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[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the percentage of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[max] percent of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
generated: This statistic shows the degree of Urbanization in Belgium from 2008 to 2018 and details the percentage of the entire population , living in urban areas . In 2018 , 98 percent of the total population in Belgium lived in cities .

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: As of 2019 , Puerto Rican rapper templateXValue[0] templateXValue[0] templateXValue[0] , often known as 'Residente ' and templateXValue[1] templateXValue[1] or 'Visitante ' _ , both from the band templateXValue[3] templateXValue[3] , had won the templateTitle[0] templateYLabel[2] templateYLabel[3] of templateTitle[5] templateTitle[6] with a total of templateYValue[max] each .
generated: As of 2019 , Puerto Rican rapper Dryvit , often known as 'Residente ' and STO or 'Visitante ' _ , both from the band Senergy , had won the Most respondents of U.S. 2018 with a total of 34.6 each .

Example 3:
titleEntities: {'Subject': ['Pennsylvania'], 'Date': ['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[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of templateTitleSubject[0] was about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the development of Pennsylvania 's Real GDP from 2000 to 2018 . In 2018 , the GDP of Pennsylvania was about 712.1 billion U.S. dollars .

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 September templateTitle[5] , 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: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] on templateYLabel[0] in the templateTitle[6] in templateTitleDate[0] . In that year , templateXValue[0] was ranked first with a templateXLabel[0] templateYLabel[1] of templateYValue[3] templateYLabel[2] templateYLabel[3] .
generated: This statistic gives information on the Leading video gaming brands Twitter on Brand in the by in 2018 . In that year , PlayStation was ranked first with a Brand fans of 9.31 millions .

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[max] . 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: This statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitleSubject[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitleSubject[0] templateTitle[4] was approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Number Employees thousands of UK industry from 2010 to 2018 . In 2018 , the Number Employees thousands of UK industry was approximately 160 thousands .

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: This statistic illustrates templateTitleSubject[0] 's templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] from the second templateXLabel[0] of templateTitleDate[0] to the second templateXLabel[0] of templateTitleDate[1] . As of the last reported templateXLabel[0] , the website 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] with the second templateXLabel[0] of templateTitleDate[1] .
generated: This statistic illustrates Dublin 's office rental prices Cost per from the second Quarter of 2019 to the second Quarter of 2019 . As of the last reported Quarter , the website 's Cost per amounted to 538 square meter with the second Quarter of 2019 .

Example 7:
titleEntities: {'Subject': ['Baltimore 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[0] 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] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] dollars.The templateTitleSubject[0] templateTitleSubject[0] are owned by Jim Crane , who bought the franchise for 465 templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[7] .
generated: 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 253 million U.S. dollars.The Baltimore Orioles are owned by Jim Crane , who bought the franchise for 465 million U.S. dollars in 2011 .

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[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] in templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateYValue[0] percent of templateTitleSubject[0] templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[2] line in templateXValue[max] .
generated: This statistic shows the rate from in Wisconsin from 2000 to 2018 . 11 percent of Wisconsin 's population lived below the rate line in 2018 .

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: As of templateXValue[0] templateXValue[0] , it was estimated that templateTitleSubject[0] had just templateYValue[0] thousand templateYLabel[1] in the templateTitle[2] , a significant drop from the templateYValue[max] templateYLabel[2] recorded in templateXValue[1] templateXValue[1] . templateTitleSubject[0] is no stranger to media attention , but after the recent claims from a source that they had obtained internal data about templateTitleSubject[0] 's floundering subscriber base , the company is once again in the spotlight . What is templateTitleSubject[0] ? templateTitleSubject[0] is a subscription-based movie ticketing service , designed to allow templateYLabel[1] to buy a select amount of movie tickets each templateXLabel[0] for a single fee .
generated: As of North America , it was estimated that Ultra had just 84054 thousand UHNW in the net , a significant drop from the 84054 individuals recorded in Europe . Ultra is no stranger to media attention , but after the recent claims from a source that they had obtained internal data about Ultra 's floundering subscriber base , the company is once again in the spotlight . What is Ultra ? Ultra is a subscription-based movie ticketing service , designed to allow UHNW to buy a select amount of movie tickets each Region for a single fee .

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: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] templateTitleSubject[0] templateTitle[6] of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Chicago Blackhawks 2014/15 of the National Basketball Association from 2005/06 to 2014/15 . In the 2005/06 season , the Average ticket price was 34.88 U.S. dollars .

Example 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: This statistic represents templateTitleSubject[0] 's templateTitle[1] templateTitle[2] from the fiscal templateXLabel[0] of templateXValue[min] through the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , templateTitleSubject[0] 's templateTitle[1] templateTitle[2] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic represents Audi 's total assets from the fiscal Year of 2002 through the fiscal Year of 2018 . In the fiscal Year of 2018 , Audi 's total assets amounted to 65598 assets million euros .

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 November 5 , templateXValue[last] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] grew by templateYValue[7] 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: This statistic shows the number of templateYLabel[0] templateYLabel[1] templateXValue[last] on 31st of March , templateTitleDate[0] , templateTitle[7] templateXValue[last] of templateXValue[last] . There were a total of 82,634 templateYLabel[0] templateYLabel[1] templateXValue[last] in templateTitleSubject[0] and templateTitleSubject[1] on this date , the largest share of whom , templateYValue[max] , were serving templateXValue[2] of templateXValue[0] or templateXValue[0] templateXValue[0] .
generated: This statistic shows the number of Percentage growth 05 Nov 2017 on 31st of March , 2010 , 2017 05 Nov 2017 of 05 Nov 2017 . There were a total of 82,634 Percentage growth 05 Nov 2017 in Great Britain and Great Britain on this date , the largest share of whom , 4.2 , were serving 25 Nov 2012 of 28 Nov 2010 or 28 Nov 2010 .

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] 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] . templateTitleSubject[0] templateTitleSubject[0] - additional information The templateTitleSubject[0] templateTitleSubject[0] are a franchise of the National Basketball Association ( NBA ) that became part of the newly formed NBA in 1949 .
generated: The statistic shows the Revenue of the Brooklyn Nets franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 304 million U.S. dollars . Brooklyn Nets - additional information The Brooklyn Nets are a franchise of the National Basketball Association ( NBA ) that became part of the newly formed NBA in 1949 .

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: As of March of templateTitleDate[0] , around templateYValue[max] percent of Americans felt that templateTitle[1] templateTitle[2] websites templateXValue[0] report templateTitle[5] templateTitle[2] stories in the templateTitle[7] . Another templateYValue[1] percent of templateYLabel[1] stated that they believed that templateTitle[1] templateTitle[2] websites templateXValue[1] report templateTitle[5] templateTitle[2] stories . Just templateYValue[2] percent of adults said that they did templateXValue[2] believe that templateTitle[5] templateTitle[2] stories were being reported templateTitle[1] .
generated: As of March of 2018 , around 51 percent of Americans felt that consumers who websites Yes more than once report experienced who stories in the 2018 . Another 22 percent of respondents stated that they believed that consumers who websites Yes but only once report experienced who stories . Just 51 percent of adults said that they did No never believe that experienced who stories were being reported consumers .

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: This statistic illustrates the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] in templateTitle[4] templateTitleSubject[0] templateTitle[6] in templateTitleDate[0] . The survey was conducted among 15 to 20 year-olds and is part of the Face of Global sex Report by Durex . According to the source , the respondents in templateXValue[0] had templateYValue[max] sex templateYLabel[2] on average .
generated: This statistic illustrates the Export value of top billion in partners U.S. 2018 in 2018 . The survey was conducted among 15 to 20 year-olds and is part of the Face of Global sex Report by Durex . According to the source , the respondents in Canada had 298.7 sex billion on average .

Example 16:
titleEntities: {'Subject': ['Kansas'], 'Date': ['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[0] percent of templateTitleSubject[0] ' templateYLabel[1] lived below the templateTitle[1] line .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the poverty rate in Kansas from 2000 to 2018 . In 2018 , about 12 percent of Kansas 's population lived below the poverty line .

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 2016* . 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: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[3] templateYValue[2] templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitle[8] in templateTitleDate[0] . In that year , templateXValue[0] 's ships had a templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[3] percent . The templateXValue[3] templateXValue[1] , founded in 1892 , is headquartered in Atlanta , Georgia .
generated: This statistic shows the Economic loss billion of the major 8.0 worldwide up 2016 in the 2016 in 2016 . In that year , United States June 2012 's ships had a Economic loss billion of 6.0 percent . The Australia 1981 China P Rep January 1994 , founded in 1892 , is headquartered in Atlanta , Georgia .

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: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] were living in the templateTitle[5] , a decrease of 10 percent compared to templateYValue[max] percent compared to the previous templateXLabel[0] .
generated: This statistic shows the Percentage of Trade union density employees in the Germany from 2000 to 2013 . In 2013 , 24.57 percent of Trade union density employees were living in the 2000 , a decrease of 10 percent compared to 24.57 percent compared to the previous Year .

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[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the percentage of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[max] percent of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
generated: This statistic shows the degree of Urbanization in Finland from 2008 to 2018 and details the percentage of the entire population , living in urban areas . In 2018 , 85.38 percent of the total population in Finland lived in cities .

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[1] , 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[1] , 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: How many people use templateTitle[0] ? In the first templateXLabel[0] of templateTitleDate[1] , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitleSubject[0] templateTitle[0] templateYLabel[3] amounted to templateYValue[0] templateYLabel[4] , an increase from templateYValue[1] templateYLabel[4] in the previous period . In recent times , templateTitle[0] 's user growth has been not endeared the company to its investors - the growth compared to the previous templateXLabel[0] only amounted to three percent and has hovered in the low single digits since 2017 as the company continues to struggle to attract and retain templateYLabel[3] . templateTitle[0] audiences With more than 330 templateYLabel[4] global templateYLabel[1] templateYLabel[2] templateYLabel[3] , templateTitle[0] is one of the biggest social networks worldwide .
generated: How many people use Twitter ? In the first Quarter of 2019 , the Number of monthly active Twitter users amounted to 330.0 millions , an increase from 321.0 millions in the previous period . In recent times , Twitter 's user growth has been not endeared the company to its investors - the growth compared to the previous Quarter only amounted to three percent and has hovered in the low single digits since 2017 as the company continues to struggle to attract and retain users . Twitter audiences With more than 330 millions global monthly active users , Twitter is one of the biggest social networks worldwide .

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: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] at templateTitle[3] templateTitle[4] in templateTitle[5] templateTitle[6] as of January templateTitleDate[0] . During the measured period , the templateXValue[0] templateXValue[0] were ranked first templateTitle[1] an templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Percent university of university at degree by in country 2007 as of January 2007 . During the measured period , the Canada were ranked first population an Percent university of 48 degree .

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[max] years of templateYLabel[1] .

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[2] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , the templateYLabel[0] of the templateYLabel[2] was templateYValue[min] percent .
generated: The statistic shows the Median of the age median age 1950 in the Projected from 1950 to 2100 . In 2100 , the Median of the age was 21.5 percent .

Example 23:
titleEntities: {'Subject': ['Automobile'], 'Date': ['2010', '2015']}
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[min] 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[max] templateYLabel[0] automobiles produced worldwide in templateXValue[max] .

generated_template: This statistic illustrates the projected templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that templateYValue[3] percent of templateTitleSubject[0] scanners were between 18 and 24 years old .
generated: This statistic illustrates the projected Automobile Million transmissions of Automobile production in the Automobile from 2010 to 2015 . In 2015 , it is estimated that 27.82 percent of Automobile scanners were between 18 and 24 years old .

Example 24:
titleEntities: {'Subject': ['General Motors', 'EBIT'], '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: As per recent data exploring the number of people employed by Hewlett Packard Enterprise ( previously a part of Hewlett-Packard Company ) worldwide between templateXValue[min] and templateXValue[max] , in templateXValue[max] the company had templateYValue[0] thousand templateYLabel[0] .
generated: As per recent data exploring the number of people employed by Hewlett Packard Enterprise ( previously a part of Hewlett-Packard Company ) worldwide between 2015 and 2019 , in 2019 the company had 8393 thousand EBIT .

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: In templateXValue[max] , the annual templateYLabel[0] of visitor exports is expected to reach templateYValue[max] templateYLabel[1] templateYLabel[2] . The spending of templateTitle[0] tourists in the country slightly decreased in templateXValue[3] , after the terrorist attacks which took place in Paris and Nice respectively in templateXValue[4] and templateXValue[3] . However , in templateXValue[2] , templateTitleSubject[0] had the fifth largest templateTitle[0] tourism templateTitle[3] worldwide .
generated: In 2028 , the annual Value of visitor exports is expected to reach 411.0 billion euros . The spending of Travel tourists in the country slightly decreased in 2016 , after the terrorist attacks which took place in Paris and Nice respectively in 2015 and 2016 . However , in 2017 , Germany had the fifth largest Travel tourism total worldwide .

Example 26:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2017']}
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[min] .

generated_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of the Chilean population accessed the templateYLabel[3] , up from templateYValue[16] percent in templateXValue[16] .
generated: This statistic gives information on the internet penetration in Argentina from 2000 to 2017 . In 2017 , 74.29 percent of the Chilean population accessed the internet , up from 9.78 percent in 2001 .

Example 27:
titleEntities: {'Subject': ['Southwest Airlines'], 'Date': ['2010', '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: As of templateXValue[max] it is estimated that there is approximately templateYValue[max] thousand templateYLabel[2] of templateTitle[1] globally . In recent years , the lowest templateTitle[1] templateTitle[2] occurred in templateXValue[5] when there were just templateYValue[min] thousand templateYLabel[2] available . templateTitle[1] is considered one of the rarest and most valuable metals in the world .
generated: As of 2019 it is estimated that there is approximately 133.32 thousand billions of passenger globally . In recent years , the lowest passenger miles occurred in 2014 when there were just 78.05 thousand billions available . passenger is considered one of the rarest and most valuable metals in the world .

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[max] percent of polled Britons made purchases templateTitle[0] . This is greater templateTitle[2] than in templateXValue[min] , when templateYValue[min] percent of individuals made purchases templateTitle[0] .

generated_template: There is no use denying that the way people consume newspapers and magazines has changed . As of templateXValue[max] , more than two thirds of individuals in templateTitleSubject[0] templateTitleSubject[0] were either reading or downloading templateTitle[0] templateTitle[1] , newspapers or magazines . This was more than three times the templateYLabel[0] of templateTitle[0] readers as compared to templateXValue[min] .
generated: There is no use denying that the way people consume newspapers and magazines has changed . As of 2019 , more than two thirds of individuals in Great Britain were either reading or downloading Online purchasing , newspapers or magazines . This was more than 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: templateTitle[2] templateYLabel[1] in templateTitleSubject[0] reached an all-time high as of templateXValue[max] , when templateTitleSubject[0] templateTitle[1] auto industry sold some templateYValue[max] templateYLabel[2] templateYLabel[3] . This figure is almost double the templateTitleDate[0] templateYLabel[1] , when just templateYValue[min] templateYLabel[2] templateTitle[2] templateYLabel[3] were sold in templateTitleSubject[0] . templateTitle[2] industry in templateTitleSubject[0] In templateXValue[max] , templateTitle[2] vehicles also made up the majority of vehicles in production in templateTitleSubject[0] , followed by passenger vehicles .
generated: United university in United Kingdom reached an all-time high as of 2018 , when United Kingdom applicants auto industry sold some 674890 applicants . This figure is almost double the 2010 university , when just 616700 applicants United applicants were sold in United Kingdom . United industry in United Kingdom In 2018 , United vehicles also made up the majority of vehicles in production in United Kingdom , followed by passenger vehicles .

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 September templateXValue[8] , 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 templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitle[3] and templateTitle[4] templateTitle[5] amounted to approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the third templateXLabel[0] of templateXValue[0] . templateTitle[0] templateTitle[1] sector in the templateTitle[6] templateTitle[0] templateTitle[1] sector templateYLabel[0] in the templateTitle[6] has been steadily falling in recent years and is beginning to come out of a period of great difficulty and problems presented to it by the economic crisis of 2008 . For the previous generations in the templateTitle[6] the real estate market was quite stable .
generated: The ITV viewers of quarterly and UK Q1 amounted to approximately 55628 thousands in the third Quarter of Q1 2012 . ITV viewers sector in the 2012-Q3 ITV viewers sector Viewers in the 2012-Q3 has been steadily falling in recent years and is beginning to come out of a period of great difficulty and problems presented to it by the economic crisis of 2008 . For the previous generations in the 2012-Q3 the real estate market was quite stable .

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: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] templateTitleSubject[0] club of Major League Soccer by templateTitle[0] in templateTitle[5] . templateXValue[0] templateXValue[0] received a salary of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in that year .
generated: The statistic shows the Player expenses ( Payroll ) of the San Jose club of Major League Soccer by Player in Earthquakes . Valeri "Vako" Qazaishvili received a salary of 1604.04 thousand U.S. dollars in that 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[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . PepsiCoPepsiCo is an American food and beverage corporation , based in Purchase , NY .

generated_template: This statistic represents templateTitleSubject[0] 's templateYLabel[0] between the templateXValue[min] and templateXValue[max] fiscal years . In the templateXValue[max] fiscal templateXLabel[0] , the manufacturer of regional aircraft , business jets , mass transportation equipment and recreational equipment had revenues of templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic represents PepsiCo 's Revenue between the 2007 and 2018 fiscal years . In the 2018 fiscal Year , the manufacturer of regional aircraft , business jets , mass transportation equipment and recreational equipment had revenues of 64.66 billion U.S. dollars .

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: This statistic shows the templateTitle[0] templateTitleSubject[0] templateTitleSubject[0] templateTitle[3] templateYLabel[1] templateTitle[5] templateTitle[6] as of October 14 , templateTitleDate[0] . templateXValue[0] templateXValue[0] has hit the most templateYLabel[1] templateYLabel[2] in templateTitle[0] templateTitleSubject[0] templateTitleSubject[0] franchise history with templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Oakland Oakland Athletics home leaders 2019 as of October 14 , 2019 . Mark McGwire has hit the most home runs in Oakland Oakland Athletics franchise history with 363 home runs .

Example 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[1] . 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 presents the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateTitle[3] on templateTitleSubject[0] worldwide from templateXValue[last] to templateXValue[0] . Between templateXValue[0] and templateXValue[0] templateXValue[0] there were templateYValue[max] templateTitle[1] templateYLabel[1] templateTitle[3] on templateTitleSubject[0] , up from templateYValue[1] templateYLabel[1] templateTitle[3] in the corresponding period in templateXValue[1] .
generated: The statistic presents the Number of monthly views on BuzzFeed worldwide from May '16 to Apr '15 . Between Apr '15 and Apr '15 there were 7000 monthly views on BuzzFeed , up from 5000 monthly views in the corresponding period in Oct '15 .

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[18] templateYLabel[1] in templateTitleSubject[0] in templateXValue[max] .

generated_template: There were over templateYValue[0] thousand templateYLabel[1] in templateTitleSubject[0] during templateXValue[max] , a decrease of around 900 templateYLabel[1] when compared with the previous templateXLabel[0] . Same sex marriage was legalized in templateTitleSubject[0] on December 16 , templateXValue[4] following the Marriage and Civil Partnership Act . Compared with templateXValue[min] , there was a net decrease of around 2.77 thousand templateYLabel[1] by templateXValue[max] .
generated: There were over 30582 thousand robberies in Canada during 2018 , a decrease of around 900 robberies when compared with the previous Year . Same sex marriage was legalized in Canada on December 16 , 2004 following the Marriage and Civil Partnership Act . Compared with 2000 , there was a net decrease of around 2.77 thousand robberies by 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[0] templateYLabel[3] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] templateYLabel[3] in templateXValue[max] .
generated: This statistic shows the Per capita consumption of fresh citrus in the U.S. from 2000 to 2018 . According to the report , the U.S. Per capita consumption of fresh citrus amounted to approximately 23.95 pounds in 2018 .

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[max] , about 1.59 templateYLabel[1] vehicles were purchased in templateXValue[max] by templateTitle[1] companies in the templateTitle[3] .

generated_template: This statistic illustrates the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] is expected to increase by templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic illustrates the Car Revenue of industry U.S. revenue in the rental from 2002 to 2017 . In 2017 , the industry U.S. revenue 2002 is expected to increase by 28.63 billion U.S. dollars .

Example 38:
titleEntities: {'Subject': ['Google'], 'Date': ['2003', '2015']}
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: This statistic shows the templateYLabel[0] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] ( templateYLabel[1] ) of the templateTitle[0] in Q3 templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In the third quarter of templateTitleDate[0] , the templateXLabel[0] of templateXValue[0] added about 2.79 templateYLabel[2] templateYLabel[3] ( 2012 ) templateYLabel[5] templateYLabel[6] of value to the templateYLabel[5] templateYLabel[0] templateYLabel[1] . Total templateYLabel[0] templateYLabel[1] amounted to about 19.12 templateYLabel[2] templateYLabel[3] ( 2012 ) templateYLabel[5] templateYLabel[6] .
generated: This statistic shows the Net Google ( income ) of the Google in Q3 2003 , 2015 Financial . In the third quarter of 2003 , the Financial of Q3 '15 added about 2.79 million U.S. ( 2012 ) dollars of value to the dollars Net income . Total Net income amounted to about 19.12 million U.S. ( 2012 ) dollars .

Example 39:
titleEntities: {'Subject': ['Case Shiller National Home Price Index'], 'Date': ['17', '19']}
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 February templateTitleDate[0] to August templateTitleDate[1] . The templateYLabel[0] templateYLabel[1] was equal to 100 as of January 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 August templateTitleDate[1] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] of the templateTitle[4] templateTitle[5] templateTitle[6] from templateTitleDate[0] to templateTitle[7] . In December templateTitleDate[1] , the templateYLabel[0] templateYLabel[1] of the templateYLabel[2] templateYLabel[3] amounted to templateYValue[0] percent . templateTitleSubject[0] templateTitleSubject[0] – additional information templateTitleSubject[0] templateTitleSubject[0] , which has only known as a shot to move and further information developed by the templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateTitleSubject[0] .
generated: The statistic shows the U.S. housing Case of the Case Shiller National Home Price Index of the National Home Price from 17 to Index . In December 19 , the Index value of the value amounted to 212.06 percent . Case Shiller National Home Price Index – additional information Case Shiller National Home Price Index , which has only known as a shot to move and further information developed by the National Home Price Index Case Shiller National Home Price Index .

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 March templateXValue[max] . 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: templateTitleSubject[0] templateTitle[1] employment was around 19,500 workers as of templateXValue[max] . templateTitleSubject[0] templateTitle[1] company goals are to create solutions to food safety and access , clean water , clean air and fuel , health care , and sustainable living . templateTitleSubject[0] Company Overview templateTitleSubject[0] is one of the world templateTitle[1] leading chemicals , plastics , and refining companies globally .
generated: Alibaba number employment was around 19,500 workers as of 2019 . Alibaba number company goals are to create solutions to food safety and access , clean water , clean air and fuel , health care , and sustainable living . Alibaba Company Overview Alibaba is one of the world number leading chemicals , plastics , and refining companies globally .

Example 41:
titleEntities: {'Subject': ['Australia'], 'Date': ['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[max] templateYLabel[2] templateTitle[1] templateTitle[2] templateYLabel[1] in templateTitleSubject[0] , up from around templateYValue[5] templateYLabel[2] in templateXValue[5] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that there will be around templateYValue[max] templateYLabel[2] templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] , up from templateYValue[min] templateYLabel[2] in 2016 .
generated: This statistic shows the Number of network users in Australia from 2015 to 2022 . In 2022 , it is estimated that there will be around 19.27 millions network users in Australia , up from 15.99 millions in 2016 .

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[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: 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 .

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[0] 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: This statistic illustrates the templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] .
generated: This statistic illustrates the Number of France number internet in France from 2002 to 2016 . In 2016 , there were 55.86 France number internet in France .

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 templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] in the most popular templateTitleSubject[0] templateXLabel[0] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . According to the source , the templateXValue[4] period , templateXValue[last] had out of the top templateTitle[0] templateXLabel[0] in the templateYLabel[0] of global templateYLabel[2] on a templateYLabel[0] templateYLabel[1] of templateYValue[max] mobile templateYLabel[2] .
generated: The statistic shows the Spending per of spending resident in the most popular U.S. State in 2018 , 2018 State . According to the source , the Arlington period , Aurora had out of the top Cities State in the Spending of global resident on a Spending per of 346.97 mobile resident .

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[0] to templateTitleDate[1] . In templateTitleDate[1] , this templateYLabel[1] was templateYValue[0] percent . The monthly figure of full-time employees in the templateTitleSubject[0] can be accessed here .

generated_template: Since templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] in the templateTitle[3] has stayed more or less steady . In templateTitleDate[0] , the female templateYLabel[0] templateYLabel[1] was templateYValue[29] percent , and in 2018 , the templateYLabel[0] templateYLabel[1] was at templateYValue[0] percent . However , it reached a peak in 2000 at templateYValue[max] percent .
generated: Since 1990 , the Employment rate of men in the U.S. has stayed more or less steady . In 1990 , the female Employment rate was 72 percent , and in 2018 , the Employment rate was at 66.6 percent . However , it reached a peak in 2000 at 72 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[0] to templateTitleDate[1] , 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: During the year templateTitleDate[0] , the most templateYLabel[1] templateTitle[2] game on the physical market was templateXValue[0] templateXValue[0] for the PS4 , accounting for about templateYValue[max] thousand templateYLabel[0] templateYLabel[1] . In second place came templateXValue[1] templateXValue[1] templateXValue[1] templateXValue[1] , with sales amounting to roughly templateYValue[1] templateYLabel[0] . templateTitle[2] game market in templateTitleSubject[0] In 2016 , the French gaming market has known a peak , with its value amounting to 2.3 billion euros , before decreasing to 1.6 billion euros in templateTitleDate[0] .
generated: During the year 1900 , the most fatalities from game on the physical market was Heat wave 1980 Kansas City Missouri St Loius for the PS4 , accounting for about 1260 thousand Number fatalities . In second place came Heat wave 1936 Illinois , with sales amounting to roughly 1193 Number . from game market in U.S.from In 2016 , the French gaming market has known a peak , with its value amounting to 2.3 billion euros , before decreasing to 1.6 billion euros in 1900 .

Example 47:
titleEntities: {'Subject': ['UK'], 'Date': ['17', '19']}
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 December templateTitleDate[1] , 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 January templateTitleDate[0] . When broken down by denomination , the twenty-pound note accounted for the highest share of templateTitle[2] in templateTitle[4] .

generated_template: As of December templateTitleDate[1] , buying a residential property in templateTitleSubject[1] cost on templateTitle[1] templateYValue[17] thousand templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . This figure represented a decrease compared to the previous years . In fact , the templateTitle[1] templateYLabel[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateTitle[3] residential templateTitle[4] in templateTitleSubject[1] has decreased steadily since December templateTitleDate[0] , when it registered a value of templateYValue[0] thousand templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: As of December 19 , buying a residential property in UK cost on outstanding 81698 thousand outstanding million GBP . This figure represented a decrease compared to the previous years . In fact , the outstanding Amounts million GBP coin residential circulation in UK has decreased steadily since December 17 , when it registered a value of 82648 thousand million GBP .

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 February templateTitleDate[0] 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: templateXValue[0] was the most popular templateTitle[1] templateTitle[2] templateXLabel[0] among templateTitle[0] templateTitle[1] templateTitle[2] users in templateTitleSubject[0] in templateTitleDate[0] . templateYValue[max] percent of internet users in templateTitleSubject[0] reported to have used templateXValue[0] on a templateTitle[0] basis . The templateTitle[1] templateTitle[2] templateXLabel[0] had 2.91 million users in 2016 and was forecast to have around 3.12 million users templateTitle[6] 2022 .
generated: Android Pay was the most popular mobile payment among U.S. mobile payment users in U.S. in 2016 . 18 percent of internet users in U.S. reported to have used Android Pay on a U.S. basis . The mobile payment had 2.91 million users in 2016 and was forecast to have around 3.12 million users 2016 2022 .

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] 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] . templateTitleSubject[0] templateTitleSubject[0] The templateTitleSubject[0] templateTitleSubject[0] are a professional basketball franchise of the National Basketball Association ( NBA ) , entering the league as an 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 National Basketball Association franchise amounted to 258 million U.S. dollars . Utah Jazz The Utah Jazz are a professional basketball franchise of the National Basketball Association ( NBA ) , entering the league as an 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 templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitle[3] and templateTitle[4] templateTitle[5] amounted to approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the third templateXLabel[0] of templateXValue[0] . templateTitle[0] templateTitle[1] sector in the templateTitle[6] templateTitle[0] templateTitle[1] sector templateYLabel[0] in the templateTitle[6] has been steadily falling in recent years and is beginning to come out of a period of great difficulty and problems presented to it by the economic crisis of 2008 . For the previous generations in the templateTitle[6] the real estate market was quite stable .
generated: The MTV viewers of quarterly and United Kingdom amounted to approximately 11487 thousands in the third Quarter of Q1 2012 . MTV viewers sector in the UK MTV viewers sector Viewers in the UK has been steadily falling in recent years and is beginning to come out of a period of great difficulty and problems presented to it by the economic crisis of 2008 . For the previous generations in the UK the real estate market was quite stable .

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: According to a templateTitleDate[0] survey , templateXValue[0] for the templateTitle[1] of templateTitle[2] templateTitle[3] for electricity generation in the templateTitleSubject[1] outweighed opposition . Of templateYLabel[1] , 35 percent supported its templateTitle[1] , compared to 23 percent who opposed it . Despite templateTitle[2] disasters occurring as recently as 2011 , when a tsunami hit a power plant in Fukushima , Japan , approximately 34 percent of templateTitleSubject[1] residents believe it provides a safe source of templateTitle[3] .
generated: According to a 2019 survey , Every week for the attendance of Americans 2019 for electricity generation in the Americans outweighed opposition . Of respondents , 35 percent supported its attendance , compared to 23 percent who opposed it . Despite Americans disasters occurring as recently as 2011 , when a tsunami hit a power plant in Fukushima , Japan , approximately 34 percent of Americans residents believe it provides a safe source of 2019 .

Example 52:
titleEntities: {'Subject': ['Belgium'], 'Date': ['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[max] templateYLabel[1] in templateTitleSubject[0] in the templateXLabel[0] templateXValue[min] and by templateXValue[max] this figure had fallen to templateYValue[min] . This is a drop of over 23 percent in the provided time period .

generated_template: There were over templateYValue[0] thousand templateYLabel[1] in templateTitleSubject[0] during templateXValue[max] , a decrease of around 900 templateYLabel[1] when compared with the previous templateXLabel[0] . Same sex marriage was legalized in templateTitleSubject[0] on December 16 , templateXValue[4] following the Marriage and Civil Partnership Act . Compared with templateXValue[min] , there was a net decrease of around 2.77 thousand templateYLabel[1] by templateXValue[max] .
generated: There were over 174 thousand hospitals in Belgium during 2018 , a decrease of around 900 hospitals when compared with the previous Year . Same sex marriage was legalized in Belgium on December 16 , 2014 following the Marriage and Civil Partnership Act . Compared with 2000 , there was a net decrease of around 2.77 thousand hospitals by 2018 .

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[1] , 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: This statistic illustrates templateTitleSubject[0] 's online traffic performance , based on the templateTitle[1] templateYLabel[1] from the first templateXLabel[0] of templateTitleDate[0] to the first templateXLabel[0] of templateTitleDate[1] . During the most recent templateXLabel[0] , an average of templateYValue[0] templateYLabel[2] visitors accessed the website , mobile templateYLabel[0] and the mobile applications . Over the period in consideration , it was reported that the number of average templateYLabel[1] increased from templateYValue[20] to over 900 templateYLabel[2] .
generated: This statistic illustrates Facebook 's online traffic performance , based on the mobile-only from the first Quarter of 2011 to the first Quarter of 2016 . During the most recent Quarter , an average of 1149 users visitors accessed the website , mobile Number and the mobile applications . Over the period in consideration , it was reported that the number of average mobile-only increased from 58 to over 900 users .

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 May templateTitleDate[0] . During the survey , templateYValue[2] percent of templateYLabel[1] said that templateXValue[2] was their templateXValue[last] game to play at casinos .

generated_template: Swedish e-shoppers cared about fashion and templateXValue[1] in templateTitleDate[0] . The templateTitle[0] frequently templateTitle[1] templateXValue[1] templateTitle[3] by Swedish consumers , were templateXValue[0] and templateXValue[0] items , with templateYValue[max] percent of templateYLabel[1] ' vote . templateXValue[1] and templateXValue[1] templateXValue[1] followed , with templateYValue[1] percent of templateYLabel[1] ' vote .
generated: Swedish e-shoppers cared about fashion and Black Jack in 2014 . The Most frequently popular Black Jack casino by Swedish consumers , were Slot machines and Slot machines items , with 48 percent of respondents ' vote . Black Jack and Black Jack followed , with 16 percent of respondents ' vote .

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: templateTitleSubject[0] templateTitle[1] employment was around 19,500 workers as of templateXValue[max] . templateTitleSubject[0] templateTitle[1] company goals are to create solutions to food safety and access , clean water , clean air and fuel , health care , and sustainable living . templateTitleSubject[0] Company Overview templateTitleSubject[0] is one of the world templateTitle[1] leading chemicals , plastics , and refining companies globally .
generated: Becton Dickinson employment was around 19,500 workers as of 2018 . Becton Dickinson company goals are to create solutions to food safety and access , clean water , clean air and fuel , health care , and sustainable living . Becton Company Overview Becton is one of the world Dickinson leading chemicals , plastics , and refining companies globally .

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[9] to around templateYValue[min] thousand in templateXValue[max] .

generated_template: Between templateXValue[min] and templateXValue[max] , each templateXLabel[0] roughly between 134,000 and 153,000 people died in the templateTitleSubject[0] . From templateXValue[4] onwards , the templateYLabel[0] of templateYLabel[1] increased annually , peaking in templateXValue[max] at roughly 153,000 . That templateXLabel[0] , women were on average 81 years old when they died , men were five years younger .
generated: Between 2008 and 2018 , each Year roughly between 134,000 and 153,000 people died in the Norway . From 2014 onwards , the Number of live increased annually , peaking in 2018 at roughly 153,000 . That Year , women were on average 81 years old when they died , men were five years younger .

Example 57:
titleEntities: {'Subject': ['HPE'], '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[min] to templateXValue[max] . In templateXValue[max] , 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] 's research and development templateYLabel[0] came to about templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] , putting an end to the gradual increase in templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] efforts of the past five years . The Michigan-based company appears to be adapting to altered fuel economy regulations and the declining demand for sedans and smaller cars in the country by developing new designs and products . Focus of research and development activities In light of an increased concern from consumers and policymakers about the impact of fossil fuels on carbon dioxide emissions , templateTitleSubject[0] is working on a new fleet of electric vehicles with a goal of having sixteen plug-in electric vehicle models to market through 2022 .
generated: HPE 's research and development Expenditure came to about 1842 million U.S. dollars in 2019 , putting an end to the gradual increase in HPE development HPE efforts of the past five years . The Michigan-based company appears to be adapting to altered fuel economy regulations and the declining demand for sedans and smaller cars in the country by developing new designs and products . Focus of research and development activities In light of an increased concern from consumers and policymakers about the impact of fossil fuels on carbon dioxide emissions , HPE is working on a new fleet of electric vehicles with a goal of having sixteen plug-in electric vehicle models to market through 2022 .

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: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a change in it is a sign of economic growth .
generated: The statistic shows the 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 change in it is a sign of economic growth .

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: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[max] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Morocco from 2014 to 2017 , with projections up until 2024 . In 2018 , Morocco 's real Gross domestic product grew by around 4.55 percent compared to the previous Year .

Example 60:
titleEntities: {'Subject': ['Net'], 'Date': ['2020']}
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: In templateXValue[1] , the templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was approximately templateYValue[1] percent . In templateXValue[max] , this was forecasted to reach around templateYValue[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] 's templateTitle[2] meat templateYLabel[0] in templateXValue[1] was above the OECD average of that templateXLabel[0] .
generated: In 2019 , the airlines Net profit billion in Net was approximately 25.9 percent . In 2020 , this was forecasted to reach around 37.6 U.S. profit billion . Net 's airlines meat Net in 2019 was above the OECD average of that Year .

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: The statistic shows the templateYLabel[0] amount of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[4] templateTitle[0] templateTitle[1] templateTitle[2] generated an templateYLabel[0] total templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Capacity amount of U.S. residential sector megawatts in the PV from 2005 to 2018 . In 2018 , solar U.S. residential sector generated an Capacity total megawatts of 2583 megawatts .

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: This statistic shows the templateTitle[0] of templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateTitle[2] templateTitle[3] in templateTitleSubject[0] was templateYValue[max] templateYLabel[0] templateYLabel[1] .
generated: This statistic shows the American of corn 2001 in American from 2001 to 2019 . In 2019 , the American of corn 2001 in American was 160 Imports million .

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: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] templateTitleSubject[0] who were using templateTitle[0] as of February templateTitleDate[0] , sorted templateTitle[6] templateTitle[7] . During that period of time , templateYValue[max] percent of female templateYLabel[1] stated that they used the social networking site .
generated: This statistic shows the Share of adults in the Americans who were using Share as of February 2012 , sorted 2012 . During that period of time , 58.1 percent of female respondents stated that they used the social networking site .

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 August templateTitleDate[0] survey , templateYValue[max] percent of templateYLabel[1] stated that they were aware of templateXValue[0] . 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: This statistic shows the results of a survey , conducted in December 2018 in the country , on templateTitleSubject[1] ' templateXValue[8] templateTitleSubject[0] templateTitle[2] resolutions templateTitle[5] templateTitleDate[0] . During the survey , templateYValue[2] percent of templateYLabel[1] said their templateTitle[3] templateTitle[5] templateTitleDate[0] is to templateXValue[2] templateXValue[2] , while templateYValue[3] percent would like to templateXValue[3] .
generated: This statistic shows the results of a survey , conducted in December 2018 in the country , on U.S. ' savingstar.com U.S. website resolutions U.S. 2016 . During the survey , 62 percent of respondents said their awareness U.S. 2016 is to retailmenot.com , while 48 percent would like to livingsocial.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[min] templateYLabel[2] in templateXValue[min] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] reached templateYValue[5] templateYLabel[2] , up from templateYValue[min] templateYLabel[2] in templateXValue[min] .
generated: This statistic shows the Number of Thailand users in Thailand from 2017 to 2023 . In 2018 , the Number of Thailand users in Thailand reached 33.0 millions , up from 32.1 millions in 2017 .

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[min] percent .

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

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: The templateTitle[0] templateTitle[1] rate in templateTitleSubject[0] was forecasted at templateYValue[max] percent in templateXValue[max] . This was a decrease compared to the previous templateXLabel[0] . According to OECD , consuming meat was equated with living standards , diet , livestock production and consumer prices .
generated: The Worldwide shipments rate in Worldwide was forecasted at 400.0 percent in 2019 . This was a decrease compared to the previous Year . According to OECD , consuming meat was equated with living standards , diet , livestock production and consumer prices .

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 December templateTitleDate[0] and December templateTitleDate[1] . In December templateTitleDate[1] , the templateTitle[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[6] was some templateYValue[6] percent lower than in December templateTitleDate[0] . 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: This statistic illustrates the templateYLabel[0] of templateTitle[2] templateYLabel[1] made in templateTitle[4] templateTitle[5] in templateTitleSubject[0] templateTitle[7] . The prices concern unleaded premium ( 95 RON ) for templateXValue[1] , templateXValue[3] , templateXValue[0] , templateXValue[4] , and the templateXValue[2] templateXValue[2] , and regular unleaded for templateXValue[6] , templateXValue[5] , and the templateXValue[2] templateXValue[last] . In templateTitleSubject[0] templateTitle[7] , the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateXValue[2] templateXValue[last] stood at 0.675 templateYLabel[3] templateYLabel[4] templateYLabel[1] templateYLabel[2] .
generated: This statistic illustrates the Change of heating made in price selected in Change 2018 . The prices concern unleaded premium ( 95 RON ) for Italy , France , Canada , United Kingdom , and the Spain , and regular unleaded for Germany , Japan , and the Spain United States . In Change 2018 , the Change heating oil in the Spain United States stood at 0.675 price heating oil .

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: As of June templateTitleDate[0] , templateXValue[0] was the templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] templateYLabel[1] , with a total of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateXValue[1] and templateXValue[2] with templateYValue[1] percent of templateYLabel[1] templateTitle[4] templateTitle[5] templateTitle[6] . templateXValue[3] , and templateXValue[2] ranked second with the highest templateTitle[0] templateTitle[1] templateTitle[2] of templateYValue[13] percent .
generated: As of June 2018 , Germany was the European Union dogs the European Union 2018 by country Number dogs , with a total of 9400 thousands . United Kingdom and Poland with 9000 percent of dogs 2018 by country . Italy , and Poland ranked second with the highest Number dogs European of 880 percent .

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] of worldwide internet templateTitle[4] templateYLabel[1] templateYLabel[2] between November 2017 and April templateTitleDate[0] , by originating templateTitle[2] . During that period of time , templateYValue[max] percent of templateTitle[3] app templateYLabel[1] templateYLabel[2] originated from IP addresses in the templateXValue[0] of templateXValue[0] . The templateXValue[0] templateXValue[0] were the templateXLabel[0] most targeted by templateTitle[3] attacks , suffering from over 238.6 million attacks during the fourth quarter of 2017 .
generated: The statistic shows the Percentage of worldwide internet by people between November 2017 and April 2018 , by originating U.S. . During that period of time , 34 percent of 2018 app people originated from IP addresses in the 0-4 years of 0-4 years . The 0-4 years were the Age most targeted by 2018 attacks , suffering from over 238.6 million attacks during the fourth quarter of 2017 .

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[14] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitleSubject[0] templateTitle[7] from templateXValue[0] to templateXValue[last] . In the first half of templateXValue[12] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was 17.6 templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh and had increased to templateYValue[max] the following templateXLabel[0] .
generated: This 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 cents per kWh 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[min] deaths occurred templateYLabel[2] thousand templateYLabel[4] templateYLabel[5] . Just over 92 thousand individuals in templateTitleSubject[0] died during templateXValue[max] , 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: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateTitle[2] in the templateTitleSubject[0] was templateYValue[0] . This means that out of every 1,000 newborns , nearly templateYValue[0] died before their first birthday . templateYLabel[0] templateYLabel[1] was lowest in templateXValue[3] , when the templateTitle[2] was templateYValue[min] deaths templateYLabel[2] 1,000 templateYLabel[4] templateYLabel[5] templateYLabel[6] .
generated: In 2018 , the Death rate in the Sweden was 9.1 . This means that out of every 1,000 newborns , nearly 9.1 died before their first birthday . Death rate was lowest in 2015 , when the rate was 9.1 deaths per 1,000 mid-year population .

Example 73:
titleEntities: {'Subject': ['Tesco', 'United Kingdom', 'UK'], 'Date': ['2020']}
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[max] .

generated_template: templateTitle[0] advertising templateTitle[2] is increasing year-on-year , with templateXValue[2] figures estimated at over 320 templateYLabel[2] templateYLabel[3] templateYLabel[4] . By templateXValue[max] , these expenditures are forecast to surpass half a templateYLabel[2] templateYLabel[4] . The growing advertising investments are most probably a response to the potential customer exposure posed by this medium , as podcasts reach more and more listeners in the templateTitle[3] each templateXLabel[0] .
generated: Edible advertising sales is increasing year-on-year , with 2018 figures estimated at over 320 US dollars . By 2020 , these expenditures are forecast to surpass half a US dollars . The growing advertising investments are most probably a response to the potential customer exposure posed by this medium , as podcasts reach more and more listeners in the forecast each Year .

Example 74:
titleEntities: {'Subject': ['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[0] templateYLabel[2] people visited the templateTitleSubject[0] art museum in templateTitleSubject[1] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of recreational templateYLabel[1] to the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] to the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] amounted to approximately templateYValue[0] templateYLabel[2] in templateXValue[max] .
generated: This statistic shows the Number of recreational visitors to the London in the 2018 from 2007 to 2018 . The Number of visitors to the London amounted to approximately 5.83 millions in 2018 .

Example 75:
titleEntities: {'Subject': ['NFL New England Patriots'], '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[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitle[4] templateTitle[5] gate receipts in templateXValue[max] .

generated_template: In templateXValue[max] , approximately templateYValue[0] templateYLabel[1] templateYLabel[0] in the templateTitle[0] had no templateTitle[3] templateTitle[4] . The share of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] was steadily decreasing until templateXValue[3] , but has been increasing since templateXValue[1] . The Tax Cuts and Jobs Act of templateXValue[1] caused this change , because it repealed the individual mandate , which penalized individuals for failing to maintain templateTitle[3] coverage .
generated: In 2018 , approximately 104 sales Ticket in the NFL had no Patriots revenue . The share of NFL New England Patriots England Patriots revenue was steadily decreasing until 2015 , but has been increasing since 2017 . The Tax Cuts and Jobs Act of 2017 caused this change , because it repealed the individual mandate , which penalized individuals for failing to maintain Patriots coverage .

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: In the academic templateXLabel[0] templateXValue[0] there were approximately templateYValue[min] thousand templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] , around 655 templateYLabel[1] less than there were in the templateXValue[last] academic templateXLabel[0] . Throughout most of this period there has been a steady decline in the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] .
generated: In the academic Year 2017/18 there were approximately 56626 thousand players in the Finland , around 655 players less than there were in the 2010/11 academic Year . Throughout most of this period there has been a steady decline in the Number of players in the Finland .

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: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: 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 1625 million U.S. dollars .

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[max] templateYLabel[4] templateYLabel[5] templateYLabel[6] templateYLabel[7] in templateXValue[max] . 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 above presents the global templateYLabel[0] of templateTitleSubject[0] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[2] templateTitle[3] templateYLabel[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic above presents the global Average of U.S. retail from 1990 to 2018 . In 2018 , U.S. retail electricity Average amounted to 10.58 electricity price U.S. .

Example 79:
titleEntities: {'Subject': ['Middle East', 'Africa'], 'Date': ['2015']}
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] templateYLabel[1] in templateXValue[9] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] per capita is projected to reach templateYValue[max] templateYLabel[1] .

generated_template: The statistic shows the templateTitleSubject[0] of the templateYLabel[0] templateYLabel[1] since the birth of Christ . templateYValue[max] templateYLabel[2] people were living on earth in templateXValue[max] . Additional information on templateYLabel[0] templateYLabel[1] The global templateYLabel[1] has risen dramatically in the last 100 years from templateYValue[3] templateYLabel[2] in templateXValue[3] to surpassing templateYValue[max] templateYLabel[2] in 2011 .
generated: The statistic shows the Middle East of the Penetration percent since the birth of Christ . 7 percent people were living on earth in 2015 . Additional information on Penetration percent The global percent has risen dramatically in the last 100 years from 2 percent in 2011 to surpassing 7 percent in 2011 .

Example 80:
titleEntities: {'Subject': ['SE'], '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[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The spirits company is based in Wolfenbüttel , Germany .

generated_template: The templateYLabel[0] of the templateTitleSubject[0] luxury brand templateTitle[4] templateTitle[5] has increased twofold over the period surveyed , growing from roughly templateYValue[min] templateYLabel[1] templateYLabel[2] in templateXValue[min] to templateYValue[max] templateYLabel[1] templateYLabel[2] in the templateXLabel[0] templateXValue[max] . Despite the steady increase in templateYLabel[0] during the period considered , the templateTitle[3] reported a net loss of approximately 25 percent in templateXValue[max] . Worldwide recognition recognition Founded in 1978 in Milan , templateTitle[4] templateTitle[5] is one of the leading international templateTitle[2] design houses .
generated: The Annual of the SE luxury brand 2009 2016 has increased twofold over the period surveyed , growing from roughly 101.88 profit million in 2009 to 135.87 profit million in the Year 2016 . Despite the steady increase in Annual during the period considered , the profit reported a net loss of approximately 25 percent in 2016 . Worldwide recognition Founded in 1978 in Milan , 2009 2016 is one of the leading international annual design houses .

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[max] templateYLabel[1] templateYLabel[2] .

generated_template: There were templateYValue[0] templateYLabel[1] of templateTitle[1] templateTitle[2] banks in the templateTitle[5] in templateXValue[max] . After a period of growth from templateXValue[min] to templateXValue[10] , this templateYLabel[0] has been slowly diminishing in the past few years . In templateXValue[max] , Wells Fargo led the ranking of banks with the highest templateYLabel[0] of templateYLabel[1] .
generated: There were 84.7 billion of from used banks in the from in 2018 . After a period of growth from 2000 to 2008 , this Revenue has been slowly diminishing in the past few years . In 2018 , Wells Fargo led the ranking of banks with the highest Revenue of billion .

Example 82:
titleEntities: {'Subject': ['Europe'], 'Date': ['14', '15']}
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 March templateTitleDate[0] to June templateTitleDate[1] . There were templateYValue[0] templateTitleSubject[0] templateTitle[2] A templateYLabel[1] in templateTitleSubject[1] in June templateTitleDate[1] . The templateYLabel[0] of merger and acquisition transactions in templateTitleSubject[1] remained fairly steady between March templateTitleDate[0] and June templateTitleDate[1] .

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] from December templateTitleDate[0] to January templateTitleDate[1] . In that year , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] stood at templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic presents the Number deals of & deals Europe from December 14 to January 15 . In that year , the Number deals of Europe & deals Europe stood at 1222 deals .

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 templateXValue[max] , the templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] Group reported a templateYLabel[0] of almost templateYValue[min] templateYLabel[1] templateYLabel[2] . Despite the impressive figure , the templateYLabel[0] of templateXValue[max] represented a decrease for templateTitle[4] compared to the previous years . Indeed , in templateXValue[min] , the first templateXLabel[0] considered in this graph , the templateYLabel[0] of the templateTitle[3] amounted to approximately templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: In 2017 , the Bulgari 2011 2017 Group reported a Turnover of almost 96.4 million euros . Despite the impressive figure , the Turnover of 2017 represented a decrease for 2017 compared to the previous years . Indeed , in 2011 , the first Year considered in this graph , the Turnover of the 2017 amounted to approximately 194.9 million euros .

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[9] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] templateTitleSubject[0] templateTitle[6] of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Washington Wizards games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the Average ticket price was 23.64 U.S. dollars .

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: This statistic shows the leading templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[0] . That year , the templateXValue[1] templateXValue[1] was the second leading importer of templateTitleSubject[0] templateTitle[5] , with imports valued at approximately templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the leading Employees working hours per in 2011 . That year , the India was the second leading importer of Employees per , with imports valued at approximately 42 hours per week .

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 March templateXValue[max] , templateTitleSubject[0] sold templateYValue[0] templateYLabel[1] 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: In templateXValue[max] , the templateTitle[0] total templateYLabel[0] of templateTitle[5] templateTitle[6] traded per day was over 547 templateYLabel[1] templateYLabel[2] . This means that an increase of over 240 templateYLabel[1] templateYLabel[2] templateYLabel[3] , compared to templateYValue[4] templateYLabel[1] templateYLabel[2] in templateXValue[2] .
generated: In 2018 , the Nintendo total Sales of 2018 traded per day was over 547 million units . This means that an increase of over 240 million units , compared to 12.24 million units in 2016 .

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[max] templateYLabel[5] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] templateYLabel[5] people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[max] templateYLabel[5] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Number of mobile internet users in Japan from 2017 to 2023 . In 2017 , 64.0 millions people accessed the internet through their mobile phone . In 2023 , this figure is projected to amount to 71.9 millions mobile phone internet users .

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[0] people were employed by the company worldwide , down from templateYValue[1] a templateXLabel[0] earlier .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[1] employed approximately templateYValue[0] thousand people worldwide . templateTitleSubject[0] was founded in 1890 in Berlin as a transport and accident insurer .
generated: This statistic shows the Number of Penguin Random House employees from 2005 to 2018 . In 2018 , Penguin Random House employees employed approximately 10351 thousand people worldwide . Penguin Random House was founded in 1890 in Berlin as a transport and accident insurer .

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: Video streaming giant templateTitleSubject[0] had a total templateYLabel[0] of approximately templateYValue[0] thousand templateYLabel[2] templateYLabel[3] in templateXValue[max] , whilst the company 's annual sales reached 20.15 thousand templateYLabel[2] templateYLabel[3] . This figure has almost doubled its templateYLabel[0] figures between templateXValue[5] and templateXValue[max] . , the British economy include revenue thanks to templateXValue[11] , the templateYLabel[0] increased annually .
generated: Video streaming giant United Kingdom had a total Revenue of approximately 1251.84 thousand GBP in 2019 , whilst the company 's annual sales reached 20.15 thousand GBP . This figure has almost doubled its Revenue figures between 2014 and 2019 . , the British economy include revenue thanks to 2008 , the Revenue increased annually .

Example 90:
titleEntities: {'Subject': ['UAE'], 'Date': ['2012', '2017']}
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[0] , implying a 7.83 increase in the templateYLabel[1] level .

generated_template: This statistic illustrates the average templateTitle[0] templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . Over the six templateXLabel[0] period the average templateTitle[0] templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] increased to almost templateYValue[max] templateYLabel[1] in templateXValue[max] .
generated: This statistic illustrates the average Consumer UAE 2012 2017 in the UAE ( UAE ) from 2012 to 2017 . Over the six Year period the average Consumer UAE 2012 2017 increased to almost 125.75 price in 2017 .

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: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitleSubject[0] and templateTitleSubject[0] worldwide from the second templateXLabel[0] of templateTitleDate[0] to the fourth templateXLabel[0] of templateTitleDate[1] . In the fourth templateXLabel[0] of templateTitleDate[0] , templateTitleSubject[0] templateTitleSubject[0] templateTitle[2] templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] ( 2012 ) .
generated: This statistic shows the Net income of Amazon and Amazon worldwide from the second Quarter of 2019 to the fourth Quarter of 2019 . In the fourth Quarter of 2019 , Amazon Q4 Net income amounted to 3268 million U.S. dollars ( 2012 ) .

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: According to a templateTitleDate[0] survey , templateXValue[0] for the templateTitle[1] of templateTitle[2] templateTitle[3] for electricity generation in the templateTitleSubject[1] outweighed opposition . Of templateYLabel[1] , 35 percent supported its templateTitle[1] , compared to 23 percent who opposed it . Despite templateTitle[2] disasters occurring as recently as 2011 , when a tsunami hit a power plant in Fukushima , Japan , approximately 34 percent of templateTitleSubject[1] residents believe it provides a safe source of templateTitle[3] .
generated: According to a 2015 survey , Less than once per week for the food of restaurant visit for electricity generation in the UK outweighed opposition . Of respondents , 35 percent supported its food , compared to 23 percent who opposed it . Despite restaurant disasters occurring as recently as 2011 , when a tsunami hit a power plant in Fukushima , Japan , approximately 34 percent of UK residents believe it provides a safe source of visit .

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 enjoyed all over the world . The templateTitleSubject[0] templateTitle[2] volume of mangos , mangosteens , and guavas reached templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[max] , an increase from templateYValue[min] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateXValue[1] . templateTitle[1] templateTitle[2] in Asia Much of the world 's mangos come from the Asia Pacific region , specifically India and China .
generated: Mangos are native to South and Southeast Asia and are now enjoyed all over the world . The Global production volume of mangos , mangosteens , and guavas reached 13.05 Million metric tons in 2017 , an increase from 7.25 Million metric tons in the 2016 . papaya production in Asia Much of the world 's mangos come from the Asia Pacific region , specifically India and China .

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: templateTitleSubject[1] templateTitle[4] templateTitleSubject[1] is a luxury templateTitleSubject[0] fashion house founded in a small town cose to Milan in 1985 by the two templateTitleSubject[0] designers Domenico templateTitleSubject[1] and Stefano templateTitleSubject[1] . This timeline illustrates the turrnover of the templateTitleSubject[0] luxury fashion templateTitle[2] templateTitleSubject[1] templateTitle[4] templateTitleSubject[1] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the total templateTitle[0] of templateTitleSubject[1] templateTitle[4] templateTitleSubject[1] amounted to over templateYValue[0] templateYLabel[1] templateYLabel[2] , a decrease compared to the previous templateXLabel[0] .
generated: Italian Gucci Italian is a luxury Italian fashion house founded in a small town cose to Milan in 1985 by the two Italian designers Domenico Italian and Stefano Italian . This timeline illustrates the turrnover of the Italian luxury fashion company Italian Gucci Italian between 2011 and 2018 . In 2018 , the total Revenues of Italian Gucci Italian amounted to over 267.63 million euros , a decrease compared to the previous Year .

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: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the value of the Houston Rockets Franchise of the National Basketball Association from 2003 to 2020 . In 2020 , the Franchise had an estimated value of 2475 million U.S. dollars .

Example 96:
titleEntities: {'Subject': ['Snapchat'], 'Date': ['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 January templateTitle[5] , 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: templateXValue[0] was by far the largest 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] templateYLabel[2] templateYLabel[1] . templateYLabel[1] – additional information on the templateTitle[1] templateYLabel[1] templateYLabel[0] templateYLabel[1] are driven as of January templateTitleDate[0] .
generated: United States was by far the largest Snapchat Country in terms of total Audience of size , with about 250 thousand size by the end of 2020 . India was ranked second , among the leading ten Snapchat , with 22.95 millions size . size – additional information on the most size Audience size are driven as of January 2020 .

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 statistic shows the templateYLabel[0] of the templateTitleSubject[0] 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] templateTitleSubject[0] - additional information The templateTitleSubject[0] templateTitleSubject[0] are a professional basketball team that is part of the National Basketball Association ( NBA ) .
generated: 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 that is part of the National Basketball Association ( NBA ) .

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[min] templateYLabel[2] US templateYLabel[4] in templateXValue[min] to templateYValue[max] templateYLabel[2] US templateYLabel[4] in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[2] a templateTitleSubject[0] subscription in the templateTitle[4] in templateXValue[min] and templateXValue[max] . In the presented time period , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[2] templateTitleSubject[0] rose from templateYValue[min] percent to templateYValue[max] percent .
generated: The statistic shows the Market of cap billion NASDAQ a NASDAQ subscription in the 1999 in 1999 and 2013 . In the presented time period , the Market of cap billion NASDAQ rose 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[0] percent .

generated_template: templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] was templateYValue[0] percent in templateXValue[max] . This represents a steady decline from the increase after the financial crisis , but pre-recession levels were still above templateYValue[3] percent . What is templateYLabel[0] ? The templateYLabel[0] templateYLabel[1] of a country refers to the share of people who want to work but can not find jobs .
generated: Armenia 's Unemployment rate was 17.71 percent in 2019 . This represents a steady decline from the increase after the financial crisis , but pre-recession levels were still above 17.62 percent . What is Unemployment ? The Unemployment rate of a country refers to the share of people who want to work but can not find jobs .

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 2050*.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 depicts 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: The statistic depicts 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 .

Example 101:
titleEntities: {'Subject': ['Americans', 'American'], '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: As of December templateTitleDate[1] , templateXValue[0] templateXValue[0] is the European football player with the most templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] with a total of templateYValue[max] templateYLabel[0] , followed by templateXValue[1] templateXValue[1] templateXValue[1] with templateYValue[1] . The record for most matches played in the templateTitleSubject[0] templateTitleSubject[0] is held by Giuseppe Bergomi with 96 games played before Frank Rost with 90 games under his belt . Further records When it comes to number of appearances of clubs in the templateTitleSubject[0] templateTitleSubject[0] , Sporting Lisbon ranked first , having taken part 32 times in the templateTitleSubject[0] and Club Brugge ranked second with a Device .
generated: As of December 2017 , Personal freedom is the European football player with the most Share respondents in the Americans with a total of 66 Share , followed by Religious freedom with 56 . The record for most matches played in the Americans is held by Giuseppe Bergomi with 96 games played before Frank Rost with 90 games under his belt . Further records When it comes to number of appearances of clubs in the Americans , Sporting Lisbon ranked first , having taken part 32 times in the Americans and Club Brugge ranked second with a Device .

Example 102:
titleEntities: {'Subject': ['U.S.'], 'Date': ['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[min] 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 revenue of the templateTitle[0] of young people between the ages of templateTitle[9] to templateTitle[10] in the templateTitle[1] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[0] templateYLabel[3] young people templateTitle[4] that templateTitle[7] templateTitle[8] lived in the templateTitle[1] .
generated: The statistic shows the revenue of the Attendance of young people between the ages of 2013 to 2013 in the performing U.S. 2003 to 2013 . In 2013 , approximately 73.54 millions young people U.S. that 2013 lived in the performing .

Example 103:
titleEntities: {'Subject': ['British Telecommunications', '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 June 30 , the templateTitleSubject[1] was templateYValue[0] templateTitleSubject[0] pounds templateYLabel[2] templateYLabel[6] .

generated_template: In the fourth templateXLabel[0] of templateTitleDate[1] , California-based web company templateTitleSubject[0] had an templateYLabel[0] of templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] , up from templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in the preceding templateXLabel[0] . The number of templateTitleSubject[0] operates under the parent company Alphabet Inc .
generated: In the fourth Quarter of , California-based web company British Telecommunications had an Average of 37.9 per user GBP , up from 38.8 per user GBP in the preceding Quarter . The number of British Telecommunications 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[0] templateYLabel[2] U.S templateYLabel[4] .

generated_template: This statistic illustrates the templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitleSubject[0] in the templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company 's templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateYLabel[3] amounted to 231 templateYLabel[4] templateYLabel[5] . templateTitle[0] templateTitleSubject[0] is a chain of discount variety stores that operates in 44 templateYLabel[4] states .
generated: This statistic illustrates the Net income of million U.S. of Regal Regal Entertainment Group in the 2017 from 2006 to 2017 . In 2017 , the company 's Net income of million U.S. amounted to 231 dollars . Regal Regal Entertainment Group is a chain of discount variety stores that operates in 44 dollars states .

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: 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[max] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: 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 increased by around 4.05 percent compared to the previous 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: This statistic shows the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[2] templateTitle[3] in the templateTitle[4] as of templateTitleDate[0] . The survey revealed that templateYValue[max] percent of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] templateTitle[2] the templateTitle[0] . Residential involves the building and selling of both individual and multi-family dwellings .
generated: This statistic shows the Most used U.S. garage door in the openers as of 2018 . The survey revealed that 50 percent of the respondents used Liftmaster Brand garage the Most . Residential involves the building and selling of both individual and multi-family dwellings .

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: This statistic shows the average templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateTitle[0] templateTitle[1] in templateTitleSubject[0] was around templateYValue[6] percent compared to the previous templateXLabel[0] . templateTitle[0] templateTitle[1] in templateTitleSubject[0] – additional information The Consumer Price Index for templateTitleSubject[0] is calculated using a product basket that contains a predefined range of products and services on which the average consumer spends money throughout the templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Argentina from 2004 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Argentina was around 34.28 percent compared to the previous Year . Inflation rate in Argentina – additional information The Consumer Price Index for Argentina is calculated using a product basket that contains a predefined range of products and services on which the average consumer spends money throughout the 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 graph shows the templateTitle[0] of templateTitle[1] to Disney 's new online video streaming service ( templateTitleSubject[0] ) among adults in the templateTitle[3] as of February templateTitleDate[0] . The data reveals templateXValue[2] just templateYValue[min] percent of surveyed templateTitleSubject[0] adults said templateXValue[2] they were templateXValue[0] templateXValue[0] to sign up to templateTitleSubject[0] upon its launch in templateTitleDate[0] , and templateYValue[max] percent said templateXValue[2] they were templateXValue[2] at templateXValue[3] templateXValue[0] to subscribe to the streaming service .
generated: The graph shows the Tourism of industry to Disney 's new online video streaming service ( Tourism ) among adults in the expectations as of February 2018 . The data reveals No change just 1 percent of surveyed Tourism adults said No change they were Significantly decline to sign up to Tourism upon its launch in 2018 , and 56 percent said No change they were No change at Slightly grow Significantly decline to subscribe to the streaming service .

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: In the UK , templateYValue[max] percent of the population shop templateTitle[4] and the consumers ' favorite item to buy templateTitle[4] were templateXValue[0] and templateXValue[0] templateXValue[0] . According to the templateTitleDate[0] results of an annual interview conducted by the Office templateXValue[4] National Statistics , more than 40 percent of individuals in the United Kingdom ( UK ) templateTitle[3] templateXValue[2] templateXValue[2] , templateXValue[1] templateXValue[0] and event templateXValue[3] templateTitle[4] . templateXValue[10] and products least templateTitle[1] with templateTitle[4] consumers were templateXValue[12] , templateXValue[9] templateXValue[11] and templateXValue[10] templateXValue[10] , which only less than 20 percent of households choose to purchase templateTitle[4] .
generated: In the UK , 38.2 percent of the population shop 's and the consumers ' favorite item to buy 's were Crime and Crime . According to the 2018 results of an annual interview conducted by the Office Education National Statistics , more than 40 percent of individuals in the United Kingdom ( UK ) country Corruption , Unemployment Crime and event Health 's . Political situation and products least public with 's consumers were Poverty , Lack of care for the elderly The economy and Political situation , which only less than 20 percent of households choose to purchase 's .

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[min] .

generated_template: The graph shows the templateTitle[2] templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitleSubject[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In its templateXValue[max] fiscal templateXLabel[0] , templateTitleSubject[0] 's paid out a templateYLabel[0] of approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The graph shows the home attendance of Regular Los Angeles Chargers from the fiscal Year of 2006 to the fiscal Year of 2019 . In its 2019 fiscal Year , Los Angeles Chargers 's paid out a Home of approximately 177755 attendance .

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[min] and reached 5,978.05 in templateXValue[max] .

generated_template: As of templateXValue[max] , the templateTitleSubject[0] templateTitleSubject[0] men 's templateTitleSubject[0] templateTitleSubject[0] was ranked 96th on the templateYLabel[0] templateYLabel[1] templateYLabel[2] and had an average templateYLabel[3] of 133 since the templateYLabel[1] templateYLabel[2] creation . The best templateYLabel[3] the country held was in templateXValue[2] with an 83rd templateYLabel[3] , whereas the lowest templateYLabel[2] was in templateXValue[13] when the templateTitleSubject[0] templateTitleSubject[0] was placed 186th . The templateTitleSubject[0] templateTitleSubject[0] is a number of living in the templateTitleSubject[0] templateTitleSubject[0] is determined by in used and templateXValue[max] .
generated: As of 2019 , the CAC men 's CAC was ranked 96th on the Index points and had an average points of 133 since the points creation . The best points the country held was in 2017 with an 83rd points , whereas the lowest points was in 2006 when the CAC was placed 186th . The CAC is a number of living in the CAC is determined by in used and 2019 .

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: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] internet users in the templateTitle[0] who were using selected templateTitle[3] networks as of February templateTitleDate[0] . During that period of time , templateYValue[4] percent of templateYLabel[1] templateYLabel[4] aged between 50 and 64 years used the photo sharing app templateXValue[4] .
generated: This statistic shows the Installed of millions internet users in the Video who were using selected U.S. networks as of February 2017 . During that period of time , 10 percent of base millions aged between 50 and 64 years used the photo sharing app PlayStation 4 .

Example 113:
titleEntities: {'Subject': ['U.S.'], 'Date': ['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: In templateXValue[max] , the templateTitle[3] of templateTitle[0] and calves in the templateTitleSubject[0] templateTitleSubject[0] amounted to approximately templateYValue[max] templateYLabel[2] templateYLabel[3] . This represented a significant decrease of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: In 2018 , the chemical of Capital and calves in the U.S. amounted to approximately 33200 million U.S. . This represented a significant decrease of 33200 million U.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: This statistic illustrates the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] templateTitleSubject[0] as of December templateTitleDate[0] . As of the last measured period , the mobile messenger hat an average of templateYValue[max] percent more followers than in the previous templateXLabel[0] .
generated: This statistic illustrates the Number poker players who in Number as of December 2006 . As of the last measured period , the mobile messenger hat an average of 22.2 percent more followers than in the previous Month .

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[5] templateYLabel[2] templateYLabel[3] templateTitle[2] a templateTitle[3] templateTitle[4] of templateTitle[5] templateTitle[6] in templateXValue[max] .

generated_template: This statistic illustrates the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateYLabel[2] templateYLabel[3] templateTitle[2] from templateXValue[min] to templateXValue[max] . For the 52 weeks ended on September 1 , templateXValue[max] , there were approximately templateYValue[max] million templateTitleSubject[0] cardholders all over the world .
generated: This statistic illustrates the Average price of U.S. dollars for from 2009 to 2014 . For the 52 weeks ended on September 1 , 2014 , there were approximately 39.16 million U.S. cardholders all over the world .

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 February 2014 , social network templateXValue[1] acquired the templateXValue[3] app for 19 templateYLabel[3] U.S. dollars .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] templateTitleSubject[0] used by templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitleDate[0] . During the measured period , it was found that templateYValue[3] percent of templateYLabel[1] templateYLabel[2] templateYLabel[3] had an active templateXValue[3] account .
generated: This statistic gives information on the Most popular global Most used by active users millions in 2019 . During the measured period , it was found that 808 percent of active users millions had an active QQ Mobile account .

Example 117:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['17', '19']}
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 November templateTitleDate[1] was templateYValue[min] percent , which together with the previous templateXLabel[0] , was the lowest templateYLabel[1] recorded in this templateYValue[min] year period . Between November templateTitleDate[0] and November templateTitleDate[1] the templateTitle[2] templateYLabel[1] was at it 's highest in November of templateTitleDate[0] , when an templateYLabel[0] templateYLabel[1] of templateYValue[max] percent was recorded .

generated_template: This statistic shows the templateYLabel[2] templateYLabel[3] in the previous templateYLabel[0] of the templateTitle[1] templateTitle[0] in templateTitleSubject[0] from January templateTitleDate[0] to January templateTitleDate[1] . In January templateTitleDate[1] , the templateTitle[1] templateTitle[0] in templateTitleSubject[0] increased by approximately 1.14 percent . November templateTitleDate[0] 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: This statistic shows the rate in the previous Inflation of the rate Inflation in United Kingdom from January 17 to January 19 . In January 19 , the rate Inflation in United Kingdom increased by approximately 1.14 percent . November 17 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: This statistic shows the trade volume of templateYLabel[0] imported to the templateTitle[0] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] templateYLabel[1] to the templateTitleSubject[0] came to templateYValue[max] thousand templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the trade volume of Spending imported to the Global sponsorships 2011 to 2017 . In 2017 , Spending billion to the Global came to 5.75 thousand U.S. dollars .

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

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] 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[0] templateYLabel[2] .
generated: This statistic shows the Number of participants in indoor soccer in the U.S. from 2006 to 2018 . In 2018 , the Number of participants ( aged six years and older ) in indoor soccer amounted to approximately 5.23 millions .

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: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] amounted to templateYValue[max] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of consumption fresh in the cucumbers from 2000 to 2018 . The U.S. Per capita consumption of consumption fresh amounted to 8.1 pounds in 2018 .

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[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] on advertising and promotion .

generated_template: Nearly 295 thousand templateYLabel[2] templateYLabel[3] of templateTitle[1] was consumed across templateTitleSubject[0] in templateXValue[max] . This was a decrease compared to the previous templateXLabel[0] . According to OECD , consuming meat was equated with living standards , diet , livestock production and consumer prices .
generated: Nearly 295 thousand million U.S. of ad was consumed across Mattel in 2019 . This was a decrease compared to the previous Year . According to OECD , consuming meat was equated with living standards , diet , livestock production and consumer prices .

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 January templateTitleDate[0] , about templateYValue[max] templateYLabel[2] templateYLabel[0] templateYLabel[1] from templateXValue[0] were living in the templateTitle[3] .

generated_template: The statistic shows the list of templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] of templateYLabel[2] caused templateTitle[5] terrorist activities in templateTitleDate[0] . The templateTitle[2] templateTitle[3] of templateYLabel[2] from templateYLabel[1] in templateTitleDate[0] occurred in templateXValue[0] , accounting for templateYValue[max] percent of all templateYLabel[2] . In that year alone , terrorist incidents worldwide resulted in approximately 15,952 fatalities .
generated: The statistic shows the list of U.S. illegal the immigrants U.S. of thousands caused 2015 terrorist activities in 2015 . The immigrants U.S. of thousands from immigrants in 2015 occurred in Mexico , accounting for 6580 percent of all thousands . In that year alone , terrorist incidents worldwide resulted in approximately 15,952 fatalities .

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 August templateTitleDate[0] , 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: This statistic shows the results of a survey conducted in the templateTitleSubject[0] templateTitleSubject[0] in templateTitleDate[0] on how long the templateYLabel[1] thought the feeling of templateTitle[3] could last in a templateTitle[4] . According to templateYValue[max] percent of templateYLabel[1] , the feeling of templateTitle[3] in a templateTitle[4] can last a templateXValue[last] .
generated: This statistic shows the results of a survey conducted in the Amazon in 2018 on how long the respondents thought the feeling of membership could last in a penetration . According to 63.3 percent of respondents , the feeling of membership in a penetration can last a 150000 USD and over .

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[0] to templateTitleDate[1] . In templateTitleDate[1] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[min] percent .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[0] percent . You can access the monthly templateYLabel[0] templateYLabel[1] for the country here .
generated: This statistic displays the Unemployment rate in Texas from 1992 to 2018 . In 2018 , Unemployment rate in Texas was 3.9 percent . You can access the monthly Unemployment rate for the country here .

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] . 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: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] parent families with a female householder and no spouse present in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[28] percent of all templateTitleSubject[0] families with a templateTitle[3] mother in the templateTitle[5] lived below the templateYLabel[0] level . In templateXValue[max] , that figure was at templateYValue[min] percent .
generated: This statistic shows the Number arrests of USA for parent families with a female householder and no spouse present in the USA from 1990 to 2018 . In 1990 , 14195100 percent of all USA families with a for mother in the offenses lived below the Number level . In 2018 , that figure was at 10310960 percent .

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: Cosmetics and templateTitle[1] are bought in larger quantities each passing templateXLabel[0] in the UK . Most recently , in September templateTitleDate[1] , the templateTitle[2] templateYLabel[2] templateTitle[4] of articles in these two categories reached an templateYLabel[0] templateYLabel[1] of templateYValue[0] as presented in the graph . This was lower than the templateTitle[4] recorded for the previous months of the year , but still higher than the figures recorded in templateTitleDate[0] , 2017 and the first half of 2018 .
generated: Cosmetics and women are bought in larger quantities each passing U.S. in the UK . Most recently , in September 2017 , the murdered victims men of articles in these two categories reached an Number homicide of 232 as presented in the graph . This was lower than the men recorded for the previous months of the year , but still higher than the figures recorded in 2017 , 2017 and the first half of 2018 .

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 January templateTitleDate[0] . 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[5] percent of people in templateTitleSubject[0] .

generated_template: This statistic shows the share of internet users in the templateTitleSubject[0] templateTitleSubject[0] who were using templateTitle[0] as of January templateTitleDate[0] , sorted templateTitle[6] annual templateTitle[7] templateTitle[8] . We Are Flint found that templateYValue[0] percent of internet users with templateTitle[7] earnings of templateXValue[0] templateXValue[0] 30,000 US dollars per templateXLabel[0] used the social networking site .
generated: This statistic shows the share of internet users in the Canada who were using Canada as of January 2015 , sorted age annual age . We Are Flint found that 75 percent of internet users with age earnings of 18-34 30,000 US dollars per Year used the social networking site .

Example 128:
titleEntities: {'Subject': ['Current'], 'Date': ['2020']}
title: Current year in various historical and world calendars 2020
X_Axis['Calendar']: ['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] templateXLabel[0] , Named after Pope Gregory XIII , who introduced it in 1582 . The templateXValue[5] templateXLabel[0] replaced the templateXValue[3] templateXLabel[0] , which had been the most used templateXLabel[0] in Europe until this point . The templateXValue[5] templateXLabel[0] 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: During the year templateTitleDate[0] , the most templateYLabel[1] templateTitle[2] game on the physical market was templateXValue[0] templateXValue[0] for the PS4 , accounting for about templateYValue[max] thousand templateYLabel[0] templateYLabel[1] . In second place came templateXValue[1] templateXValue[1] templateXValue[1] templateXValue[1] , with sales amounting to roughly templateYValue[1] templateYLabel[0] . templateTitle[2] game market in templateTitleSubject[0] In 2016 , the French gaming market has known a peak , with its value amounting to 2.3 billion euros , before decreasing to 1.6 billion euros in templateTitleDate[0] .
generated: During the year 2020 , the most year various game on the physical market was Assyrian for the PS4 , accounting for about 6770 thousand Current year . In second place came Hebrew , with sales amounting to roughly 5780 Current . various game market in Current In 2016 , the French gaming market has known a peak , with its value amounting to 2.3 billion euros , before decreasing to 1.6 billion euros in 2020 .

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[0] templateTitle[0] templateTitle[1] U.S. templateYLabel[1] templateYLabel[2] members .

generated_template: In templateXValue[max] , the templateTitle[1] templateYLabel[0] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] amounted to about templateYValue[0] templateYLabel[1] templateYLabel[2] , all types included . The templateTitle[0] realized templateYLabel[0] of templateTitle[3] templateTitle[4] was fairly steady throughout the 25 years until templateXValue[3] , when it decreased to below templateYValue[0] templateYLabel[1] templateYLabel[2] . The templateTitle[0] market The templateTitle[0] templateYLabel[0] serves as an indicator for a variety of different selling prices on the templateTitle[4] market , gathering all templateYLabel[0] ranges of templateTitle[3] wines purchased in templateTitleSubject[0] .
generated: In 2010 , the duty Number of Coast Guard in Coast Guard amounted to about 41327 Coast Guard , all types included . The Active realized Number of Coast Guard was fairly steady throughout the 25 years until 2007 , when it decreased to below 41327 Coast Guard . The Active market The Active Number serves as an indicator for a variety of different selling prices on the Guard market , gathering all Number ranges of Coast wines purchased in Coast Guard .

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: As of January templateTitleDate[0] , templateXValue[0] was home to the largest templateYLabel[0] of templateYLabel[1] of templateTitle[4] templateTitle[5] , with templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateXValue[1] , there were about templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] of internet users in that year . Global templateTitle[3] templateXLabel[0] In second , templateXValue[0] had the second largest templateYLabel[0] templateYLabel[1] of the templateTitle[1] worldwide , with a templateYLabel[1] of approximately templateYValue[1] templateYLabel[2] people in the middle templateXValue[0] already each templateXValue[last] in a fight to medical advances , better living conditions and the increase of agricultural productivity the world templateTitle[2] is continuously rising . The development of the world templateTitle[2] from 1950 to 2030 is estimated to be tripled according to United Nations ' data.The majority of the world templateTitle[2] lives in templateXValue[4] , but the templateTitle[2] in templateXValue[0] is forecasted to rise from 1,031 in year 2010 up to 4,185 in year 2100 .
generated: As of January 2013 , Europe was home to the largest Physicians of per of region 2013 , with 32.1 10,000 population . Americas , there were about 21.5 10,000 population of internet users in that year . Global by Country In second , Europe had the second largest Physicians per of the density worldwide , with a per of approximately 21.5 10,000 people in the middle Europe already each Africa in a fight to medical advances , better living conditions and the increase of agricultural productivity the world worldwide is continuously rising . The development of the world worldwide from 1950 to 2030 is estimated to be tripled according to United Nations ' data.The majority of the world worldwide lives in Eastern Mediterranean , but the worldwide in Europe is forecasted to rise from 1,031 in year 2010 up to 4,185 in year 2100 .

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[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] , up from templateYValue[1] templateYLabel[3] templateYLabel[2] in the previous templateXLabel[0] .

generated_template: This statistic presents the average templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , a templateYLabel[0] of templateYValue[0] templateYLabel[2] templateYLabel[1] were sold in templateTitleSubject[0] .
generated: This statistic presents the average Number of active of Wayfair from 2013 to 2018 . In 2018 , a Number of 15.16 customers active were sold in Wayfair .

Example 132:
titleEntities: {'Subject': ['Italians'], 'Date': ['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: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] templateTitle[3] on templateTitle[4] templateTitle[5] in the templateTitle[6] as forecasted by professional chefs for templateTitleDate[0] . During the survey , templateYValue[max] percent of the templateYLabel[1] stated that templateXValue[0] templateXValue[0] templateXValue[0] was a `` hot trend '' for templateTitle[4] templateTitle[5] in templateTitleDate[0] .
generated: This statistic shows the Persons whom in Italians cheat on their partners in the 2017 as forecasted by professional chefs for 2017 . During the survey , 25.4 percent of the respondents stated that Friend was a `` hot trend '' for their partners in 2017 .

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: This statistic shows the number of metal and metal products that are produced from templateTitleSubject[0] templateTitle[1] and other low-grade residues worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[7] , the templateTitle[2] templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] templateTitle[1] amounted to templateYValue[3] percent .
generated: This statistic shows the number of metal and metal products that are produced from U.S. rental and other low-grade residues worldwide from 2002 to 2012 . In 2009 , the U.S. Cars service 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 templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] , ranked templateTitle[5] cumulative templateTitle[6] templateYLabel[1] . templateXValue[0] 's templateYLabel[0] templateTitle[8] results in templateTitleSubject[0] was templateXValue[0] , with a total of templateYValue[max] thousand templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Largest cities Russia 2015 Russia in 2015 , ranked 2015 cumulative 2015 million . Moscow 's Residents 2015 results in Russia was Moscow , with a total of 12.05 thousand million .

Example 135:
titleEntities: {'Subject': ['Spain'], 'Date': ['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[max] , a rise since the previous templateXLabel[0] .

generated_template: templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] have been steadily rising each templateXLabel[0] since the start of the decade . In templateXValue[max] , a record templateYValue[max] templateYLabel[2] tourists ( including residents and non-residents ) arrived at templateTitle[3] in templateTitleSubject[0] . Tourism in templateTitleSubject[0] In line with trends across Europe , tourism in templateTitleSubject[0] has experienced a boost in the last ten years .
generated: tourist arrivals at accommodation establishments in Spain have been steadily rising each Year since the start of the decade . In 2017 , a record 129.4 millions tourists ( including residents and non-residents ) arrived at accommodation in Spain . Tourism in Spain In line with trends across Europe , tourism in Spain has experienced a boost in the last ten years .

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: templateTitleSubject[0] , the warehouse shopping club , continued to grow in templateTitleDate[0] with 782 locations worldwide at year 's end . The U.S. is still home to the vast majority of the American retailer 's templateYLabel[1] , but it has expanded into templateYValue[7] other markets . With the expansion in store count comes an increase of members , and at the end of 2018 templateTitleSubject[0] could boast over 98 million cardholders .
generated: Europe , the warehouse shopping club , continued to grow in 2014 with 782 locations worldwide at year 's end . The U.S. is still home to the vast majority of the American retailer 's users , but it has expanded into 21000 other markets . With the expansion in store count comes an increase of members , and at the end of 2018 Europe could boast over 98 million cardholders .

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: This statistic shows the templateTitle[4] templateTitle[5] templateYLabel[0] templateYLabel[1] templateYLabel[2] worldwide in templateTitleDate[0] , broken down templateTitle[7] templateXLabel[0] . According to the report , the templateXValue[0] templateXValue[0] accounted for approximately templateYValue[max] percent of templateTitleSubject[0] templateTitle[4] templateYLabel[0] templateYLabel[1] that year .
generated: This statistic shows the market by Share market worldwide in 2015 , broken down 2015 Country . According to the report , the European Union accounted for approximately 26 percent of Share market Share market that 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[0] percent .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent of the total labor force . The templateYLabel[0] templateYLabel[1] is the percentage of a country 's labor force that are without jobs but are available to work and actively seeking employment . templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] is above the worldwide templateYLabel[0] templateYLabel[1] , and compared to other Sub-Saharan African countries and other regions , templateTitleSubject[0] has a relatively average templateYLabel[1] of templateYLabel[0] .
generated: In 2019 , the Unemployment rate in Tonga was at approximately 1.02 percent of the total labor force . The Unemployment rate is the percentage of a country 's labor force that are without jobs but are available to work and actively seeking employment . Tonga 's Unemployment rate is above the worldwide Unemployment rate , and compared to other Sub-Saharan African countries and other regions , Tonga has a relatively average rate of Unemployment .

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: During the year templateTitleDate[0] , the most templateYLabel[1] templateTitle[2] game on the physical market was templateXValue[0] templateXValue[0] for the PS4 , accounting for about templateYValue[max] thousand templateYLabel[0] templateYLabel[1] . In second place came templateXValue[1] templateXValue[1] templateXValue[1] templateXValue[1] , with sales amounting to roughly templateYValue[1] templateYLabel[0] . templateTitle[2] game market in templateTitleSubject[0] In 2016 , the French gaming market has known a peak , with its value amounting to 2.3 billion euros , before decreasing to 1.6 billion euros in templateTitleDate[0] .
generated: During the year 2017 , the most thousand groups game on the physical market was Carps barbels and other cyprinids for the PS4 , accounting for about 28345 thousand Production thousand . In second place came Miscellaneous freshwater fishes , with sales amounting to roughly 10433 Production . groups game market in Major In 2016 , the French gaming market has known a peak , with its value amounting to 2.3 billion euros , before decreasing to 1.6 billion euros in 2017 .

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[min] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] in templateXValue[min] , but were expected to grow to templateYValue[max] templateYLabel[1] templateYLabel[2] in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] cloud-based online service templateYLabel[1] templateTitle[4] . In templateXValue[max] , approximately templateYValue[max] templateYLabel[2] internet templateYLabel[1] are projected to access templateTitle[1] templateTitle[2] services , up from templateYValue[min] templateYLabel[2] templateYLabel[1] in templateXValue[min] .
generated: The statistic shows the Sales of Germany cloud-based online service billion value . In 2018 , approximately 41 euros internet billion are projected to access Online retail services , up from 25 euros billion in 2013 .

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 August templateXValue[max] . Following the templateTitleSubject[0] accident in Japan in March templateXValue[8] , 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: This statistic shows the number of templateTitle[0] templateTitle[2] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , this was estimated to produce around templateYValue[0] templateYLabel[2] templateYLabel[1] in templateTitle[0] .
generated: This statistic shows the number of Nuclear plants shutdowns in Nuclear from 2005 to 2019 . In 2019 , this was estimated to produce around 3 shutdowns in Nuclear .

Example 142:
titleEntities: {'Subject': ['U.S. Black Friday'], 'Date': ['2015', '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[4] 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[max] percent of templateYLabel[1] were planning to do the majority of their templateTitle[3] on templateTitleSubject[0] .

generated_template: As of June templateXValue[max] , templateYValue[4] percent of all templateYLabel[1] stated that their favorite moving watching was at templateXValue[1] . Only around templateYValue[0] percent of templateYLabel[1] stated that they templateTitle[0] to watch movies in templateXValue[3] , while templateYValue[3] percent stated that they like the theatre and compared to the same templateXLabel[0] . the Movies was founded in templateTitleSubject[1] areas .
generated: As of June 2019 , 36 percent of all respondents stated that their favorite moving watching was at 2016 . Only around 59 percent of respondents stated that they Share to watch movies in 2018 , while 37 percent stated that they like the theatre and compared to the same Year . the Movies was founded in U.S. Black Friday areas .

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: templateTitle[2] templateYLabel[1] in templateTitleSubject[0] reached an all-time high as of templateXValue[max] , when templateTitleSubject[0] templateTitle[1] auto industry sold some templateYValue[max] templateYLabel[2] templateYLabel[3] . This figure is almost double the templateTitleDate[0] templateYLabel[1] , when just templateYValue[min] templateYLabel[2] templateTitle[2] templateYLabel[3] were sold in templateTitleSubject[0] . templateTitle[2] industry in templateTitleSubject[0] In templateXValue[max] , templateTitle[2] vehicles also made up the majority of vehicles in production in templateTitleSubject[0] , followed by passenger vehicles .
generated: motorcycle units in BMW Group reached an all-time high as of 2018 , when BMW Group Group auto industry sold some 185682 units . This figure is almost double the 2010 units , when just 99236 units motorcycle units were sold in BMW Group . motorcycle industry in BMW Group In 2018 , motorcycle vehicles also made up the majority of vehicles in production in BMW Group , followed by passenger vehicles .

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 April templateTitleDate[0] to September templateTitleDate[1] . 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: How many people play templateTitleSubject[0] ? PlayerUnknown 's Battlegrounds ( templateTitleSubject[0] ) , which is available on templateTitle[1] and templateTitle[2] , crossed the templateYValue[max] templateYLabel[2] templateTitle[3] templateTitle[4] mark in templateXValue[0] templateXValue[0] - an impressive figure considering it was released little over a year earlier . The Battle of templateXValue[0] , this was made to templateYValue[max] templateYLabel[2] concurrent templateYLabel[1] .
generated: How many people play Messenger ? PlayerUnknown 's Battlegrounds ( Messenger ) , which is available on Messenger and number , crossed the 1300 active monthly active mark in Sep '17 - an impressive figure considering it was released little over a year earlier . The Battle of Sep '17 , this was made to 1300 active concurrent monthly .

Example 145:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2017', '2023']}
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[min] 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: This statistic presents the templateTitle[0] networking reach in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the templateYLabel[1] in the country accessed templateTitle[0] templateTitle[1] . In templateXValue[4] , this templateYLabel[0] is projected to reach templateYValue[4] percent .
generated: This statistic presents the Vietnam networking reach in Vietnam from 2017 to 2023 . In 2017 , 46 percent of the population in the country accessed Vietnam social . In 2019 , this Share is projected to reach 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: This statistic illustrates the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] generated templateYLabel[0] of approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic illustrates the Revenue of industry Revenue Easton Bell in the goods from 2006 to 2013 . In 2013 , the industry Revenue Easton Bell generated Revenue of approximately 834.9 (in million U.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 August templateTitleDate[0] . 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: As of September templateTitleDate[0] , templateXValue[0] templateXValue[0] had the highest amount of the templateTitleSubject[0] templateTitleSubject[0] templateXLabel[1] in the templateTitleSubject[1] templateTitleSubject[1] with a total of templateYValue[max] templateYLabel[0] templateYLabel[1] . In this year , they had a total of templateYValue[min] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] templateTitleSubject[0] . The ( templateTitleSubject[1] ) user had the highest lowest amount to the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) .
generated: As of September 2019 , Nissan had the highest amount of the Mexico Automaker in the Mexico with a total of 174706 Number units . In this year , they had a total of 30 Number units sold in the Mexico . The ( Mexico ) user had the highest lowest amount to the Mexico ( Mexico ) .

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[min] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , there were approximately templateYValue[5] templateYLabel[2] templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] , and this figure is projected to grow to templateYValue[max] templateYLabel[2] in templateXValue[max] .
generated: This statistic shows the Number of social network users in Mexico from 2017 to 2023 . In 2018 , there were approximately 61.7 millions social network users in Mexico , and this figure is projected to grow to 73.0 millions in 2023 .

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: This statistic represents templateTitleSubject[0] 's templateYLabel[0] between the templateXValue[min] and templateXValue[max] fiscal years . In the templateXValue[max] fiscal templateXLabel[0] , the manufacturer of regional aircraft , business jets , mass transportation equipment and recreational equipment had revenues of templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic represents Bombardier 's Revenue between the 2007 and 2018 fiscal years . In the 2018 fiscal Year , the manufacturer of regional aircraft , business jets , mass transportation equipment and recreational equipment had revenues of 8.91 billion U.S. dollars .

Example 150:
titleEntities: {'Subject': ['Lauder'], 'Date': ['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 graph shows templateTitleSubject[0] 's templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] templateTitle[2] templateYLabel[1] of the global templateTitle[5] templateTitle[6] templateYLabel[0] is estimated to be templateYValue[7] percent . The company is a manufacturer and marketer of prestige templateTitle[4] templateTitle[5] , makeup , fragrance and hair templateTitle[5] templateTitle[6] , with global net sales of over 11.2 billion U.S. dollars .
generated: The graph shows Lauder 's Estée Lauder 's share of the makeup products market 2012 from 2012 to 2024 . In 2018 , Lauder 's share of the global products market is estimated to be 16.4 percent . The company 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': ['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[max] templateYLabel[2] templateTitle[3] templateTitle[4] templateYLabel[1] in templateTitleSubject[0] , up from templateYValue[5] templateYLabel[2] in templateXValue[5] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , there were approximately templateYValue[5] templateYLabel[2] templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] , and this figure is projected to grow to templateYValue[max] templateYLabel[2] in templateXValue[max] .
generated: This statistic shows the Number of number social users in South Korea from 2015 to 2022 . In 2018 , there were approximately 24.77 millions number social users in South Korea , and this figure is projected to grow to 28.16 millions in 2022 .

Example 152:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['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[max] thousand templateYLabel[1] are templateTitle[0] to have templateTitle[4] by templateXValue[max] in templateTitleSubject[0] .

generated_template: This statistic illustrates the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateTitle[3] templateTitle[4] in templateTitleSubject[0] in templateXValue[min] , templateXValue[1] and templateXValue[max] . According to the source , approximately 240 thousand templateYLabel[1] are templateTitle[0] to have templateTitle[4] by templateXValue[max] in templateTitleSubject[0] .
generated: This statistic illustrates the Projected Number of people dementia Malaysia in Malaysia in 2015 , 2030 and 2050 . According to the source , approximately 240 thousand people are Projected to have Malaysia by 2050 in Malaysia .

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[0] percent of the total labor force in templateTitleSubject[0] was unemployed .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Qatar from 1999 to 2019 . In 2019 , the Unemployment rate in Qatar was at approximately 0.15 percent .

Example 154:
titleEntities: {'Subject': ['Twitter'], 'Date': ['08', '19']}
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[0] to templateTitleDate[1] . 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: As of December templateTitleDate[1] , there were approximately templateYValue[max] thousand in templateTitleSubject[0] as of December templateTitleDate[0] . The New York-based trend in templateTitleSubject[0] , it is also known as the most-followed country in templateTitleSubject[0] . As of the total numbers were almost 60 thousand templateYLabel[1] in templateTitleSubject[0] during this time .
generated: As of December 19 , there were approximately 4900 thousand in Twitter as of December 08 . The New York-based trend in Twitter , it is also known as the most-followed country in Twitter . As of the total numbers were almost 60 thousand employees in Twitter during this time .

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: As of templateTitleDate[0] , right-wing American news website templateTitle[0] was rated as templateXValue[2] at templateXValue[2] templateXValue[2] by templateYValue[2] percent of templateYLabel[1] from a survey of over two thousand templateTitleSubject[0] adults . Additionally , over half of templateYLabel[1] had either templateXValue[0] templateXValue[0] of the publication or had templateXValue[1] templateXValue[1] about templateTitle[0] 's templateTitle[1] . Conservative view of news media While right-wing websites and online publications have grown in number and popularity in recent years , Fox News remains the largest and perhaps most influential conservative news source in the templateTitle[2] .
generated: As of 2018 , right-wing American news website Americans was rated as Moderately at Moderately by 16 percent of respondents from a survey of over two thousand Americans adults . Additionally , over half of respondents had either Extremely of the publication or had Very about Americans 's ' . Conservative view of news media While right-wing websites and online publications have grown in number and popularity in recent years , Fox News remains the largest and perhaps most influential conservative news source in the level .

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[min] percent .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent of the total labor force . The templateYLabel[0] templateYLabel[1] is the percentage of a country 's labor force that are without jobs but are available to work and actively seeking employment . templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] is above the worldwide templateYLabel[0] templateYLabel[1] , and compared to other Sub-Saharan African countries and other regions , templateTitleSubject[0] has a relatively average templateYLabel[1] of templateYLabel[0] .
generated: In 2019 , the Unemployment rate in Bulgaria was at approximately 4.82 percent of the total labor force . The Unemployment rate is the percentage of a country 's labor force that are without jobs but are available to work and actively seeking employment . Bulgaria 's Unemployment rate is above the worldwide Unemployment rate , and compared to other Sub-Saharan African countries and other regions , Bulgaria has a relatively average rate of Unemployment .

Example 157:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['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 templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] amounted to templateYValue[0] thousand , up from templateYValue[min] thousand in the previous templateXLabel[0] .
generated: The statistic shows the Retail of value at United Kingdom from 2009 to 2018 . In 2018 , the Retail of United Kingdom value amounted to 2984.2 thousand , up from 2633.5 thousand in the previous Year .

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: The statistics depicts the templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , there were a total of templateYValue[max] registered templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] according to the International templateTitle[0] templateTitle[1] Federation .
generated: The statistics depicts the Number of registered Ice hockey players in the Sweden from 2010/11 to 2018/19 . In the 2018/19 season , there were a total of 69921 registered Ice hockey players in the Sweden according to the International Ice hockey Federation .

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: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] .
generated: This statistic shows the Median age of the population in 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 .

Example 160:
titleEntities: {'Subject': ['Obamacare'], '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 February templateTitleDate[0] , templateTitle[7] templateTitleSubject[0] templateXLabel[0] . Until February templateTitleDate[0] , 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: As of templateTitleDate[0] , the states with the highest rates of templateTitle[0] included templateXValue[1] templateXValue[1] , templateXValue[1] templateXValue[2] , templateXValue[3] , and templateXValue[4] . As of that time , there were around templateYValue[1] templateYLabel[1] templateYLabel[2] 1,000 templateYLabel[4] in the templateXLabel[0] of templateXValue[1] templateXValue[1] . The overall templateTitle[1] of legal templateYLabel[1] in the templateTitle[2] has gradually decreased since the mid-1980s .
generated: As of 2019 , the states with the highest rates of Number included California , California Texas , North Carolina , and Georgia . As of that time , there were around 1513883 signups 1,000 signups in the State of California . The overall sign-ups of legal signups in the during has gradually decreased since the mid-1980s .

Example 161:
titleEntities: {'Subject': ['GDP Michigan'], 'Date': ['2018', '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: This graph shows the templateXValue[1] templateYLabel[1] templateYLabel[2] to the Gross Domestic Product ( templateTitle[1] ) of templateTitleSubject[0] templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[9] templateXLabel[0] templateYLabel[2] templateYValue[min] templateYLabel[3] templateYLabel[4] 2012 templateYLabel[6] templateYLabel[7] of templateYLabel[1] to the state templateTitle[1] .
generated: This graph shows the Finance insurance real estate rental and leasing value added to the Gross Domestic Product ( GDP ) of GDP Michigan in 2018 , by Industry . In 2018 , the Information Industry added 2.36 billion chained 2012 U.S. dollars of value to the state 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: According to a templateTitleDate[0] Statista survey , templateYValue[max] percent of the templateTitle[6] templateYLabel[1] use their templateTitle[1] to templateXValue[0] to templateXValue[0] . templateXValue[last] common uses of templateTitle[1] included templateXValue[1] templateXValue[1] or templateXValue[1] templateXValue[1] and listening to the templateXValue[2] . Headphone Market The retail value of global headphone shipments was forecast to reach 26.1 billion templateTitle[6] dollars in 2019 , a more than threefold increase in templateYValue[min] years .
generated: According to a 2012 Statista survey , 38 percent of the decisions respondents use their friends to Parents to Parents . Someone else common uses of friends included Friends or Friends and listening to the The media . Headphone Market The retail value of global headphone shipments was forecast to reach 26.1 billion decisions dollars in 2019 , a more than threefold increase in 4 years .

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: This statistic shows the templateTitle[0] ten templateTitle[1] in templateTitle[2] templateTitle[3] on templateTitle[4] templateTitle[5] in the templateTitle[6] as forecasted by professional chefs for templateTitleDate[0] . During the survey , templateYValue[max] percent of the templateYLabel[1] stated that templateXValue[3] templateXValue[0] was a `` hot trend '' for templateTitle[4] templateTitle[5] in templateTitleDate[0] .
generated: This statistic shows the functions ten outsourced in worldwide 2017 on 2017 in the 2017 as forecasted by professional chefs for 2017 . During the survey , 64 percent of the respondents stated that IT infrastructure Software application development was a `` hot trend '' for 2017 in 2017 .

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[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: In templateXValue[max] , e-books accounted for templateYValue[min] percent of all book sales in the templateTitle[3] , three percent less than in the previous templateXLabel[0] . This is due to templateYLabel[0] unit sales dropping from 180 million in templateXValue[min] to 162 million in templateXValue[max] . In the latter templateXLabel[0] , the top-selling templateYLabel[0] was 'The Handmaid 's Tale ' by Margaret Atwood , a novel which has seen become a highly popular and successful TV series .
generated: In 2011 , e-books accounted for 191.76 percent of all book sales in the U.S. , three percent less than in the previous Year . This is due to Equipment unit sales dropping from 180 million in 2010 to 162 million in 2011 . In the latter Year , the top-selling Equipment was 'The Handmaid 's Tale ' by Margaret Atwood , a novel which has seen become a highly popular and successful TV series .

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[max] .

generated_template: As of templateXValue[max] , the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] amounted to approximately templateYValue[max] templateYLabel[2] . As one of the biggest economies in Southeast Asia , templateTitleSubject[0] is an attractive destination for workers from neighboring countries seeking better wages and employment . Indonesians make up the majority of templateYLabel[1] With their shared culture , religion and a very similar language , Indonesia was the single biggest contributor of templateYLabel[1] to templateTitleSubject[0] .
generated: As of 2018 , the Average of sales in Outback Steakhouse amounted to approximately 3580 thousand . As one of the biggest economies in Southeast Asia , Outback Steakhouse is an attractive destination for workers from neighboring countries seeking better wages and employment . Indonesians make up the majority of sales With their shared culture , religion and a very similar language , Indonesia was the single biggest contributor of sales to Outback Steakhouse .

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: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitle[6] 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[0] templateYLabel[2] .
generated: This graph depicts the total Franchise value Franchise value of the Chicago Blackhawks 2019 franchise of the National Football League from 2006 to 2019 . In 2019 , the Franchise value Franchise value of the franchise was 1085 million .

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: As of September templateTitleDate[0] , templateXValue[0] had the highest prevalence of templateTitle[0] templateTitle[1] in templateTitleSubject[0] at templateYValue[max] percent , the highest of any templateTitleSubject[0] templateTitle[3] state . Only templateYValue[7] other templateTitle[3] templateTitle[4] contributed more than ten percent to the templateTitleSubject[0] templateTitle[7] , templateXValue[1] ( templateYValue[1] percent ) the United Kingdom ( templateYValue[2] percent ) and templateXValue[3] ( templateYValue[3] percent ) . The small island-nation of templateXValue[last] contributed the least to the templateTitleSubject[0] templateTitle[7] , at templateYValue[min] percent .
generated: As of September 2018 , New Zealand had the highest prevalence of Corruption perception in APAC at 87 percent , the highest of any APAC state . Only 57 other APAC 2018 contributed more than ten percent to the APAC country , Singapore ( 85 percent ) the United Kingdom ( 77 percent ) and Hong Kong ( 76 percent ) . The small island-nation of North Korea contributed the least to the APAC country , at 14 percent .

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: This statistic represents the regional templateTitle[0] of templateTitleSubject[0] templateYLabel[1] in the last three months . As of October templateTitleDate[0] , the templateXValue[0] templateXValue[0] accounted for templateYValue[max] percent of desktop templateYLabel[1] to the visual blogging site during this period of time .
generated: This statistic represents the regional Metro of Metro Group million in the last three months . As of October , the Western Europe (excluding Germany) accounted for 8885 percent of desktop million to the visual blogging site during this period of time .

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: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .
generated: The statistic shows the Gross domestic product ( GDP ) per capita from 1994 to 2018 , with projections up until 2024 . GDP refers to the total market value of all goods and services that are produced within a country per Year . It is an important indicator of the economic strength of a country .

Example 170:
titleEntities: {'Subject': ['Latin America', 'Caribbean'], 'Date': ['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: This 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[max] . In templateXValue[6] , templateTitleSubject[0] 's templateYLabel[0] amounted approximately templateYValue[6] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Gross domestic product ( GDP ) of Latin America from 2014 to 2018 , with projections up until 2024 . In 2018 , Latin America 's GDP amounted approximately 5249.66 billion U.S. dollars .

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: This statistic illustrates the distribution of templateTitleSubject[0] templateYLabel[0] farm projects templateYLabel[2] templateYLabel[3] in leading templateYLabel[0] power countries as of October templateTitleDate[0] . There were templateYValue[1] templateYLabel[0] farm projects templateYLabel[2] templateYLabel[3] in templateXValue[2] . Higher templateYLabel[0] speeds that occur templateTitleSubject[0] in comparison to land , allows for a higher supply of electricity from templateTitleSubject[0] templateYLabel[0] energy .
generated: This statistic illustrates the distribution of Terrorism Number farm projects taken in leading Number power countries as of October . There were 1748 Number farm projects taken in Near East . Higher Number speeds that occur Terrorism in comparison to land , allows for a higher supply of electricity from Terrorism Number energy .

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: As of September templateTitleDate[0] , templateXValue[0] templateXValue[0] from KRC Genk had the templateTitle[6] templateXLabel[0] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) in 2018 , with an templateYLabel[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] , followed by templateXValue[1] with templateYValue[1] templateYLabel[2] templateYLabel[3] . The Big templateYValue[22] Of all the consultancy templateTitle[2] , the best known are the Big templateYValue[22] , accounting for almost 40 percent of the templateXValue[10] market - templateXValue[3] , templateXValue[13] ( Pricewaterhouse Coopers ) , templateXValue[6] ( Ernst templateXValue[0] Young ) .
generated: As of September 2017 , Valentino (Demi Lovato) from KRC Genk had the by Brand in the Luxury ( Luxury ) in 2018 , with an Number of 1385467 media actions , followed by Tory Burch (Shay Mitchell) with 134751 media actions . The Big 341862 Of all the consultancy social , the best known are the Big 341862 , accounting for almost 40 percent of the Hermes (Xenia Tchoumi) market - Tiffany & Co. (Reese Witherspoon) , Fendi (Gigi Hadid) ( Pricewaterhouse Coopers ) , Prada (Chiara Ferragni) ( Ernst Valentino (Demi Lovato) Young ) .

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: This statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitleDate[0] . In templateXValue[0] templateXValue[0] , the templateYLabel[0] grew by about templateYValue[max] percent templateYLabel[2] to the templateYLabel[3] templateYLabel[4] , making it the templateXLabel[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitleDate[0] . The global templateYLabel[0] Today , the global templateYLabel[0] amounts to around 7 billion people , i.e .
generated: This statistic shows the 20 Euro inflation the rate Inflation rate countries in 2019 . In Latvia , the Inflation grew by about 3.1 percent compared to the previous year , making it the Country inflation the rate Inflation rate countries in 2019 . The global Inflation Today , the global Inflation amounts to around 7 billion people , i.e .

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[22] 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: This statistic shows the statutory templateYLabel[0] templateYLabel[1] on templateTitle[0] templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . On August templateXValue[max] , the total templateYLabel[0] templateYLabel[1] was raised to 21.9 templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the statutory Price U.S. on U.S. Price of the U.S. of U.S. from 1995 to 2019 . On August 2019 , the total Price U.S. was raised to 21.9 dollars per pound .

Example 175:
titleEntities: {'Subject': ['Bolivia'], 'Date': ['2017']}
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[max] 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[0] ( templateTitleSubject[1] ) used the templateYLabel[3] in templateXValue[max] , according to figures from Eurostat and the International Telecommunications Union . The figure is unchanged from templateXValue[1] , marking the first templateXLabel[0] without growth since templateXValue[10] . templateYLabel[3] users in Great Britain44.1 million people used the templateYLabel[3] daily in Great Britain during 2018 according to the templateTitleSubject[0] templateTitleSubject[0] 's ( templateTitleSubject[1] ) Office for National Statistics ( ONS ) , breaking the record set in templateXValue[1] .
generated: 43.83 percent of the population of the Bolivia ( Bolivia ) used the internet in 2017 , according to figures from Eurostat and the International Telecommunications Union . The figure is unchanged from 2016 , marking the first Year without growth since 2007 . internet users in Great Britain44.1 million people used the internet daily in Great Britain during 2018 according to the Bolivia 's ( Bolivia ) Office for National 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 templateYLabel[0] templateYLabel[1] templateYLabel[2] rose to templateYValue[max] percent in templateXValue[max] , down from templateYValue[1] percent in December templateXLabel[0] . The highest templateYLabel[0] templateYLabel[1] templateYLabel[2] was recorded in templateXValue[min] to templateYValue[min] percent . The average templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[max] percent in templateXValue[max] .
generated: The Life expectancy birth rose to 78.83 percent in 2017 , down from 78.57 percent in December Year . The highest Life expectancy birth was recorded in 1960 to 45.19 percent . The average Life expectancy birth was at 78.83 percent in 2017 .

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[max] 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: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[2] templateYLabel[1] by one templateYLabel[4] while being of child-bearing age . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Fertility rate in China from 2007 to 2017 . The Fertility rate is the average Number of born children by one woman while being of child-bearing age . In 2017 , the Fertility rate in China amounted to 1.53 born per woman .

Example 178:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2013']}
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 November templateTitle[10] . The majority of templateYLabel[1] said that their templateXValue[0] is the templateTitle[0] templateTitle[1] thing to them .

generated_template: As of the third quarter of templateTitleDate[0] , it was found that templateYValue[0] percent of templateTitleSubject[0] users in the templateTitleSubject[1] templateTitleSubject[1] accessed the photo sharing app templateXValue[0] templateXValue[0] a templateXValue[0] . A further templateYValue[1] percent of templateYLabel[1] claimed that they used templateTitleSubject[0] on a templateXValue[1] basis .
generated: As of the third quarter of 2013 , it was found that 51 percent of Great Britain users in the Great Britain accessed the photo sharing app Family a Family . A further 14 percent of respondents claimed that they used Great Britain on a Friends basis .

Example 179:
titleEntities: {'Subject': ['Global R D expenditure', '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[max] – an increase of 7.7 templateYLabel[2] templateYLabel[3] on the previous templateXLabel[0] . What is the role of research and development ? As of December templateXValue[max] , templateTitleSubject[0] had 14,332 employees worldwide , of which 986 were employed in the field of research and development/product management . In a sportswear market that is constantly changing , templateTitleSubject[0] 's research and development team is responsible for providing insights into product design and new technologies .

generated_template: This statistic presents the templateTitle[2] templateYLabel[0] of templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it was estimated that there will be around templateYValue[max] templateYLabel[2] templateYLabel[1] in the templateTitleSubject[1] , up from templateYValue[1] templateYLabel[2] in templateXValue[3] .
generated: This statistic presents the & R&D of Global R in Global R D expenditure from 2013 to 2019 . In 2019 , it was estimated that there will be around 61.7 million expenditure in the Puma , up from 54.0 million in 2016 .

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: 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: 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 .

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] templateTitle[1] templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[6] in templateTitle[4] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[min] percent of the templateTitle[4] templateTitle[5] templateTitle[6] . See templateYLabel[2] of templateTitleSubject[0] for additional information .
generated: The statistic shows Argentina 's Budget balance from 2014 to 2018 in relation to the relation GDP 2024* ( relation ) , with projections up until 2024 . In 2018 , the Budget balance in Argentina amounted to about -6.7 percent of the relation GDP 2024* . See relation of Argentina for additional information .

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 by far the largest templateTitleSubject[0] templateXLabel[0] in terms of total templateYLabel[0] of templateYLabel[1] , with about 250 thousand templateYLabel[1] by the end of templateTitleDate[0] . templateXValue[1] was ranked second , among the leading ten templateTitle[2] , with templateYValue[1] thousand templateYLabel[1] as of templateTitleDate[0] . templateYLabel[1] classification In the templateTitle[1] templateYLabel[1] are classified templateTitle[3] on the farm income and government payments into six sales classes .
generated: European Union (28) was by far the largest Value Country in terms of total Import of value , with about 250 thousand value by the end of 2018 . United States was ranked second , among the leading ten 10 , with 30 thousand value as of 2018 . value classification In the leading value are classified textile on the farm income and government payments into six sales classes .

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: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] 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] templateTitleSubject[0] templateTitleSubject[0] are owned by Stuart Sternberg , who bought the templateYLabel[0] for 200 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 2000 .
generated: 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 1900 million U.S. dollars . The Los Angeles Angels are owned by Stuart Sternberg , who bought the Franchise for 200 million U.S. dollars in 2000 .

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[9] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] templateTitleSubject[0] templateTitle[6] of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] .
generated: 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 31.42 U.S. dollars .

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[0] to templateTitleDate[1] . In the period 2013 to templateTitleDate[1] , on templateYLabel[0] those aged 12 to 20 templateYLabel[2] templateTitle[2] drank templateTitle[3] when they were templateYValue[max] templateYLabel[2] old .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[max] templateYLabel[2] .
generated: This statistic shows the Average age of first alcohol use in U.S. from 2013–2015 to 2013–2015 . In 2013–2015 , the Average age of first alcohol use was 14.64 years .

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: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[0] templateYLabel[2] templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitle[3] .
generated: This statistic shows the Number of Companies insurance companies in the United from 2004 to 2017 . In 2017 , there were approximately 436 companies insurance companies in the United .

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: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] 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] templateTitleSubject[0] templateTitleSubject[0] are owned by Stuart Sternberg , who bought the templateYLabel[0] for 200 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 2000 .
generated: This graph depicts the value of the Los Angeles Rams Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 3800 million U.S. dollars . The Los Angeles Rams are owned by Stuart Sternberg , who bought the Franchise for 200 million U.S. dollars in 2000 .

Example 188:
titleEntities: {'Subject': ['GDP'], 'Date': ['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[0] % of the nation 's Gross Domestic Product , making it one of the largest sectors of the U.S. economy .

generated_template: As of templateXValue[max] , the templateYLabel[0] templateYLabel[2] per consumer unit on templateTitle[3] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] in the templateTitle[0] . In comparison , this figure was significantly higher in affluent households where the templateYLabel[0] templateYLabel[1] spend on templateTitle[3] amounted to 1,098 templateYLabel[3] templateYLabel[4] in templateXValue[max] . templateYLabel[3] templateTitle[3] Market templateTitle[3] and furnishings make up a central part of the home goods industry , which itself covers the manufacturing , distribution and retail of home templateTitle[3] , templateTitle[1] decorative accessories , home furnishings , appliances , cookware and gardening equipment .
generated: As of 2018 , the Value percentage per consumer unit on percentage amounted to 12.8 GDP in the Value . In comparison , this figure was significantly higher in affluent households where the Value added spend on percentage amounted to 1,098 GDP in 2018 . GDP percentage Market percentage and furnishings make up a central part of the home goods industry , which itself covers the manufacturing , distribution and retail of home percentage , added decorative accessories , home furnishings , appliances , cookware and gardening equipment .

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 August templateTitleDate[0] . templateTitle[4] was templateYValue[max] percent in templateXValue[0] , templateXValue[1] and templateXValue[2] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] 10,000 templateYLabel[4] templateYLabel[5] in templateTitle[2] templateTitle[4] in Europe in templateTitleDate[0] . In this year , templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] with templateYValue[max] percent templateTitle[4] templateTitle[5] , this was closely followed by templateXValue[1] with templateYValue[1] percent and templateYValue[2] percent on the templateXValue[2] at templateYValue[2] percent . templateTitleSubject[0] templateTitle[1] in the European Union is predominantly using the professions of Optometry and Optics in Spain and the Nordic templateTitle[4] are combined in templateYValue[14] , as optometrists .
generated: This statistic shows the Share of respondents 10,000 respondents in regular penetration in Europe in 2013 . In this year , Belgium had the highest Share of respondents with 85 percent penetration 2013 , this was closely followed by Norway with 85 percent and 85 percent on the South Africa at 85 percent . Search engines in the European Union is predominantly using the professions of Optometry and Optics in Spain and the Nordic penetration are combined in 74 , as optometrists .

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: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitleSubject[0] templateTitle[4] to templateTitle[5] templateTitle[6] templateXValue[0] in templateTitleDate[0] . The survey revealed templateTitle[1] templateYValue[4] percent of the templateYLabel[1] said templateTitle[1] the templateXValue[4] influences their templateTitle[6] templateXValue[0] purchases.Luggage industry in the templateTitle[3] To understand the templateXValue[0] industry , one must understand how the airline industry impacts templateXValue[0] demands . With airlines continually increasing their baggage fees , the majority of travellers are adjusting all aspects of their travel , including considerations when it comes to purchasing luggage.Consumers are n't going for the lightweight templateXValue[0] without checking out some additional templateXValue[2] .
generated: This statistic presents the U.S. parental digital U.S. teen to online behavior What sites he/she can access in 2015 . The survey revealed parental 67 percent of the respondents said parental the May only download apps with age ratings influences their behavior What sites he/she can access purchases.Luggage industry in the monitoring To understand the What sites he/she can access industry , one must understand how the airline industry impacts What sites he/she can access demands . With airlines continually increasing their baggage fees , the majority of travellers are adjusting all aspects of their travel , including considerations when it comes to purchasing luggage.Consumers are n't going for the lightweight What sites he/she can access without checking out some additional What he/she can post online for others to see .

Example 191:
titleEntities: {'Subject': ['Chained'], 'Date': ['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[min] to templateXValue[max] , at year-end . In templateXLabel[0] templateXValue[max] , 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: As of templateXValue[max] , templateTitleSubject[0] had an estimated templateYLabel[0] templateYLabel[1] of approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] , a templateYLabel[0] templateYLabel[1] of templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] in the previous templateXLabel[0] . The number of templateTitleSubject[0] 's streaming subscribers worldwide has continued to grow in recent years , reaching 167 templateYLabel[2] in the fourth quarter of templateXValue[max] . templateTitleSubject[0] and competitors templateTitleSubject[0] is a music streaming service originally founded in 2006 in Sweden .
generated: As of 2019 , Chained had an estimated Chained Consumer of approximately 144.73 Price Index (1999=100) , a Chained Consumer of 102.6 Price Index (1999=100) in the previous December . The number of Chained 's streaming subscribers worldwide has continued to grow in recent years , reaching 167 Price in the fourth quarter of 2019 . Chained and competitors Chained is a music streaming service originally founded in 2006 in Sweden .

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[max] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] parent families with a female householder and no spouse present in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[28] percent of all templateTitleSubject[0] families with a templateTitle[3] mother in the templateTitle[5] lived below the templateYLabel[0] level . In templateXValue[max] , that figure was at templateYValue[min] percent .
generated: This statistic shows the Household income of Texas income parent families with a female householder and no spouse present in the Texas from 1990 to 2018 . In 1990 , 28228 percent of all Texas families with a income mother in the 2018 lived below the Household level . In 2018 , that figure was at 27733 percent .

Example 193:
titleEntities: {'Subject': ['UK'], 'Date': ['2013', '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: This statistic shows templateTitle[0] sector templateYLabel[0] on templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . The templateYLabel[0] on templateTitle[2] templateTitle[3] has continuously decreased during this period . In templateXValue[0] , this templateYLabel[0] totalled approximately templateYValue[min] templateYLabel[1] British pounds .
generated: This statistic shows Public sector Expenditure on recreational sporting in the UK from 2013/14 to 2018//19 . The Expenditure on recreational sporting has continuously decreased during this period . In 2018//19 , this Expenditure totalled approximately 2947 million British pounds .

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] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Czech Republic from 1999 to 2019 . In 2019 , the Unemployment rate in Czech Republic was at approximately 2.47 percent .

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] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was around templateYValue[6] templateYLabel[2] templateYLabel[3] dollars . In a ranking of templateYLabel[1] to GDP per country , templateTitleSubject[0] is currently ranked second .
generated: The statistic shows the Budget balance of Italy from 2014 to 2018 , with projections up until 2024 . In 2018 , the Budget balance in Italy was around -37.51 billion euros dollars . In a ranking of balance to GDP per country , Italy is currently ranked second .

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: Since the first half of templateXValue[17] , household templateTitle[0] templateTitle[1] in templateTitleSubject[0] have seen an overall increase , rising to templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( kWh ) in the first half of templateXValue[1] . The highest price during the reporting period was in the first half of templateXValue[5] , at templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh . templateTitle[3] with an annual consumption of 2,500 kWh to 5,000 kWh tended to pay more templateTitle[2] their templateTitle[0] compared to those with a consumption of between 1,000 kWh and 2,499 kWh .
generated: Since the first half of 2010 S2 , household Electricity prices in Germany have seen an overall increase , rising to 30.88 Euro cents per kilowatt-hour ( kWh ) in the first half of 2018 S2 . The highest price during the reporting period was in the first half of 2016 S2 , at 30.88 Euro cents per kWh . households with an annual consumption of 2,500 kWh to 5,000 kWh tended to pay more for their Electricity compared to those with a consumption of between 1,000 kWh and 2,499 kWh .

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: 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: The statistic shows the growth in real GDP in Cyprus from 2014 to 2018 , with projections up until 2024 . In 2018 , Cyprus 's real Gross domestic product increased by around 3.88 percent compared to the previous Year .

Example 198:
titleEntities: {'Subject': ['NFL'], 'Date': ['2015', '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] templateTitleSubject[0] 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: templateTitle[1] templateTitle[2] templateTitle[3] averaged templateYValue[0] templateYLabel[1] templateYLabel[0] in the country during the templateXValue[max] templateTitleSubject[0] season , an increase of around one templateYLabel[1] on the previous templateXLabel[0] . Although viewing figures have increased , they are still down on the templateYValue[max] templateYLabel[1] templateYLabel[0] in templateXValue[min] . Sunday , templateTitle[1] , happy nights The templateTitleSubject[0] has prime-time games that are broadcast across the entire country by one major network on Thursday templateTitle[2] , Sunday templateTitle[2] , and templateTitle[1] templateTitle[2] – these games start at around 8:30 pm Eastern Time .
generated: Thursday Night Football averaged 15.4 millions Viewers in the country during the 2019 NFL season , an increase of around one millions on the previous Year . Although viewing figures have increased , they are still down on the 15.4 millions Viewers in 2015 . Sunday , Thursday , happy nights The NFL has prime-time games that are broadcast across the entire country by one major network on Thursday Night , Sunday Night , and Thursday Night – these games start at around 8:30 pm Eastern Time .

Example 199:
titleEntities: {'Subject': ['U.S.'], 'Date': ['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] .

generated_template: templateTitle[2] templateTitleSubject[0] robot shipments increased by one percent from about templateYValue[1] in templateXValue[1] to around templateYValue[max] in templateXValue[max] . Global spending on templateTitleSubject[0] templateTitle[1] is projected to exceed 24 templateYLabel[1] U.S. dollars by 2025 . Leading markets Japan , China , the country , South Korea , and Germany are counted among the five leading templateTitleSubject[0] robot markets templateTitle[2] .
generated: domestic U.S. robot shipments increased by one percent from about 741.73 in 2017 to around 777.91 in 2018 . Global spending on U.S. airlines is projected to exceed 24 enplanements U.S. dollars by 2025 . Leading markets Japan , China , the country , South Korea , and Germany are counted among the five leading U.S. robot markets domestic .

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[0] 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: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[2] templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] generated approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] .
generated: This statistic shows the Revenue of Bloomin Brands Brands Bloomin Brands worldwide from 2009 to 2018 . In 2018 , the U.S. Bloomin Brands Brands Bloomin Brands generated approximately 4.44 billion U.S. dollars in Revenue .

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 templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the size of the Autonomous Market from 2015 to 2030 . In 2020 , the Autonomous driving sensor Market amounted to 25.0 billion U.S. dollars .

Example 202:
titleEntities: {'Subject': ['U.S.'], 'Date': ['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[max] templateYLabel[3] in templateXValue[max] .

generated_template: This statistic illustrates 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: This statistic illustrates the Per capita consumption of oat products in the U.S. from 2000 to 2018 . According to the report , the U.S. Per capita consumption of oat products amounted to approximately 4.8 pounds in 2018 .

Example 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 templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] is called the templateXValue[0] templateTitle[3] Complex , located in the country , with a templateYLabel[0] of templateYValue[max] templateYLabel[1] . It is made up of 22 power plants and spread over several kilometers , located north of San Francisco . It sits on top of a deep magma chamber that spans over 30 square miles .
generated: The UHNW super rich in the U.S. UHNW is called the New York super Complex , located in the country , with a Number of 8655 Ultra-High-Net-Worth . It is made up of 22 power plants and spread over several kilometers , located north of San Francisco . It sits on top of a deep magma chamber that spans over 30 square miles .

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: The statistic above presents the distribution of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , templateXValue[0] templateTitle[0] templateTitle[1] templateTitle[2] accounted for templateYValue[max] percent of all templateTitle[0] templateTitle[1] templateTitle[2] .
generated: The statistic above presents the distribution of most successful music in the tours in 2019 , America Artist . In 2019 , The Rolling Stones most successful music accounted for 177.8 percent of all most successful music .

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[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: 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 .

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 degree of templateTitle[3] templateTitleSubject[0] in templateTitle[5] templateTitle[6] worldwide . According to the templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] , templateXValue[last] occupied the last place in templateTitle[3] templateTitleSubject[0] with templateYValue[min] templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . templateXValue[1] and templateXValue[0] were ranked first and second with templateYValue[max] and templateYValue[1] out of 100 templateYLabel[0] templateYLabel[1] respectively .
generated: The statistic shows the degree of M H M in openings worldwide . According to the H M Number , Czech Republic occupied the last place in M H M with 0 Number H&M in 2019 . Franchise and Total were ranked first and second with 375 and 28 out of 100 Number H&M respectively .

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[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , this was estimated that around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[1] .
generated: This statistic shows the Production of production in the veneer from 2003 to 2016 . In 2016 , this was estimated that around 1082 million cubic feet in the veneer .

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: This statistic illustrates the templateTitle[1] templateTitle[2] templateTitle[3] sales of templateTitle[0] Inc. as of the fourth templateXLabel[0] of templateTitle[6] . During that period , the C2C e-commerce company generated templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from templateYValue[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the corresponding templateXLabel[0] of the previous year . This represents a 32.8 percent year-on-year growth .
generated: This statistic illustrates the mobile internet users sales of Number Inc. as of the fourth Quarter of UK . During that period , the C2C e-commerce company generated Users of 42.77 millions , up from 39.56 millions in the corresponding Quarter of the previous year . This represents a 32.8 percent year-on-year growth .

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[0] templateYLabel[2] templateYLabel[1] in templateTitle[2] in the templateTitleSubject[0] , down from templateYValue[1] templateYLabel[2] the previous templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] templateTitle[2] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] templateTitle[2] amounted to approximately templateYValue[0] templateYLabel[2] .
generated: This statistic shows the Number of participants in participants sailing in the U.S. 2006 to 2018 . In 2018 , the Number of participants ( aged six years and older ) in participants sailing amounted to approximately 3.75 millions .

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[max] . 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: In templateXValue[max] , templateTitleSubject[0] 's total templateYLabel[0] templateYLabel[1] amounted to more than templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] , representing a 23 percent growth from templateXValue[1] . In templateXValue[1] , the templateYLabel[0] provider 's mobile templateYLabel[0] templateYLabel[1] amounted to 227 templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: In 2028 , France 's total Spending billion amounted to more than 131.4 euros , representing a 23 percent growth from 2018 . In 2018 , the Spending provider 's mobile Spending billion amounted to 227 euros .

Example 211:
titleEntities: {'Subject': ['U.S.'], 'Date': ['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[max] templateTitle[0] templateTitle[1] templateTitle[2] poisoning templateYLabel[2] 100,000 people in the templateTitle[5] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] templateYLabel[3] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitleSubject[0] templateTitle[3] templateTitle[4] were living below the templateYLabel[0] line in the templateTitle[0] .
generated: This statistic shows the Death rate of U.S. drug 100,000 in the U.S. from 1950 to 2017 . In 2017 , 20.1 percent of U.S. drug overdose were living below the Death line in the Deaths .

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 2016* . The dry period of April 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: As of templateTitleSubject[0] templateYValue[6] , templateTitle[5] , the outbreak of the coronavirus disease ( templateTitle[0] ) had spread to templateYValue[4] continents , and approximately 3,000 people had died after contracting the respiratory virus . The majority of the confirmed cases were located in templateXValue[0] , where around 2,900 templateYLabel[1] had been reported . WHO raises global risk level Approximately 60 countries templateTitle[2] have been affected templateTitle[6] the virus .
generated: As of Number 500000 , droughts , the outbreak of the coronavirus disease ( Number ) had spread to 1250000 continents , and approximately 3,000 people had died after contracting the respiratory virus . The majority of the confirmed cases were located in China (1928) , where around 2,900 deaths had been reported . WHO raises global risk level Approximately 60 countries caused have been affected worldwide the virus .

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: This statistic illustrates templateTitleSubject[0] 's templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] from the second templateXLabel[0] of templateTitleDate[0] to the second templateXLabel[0] of templateTitleDate[1] . As of the last reported templateXLabel[0] , templateYValue[0] percent of the group 's templateTitle[1] visits converted to purchases .
generated: This statistic illustrates Alibaba 's mobile share Percentage mobile from the second Quarter of 2016 to the second Quarter of 2016 . As of the last reported Quarter , 75 percent of the group 's mobile visits converted 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: Since the first half of templateXValue[17] , household templateTitle[0] templateTitle[1] in templateTitleSubject[0] have seen an overall increase , rising to templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( kWh ) in the first half of templateXValue[1] . The highest price during the reporting period was in the first half of templateXValue[5] , at templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh . templateTitle[3] with an annual consumption of 2,500 kWh to 5,000 kWh tended to pay more templateTitle[2] their templateTitle[0] compared to those with a consumption of between 1,000 kWh and 2,499 kWh .
generated: Since the first half of 2010 S2 , household Electricity prices in Latvia have seen an overall increase , rising to 16.5 Euro cents per kilowatt-hour ( kWh ) in the first half of 2018 S2 . The highest price during the reporting period was in the first half of 2016 S2 , at 16.5 Euro cents per kWh . households with an annual consumption of 2,500 kWh to 5,000 kWh tended to pay more for their Electricity compared to those with a consumption of between 1,000 kWh and 2,499 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: Since the first half of templateXValue[17] , household templateTitle[0] templateTitle[1] in templateTitleSubject[0] have seen an overall increase , rising to templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( kWh ) in the second half of templateXValue[1] . The highest price during the reporting period was in the first half of templateXValue[5] , at templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh . templateTitle[3] with an annual consumption of 2,500 kWh to 5,000 kWh tended to pay more templateTitle[2] their templateTitle[0] compared to those with a consumption of between 1,000 kWh and 2,499 kWh .
generated: Since the first half of 2010 S2 , household Electricity prices in Luxembourg have seen an overall increase , rising to 17.98 Euro cents per kilowatt-hour ( kWh ) in the second half of 2018 S2 . The highest price during the reporting period was in the first half of 2016 S2 , at 17.98 Euro cents per kWh . households with an annual consumption of 2,500 kWh to 5,000 kWh tended to pay more for their Electricity compared to those with a consumption of between 1,000 kWh and 2,499 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[0] templateYLabel[1] were recorded on Swiss roads . Between templateXValue[min] and templateXValue[max] , traffic related templateTitle[2] declined by over one third , with the lowest templateYLabel[0] seen in templateXValue[2] at templateYValue[min] such incidences . templateTitleSubject[0] was one of the safest countries in Europe for templateTitle[1] users .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] due to templateTitle[0] between templateXLabel[0] templateXValue[min] and templateXValue[max] . In templateXValue[max] , around templateYValue[min] people were killed by terrorists in templateTitleSubject[0] . templateTitleSubject[0] suffered the sixth-highest templateYLabel[0] people killed by terrorist attacks and the second-most attacks in templateXValue[max] .
generated: The statistic shows the Number of fatalities in Switzerland due to Number between Year 2006 and 2018 . In 2018 , around 216 people were killed by terrorists in Switzerland . Switzerland suffered the sixth-highest Number people killed by terrorist attacks and the second-most attacks in 2018 .

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[min] to templateXValue[max] . 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: The timeline shows templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateXValue[min] to templateXValue[max] . In templateXValue[1] , templateTitleSubject[0] templateTitle[1] global templateTitle[2] templateTitle[3] amounted to about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The timeline shows Walmart 's operating income worldwide 2006 to 2019 . In 2018 , Walmart 's global operating income amounted to about 27.73 billion U.S. dollars .

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[0] percent .

generated_template: In templateXValue[max] , about templateYValue[0] babies were born in templateTitleSubject[0] , a slight decrease from the templateXLabel[0] before . templateTitleSubject[0] is the country with the highest templateYLabel[0] of templateYLabel[2] in the European Union , with over 20 thousand more templateYLabel[2] more than France , the country with the next highest templateYLabel[0] of templateYLabel[1] templateYLabel[2] in Europe . Population growth in templateTitleSubject[0] has about 83 million inhabitants , making it the most populous country in Europe .
generated: In 2018 , about 8.2 babies were born in BNP Paribas , a slight decrease from the Year before . BNP Paribas is the country with the highest Return of equity in the European Union , with over 20 thousand more equity more than France , the country with the next highest Return of equity in Europe . Population growth in BNP Paribas has about 83 million inhabitants , making it the most populous country in Europe .

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

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

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 December templateTitleDate[0] , 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: This statistic presents the templateTitle[1] of templateTitle[0] templateYLabel[1] in the templateTitle[2] as of July templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] . During the survey period it was found that templateYValue[max] percent of templateTitleSubject[0] templateTitle[0] templateYLabel[1] were templateXValue[0] .
generated: This statistic presents the cannabis of Marijuana respondents in the consumption as of July 2019 , sorted months Canada . During the survey period it was found that 18.4 percent of Canada Marijuana respondents 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: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] and templateTitle[4] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] and templateTitle[4] in the templateTitleSubject[0] amounted to approximately templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Average price of fill and nail in the U.S. from 2006 to 2019 . In 2019 , the Average price of fill and nail in the U.S. amounted to approximately 30.57 U.S. dollars .

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 templateYLabel[0] of the templateTitleSubject[0] luxury brand templateTitle[4] templateTitle[5] has increased twofold over the period surveyed , growing from roughly templateYValue[min] templateYLabel[1] templateYLabel[2] in templateXValue[min] to templateYValue[max] templateYLabel[1] templateYLabel[2] in the templateXLabel[0] templateXValue[max] . Despite the steady increase in templateYLabel[0] during the period considered , the templateTitle[3] reported a net loss of approximately 25 templateYLabel[1] templateYLabel[2] in templateXValue[max] . Worldwide recognition Founded in 1978 in Milan , templateTitle[4] templateTitle[5] is one of the leading international templateTitle[2] design houses .
generated: The Turnover of the Italian luxury brand Giorgio Armani has increased twofold over the period surveyed , growing from roughly 1275 million euros in 2011 to 1702 million euros in the Year 2018 . Despite the steady increase in Turnover during the period considered , the company reported a net loss of approximately 25 million euros in 2018 . Worldwide recognition Founded in 1978 in Milan , Giorgio Armani is one of the leading international fashion design houses .

Example 224:
titleEntities: {'Subject': ['Latin American'], 'Date': ['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: This statistic illustrates the global banana templateYLabel[0] of templateTitleSubject[0] templateTitle[4] in templateTitleDate[0] . In that year , the templateTitleSubject[0] templateYLabel[0] of templateTitleSubject[0] averaged templateYValue[3] templateYLabel[1] templateYLabel[2] in the templateXValue[3] . templateTitleSubject[0] is a prescription drug used to treat some types of cancers , including some types of leukemia .
generated: This statistic illustrates the global banana Value of Latin American most in 2018 . In that year , the Latin American Value of Latin American averaged 251.1 million U.S. in the Colombia . Latin American is a prescription drug used to treat some types of cancers , including some types of leukemia .

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] is an international pharmaceutical company based out of Germany . As of templateXValue[max] , the company had a total of templateYValue[0] templateYLabel[1] globally . This is a significant increase from the previous years .
generated: Vinci Group revenue is an international pharmaceutical company based out of Germany . As of 2018 , the company had a total of 43.52 billion globally . This is a significant increase from the previous years .

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: This statistic presents the expenditure of templateTitleSubject[0] on research and development from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateTitleSubject[0] spent some templateYValue[max] templateYLabel[2] templateYLabel[1] on research and development . Abbot was a U.S.-based global pharmaceutical and healthcare products company , headquartered in Chicago , Illinois .
generated: This statistic presents the expenditure of Portugal on research and development from 2012 to 2028 . In 2017 , Portugal spent some 26.4 euros billion on research and development . Abbot was a U.S.-based global pharmaceutical and healthcare products company , headquartered in Chicago , Illinois .

Example 227:
titleEntities: {'Subject': ['Johnson Johnson'], 'Date': ['2013', '2021']}
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: This statistic illustrates the templateTitle[0] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[1] templateTitleSubject[1] ( UK ) based templateTitleSubject[0] Building Society from templateXValue[min] to templateXValue[max] . It can be seen that templateTitleSubject[0] increased its templateYLabel[1] of the templateTitle[2] templateYLabel[0] overall during the period under observation , reaching a templateYLabel[1] of templateYValue[0] percent as of the templateXLabel[0] ending April templateXValue[max] . The smallest templateTitle[2] templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] was found in templateXValue[min] , when a templateYLabel[0] templateYLabel[1] of templateYValue[min] percent was recorded .
generated: This statistic illustrates the Johnson Market share of the Johnson Johnson ( UK ) based Johnson Johnson Building Society from 2013 to 2021 . It can be seen that Johnson Johnson increased its share of the Johnson Market overall during the period under observation , reaching a share of 4.3 percent as of the Year ending April 2021 . The smallest Johnson Market share for Johnson Johnson was found in 2013 , when a Market share of 3.65 percent was recorded .

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 templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , the most important templateTitle[1] partner templateTitle[3] templateTitleSubject[0] was templateXValue[0] , accounting templateTitle[3] templateYValue[max] percent of all templateYLabel[2] .
generated: The statistic shows the Main export partners Iran in 2017 . In 2017 , the most important export partner Iran was Japan , accounting Iran 27.5 percent of all exports .

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[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the percentage of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[max] percent of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
generated: This statistic shows the degree of Urbanization in Jamaica from 2008 to 2018 and details the percentage of the entire population , living in urban areas . In 2018 , 55.67 percent of the total population in Jamaica lived in cities .

Example 230:
titleEntities: {'Subject': ['Reddit.com'], 'Date': ['2019', '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 August templateTitleDate[0] , 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: This statistic represents the regional templateTitle[0] of templateTitleSubject[0] templateYLabel[1] in the last three months . As of October templateTitleDate[0] , the templateXValue[0] templateXValue[1] accounted for templateYValue[1] percent of templateYLabel[1] to the website and templateXValue[2] accounted for templateYValue[2] percent of templateYLabel[1] .
generated: This statistic represents the regional Distribution of Reddit.com traffic in the last three months . As of October 2019 , the United States United Kingdom accounted for 7.79 percent of traffic to the website and Canada accounted for 7.75 percent of traffic .

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] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] dollars.The templateTitleSubject[0] templateTitleSubject[0] are owned by Jim Crane , who bought the franchise for 465 templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[7] .
generated: The statistic depicts the Revenue of the Green Bay Packers from 2001 to 2018 . In 2018 , the Revenue of the Major League Baseball franchise amounted to 456 million U.S. dollars.The Green Bay Packers are owned by Jim Crane , who bought the franchise for 465 million U.S. dollars in 2011 .

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[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] dollars.The templateTitleSubject[0] templateTitleSubject[0] are owned by Jim Crane , who bought the franchise for 465 templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[12] .
generated: The statistic depicts the Revenue of the NFL from 2001 to 2018 . In 2018 , the Revenue of the Major League Baseball franchise amounted to 438 million U.S. dollars.The NFL are owned by Jim Crane , who bought the franchise for 465 million U.S. dollars in 2006 .

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: This statistic shows the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] was around templateYValue[0] templateYLabel[1] templateYLabel[2] . According to the same templateXLabel[0] .
generated: This statistic shows the domestic product GDP in Denmark from 2008 to 2018 . In 2008 , the domestic product GDP in Denmark was around 1801470 million Danish . According to the same Year .

Example 234:
titleEntities: {'Subject': ['Malawi'], 'Date': ['2017']}
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[max] percent of the population accessed the templateYLabel[3] , up from templateYValue[min] percent in templateXValue[min] . In templateXValue[2] , templateTitleSubject[0] 's population amounted to 18.11 million inhabitants .

generated_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In the most recently measured period , templateYValue[max] percent of the population accessed the templateYLabel[3] , up from templateYValue[min] percent in templateXValue[min] . In templateXValue[2] , templateTitleSubject[0] 's population increased by approximately 2.48 percent compared to the previous templateXLabel[0] .
generated: This statistic gives information on the internet penetration in Malawi from 2000 to 2017 . In the most recently measured period , 13.78 percent of the population accessed the internet , up from 0.13 percent in 2000 . In 2015 , Malawi 's population increased by approximately 2.48 percent compared to the previous Year .

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] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[min] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: 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 .

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[min] . In templateXValue[max] , total templateYLabel[0] dropped just above templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the total global templateYLabel[0] of templateTitleSubject[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] templateTitle[1] templateYLabel[0] In templateXValue[max] , some templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] templateTitle[1] was produced globally . The base templateTitle[1] is usually extracted in combination with zinc , silver and copper .
generated: This statistic shows the total global Production of Global mine from 2007 to 2019 . Global mine Production In 2019 , some 2200 thousand metric tons of Global mine was produced globally . The base mine is usually extracted in combination with zinc , silver and copper .

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: This statistic illustrates the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] in the templateTitle[0] in templateTitleDate[0] , templateTitle[6] templateXValue[1] . In that year , templateYValue[max] percent of templateXValue[0] drank templateTitle[2] templateTitle[3] every week in the United States.Craft templateTitle[3] in the United StatesCraft templateTitle[3] is paving the way for a new crop of templateTitle[3] makers ! While the 1990 's and early 2000 's saw a big rise in brew pubs , the trend today leans more toward microbreweries that aim to distribute their product . Over the last couple of years , the number of new brewery openings has been at near unprecedented levels ; the highest total since the 1880 's .
generated: This statistic illustrates the Share of content watching worldwide time in the Netflix in 2017 , 2017 PC/laptop . In that year , 70 percent of Television drank watching worldwide every week in the United States.Craft worldwide in the United StatesCraft worldwide is paving the way for a new crop of worldwide makers ! While the 1990 's and early 2000 's saw a big rise in brew pubs , the trend today leans more toward microbreweries that aim to distribute their product . Over the last couple of years , the number of new brewery openings has been at near unprecedented levels ; the highest total since the 1880 's .

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: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] templateTitleSubject[0] who were using templateTitle[0] as of February templateTitleDate[0] , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateYValue[0] percent of templateYLabel[1] aged between 18 and 29 years stated that they used the visual blogging site .
generated: This statistic shows the Share of adults in the U.S. who were using Book as of February 2019 , sorted group . During that period of time , 81 percent of respondents aged between 18 and 29 years stated that they used the visual blogging site .

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 December templateTitleDate[0] , 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: This statistic gives information on the most popular multi-platform templateTitle[1] templateTitle[2] in the templateTitle[3] as of October templateTitleDate[0] . During that month , templateXValue[0] was ranked first with almost templateYValue[max] monthly active templateTitle[3] users . templateXValue[0] templateXValue[2] was ranked third in terms of templateTitleSubject[0] but came first in regards of mobile-only users .
generated: This statistic gives information on the most popular multi-platform engagement rate in the U.S. as of October 2019 . During that month , Facebook was ranked first with almost 63.7 monthly active U.S. users . Facebook Snapchat was ranked third in terms of U.S. but came first in regards of mobile-only users .

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: This statistic shows the results of a survey poll conducted in templateTitleDate[0] asking Americans to describe templateTitle[0] templateTitleSubject[0] in one templateTitle[3] . In templateTitleDate[0] , templateYValue[max] percent of Americans chose the words `` templateXValue[0] '' to describe templateTitle[0] templateTitleSubject[0] , templateYValue[1] percent chose `` templateXValue[1] '' and templateYValue[2] percent chose `` templateXValue[2] '' . templateTitleSubject[0] 's perception as a presidential candidate In templateTitleDate[0] , when Americans who were asked to describe templateTitle[0] templateTitleSubject[0] in one templateTitle[3] , close to templateYValue[max] percent of Americans chose the words `` templateXValue[0] '' .
generated: This statistic shows the results of a survey poll conducted in 2019 asking Americans to describe Most in one among . In 2019 , 49.9 percent of Americans chose the words `` Node.js '' to describe Most , 37.4 percent chose `` .NET '' and 23.7 percent chose `` .NET Core '' . Most 's perception as a presidential candidate In 2019 , when Americans who were asked to describe Most in one among , close to 49.9 percent of Americans chose 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: In templateXValue[max] , the templateTitle[0] templateYLabel[0] to businesses affected by a templateTitle[3] templateTitle[4] in the templateTitleSubject[0] templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] . The global templateTitle[0] templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] was 3.92 templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: In 2019 , the SAP Net to businesses affected by a profit 2006 in the SAP amounted to 4083 profit million euros , up from 4083 profit million euros in the previous Year . The global SAP Net profit 2006 was 3.92 profit million euros .

Example 242:
titleEntities: {'Subject': ['Texas'], 'Date': ['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[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line in templateXValue[max] .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the poverty rate in Texas from 2000 to 2018 . In 2018 , about 14.9 percent of Texas 's population lived below the poverty line .

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: templateTitleSubject[0] Inc. is a Canadian owned retail chain headquartered in Montreal . There were 1,225 templateTitleSubject[0] templateYLabel[1] in templateTitleSubject[1] as of February templateTitleDate[0] . templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] per templateTitle[5] , templateYValue[max] , and templateXValue[last] templateXValue[last] templateXValue[last] had just templateYValue[min] .
generated: France Inc. is a Canadian owned retail chain headquartered in Montreal . There were 1,225 France units in France as of February 2018 . FIFA 19 had the highest Number of units per gaming , 1353.4 , and God of War had just 301.4 .

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: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a change in it is a sign of economic growth .
generated: The statistic shows the 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 change in it is a sign of economic growth .

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[0] to templateTitleDate[1] . In templateTitleDate[1] , 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: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , templateYLabel[1] templateTitle[0] and templateTitle[0] templateTitle[2] templateTitle[3] amounted to about templateYValue[0] templateYLabel[0] on the templateYLabel[2] . According to the NAICS definition , companies operating in this sector are retailing templateTitle[0] and templateTitle[0] templateTitle[2] from fixed point-of-sale locations.The templateTitle[0] Industry in the templateTitleSubject[0] StatesIn 2012 , the global apparel market was valued at 1.7 templateYLabel[0] templateYLabel[1] templateYLabel[2] and was estimated to employ approximately 75 templateYLabel[0] people , making it templateXValue[16] of the largest industries in the world .
generated: This statistic shows the Luggage Billion of goods store in the U.S. from 1992 to 2017 . In 2017 , U.S. Luggage and Luggage goods store amounted to about 33.92 Billion on the dollars . According to the NAICS definition , companies operating in this sector are retailing Luggage and Luggage goods from fixed point-of-sale locations.The Luggage Industry in the U.S. StatesIn 2012 , the global apparel market was valued at 1.7 Billion U.S. dollars and was estimated to employ approximately 75 Billion people , making it 1 of the largest industries in the world .

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[max] to templateYValue[min] 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: In templateXValue[max] , the templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] Group reported a templateYLabel[0] of almost templateYValue[min] templateYLabel[1] templateYLabel[2] . Despite the impressive figure , the templateYLabel[0] of templateXValue[max] represented a decrease for templateTitle[4] compared to the previous years . Indeed , in templateXValue[min] , the first templateXLabel[0] considered in this graph , the templateYLabel[0] of the templateTitle[3] amounted to approximately templateYValue[max] thousand templateYLabel[1] templateYLabel[2] .
generated: In 2018 , the Italy banks 2011 2018 Group reported a Number of almost 505 banks . Despite the impressive figure , the Number of 2018 represented a decrease for 2018 compared to the previous years . Indeed , in 2011 , the first Year considered in this graph , the Number of the 2011 amounted to approximately 740 thousand banks .

Example 247:
titleEntities: {'Subject': ['Trade'], 'Date': ['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: As of templateXValue[max] , around templateYValue[0] percent of templateTitle[5] in the templateTitle[4] templateTitle[6] templateTitle[7] to templateTitle[8] templateTitle[9] had been vaccinated against measles , mumps and rubella ( templateTitleSubject[0] ) . This is a significant decrease from templateYValue[max] percent of templateTitle[5] templateTitle[6] who templateTitle[2] the drug to be easy to obtain in the templateXLabel[0] templateXValue[20] . templateTitleSubject[0] is a hallucinogenic drug that can alter the user 's thoughts , feelings , and perception of reality .
generated: As of 2018 , around 19453.36 percent of 2018 in the 1950 2018 to 2018 had been vaccinated against measles , mumps and rubella ( Trade ) . This is a significant decrease from 19453.36 percent of 2018 who volume the drug to be easy to obtain in the Year 1970 . Trade is a hallucinogenic drug that can alter the user 's thoughts , feelings , and perception of reality .

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: templateTitleSubject[0] Division 's research and development ( templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] ) efforts amounted to over templateYValue[0] templateYLabel[1] templateYLabel[2] in the fiscal templateXLabel[0] of templateXValue[max] . templateTitleSubject[0] is an aircraft manufacturing subsidiary of the templateTitleSubject[0] Group , which was known as EADS ( European Aeronautic Defence and Space Company ) prior to the group 's restructuring in January templateXValue[4] .
generated: Research Division 's research and development ( Research spending Research ) efforts amounted to over 1911 million U.S. in the fiscal Year of 2019 . Research is an aircraft manufacturing subsidiary of the Research Group , which was known as EADS ( European Aeronautic Defence and Space Company ) prior to the group 's restructuring in January 2015 .

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 25 templateTitleSubject[0] templateYLabel[2] the templateTitle[2] templateTitle[3] in templateTitleDate[0] . In templateTitleDate[0] there were approximately templateYValue[max] templateYLabel[7] individuals templateYLabel[2] a templateYLabel[3] worth templateYLabel[5] templateYValue[6] templateYLabel[7] dollars in the U.S .
generated: The statistic shows the 25 Latin America score the gender gap in 2020 . In 2020 there were approximately 0.81 score individuals score a score worth score 0.75 score dollars in the U.S .

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[min] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In the templateXLabel[0] templateXValue[max] , about templateYValue[min] templateYLabel[1] were templateYLabel[2] templateYLabel[3] templateYLabel[4] of childbearing age . Despite a decreasing templateTitle[0] templateTitle[1] , the total population of templateTitleSubject[0] is expected to increase over the next few years .
generated: The statistic shows the Fertility rate in Nepal from 2007 to 2017 . In the Year 2017 , about 1.97 children were born per woman of childbearing age . Despite a decreasing Fertility rate , the total population of Nepal is expected to increase over the next few years .

Example 251:
titleEntities: {'Subject': ['Forecast'], 'Date': ['2009', '2025']}
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: This statistic represents the average templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateTitle[2] templateTitle[3] templateTitle[4] is templateTitle[1] to grow by templateYValue[9] templateYLabel[1] templateYLabel[2] . In templateXValue[8] , templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] is projected to amount to templateYValue[max] templateYLabel[1] templateYLabel[2] in templateXValue[max] .
generated: This statistic represents the average Price of Forecast earth oxide gadolinium from 2010 to 2025 . The earth oxide gadolinium is rare to grow by 23916 U.S. dollars . In 2018 , Forecast earth oxide gadolinium is projected to amount to 62724 U.S. dollars in 2025 .

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] templateTitleSubject[0] had 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. had 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[max] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] parent families with a female householder and no spouse present in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[28] percent of all templateTitleSubject[0] families with a templateTitle[3] mother in the templateTitle[5] lived below the templateYLabel[0] level . In templateXValue[max] , that figure was at templateYValue[min] percent .
generated: This statistic shows the Household income of Illinois income parent families with a female householder and no spouse present in the Illinois from 1990 to 2018 . In 1990 , 32542 percent of all Illinois families with a income mother in the 2018 lived below the Household level . In 2018 , that figure was at 31551 percent .

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 March templateTitleDate[0] . 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 June 2018 .

generated_template: The statistic shows the templateTitle[2] templateTitle[3] in the templateTitleSubject[0] soccer templateTitleSubject[1] templateTitle[4] the templateTitle[5] templateYLabel[0] fees up to September templateTitleDate[0] . templateXValue[0] templateXValue[1] München templateYLabel[2] templateYValue[14] templateYLabel[3] templateYLabel[4] as a templateYLabel[0] templateYLabel[1] for the Spanish templateTitle[2] templateXValue[16] templateXValue[16] .
generated: The statistic shows the leading accounting in the U.S. soccer U.S. firms the U.S. Number fees up to September 2019 . Deloitte PwC München professionals 1219 professionals as a Number professionals for the Spanish leading Dixon Hughes Goodman .

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[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the percentage of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[max] percent of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
generated: This statistic shows the degree of Urbanization in Ireland from 2008 to 2018 and details the percentage of the entire population , living in urban areas . In 2018 , 63.17 percent of the total population in Ireland lived in cities .

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[min] templateYLabel[5] people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[max] templateYLabel[5] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , templateYValue[5] templateYLabel[5] people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[max] templateYLabel[5] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Number of mobile internet users in Singapore from 2017 to 2023 . In 2018 , 4.5 millions people accessed the internet through their mobile phone . In 2023 , this figure is projected to amount to 4.9 millions mobile phone internet users .

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] 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[min] percent of templateTitle[4] templateTitle[5] templateTitle[6] . An eye on templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] ranks first among countries with the highest templateYLabel[1] levels in the world , far surpassing the templateYLabel[1] levels of Greece - which ranks number two - whose financial crisis has been in the spotlight recently .
generated: 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 65.61 percent of gross domestic product . An eye on India debt National debt India debt National debt ranks first among countries with the highest debt levels in the world , far surpassing the debt levels of Greece - which ranks number two - whose financial crisis has been in the spotlight recently .

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 15 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . templateTitle[1] defense templateYLabel[1] totaling USD templateYValue[max] templateYLabel[2] , the templateXValue[0] ranked first . Worldwide leaders in templateYLabel[0] templateYLabel[1] The templateXValue[6] States lead the globe in templateYLabel[0] templateYLabel[1] in templateTitleDate[0] .
generated: The statistic shows the 15 Terrorism kidnappings the grouped by country in . kidnappings defense kidnappings totaling USD 2527 kidnappings , the Somalia ranked first . Worldwide leaders in Number kidnappings The Sudan States lead the globe in Number kidnappings in .

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] 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] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[6] percent of the templateYLabel[3] .
generated: The 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 , Pakistan 's National debt amounted to approximately 71.69 percent of the GDP .

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: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[3] ) , with projections up until templateXValue[max] . The figures refer to the whole country and include the debts of the state , the communities , the municipalities and the social insurances . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateYLabel[2] of the templateYLabel[3] .
generated: 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 approximately 63.86 percent of the GDP .

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

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: As of December templateTitleDate[0] , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] in the templateTitle[2] , with templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . When there were based in the previous year , there were approximately 16.5 templateYLabel[2] templateYLabel[3] templateYLabel[4] since the U.S . The world 's second on the templateXLabel[0] with the largest templateYLabel[0] templateYLabel[1] .
generated: As of December 2018 , Turkey had the highest Volume thousand in the tomatoes , with 12150.0 tonnes . When there were based in the previous year , there were approximately 16.5 tonnes since the U.S . The world 's second on the Country with the largest Volume thousand .

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[min] 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[max] percent in templateXValue[max] .

generated_template: This statistic illustrates the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per day in the templateTitleSubject[0] templateTitleSubject[0] between templateTitle[6] and templateTitle[7] , templateTitle[8] templateTitle[9] . In this templateYValue[min] templateXLabel[0] period , individuals aged between 11 and 18 templateXValue[0] old templateTitle[3] templateYValue[max] templateYLabel[1] of templateTitle[1] templateTitle[2] per day .
generated: This statistic illustrates the Growth of crowdfunding platforms worldwide per day in the Growth between 2012 and 2012 , 2012 . In this 38 Year period , individuals aged between 11 and 18 2012 old worldwide 60 number of crowdfunding platforms per day .

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: Between templateXValue[min] and templateXValue[max] the templateTitle[0] templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] has increased from templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[min] to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . From templateXValue[min] and templateXValue[max] the templateYLabel[0] templateTitle[2] templateTitle[3] has increased each templateXLabel[0] since templateXValue[6] , reaching templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Production and templateXValue[max] alone templateTitle[3] are the most commonly period .
generated: Between 1990 and 2018 the Fresh Production worldwide 1990 in the 2018 has increased from 401.61 million metric tons in 1990 to 868.09 million metric tons in 2018 . From 1990 and 2018 the Production worldwide has increased each Year since 2005 , reaching 868.09 million metric tons . Production and 2018 alone worldwide are the most commonly period .

Example 265:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['13', '19']}
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 January templateTitleDate[0] to March templateTitleDate[1] . In March templateTitleDate[1] , templateTitle[1] templateTitle[2] increased by templateYValue[0] percent .

generated_template: This statistic shows the templateTitle[0] templateTitleSubject[1] templateTitleSubject[1] ( UK ) templateYLabel[0] templateYLabel[1] templateYLabel[2] from July templateTitleDate[0] to December templateTitleDate[1] . The templateTitle[0] templateYLabel[0] templateYLabel[1] had a range of templateYValue[0] percent as of December templateTitleDate[1] . The highest templateYLabel[0] templateYLabel[1] templateYLabel[2] was recorded in templateTitleDate[1] , at templateYValue[max] percent .
generated: This statistic shows the Internet UK ( UK ) Percentage change from July 13 to December 19 . The Internet Percentage change had a range of 12.4 percent as of December 19 . The highest Percentage change was recorded in 19 , at 30.1 percent .

Example 266:
titleEntities: {'Subject': ['Germany'], 'Date': ['2017']}
title: Life expectancy at birth in Germany 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Life', 'expectancy', 'at', 'birth', 'in', 'years']: ['80.99', '80.99', '80.64', '81.09', '80.49', '80.54', '80.44', '79.99', '79.84', '79.74', '79.53']

gold: This statistic shows the average life expectancy at birth in Germany from 2007 to 2017 . A child born in the year 2017 had an average life expectancy of 80.99 years .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . A child born in the templateXLabel[0] templateXValue[max] had an average templateYLabel[0] templateYLabel[1] of templateYValue[0] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] of templateTitle[2] at templateYLabel[2] in templateTitleSubject[0] was about templateYValue[max] templateYLabel[3] .
generated: The statistic shows the Life expectancy of birth at birth in Germany from 2007 to 2017 . In 2017 , the average Life expectancy of birth at birth in Germany was about 81.09 years .

Example 267:
titleEntities: {'Subject': ['Illinois'], 'Date': ['1960', '2018']}
title: Population density in Illinois 1960 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2010', '2000', '1990', '1980', '1970', '1960']
Y_Axis['Number', 'of', 'residents', 'per', 'square', 'mile']: ['229.5', '230.6', '230.6', '231.6', '232.0', '232.0', '231.9', '231.1', '223.7', '205.9', '205.8', '200.1', '181.3']

gold: This graph shows the population density in the federal state of Illinois from 1960 to 2018 . In 2018 , the population density of Illinois stood at 229.5 residents per square mile of land area .
gold_template: This graph shows the templateTitle[0] templateTitle[1] in the federal state of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] stood at templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] of land area .

generated_template: This graph shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] .
generated: This graph shows the Population density in Illinois 1960 to 2018 . In 2018 , the Population density of Illinois 1960 amounted to 232.0 residents per square mile in 2018 .

Example 268:
titleEntities: {'Subject': ['European Union'], 'Date': ['2018']}
title: Number of textile and clothing manufacturers in the European Union ( EU28 ) 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'companies']: ['171072', '176354', '177684', '174862', '171437', '174254', '180200', '186101', '191459', '188464']

gold: In the European Union , the number of companies in the business of textile and clothing manufacturing has ebbed and flowed over the period of time between 2009 and 2018 . In 2018 , the total number of textile and clothing manufacturers was recorded as 171,072 in EU28 countries , marking a loss of about 17,000 of them since 2009 . In contrast , the turnover figures of the industry demonstrated a consistently positive trend before peaking at 181 billion euros in 2017 .
gold_template: In the templateTitleSubject[0] , the templateYLabel[0] of templateYLabel[1] in the business of templateTitle[1] and templateTitle[2] manufacturing has ebbed and flowed over the period of time between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the total templateYLabel[0] of templateTitle[1] and templateTitle[2] templateTitle[3] was recorded as templateYValue[min] in templateTitle[6] countries , marking a loss of about 17,000 of them since templateXValue[min] . In contrast , the turnover figures of the industry demonstrated a consistently positive trend before peaking at 181 billion euros in templateXValue[1] .

generated_template: This statistic represents the templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[min] thousand , up from templateYValue[min] thousand in templateXValue[min] .
generated: This statistic represents the Number of companies at European Union from 2009 to 2018 . In 2018 , the Number of companies in European Union amounted to 171072 thousand , up from 171072 thousand in 2009 .

Example 269:
titleEntities: {'Subject': ['Philadelphia Union'], 'Date': ['2019']}
title: Player expenses ( payroll ) of Philadelphia Union 2019
X_Axis['Month']: ['Marco_Fabian', 'Alejandro_Bedoya', 'Sergio_Santos', 'Haris_Medunjanin', 'Jamiro_Monteiro', 'Andre_Blake', 'Kai_Wagner', 'Ilsinho', 'Kacper_Przybylko', 'Jack_Elliott', 'Raymon_Gaddis', 'Warren_Creavalle', 'Aurelien_Collin', 'Fafa_Picault', 'Carlos_Miguel_Coronel', 'Auston_Trusty', 'Fabinho_Alves', 'Brenden_Aaronson', 'Mark_McKenzie', 'Derrick_Jones', 'Cory_Burke', 'Matt_Freese', 'Olivier_Mbaizo', 'Anthony_Fontana', 'Matthew_Real', 'Michee_Ngalina']
Y_Axis['Payroll', 'in', 'thousand', 'U.S.', 'dollars']: ['2274.09', '1266.25', '668.5', '595.01', '569.2', '550.0', '360.15', '357.0', '277.0', '265.0', '190.0', '175.3', '175.0', '173.67', '148.43', '124.1', '120.0', '95.81', '82.23', '80.9', '79.72', '77.65', '70.88', '70.26', '57.23', '56.25']

gold: The statistic shows the player expenses ( payroll ) of the Philadelphia Union club of Major League Soccer by player in 2019 . Marco Fabian received a salary of 2.27 million U.S. dollars .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] templateTitle[4] club of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[0] received a salary of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] templateTitleSubject[0] club of Major League Soccer by templateTitle[0] in templateTitle[5] . templateXValue[0] templateXValue[0] received a salary of templateYValue[max] thousand templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Player expenses ( Payroll ) of the Philadelphia Union club of Major League Soccer by Player in 2019 . Marco Fabian received a salary of 2274.09 thousand U.S. dollars .

Example 270:
titleEntities: {'Subject': ['French'], 'Date': ['2010', '2017']}
title: French households savings rate 2010 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Savings', 'rate']: ['14.6', '14', '14.5', '14.4', '14.3', '15.1', '15.6', '15.8']

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

generated_template: In templateXValue[max] , the templateTitle[0] total templateYLabel[0] of templateTitle[5] templateTitle[6] traded per day was over 547 templateYLabel[1] templateYLabel[2] . This means that every day , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[1] has increased over the past few years , reaching an increase of templateYValue[max] percent compared to the previous templateXLabel[0] . Nevertheless , the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] was 856.7 percent .
generated: In 2017 , the French total Savings of 2017 traded per day was over 547 rate . This means that every day , the Savings of rate in the French has increased over the past few years , reaching an increase of 15.8 percent compared to the previous Year . Nevertheless , the average Savings of rate in the French was 856.7 percent .

Example 271:
titleEntities: {'Subject': ['Philadelphia', 'NBA'], 'Date': ['2003', '2020']}
title: Franchise value of the Philadelphia 76ers ( 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']: ['2000', '1650', '1180', '800', '700', '700', '469', '418', '314', '330', '344', '360', '380', '373', '351', '342', '328', '298']

gold: This graph depicts the value of the Philadelphia 76ers franchise of the National Basketball Association from 2003 to 2020 . In 2020 , the franchise had an estimated value of 2 billion U.S. dollars .
gold_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateYLabel[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the value of the Philadelphia Franchise of the National Basketball Association from 2003 to 2020 . In 2020 , the Franchise had an estimated value of 2000 million U.S. dollars .

Example 272:
titleEntities: {'Subject': ['Guatemala'], 'Date': ['2024']}
title: Inflation rate in Guatemala 2024*
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['4.33', '4.23', '4.18', '4.15', '4.18', '4.21', '3.75', '4.43', '4.45', '2.39', '3.42', '4.34', '3.78', '6.22', '3.86', '1.86', '11.36', '6.82', '6.56', '9.11', '7.58', '5.6', '8.14', '7.28', '5.98', '5.21', '6.62', '9.24', '11.06', '8.41', '12.51', '13.36', '10.24', '35.11', '37.98', '12.99', '10.3', '10.85', '32.78', '19.18', '3.21']

gold: This statistic shows the average inflation rate in Guatemala from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Guatemala amounted to about 3.75 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Guatemala from 1984 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Guatemala amounted to about 3.75 percent compared to the previous Year .

Example 273:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Leading diversified financial service companies in the U.S. 2018 , by revenue
X_Axis['Month']: ['Fannie_Mae', 'Freddie_Mac', 'American_Express', 'INTL_FCStone', 'Icahn_Enterprises', 'Synchrony_Financial', 'Marsh_&_McLennan', 'Ameriprise_Financial', 'Ally_Financial', 'Voya_Financial']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['120.1', '73.6', '43.28', '27.66', '18.98', '18.25', '14.95', '12.92', '10.47', '8.93']

gold: The statistic displays the leading diversified financial service companies in the United States in 2018 , by revenue . In that year , Fannie Mae was ranked first with revenue of around 120.1 billion U.S. dollars .
gold_template: The statistic displays the templateTitle[0] templateTitle[1] templateXValue[5] templateTitle[3] templateTitle[4] in the templateTitle[5] in templateTitleDate[0] , templateTitle[7] templateYLabel[0] . In that year , templateXValue[0] was ranked first with templateYLabel[0] of around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: With templateYLabel[0] amounting to over templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitleDate[0] , templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] topped the list of templateTitle[0] templateXValue[4] templateTitle[2] in the country . In that year , they were followed templateTitle[5] templateXValue[1] templateXValue[1] , templateXValue[2] templateXValue[2] templateXValue[2] P.L.C . and templateXValue[3] templateXValue[3] templateXValue[3] templateXValue[0] Co , in terms of templateYLabel[0] .
generated: With Revenue amounting to over 120.1 billion U.S. dollars in 2018 , Fannie Mae topped the list of Leading Icahn Enterprises financial in the country . In that year , they were followed U.S. Freddie Mac , American Express P.L.C . and INTL FCStone Fannie Mae Co , in terms of Revenue .

Example 274:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Change in U.S. exports of trade goods 1990 to 2018
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Percentage', 'of', 'change']: ['-1.3', '7.8', '6.6', '-3.6', '-7.6', '2.6', '2', '4.3', '16.2', '20.5', '-18.2', '12.3', '11.9', '14', '10.9', '12.8', '4.6', '-4.6', '-6.8', '12.4', '4.2', '-1.2', '10.8', '6.4', '14.4', '10', '3.9', '6.2', '6.9', '7.6']

gold: This graph shows the percentage of change in the U.S. export volume of trade goods from 1990 to 2019 . In 2019 , U.S. exports decreased by 1.3 percent from the previous year .
gold_template: This graph shows the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] export volume of templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[2] decreased by 1.3 percent from the previous templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[2] templateYLabel[3] templateYLabel[4] for templateTitle[3] in the country from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] amounted to approximately templateYValue[0] percent . The PPI for templateTitle[3] stood at 199.8 in templateTitleDate[1] .
generated: This statistic shows the Percentage change of the change for trade in the country from 1990 to 2019 . In 2019 , the Percentage change of exports trade amounted to approximately -1.3 percent . The PPI for trade stood at 199.8 in 2018 .

Example 275:
titleEntities: {'Subject': ['China'], 'Date': ['2016']}
title: Consumption of cement in China 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Consumption', 'in', 'thousand', 'metric', 'tons']: ['2395070', '2339000', '2454081', '2206000', '2160000', '2055224', '1872921', '1642216', '1375720', '1345338', '1218128']

gold: The statistic displays the consumption of cement in China from 2006 to 2016 . In 2016 , the country consumed around 2.4 billion metric tons of cement .
gold_template: The statistic displays the templateYLabel[0] of templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the country consumed around templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[1] .

generated_template: This statistic shows the percentage of metal and acquisition products that are produced from templateTitleSubject[0] templateTitle[1] and other low-grade residues worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] were sold in templateTitleSubject[0] .
generated: This statistic shows the percentage of metal and acquisition products that are produced from China cement and other low-grade residues worldwide from 2006 to 2016 . In 2016 , approximately 2395070 thousand metric tons were sold in China .

Example 276:
titleEntities: {'Subject': ['United Kingdom'], 'Date': []}
title: Budgeted public sector spending in the United Kingdom 2019/20 by function
X_Axis['Industry']: ['Social_protection', 'Health', 'Education', 'Other_(including_EU_transactions', 'Defense', 'Net_debt_increase', 'Transport', 'Public_order_and_safety', 'Personal_social_services', 'Housing_and_environment', 'Housing_and_community_amenities', 'Industry_agriculture_and_employment']
Y_Axis['Amount', 'budgeted', 'in', 'billion', 'GBP']: ['256', '166', '103', '58', '52', '43', '37', '35', '34', '32', '32', '25']

gold: This statistic presents what the government of the United Kingdom has budgeted for public sector spending for the fiscal year 2019/20 , in billion British Pounds . During this year the government has budgeted 256 billion pounds for social protection , which includes spending on pensions and other welfare benefits . Government spending on health is expected to be 166 billion pounds , the second highest spending function in this fiscal year .
gold_template: This statistic presents what the government of the templateTitleSubject[0] has templateYLabel[1] for templateXValue[7] templateTitle[2] templateTitle[3] for the fiscal year templateTitle[6] , in templateYLabel[2] British Pounds . During this year the government has templateYLabel[1] templateYValue[max] templateYLabel[2] pounds for templateXValue[0] , which includes templateTitle[3] on pensions and templateXValue[3] welfare benefits . Government templateTitle[3] on templateXValue[1] is expected to be templateYValue[1] templateYLabel[2] pounds , the second highest templateTitle[3] templateTitle[8] in this fiscal year .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[0] templateXValue[0] accounted for more than one third of the total templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide .
generated: This statistic shows the Amount of United Kingdom budgeted billion GBP in , United Industry . In , the Social protection accounted for more than one third of the total budgeted billion GBP worldwide .

Example 277:
titleEntities: {'Subject': ['Video'], 'Date': ['2016']}
title: Video game industry 's wealthiest entrepreneurs 2016
X_Axis['Entrepreneur', '(company)']: ['Ma_Huateng_(Tencent)', 'William_Ding_(NetEase)', 'Shi_Yuzhu_(Giant_Interactive)', 'Kwon_Hyuk-Bin_(SmileGate)', 'Kim_Jung-Ju_(Nexon)']
Y_Axis['Net', 'worth', 'in', 'billion', 'U.S.', 'dollars']: ['21.9', '11.5', '5.4', '4.9', '3.5']

gold: The graph shows the estimated net worth of the wealthiest entrepreneurs in the video game industry worldwide as of July 2016 . According to the source , Ma Huateng , the founder and chairman as well as CEO of Tencent , was worth 21.9 billion U.S. dollars in the measured period . Overall , Tencent reported 70.84 billion Chinese yuan revenue from its online games in 2016 .
gold_template: The graph shows the estimated templateYLabel[0] templateYLabel[1] of the templateTitle[4] templateTitle[5] in the templateTitleSubject[0] templateTitle[1] templateTitle[2] worldwide as of July templateTitleDate[0] . According to the source , templateXValue[0] , the founder and chairman as well as CEO of Tencent , was templateYLabel[1] templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in the measured period . Overall , Tencent reported 70.84 templateYLabel[2] Chinese yuan revenue from its online games in templateTitleDate[0] .

generated_template: This statistic illustrates the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , around templateYValue[max] percent of templateXValue[0] 's total templateYLabel[1] was estimated to be aged between 0 and 14 templateXValue[0] .
generated: This statistic illustrates the Video game industry in Video in 2016 . In 2016 , around 21.9 percent of Ma Huateng (Tencent) 's total worth was estimated to be aged between 0 and 14 Ma Huateng (Tencent) .

Example 278:
titleEntities: {'Subject': ['Senegal'], 'Date': ['2019']}
title: Unemployment rate in Senegal 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['6.52', '6.46', '6.44', '6.61', '6.76', '7.63', '8.51', '9.31', '10.36', '10.54', '10.36', '9.32', '9.5', '10.03', '9.09', '7.96', '6.78', '5.65', '5.61', '5.6', '5.7']

gold: This statistic shows the unemployment rate in Senegal from 1999 to 2019 . The figures are ILO estimates . In 2019 , the estimated unemployment rate in Senegal was 6.52 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The figures are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Senegal from 1999 to 2019 . In 2019 , the Unemployment rate in Senegal was at approximately 6.52 percent .

Example 279:
titleEntities: {'Subject': ['Canada'], 'Date': ['2024']}
title: Canada 's share of global gross domestic product ( GDP ) 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Share', 'of', 'global', 'GDP']: ['1.23', '1.25', '1.27', '1.3', '1.32', '1.34', '1.36', '1.38', '1.39', '1.42', '1.46']

gold: The statistic shows Canada 's share of the global gross domestic product ( GDP ) adjusted for Purchasing Power Parity ( PPP ) from 2014 to 2024 . In 2018 , Canada 's share of the global GDP ( in relation to PPP dollars ) amounted to about 1.36 percent .
gold_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] of the templateYLabel[1] templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) adjusted for Purchasing Power Parity ( PPP ) from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] of the templateYLabel[1] templateYLabel[2] ( in relation to PPP dollars ) amounted to about templateYValue[6] percent .

generated_template: The statistic shows the templateTitleSubject[0] templateTitleSubject[0] templateTitle[2] templateYLabel[0] of the templateYLabel[1] templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[2] ) adjusted for Purchasing Power Parity ( PPP ) from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitleSubject[0] templateTitleSubject[0] templateTitle[2] templateYLabel[0] of templateYLabel[1] templateYLabel[2] ( in relation to PPP dollars ) amounted to about templateYValue[6] percent .
generated: The statistic shows the Canada share of the global domestic product GDP ( GDP ) adjusted for Purchasing Power Parity ( PPP ) from 2014 to 2024 . In 2018 , the Canada share of global GDP ( in relation to PPP dollars ) amounted to about 1.36 percent .

Example 280:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2008', '2018']}
title: Poverty risk rate in Belgium 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['At-risk-of-poverty', 'rate']: ['16.4', '15.9', '15.5', '14.9', '15.5', '15.1', '15.3', '15.3', '14.6', '14.6', '14.7']

gold: Between 2008 and 2018 , roughly 15 percent of the Belgium population was at risk of poverty , defined by Statistics Belgium as 60 percent of the average disposable income in the country . People with an income lower than this are considered poor . In 2018 , this definition matched nearly 16 percent of the Belgians .
gold_template: Between templateXValue[min] and templateXValue[max] , roughly templateYValue[3] percent of the templateTitleSubject[0] population was at templateTitle[1] of templateTitle[0] , defined by Statistics templateTitleSubject[0] as 60 percent of the average disposable income in the country . People with an income lower than this are considered poor . In templateXValue[max] , this definition matched nearly templateYValue[max] percent of the Belgians .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] has been fluctuating in this period . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] to templateTitleSubject[0] was at templateYValue[0] templateYLabel[2] .
generated: The statistic shows the At-risk-of-poverty of rate in Belgium from 2008 to 2018 . The At-risk-of-poverty of rate has been fluctuating in this period . In 2018 , the At-risk-of-poverty of rate to Belgium was at 16.4 rate .

Example 281:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2017']}
title: Most popular mobile payment services in Sweden 2017
X_Axis['Response']: ['Swish', 'PayPal_Mobile', 'WyWallet', 'Other', 'None']
Y_Axis['Share', 'of', 'respondents']: ['75', '14', '3', '5', '21']

gold: Swish was the most popular mobile payment service in Sweden in 2017 . The share of respondents who used Swish amounted to 75 percent . It was followed by PayPal Mobile , which was used by 14 percent of respondents that year .
gold_template: templateXValue[0] was the templateTitle[0] templateTitle[1] templateXValue[1] templateTitle[3] service in templateTitleSubject[0] in templateTitleDate[0] . The templateYLabel[0] of templateYLabel[1] who used templateXValue[0] amounted to templateYValue[max] percent . It was followed by templateXValue[1] , which was used by templateYValue[1] percent of templateYLabel[1] that year .

generated_template: The graph shows the templateTitle[0] of templateTitle[1] to Disney 's new online video streaming service ( templateTitleSubject[0] ) among adults in the templateTitle[3] as of February templateTitleDate[0] . The data reveals templateXValue[2] just templateYValue[min] percent of surveyed templateTitleSubject[0] adults said templateXValue[2] they were templateXValue[0] templateXValue[0] to sign up to templateTitleSubject[0] upon its launch in templateTitleDate[0] , and templateYValue[max] percent said templateXValue[2] they were templateXValue[2] at templateXValue[3] templateXValue[0] to subscribe to the streaming service .
generated: The graph shows the Most of popular to Disney 's new online video streaming service ( Sweden ) among adults in the payment as of February 2017 . The data reveals WyWallet just 3 percent of surveyed Sweden adults said WyWallet they were Swish to sign up to Sweden upon its launch in 2017 , and 75 percent said WyWallet they were WyWallet at Other Swish to subscribe to the streaming service .

Example 282:
titleEntities: {'Subject': ['Q1'], 'Date': ['2017']}
title: Natural and synthetic diamonds price difference Q1 2016-Q3 2017
X_Axis['Quarter']: ['Q3_2017', 'Q2_2017', 'Q1_2017', 'Q4_2016', 'Q3_2016', 'Q2_2016', 'Q1_2016']
Y_Axis['Price', 'difference', 'in', 'percent']: ['-18.3', '-15.7', '-20.1', '-18.1', '-15.3', '-17.8', '-17.6']

gold: This statistic shows the price difference between synthetic/lab-created diamonds and natural diamonds from the first quarter of 2016 ( Q1 2016 ) to the third quarter of 2017 ( Q3 2017 ) in percent . In the second quarter of 2017 , lab-created diamonds cost some 15.7 percent less than naturally occurring diamonds .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] between synthetic/lab-created templateTitle[2] and templateTitle[0] templateTitle[2] from the first templateXLabel[0] of templateXValue[3] ( templateXValue[2] templateXValue[3] ) to the third templateXLabel[0] of templateXValue[0] ( templateXValue[0] ) in templateYLabel[2] . In the second templateXLabel[0] of templateXValue[0] , lab-created templateTitle[2] cost some 15.7 templateYLabel[2] less than naturally occurring templateTitle[2] .

generated_template: This statistic illustrates templateTitleSubject[0] 's templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] from the second templateXLabel[0] of templateTitleDate[0] to the second templateXLabel[0] of templateTitleDate[1] . As of the second templateXLabel[0] of templateTitleDate[0] , Mexicans paid on average templateYValue[0] templateYLabel[2] worldwide .
generated: This statistic illustrates Q1 's synthetic diamonds Price difference from the second Quarter of 2017 to the second Quarter of 2017 . As of the second Quarter of 2017 , Mexicans paid on average -18.3 percent worldwide .

Example 283:
titleEntities: {'Subject': ['Capital'], 'Date': ['2020']}
title: Capital spending in the semiconductor industry worldwide 2000 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Spending', 'in', 'billion', 'U.S.', 'dollars']: ['94.3', '97.8', '105.9', '95.6', '67.8', '65.2', '66.1', '55.3', '59.0', '67.4', '54.0', '26.1', '43.4', '61.0', '57.5', '48.1', '47.7', '31.3', '27.5', '38.7', '61.3']

gold: The statistic depicts the total capital spending in the global semiconductor industry from 2000 to 2018 and shows a forecast for 2019 and 2020 . In 2019 , global semiconductor capital spending is projected to amount to 97.8 billion U.S. dollars , down from the all-time highest spending of 105.9 billion U.S. dollars in 2018 .
gold_template: The statistic depicts the total templateTitleSubject[0] templateYLabel[0] in the global templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[2] and shows a forecast for templateXValue[1] and templateXValue[max] . In templateXValue[1] , global templateTitle[2] templateTitleSubject[0] templateYLabel[0] is projected to amount to templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] , down from the all-time highest templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[2] .

generated_template: This statistic shows the templateTitle[0] amount of templateTitle[1] templateYLabel[0] that was offered by different sources to students in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] had a templateTitle[0] of templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Capital amount of spending that was offered by different sources to students in the Capital ( Capital ) from 2000 to 2020 . In 2020 , Capital had a Capital of 26.1 billion U.S. dollars .

Example 284:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Number of forcible rape cases by state U.S. 2018
X_Axis['State']: ['California', 'Texas', 'Florida', 'Michigan', 'New_York', 'Illinois', 'Ohio', 'Pennsylvania', 'Colorado', 'Arizona', 'Washington', 'Virginia', 'Missouri', 'Tennessee', 'Georgia', 'North_Carolina', 'Minnesota', 'South_Carolina', 'Massachusetts', 'Indiana', 'Nevada', 'Oklahoma', 'Wisconsin', 'Arkansas', 'Louisiana', 'Alabama', 'Maryland', 'Oregon', 'Utah', 'Kentucky', 'Kansas', 'New_Jersey', 'New_Mexico', 'Nebraska', 'Alaska', 'Iowa', 'Connecticut', 'Idaho', 'West_Virginia', 'Hawaii', 'South_Dakota', 'Montana', 'Mississippi', 'New_Hampshire', 'Rhode_Island', 'District_of_Columbia', 'Maine', 'North_Dakota', 'Delaware', 'Vermont', 'Wyoming']
Y_Axis['Number', 'of', 'forcible', 'rapes']: ['15505', '14693', '8438', '7690', '6575', '5859', '5300', '4483', '4070', '3638', '3413', '2924', '2912', '2821', '2651', '2633', '2462', '2434', '2410', '2370', '2329', '2299', '2248', '2196', '2085', '1996', '1979', '1975', '1753', '1707', '1567', '1424', '1354', '1233', '1192', '976', '840', '791', '652', '625', '614', '551', '537', '534', '481', '450', '446', '397', '338', '287', '243']

gold: In 2018 , California had the highest number of forcible rape cases in the United States , with 15,505 reported rapes . Vermont had the lowest number of reported forcible rape cases at 243 . Number vs. rate It is perhaps unsurprising that California had the highest number of reported rapes in the United States in 2018 , as California is the state with the highest population .
gold_template: In templateTitleDate[0] , templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] in the templateTitle[6] , with templateYValue[max] reported templateYLabel[2] . templateXValue[49] had the lowest templateYLabel[0] of reported templateYLabel[1] templateTitle[2] templateTitle[3] at templateYValue[min] . templateYLabel[0] vs. rate It is perhaps unsurprising that templateXValue[0] had the highest templateYLabel[0] of reported templateYLabel[2] in the templateTitle[6] in templateTitleDate[0] , as templateXValue[0] is the templateXLabel[0] with the highest population .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] and templateTitle[4] in the templateTitleSubject[0] templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In that year , most templateTitle[2] templateYLabel[1] and templateTitle[4] occurred in templateXValue[0] templateXValue[0] - a total of templateYValue[max] . In templateXValue[last] , no templateTitle[2] templateYLabel[1] and templateTitle[4] occurred in templateTitleDate[0] .
generated: The statistic shows the Number of rape forcible and by in the U.S. in 2018 , U.S. State . In that year , most rape forcible and by occurred in California - a total of 15505 . In Wyoming , no rape forcible and by occurred in 2018 .

Example 285:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2050']}
title: Carbon dioxide emissions per person in the U.S. 2019 to 2050
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2019']
Y_Axis['Emissions', 'in', 'metric', 'tons', 'of', 'CO2', 'equivalent/', 'person']: ['12.6', '12.5', '12.6', '12.8', '13.1', '13.7', '15.0', '15.5']

gold: Per capita carbon dioxide ( CO2 ) emissions in the United States reached an estimate of 15.5 metric tons of carbon dioxide in 2019 . The United States has forecast that a decrease in CO2 emissions will occur through 2050 , reaching 12.6 metric tons of carbon dioxide equivalent per person . This forecast is driven by increasing consumption of natural gas due to lower cost and emissions with higher efficiency .
gold_template: templateTitle[3] capita templateTitle[0] templateTitle[1] ( templateYLabel[3] ) templateYLabel[0] in the templateTitle[5] reached an estimate of templateYValue[max] templateYLabel[1] templateYLabel[2] of templateTitle[0] templateTitle[1] in templateXValue[min] . The templateTitle[5] has forecast that a decrease in templateYLabel[3] templateYLabel[0] will occur through templateXValue[max] , reaching templateYValue[0] templateYLabel[1] templateYLabel[2] of templateTitle[0] templateTitle[1] equivalent templateTitle[3] templateYLabel[5] . This forecast is driven by increasing consumption of natural gas due to lower cost and templateYLabel[0] with higher efficiency .

generated_template: This statistic shows the templateTitleSubject[0] templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , approximately templateYValue[min] templateYLabel[2] people were using the templateTitle[2] templateTitle[3] templateTitle[4] . In templateXValue[max] , this figure is projected to be some templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the U.S. Emissions of emissions per person from 2019 to 2050 . In 2019 , approximately 12.5 tons people were using the emissions per person . In 2050 , this figure is projected to be some 15.5 tons CO2 equivalent/ .

Example 286:
titleEntities: {'Subject': ['North America'], 'Date': ['2016']}
title: Online gaming traffic in North America 2011 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Data', 'volume', 'in', 'petabytes']: ['96', '62', '44', '37', '29', '22']

gold: The statistic shows a forecast for the traffic caused by online gaming in North America from 2011 until 2016 , in petabytes per month . In 2014 , online gaming IP traffic is expected to reach 44 petabytes per month .
gold_template: The statistic shows a forecast for the templateTitle[2] caused by templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] until templateXValue[max] , in templateYLabel[2] per month . In templateXValue[2] , templateTitle[0] templateTitle[1] IP templateTitle[2] is expected to reach templateYValue[2] templateYLabel[2] per month .

generated_template: This statistic illustrates the average templateTitle[0] templateYLabel[0] of templateTitle[2] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateTitle[2] templateTitle[3] has continuously decreased during this period . In templateXValue[max] , this templateYLabel[0] totalled a templateTitle[0] of templateYValue[max] percent .
generated: This statistic illustrates the average Online Data of traffic in the North America from 2011 to 2016 . The Data of traffic North has continuously decreased during this period . In 2016 , this Data totalled a Online of 96 percent .

Example 287:
titleEntities: {'Subject': ['YouTube'], 'Date': ['2017']}
title: Global YouTube usage for music consumption 2017 , by country
X_Axis['Country']: ['Mexico', 'Brazil', 'Italy', 'Spain', 'South_Korea', 'Canada', 'Total', 'Germany', 'United_States', 'France', 'Great_Britain', 'Sweden', 'Australia', 'Japan']
Y_Axis['Share', 'of', 'respondents']: ['97', '95', '90', '90', '86', '84', '83', '82', '82', '81', '79', '79', '77', '72']

gold: This statistic shows the share of global users who have accessed YouTube to consume music as of 2017 , sorted by country . During the survey period , 82 percent of respondents from the United States said that they had used YouTube for music .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] users who have accessed templateTitleSubject[0] to consume templateTitle[4] as of templateTitleDate[0] , sorted templateTitle[7] templateXLabel[0] . During the survey period , templateYValue[7] percent of templateYLabel[1] from the templateXValue[8] said that they had used templateTitleSubject[0] templateTitle[3] templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] internet users who have used templateTitle[2] templateTitle[3] templateTitle[4] as of templateTitleDate[0] , sorted templateTitle[6] templateXLabel[0] . During the survey period , templateYValue[max] percent of templateYLabel[1] from templateXValue[0] said that they had used templateTitle[2] templateTitle[3] templateTitle[4] in the past six months . In the templateXValue[5] templateXValue[5] , templateYValue[5] percent of internet users used templateTitle[2] templateTitle[3] templateTitle[4] .
generated: This statistic shows the Share of YouTube internet users who have used usage for music as of 2017 , sorted 2017 Country . During the survey period , 97 percent of respondents from Mexico said that they had used usage for music in the past six months . In the Canada , 84 percent of internet users used usage for music .

Example 288:
titleEntities: {'Subject': ['Jordan'], 'Date': ['2019']}
title: Youth unemployment rate in Jordan in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['36.68', '37.24', '34.86', '35.63', '30.9', '28.43', '29.87', '28.84', '30.03', '28.88', '29.12', '28.51', '29.04', '30.45', '31.75', '31.37', '31.06', '32.62', '31.08', '29.4', '29.65']

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

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

Example 289:
titleEntities: {'Subject': ['Russia'], 'Date': ['2017']}
title: Main export partners for Russia 2017
X_Axis['Country']: ['China', 'Netherlands', 'Germany', 'Belarus', 'Turkey']
Y_Axis['Share', 'of', 'total', 'exports']: ['10.9', '10', '7.1', '5.1', '4.9']

gold: This statistic shows the main export partners for Russia in 2017 . In 2017 , the most important export partner for Russia was China , accounting for 10.9 percent of all exports .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , the most important templateTitle[1] partner templateTitle[3] templateTitleSubject[0] was templateXValue[0] , accounting templateTitle[3] templateYValue[max] percent of all templateYLabel[2] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , the most important templateTitle[1] partner templateTitle[3] templateTitleSubject[0] were the templateXValue[0] templateXValue[0] , accounting templateTitle[3] templateYValue[max] percent of all templateYLabel[2] .
generated: The statistic shows the Main export partners for Russia in 2017 . In 2017 , the most important export partner for Russia were the China , accounting for 10.9 percent of all exports .

Example 290:
titleEntities: {'Subject': ['NCAA'], 'Date': ['2013']}
title: NCAA college basketball tournament TV/television revenue 2013
X_Axis['Year']: ['2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984', '1983', '1982', '1981', '1980']
Y_Axis['Television', 'revenue', '(in', 'million', 'U.S.', 'dollars)']: ['684.3', '670.0', '653.0', '633.8', '571.0', '543.8', '503.0', '453.0', '432.0', '400.0', '370.04', '272.78', '242.1', '227.7', '213.8', '200.9', '188.4', '178.3', '166.2', '137.06', '129.06', '120.06', '112.44', '63.51', '57.16', '57.79', '36.64', '33.03', '28.33', '20.14', '16.88', '14.63', '10.32', '8.86']

gold: The graph shows the television revenue generated through NCAA college basketball tournament from 2000 to 2013 . In 2010 , the television revenue was at 633.8 million U.S. dollars .
gold_template: The graph shows the templateYLabel[0] templateYLabel[1] generated through templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateXValue[13] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] templateYLabel[1] was at templateYValue[3] templateYLabel[3] templateYLabel[4] dollars .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[0] thousand people were living in templateTitle[1] templateYLabel[1] in the templateTitle[0] .
generated: This statistic shows the Television of basketball tournament in the NCAA from 1980 to 2013 . In 2013 , approximately 684.3 thousand people were living in college revenue in the NCAA .

Example 291:
titleEntities: {'Subject': ['Fiji'], 'Date': ['2019']}
title: Unemployment rate in Fiji 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['4.15', '4.15', '4.14', '4.32', '4.55', '4.53', '4.36', '4.52', '4.41', '4.35', '4.16', '3.62', '3.76', '3.83', '3.89', '4.06', '4.07', '4.16', '4.15', '4.15', '4.12']

gold: This statistic shows the unemployment rate in Fiji from 1999 to 2019 . In 2019 , the unemployment rate in Fiji was at approximately 4.15 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent of the total labor force . The templateYLabel[0] templateYLabel[1] is the percentage of a country 's labor force that are without jobs but are available to work and actively seeking employment . templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] is above the worldwide templateYLabel[0] templateYLabel[1] , and compared to other Sub-Saharan African countries and other regions , templateTitleSubject[0] has a relatively average templateYLabel[1] of templateYLabel[0] .
generated: In 2019 , the Unemployment rate in Fiji was at approximately 4.15 percent of the total labor force . The Unemployment rate is the percentage of a country 's labor force that are without jobs but are available to work and actively seeking employment . Fiji 's Unemployment rate is above the worldwide Unemployment rate , and compared to other Sub-Saharan African countries and other regions , Fiji has a relatively average rate of Unemployment .

Example 292:
titleEntities: {'Subject': ['Connecticut'], 'Date': ['2000', '2018']}
title: Connecticut - Poverty rate 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'population']: ['10.4', '9.6', '9.8', '10.5', '10.8', '10.7', '10.7', '10.9', '10.1', '9.4', '9.3', '7.9', '8.3', '8.3', '7.6', '8.1', '7.5', '7.3', '7.7']

gold: This statistic shows the poverty rate in Connecticut from 2000 to 2018 . In 2018 , about 10.4 percent of Connecticut 's population lived below the poverty line .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the Poverty rate in Connecticut from 2000 to 2018 . In 2018 , 10.4 percent of Connecticut 's population lived below the Poverty line .

Example 293:
titleEntities: {'Subject': ['LinkedIn'], 'Date': ['2013', '2016']}
title: LinkedIn : unique mobile visiting members 2013 to 2016
X_Axis['Quarter']: ["Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13"]
Y_Axis['Number', 'of', 'mobile', 'visiting', 'members', 'in', 'millions']: ['63', '61', '57', '55', '51', '49', '45', '42', '38', '35', '31', '29', '26', '20']

gold: This timeline displays the number of unique mobile visiting members to social network LinkedIn . As of the second quarter of 2016 , LinkedIn had an average of 63 million unique visiting members via mobile . These accounted for 59 percent of all unique visiting members .
gold_template: This timeline displays the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] to social network templateTitleSubject[0] . As of the second templateXLabel[0] of templateTitleDate[1] , templateTitleSubject[0] had an average of templateYValue[max] templateYLabel[4] templateTitle[1] templateYLabel[2] templateYLabel[3] via templateYLabel[1] . These accounted for 59 percent of all templateTitle[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[0] to the second templateXLabel[0] of templateTitleDate[1] . As of the second templateXLabel[0] of templateTitleDate[1] , the Germany-based multinational engineering and electronics company incurred approximately templateYValue[0] templateYLabel[1] templateYLabel[2] ( or about 6.8 templateYLabel[1] U.S. dollars ) .
generated: This statistic shows the LinkedIn unique mobile of LinkedIn from the first Quarter of 2013 to the second Quarter of 2016 . As of the second Quarter of 2016 , the Germany-based multinational engineering and electronics company incurred approximately 63 mobile visiting ( or about 6.8 mobile U.S. dollars ) .

Example 294:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2020']}
title: Countries with the most Facebook users 2020
X_Axis['Country']: ['India', 'United_States', 'Indonesia', 'Brazil', 'Mexico', 'Philippines', 'Vietnam', 'Thailand', 'Egypt', 'Turkey', 'United_Kingdom', 'Bangladesh', 'Pakistan', 'Colombia', 'France', 'Argentina', 'Italy', 'Germany', 'Nigeria', 'Malaysia', 'Peru', 'Canada', 'Maynmar', 'Spain']
Y_Axis['Number', 'of', 'Facebook', 'users', 'in', 'millions']: ['260', '180', '130', '120', '84', '70', '61', '47', '38', '37', '37', '34', '33', '32', '31', '29', '29', '28', '24', '22', '22', '21', '21', '21']

gold: There are over 260 million Facebook users in India alone , making it the leading country in terms of Facebook audience size . To put this into context , if India 's Facebook audience were a country then it would be ranked fourth in terms of largest population worldwide . Apart from India , there are several other markets with more than 100 million Facebook users each : The United States , Indonesia , and Brazil with 180 million , 130 million , and 120 million Facebook users respectively .
gold_template: There are over templateYValue[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] in templateXValue[0] alone , making it the leading templateXLabel[0] in terms of templateYLabel[1] audience size . To put this into context , if templateXValue[0] 's templateYLabel[1] audience were a templateXLabel[0] then it would be ranked fourth in terms of largest population worldwide . Apart from templateXValue[0] , there are several other markets templateTitle[1] more than 100 templateYLabel[3] templateYLabel[1] templateYLabel[2] each : The templateXValue[1] , templateXValue[2] , and templateXValue[3] templateTitle[1] templateYValue[1] templateYLabel[3] , templateYValue[2] templateYLabel[3] , and templateYValue[3] templateYLabel[3] templateYLabel[1] templateYLabel[2] respectively .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] in the templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in Europe by selected templateTitle[4] in templateTitleDate[0] . The generic templateYLabel[0] in the templateYLabel[1] industry 's templateYLabel[2] templateYLabel[3] in templateXValue[7] exceeded templateYValue[7] percent in templateTitleDate[0] . templateYLabel[2] templateYLabel[0] of templateTitle[2] is higher in newer EU member states due to low levels of intellectual property protection .
generated: The statistic shows the Number of Facebook in the Facebook users millions in Europe by selected 2020 in 2020 . The generic Number in the Facebook industry 's users millions in Thailand exceeded 47 percent in 2020 . users Number of Facebook is higher in newer EU member states due to low levels of intellectual property protection .

Example 295:
titleEntities: {'Subject': ['Arizona Diamondbacks'], 'Date': ['2001', '2018']}
title: Arizona Diamondbacks revenue 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['275', '258', '253', '223', '211', '192', '195', '186', '180', '172', '177', '165', '154', '145', '136', '126', '122', '127']

gold: The statistic depicts the revenue of the Arizona Diamondbacks from 2001 to 2018 . In 2018 , the revenue of the Major League Baseball franchise amounted to 275 million U.S. dollars.The Arizona Diamondbacks are owned by Ken Kendrick , who bought the franchise for 238 million U.S. dollars in 2004 .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] dollars.The templateTitleSubject[0] are owned by Ken Kendrick , who bought the franchise for 238 templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[14] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] dollars.The templateTitleSubject[0] templateTitleSubject[0] are owned by Jim Crane , who bought the franchise for 465 templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[7] .
generated: The statistic depicts the Revenue of the Arizona Diamondbacks from 2001 to 2018 . In 2018 , the Revenue of the Major League Baseball franchise amounted to 275 million U.S. dollars.The Arizona Diamondbacks are owned by Jim Crane , who bought the franchise for 465 million U.S. dollars in 2011 .

Example 296:
titleEntities: {'Subject': ['U.S. August'], 'Date': ['2014', '2014']}
title: Frequency of eating fast food in the U.S. as of August 2014
X_Axis['Response']: ['Daily', 'A_few_times_per_week', 'About_once_per_week', 'A_few_times_per_month', 'About_once_per_month', 'Rarely', 'Never']
Y_Axis['Share', 'of', 'respondents']: ['5.2', '19', '21', '22.7', '12.2', '17.5', '2.2']

gold: This statistic shows the frequency with which consumers ate fast food in the United States as of August 2014 . During the survey , 21 percent of respondents said that they ate fast food about once per week .
gold_template: This statistic shows the templateTitleSubject[0] with which consumers ate templateTitle[2] templateTitle[3] in the templateTitle[4] as of templateTitle[5] templateTitle[6] . During the survey , templateYValue[2] percent of templateYLabel[1] said that they ate templateTitle[2] templateTitle[3] templateXValue[2] per templateXValue[1] .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among templateTitleSubject[0] templateTitle[1] aged 18 to 60 about their stance on templateXValue[0] on templateTitle[4] . They were asked how they would manage to templateXValue[0] templateXValue[0] on templateTitle[4] templateTitle[5] templateTitle[6] a templateXValue[0] with others . templateYValue[max] percent of the templateTitle[1] stated templateXValue[1] they would templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] is in the templateXValue[0] .
generated: This statistic shows the results of a 2014 survey among U.S. August eating aged 18 to 60 about their stance on Daily on U.S. . They were asked how they would manage to Daily on U.S. August 2014 a Daily with others . 22.7 percent of the eating stated A few times per week they would Daily is in the Daily .

Example 297:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. social media user account privacy 2018
X_Axis['Response']: ['Yes_all_of_my_social_media_accounts_are_private', 'Yes_a_few_of_my_social_meda_accounts_are_private', 'Yes_one_of_my_social_media_accounts_is_private', 'No_none_of_my_social_media_accounts_are_private', "Don't_know/No_opinion"]
Y_Axis['Share', 'of', 'respondents']: ['45', '20', '7', '19', '8']

gold: This statistic presents the percentage of social media users in the United States who have a private social media account as of September 2018 . According to the findings , 45 percent of respondents reported that all of their social media accounts were private , while 19 percent of respondents stated the opposite saying none of their social media accounts were private at all .
gold_template: This statistic presents the percentage of templateXValue[0] users in the templateTitle[0] who have a templateXValue[0] media templateTitle[4] as of September templateTitleDate[0] . According to the findings , templateYValue[max] percent of templateYLabel[1] reported that templateXValue[0] of their templateXValue[0] accounts were templateXValue[0] , while templateYValue[3] percent of templateYLabel[1] stated the opposite saying templateXValue[3] of their templateXValue[0] accounts were templateXValue[0] at templateXValue[0] .

generated_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] . As of October templateTitleDate[0] , templateXValue[0] programs were templateTitle[0] templateTitle[1] among survey templateYLabel[1] , at templateYValue[max] percent .
generated: This statistic presents the U.S. social media user in the U.S. ( U.S. ) in 2018 . As of October 2018 , Yes all of my social media accounts are private programs were U.S. social among survey respondents , at 45 percent .

Example 298:
titleEntities: {'Subject': ['Newmont Mining'], 'Date': ['2007', '2018']}
title: Newmont Mining 's revenue 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['7253', '7379', '6680', '6085', '6819', '7891', '9297', '10441', '9610', '7705', '6124', '5465']

gold: This statistic shows Newmont Mining 's revenue from 2007 to 2018 , in million U.S. dollars . United States-based Newmont is one of the world 's largest gold producers . In 2018 , the company generated some 7.25 billion U.S. dollars of revenue .
gold_template: This statistic shows templateTitleSubject[0] 's templateYLabel[0] from templateXValue[min] to templateXValue[max] , in templateYLabel[1] templateYLabel[2] templateYLabel[3] . United States-based templateTitleSubject[0] is one of the world templateTitle[2] largest gold producers . In templateXValue[max] , the company generated some templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateYLabel[0] .

generated_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateYLabel[1] templateYLabel[2] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] . templateTitleSubject[0] Corporation is one of the world templateTitle[1] leading manufacturers of the world .
generated: Between 2007 and 2018 , the Revenue of Newmont Mining Mining million U.S. amounted to 10441 million U.S. dollars , up from 7379 million U.S. dollars in the previous Year . Newmont Mining Corporation is one of the world Mining leading manufacturers of the world .

Example 299:
titleEntities: {'Subject': ['Rakuten Group'], 'Date': ['2014', '2019']}
title: Rakuten Group : quarterly domestic e-commerce GMS 2014 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14"]
Y_Axis['Gross', 'merchandise', 'sales', 'in', 'billion', 'Japanese', 'yen']: ['1053.1', '1012.8', '921.9', '871.7', '974.4', '855.3', '805.2', '770.0', '868.0', '771.3', '735.8', '704.0', '778.4', '748.9', '705.4', '682.8', '741.8', '685.3', '640.8', '611.9', '671.0', '607.7', '548.6', '607.0']

gold: In the fourth quarter of 2019 , the domestic gross transaction value of the Rakuten Group 's e-commerce sectors amounted to approximately 1.05 trillion Japanese yen , up 8.1 percent compared to the fourth quarter of the previous year . The company is headquartered in Tokyo , where it was founded in 1997 .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[1] , the templateTitle[3] templateYLabel[0] transaction value of the templateTitleSubject[0] 's templateTitle[4] sectors amounted to approximately templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] , up 8.1 percent compared to the fourth templateXLabel[0] of the previous year . The company is headquartered in Tokyo , where it was founded in 1997 .

generated_template: In the fourth templateXLabel[0] of templateTitleDate[1] , revenues of AWS grew templateYValue[min] percent compared to the preceding templateXLabel[0] . AWS is one of templateTitleSubject[0] 's strongest revenue segments , generating 25.66 billion U.S. dollars in 2018 net sales , up from 17.46 billion U.S. dollars in 2017 . AWS ranks as one of the most popular public cloud infrastructure and platform templateTitleSubject[0] running applications worldwide in 2018 , ahead off Azure and Google cloud templateTitleSubject[0] .
generated: In the fourth Quarter of 2019 , revenues of AWS grew 548.6 percent compared to the preceding Quarter . AWS is one of Rakuten Group 's strongest revenue segments , generating 25.66 billion U.S. dollars in 2018 net sales , up from 17.46 billion U.S. dollars in 2017 . AWS ranks as one of the most popular public cloud infrastructure and platform Rakuten Group running applications worldwide in 2018 , ahead off Azure and Google cloud Rakuten Group .

Example 300:
titleEntities: {'Subject': ['Brazil'], 'Date': []}
title: Fertility rate in Brazil
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'children', 'born', 'per', 'woman']: ['1.74', '1.75', '1.75', '1.76', '1.76', '1.77', '1.78', '1.8', '1.82', '1.85', '1.88']

gold: This statistic shows the fertility rate in Brazil from 2007 to 2017 . The fertility rate is the average number of children born by one woman while being of child-bearing age . In 2017 , the fertility rate among Brazil 's population amounted to 1.74 children per woman .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] by one templateYLabel[4] while being of child-bearing age . In templateXValue[max] , the templateTitle[0] templateTitle[1] among templateTitleSubject[0] 's population amounted to templateYValue[min] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In the templateXLabel[0] templateXValue[max] , about templateYValue[min] templateYLabel[1] were templateYLabel[2] templateYLabel[3] templateYLabel[4] of childbearing age . Despite a decreasing templateTitle[0] templateTitle[1] , the total population of templateTitleSubject[0] is expected to increase over the next few years .
generated: The statistic shows the Fertility rate in Brazil from 2007 to 2017 . In the Year 2017 , about 1.74 children were born per woman of childbearing age . Despite a decreasing Fertility rate , the total population of Brazil is expected to increase over the next few years .

Example 301:
titleEntities: {'Subject': ['Annual'], 'Date': ['2009', '2015']}
title: Annual growth of the global generic market 2009 - 2015 by region
X_Axis['Country']: ['Rest_of_world', 'World', 'North_America', 'Europe', 'Japan']
Y_Axis['Average', 'annual', 'growth']: ['19', '12', '11', '9', '6']

gold: This statistic depicts the average annual growth of the global generic market between 2009 and 2015 , by region . In Japan , the average annual growth is estimated to be six percent in that period .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[2] templateTitle[3] templateTitle[4] between templateTitle[5] and templateTitle[6] , templateTitle[7] templateTitle[8] . In templateXValue[last] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] is estimated to be templateYValue[min] percent in that period .

generated_template: This statistic shows the templateTitle[2] expensive templateTitle[0] templateTitle[3] a monthly templateTitleSubject[0] subscription templateTitle[6] as of April templateTitleDate[0] . The data reveals that templateXValue[0] was the templateTitle[2] expensive place in the world to get templateTitleSubject[0] , with a monthly subscription costing templateYValue[min] templateYLabel[1] templateYLabel[2] . In templateXValue[2] , the monthly price templateTitle[3] a templateTitleSubject[0] subscription amounted to templateYValue[2] templateYLabel[1] templateYLabel[2] , and Mexican subscribers paid templateYValue[3] templateYLabel[1] templateYLabel[2] per month to use the streaming service .
generated: This statistic shows the global expensive Annual generic a monthly Annual subscription 2015 as of April 2009 . The data reveals that Rest of world was the global expensive place in the world to get Annual , with a monthly subscription costing 6 annual growth . In North America , the monthly price generic a Annual subscription amounted to 11 annual growth , and Mexican subscribers paid 9 annual growth per month to use the streaming service .

Example 302:
titleEntities: {'Subject': ['Pennsylvania'], 'Date': ['1992', '2018']}
title: Pennsylvania - Unemployment rate 1992 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92"]
Y_Axis['Unemployment', 'rate']: ['4.3', '4.9', '5.4', '5.3', '5.9', '7.4', '7.8', '7.9', '8.5', '8', '5.3', '4.4', '4.6', '5', '5.4', '5.7', '5.6', '4.8', '4.1', '4.4', '4.6', '5.1', '5.4', '5.9', '6.3', '7', '7.6']

gold: This statistic displays the unemployment rate in Pennsylvania from 1992 to 2018 . In 2018 , unemployment in Pennsylvania was 4.3 percent .
gold_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[0] percent .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[0] percent . You can access the monthly templateYLabel[0] templateYLabel[1] for the country here .
generated: This statistic displays the Unemployment rate in Pennsylvania from 1992 to 2018 . In 2018 , Unemployment rate in Pennsylvania was 4.3 percent . You can access the monthly Unemployment rate for the country here .

Example 303:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2005', '2017']}
title: Major kitchen/laundry appliances : unit shipments in the U.S. 2005 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Unit', 'shipments', 'in', 'millions']: ['64.61', '62.29', '60.52', '58.76', '55.71', '52.11', '53.53', '53.51', '52.02', '57.54', '63.41', '68.45', '69.13']

gold: The statistic illustrates the total unit shipments of kitchen and laundry appliances* in the United States from 2005 to 2012 and forecasts up to and including 2017 . For 2015 the Appliance Magazine projects total unit shipments of major kitchen/laundry appliances to reach 60.52 million units . U.S. unit shipments of major appliances - additional information Total unit shipments of major kitchen and laundry appliances in the United States are expected to reach 64.6 million units in 2017 .
gold_template: The statistic illustrates the total templateYLabel[0] templateYLabel[1] of kitchen and laundry appliances* in the templateTitle[5] from templateXValue[min] to templateXValue[5] and forecasts up to and including templateXValue[max] . For templateXValue[2] the Appliance Magazine projects total templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] to reach templateYValue[2] templateYLabel[2] units . templateTitleSubject[0] templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[2] - additional information Total templateYLabel[0] templateYLabel[1] of templateTitle[0] kitchen and laundry templateTitle[2] in the templateTitle[5] are expected to reach templateYValue[0] templateYLabel[2] units in templateXValue[max] .

generated_template: The statistic illustrates the templateTitle[0] templateYLabel[0] templateYLabel[1] of smartphones in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around templateYValue[min] templateYLabel[2] templateTitle[0] templateYLabel[0] templateYLabel[1] were living in the templateTitle[4] . Since then , this figure increased to some templateYValue[max] templateYLabel[2] in templateXValue[max] .
generated: The statistic illustrates the Major Unit shipments of smartphones in the U.S. from 2005 to 2017 . In 2017 , around 52.02 millions Major Unit shipments were living in the shipments . Since then , this figure increased to some 69.13 millions in 2017 .

Example 304:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: U.S. tea market : total wholesale value 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '1990']
Y_Axis['Sales', 'in', 'billion', 'U.S.', 'dollars']: ['12.66', '12.5', '12.0', '11.5', '10.84', '10.41', '9.79', '1.84']

gold: The statistic shows the wholesale tea value in the United States from 1990 to 2018 . U.S. wholesale tea sales amounted to approximately 1.84 billion U.S. dollars in 1990 and increased to about 12.66 billion U.S. dollars in 2018 .
gold_template: The statistic shows the templateTitle[4] templateTitle[1] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . templateYLabel[2] templateTitle[4] templateTitle[1] templateYLabel[0] amounted to approximately templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[min] and increased to about templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] .

generated_template: The statistic presents the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] was approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic presents the Sales of market total wholesale value in the tea from 1990 to 2018 . In 2018 , the Sales of the market total wholesale value in the tea was approximately 12.66 billion U.S. dollars .

Example 305:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2024']}
title: Ratio of government expenditure to gross domestic product ( GDP ) in Argentina 2024*
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Ratio', 'of', 'government', 'expenditure', 'to', 'GDP']: ['38.28', '37.74', '37.28', '37.04', '36.86', '37.56', '38.9', '41.18', '41.52', '41.37', '38.85']

gold: The statistic shows the ratio of government expenditure to the gross domestic product ( GDP ) in Argentina from 2014 to 2018 , with projections up until 2024 . In 2018 , the government expenditure in Argentina amounted to about 38.9 percent of the country 's gross domestic product .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to the templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to about templateYValue[6] percent of the country 's templateTitle[3] templateTitle[4] templateTitle[5] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to about templateYValue[max] percent of the country 's templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The statistic shows the Ratio of government expenditure to gross domestic product ( GDP ) in Argentina from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure in Argentina amounted to about 41.52 percent of the country 's gross domestic product .

Example 306:
titleEntities: {'Subject': ['U.S. U.S.'], 'Date': ['2017']}
title: Biggest U.S. government cyber security problems according to U.S. adults 2017
X_Axis['Response']: ['Hacking_by_foreign_governments', 'Securing_confidential_intelligence_reports', 'Securing_citizen_records_(ex._IRS_filings)', 'Eavesdropping_through_smart_technology', 'Securing_records_of_military_personnel', 'Interfering_with_elections_through_propaganda', 'Interfering_with_elections_by_hacking_the_counting_of_ballots']
Y_Axis['Share', 'of', 'respondents']: ['72', '23', '17', '11', '11', '10', '8']

gold: This statistic presents a ranking of the biggest cyber security problems facing the U.S. government according to adults in the United States . During the January 2017 survey period , 72 percent of respondents stated that hacking by foreign governments was the U.S. government 's biggest cyber security problem .
gold_template: This statistic presents a ranking of the templateTitle[0] templateTitle[3] templateTitle[4] templateTitle[5] facing the templateTitleSubject[0] templateTitle[2] templateTitle[6] to templateTitle[8] in the templateTitle[1] . During the January templateTitleDate[0] survey period , templateYValue[max] percent of templateYLabel[1] stated that templateXValue[0] foreign templateXValue[0] was the templateTitleSubject[0] templateTitle[2] 's templateTitle[0] templateTitle[3] templateTitle[4] problem .

generated_template: As of the third quarter of templateTitleDate[0] , it was found that templateYValue[max] percent of templateTitleSubject[0] users in the templateTitleSubject[1] templateTitleSubject[1] accessed the photo sharing app templateXValue[0] templateXValue[0] a templateXValue[0] . A further templateYValue[1] percent of templateYLabel[1] claimed that they used templateTitleSubject[0] on a templateXValue[1] basis .
generated: As of the third quarter of 2017 , it was found that 72 percent of U.S. U.S. users in the U.S. U.S. accessed the photo sharing app Hacking by foreign governments a Hacking by foreign governments . A further 23 percent of respondents claimed that they used U.S. U.S. on a Securing confidential intelligence reports basis .

Example 307:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1995', '2019']}
title: U.S. nuclear refueling outage days 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['Average', 'number', 'of', 'days']: ['32', '34', '34', '34', '36', '37', '41', '46', '45', '40', '41', '38', '40', '39', '38', '42', '40', '33', '37', '44', '40', '51', '81', '66', '66']

gold: The statistic represents the average length of outages for nuclear power plants in the United States between 1995 and 2019 . In 2019 , as of October 2019 , this figure stood at 32 outage days . US nuclear refueling outage days America 's electricity needs are met by a selection of energy carriers including coal , oil and natural gas , as well as nuclear fuel and renewable energy sources .
gold_template: The statistic represents the templateYLabel[0] length of outages for templateTitle[1] power plants in the templateTitle[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , as of October templateXValue[max] , this figure stood at templateYValue[min] templateTitle[3] templateYLabel[2] . US templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[2] America 's electricity needs are met by a selection of energy carriers including coal , oil and natural gas , as well as templateTitle[1] fuel and renewable energy sources .

generated_template: In templateXValue[max] , around templateYValue[0] percent of templateTitle[5] templateTitle[6] in the templateTitle[4] stated they felt it was `` fairly easy '' or `` very easy '' to obtain templateTitleSubject[0] . This is a significant decrease from templateYValue[max] percent of templateTitle[5] templateTitle[6] who templateTitle[2] the drug to be easy to obtain in the templateXLabel[0] templateXValue[20] . templateTitleSubject[0] is a hallucinogenic drug that can alter the user 's thoughts , feelings , and perception of reality .
generated: In 2019 , around 32 percent of 1995 2019 in the days stated they felt it was `` fairly easy '' or `` very easy '' to obtain U.S. . This is a significant decrease from 81 percent of 1995 2019 who refueling the drug to be easy to obtain in the Year 1999 . U.S. is a hallucinogenic drug that can alter the user 's thoughts , feelings , and perception of reality .

Example 308:
titleEntities: {'Subject': ['Portland Trail Blazers'], 'Date': ['2001', '2019']}
title: Portland Trail Blazers ' 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']: ['287', '246', '223', '178', '157', '153', '140', '117', '132', '127', '121', '114', '82', '77', '78', '88', '97', '96']

gold: The statistic shows the revenue of the Portland Trail Blazers 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 287 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] Blazers 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] 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: The statistic shows the Revenue of the Portland Trail Blazers 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 287 million U.S. dollars .

Example 309:
titleEntities: {'Subject': ['Teva'], 'Date': ['2006', '2019']}
title: Teva : expenditure on research and development 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Expenditure', 'in', 'million', 'U.S.', 'dollars']: ['1010', '1213', '1778', '2077', '1525', '1488', '1427', '1356', '1095', '951', '825', '786', '581', '495']

gold: This statistic shows the expenditure of pharmaceutical company Teva for research and development from 2006 to 2019 . Teva Pharmaceutical Industries is one of the largest generic drug manufacturers in the world . In 2019 , the company invested about one billion U.S. dollars in research and development .
gold_template: This statistic shows the templateYLabel[0] of pharmaceutical company templateTitleSubject[0] for templateTitle[2] and templateTitle[3] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] Pharmaceutical Industries is one of the largest generic drug manufacturers in the world . In templateXValue[max] , the company invested about templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitle[2] and templateTitle[3] .

generated_template: templateTitleSubject[0] experienced a templateYLabel[0] of seven templateYLabel[2] templateYLabel[3] in templateXValue[max] , getting closer to turning a profit than the previous templateXLabel[0] . The Finnish company once known for cell phones phones has shifted its focus to providing telecommunication networks equipment and services - a market forecast to grow through 2020 . A strong player in networks technology As part of its drive into networks technology , templateTitleSubject[0] acquired communications equipment company Alcatel-Lucent in templateXValue[3] for 15.6 templateYLabel[2] templateYLabel[3] .
generated: Teva experienced a Expenditure of seven U.S. dollars in 2019 , getting closer to turning a profit than the previous Year . The Finnish company once known for cell phones has shifted its focus to providing telecommunication networks equipment and services - a market forecast to grow through 2020 . A strong player in networks technology As part of its drive into networks technology , Teva acquired communications equipment company Alcatel-Lucent in 2016 for 15.6 U.S. dollars .

Example 310:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Brazil 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['2296.03', '2188.89', '2084.3', '1987.82', '1893.01', '1847.02', '1867.82', '2052.81', '1795.37', '1799.88', '2456.11', '2471.56', '2464.4', '2613.99', '2207.62', '1667.68', '1694.87', '1396.11', '1106.37', '890.67', '668.43', '557.68', '509.36', '559.96', '655.44', '599.87', '865.12', '884.31', '850.42', '786.54', '546.57', '429.03', '382.33', '399.11', '455.17', '439.28', '319.99', '286.44', '263.16', '226.86', '142.91']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateTitle[3] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .
generated: The statistic shows the Gross domestic product ( GDP ) in Brazil from 1984 to 2018 , with projections up until 2024 . GDP refers to the total market value of all goods and services that are produced within a country per Year . It is an important indicator of the economic strength of a country .

Example 311:
titleEntities: {'Subject': ['New Brunswick', 'Canada'], 'Date': ['2000', '2018']}
title: GDP of New Brunswick , Canada 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['GDP', 'in', 'million', 'chained', '2012', 'Canadian', 'dollars']: ['30446.6', '30223.9', '29546.0', '29275.7', '29039.6', '29017.0', '29117.4', '29467.6', '29404.4', '28802.1', '29215.2', '28962.4', '28787.2', '28281.0', '28008.6', '27240.8', '26653.8', '25444.8', '25058.0']

gold: This statistic shows the gross domestic product ( GDP ) of New Brunswick from 2000 to 2018 . In 2018 , New Brunswick 's GDP was 30.44 billion chained 2012 Canadian dollars .
gold_template: This statistic shows the gross domestic product ( templateYLabel[0] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] was 30.44 templateYLabel[1] templateYLabel[2] templateXValue[6] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the number of people employed in the templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . Once a major powerhouse of the templateTitleSubject[1] 's economy , the templateTitle[1] templateTitle[3] was the lifeblood of several regions , providing templateTitle[0] for more than templateYValue[min] templateYLabel[1] workers . Since that time , shifting attitudes towards templateTitle[1] and the emergence of alternative energy sources such as wind and solar have seen templateTitle[1] 's role in the templateTitleSubject[1] 's energy mix diminish in recent .
generated: This statistic shows the number of people employed in the New Brunswick Canada in the New Brunswick ( Canada ) from 2000 to 2018 . Once a major powerhouse of the Canada 's economy , the New Canada was the lifeblood of several regions , providing GDP for more than 25058.0 million workers . Since that time , shifting attitudes towards New and the emergence of alternative energy sources such as wind and solar have seen New 's role in the Canada 's energy mix diminish in recent .

Example 312:
titleEntities: {'Subject': ['Top'], 'Date': ['2014']}
title: Top countries by pharmaceutical industry employment 2014
X_Axis['Country']: ['China', 'India', 'United_States', 'Germany', 'Brazil', 'Japan', 'Russian_Federation', 'Indonesia', 'Italy', 'Mexico', 'Thailand', 'Egypt', 'France', 'Spain', 'Switzerland', 'United_Kingdom', 'Vietnam', 'Poland', 'Pakistan', 'Turkey']
Y_Axis['Number', 'of', 'employees']: ['2301534', '567469', '251995', '128000', '105253', '91529', '66523', '64059', '59300', '51125', '48000', '47753', '47000', '43723', '43258', '41690', '39749', '38000', '36336', '35100']

gold: This statistic displays the top 20 countries worldwide based on the number of people employed in the pharmaceutical industry as of 2014 . The pharmaceutical industry in Japan counted over 91,500 employees that year .
gold_template: This statistic displays the templateTitleSubject[0] 20 templateTitle[1] worldwide based on the templateYLabel[0] of people employed in the templateTitle[3] templateTitle[4] as of templateTitleDate[0] . The templateTitle[3] templateTitle[4] in templateXValue[5] counted over 91,500 templateYLabel[1] that year .

generated_template: As of June templateTitleDate[0] , templateXValue[0] was ranked first among the templateTitleSubject[0] templateTitle[1] the most templateYLabel[1] templateYLabel[2] . templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] templateYLabel[2] , with templateYValue[max] thousand people , followed by templateXValue[1] with templateYValue[1] thousand templateYLabel[1] templateYLabel[2] .
generated: As of June 2014 , China was ranked first among the Top countries the most employees . China had the highest Number of employees , with 2301534 thousand people , followed by India with 567469 thousand employees .

Example 313:
titleEntities: {'Subject': ['Southwest Airlines'], 'Date': ['2011', '2019']}
title: Southwest Airlines - available seat miles 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['ASMs', 'in', 'billions']: ['157.25', '159.8', '153.81', '148.52', '140.5', '131.0', '130.34', '128.14', '120.58']

gold: Southwest Airlines grew its available seat miles ( ASMs ) from 120.58 billion in 2011 to 157.25 billion in 2019 . ASMs are calculated by the total number of seats multiplied by the total distance traveled . When describing the airline industry , people consider ASMs as a measure of capacity .
gold_template: templateTitleSubject[0] grew its templateTitle[2] templateTitle[3] templateTitle[4] ( templateYLabel[0] ) from templateYValue[min] templateYLabel[1] in templateXValue[min] to templateYValue[0] templateYLabel[1] in templateXValue[max] . templateYLabel[0] are calculated by the total number of seats multiplied by the total distance traveled . When describing the airline industry , people consider templateYLabel[0] as a measure of capacity .

generated_template: This statistic shows the templateTitle[2] templateYLabel[0] of templateTitle[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateYLabel[2] was at templateYValue[max] percent , up from templateYValue[1] percent in the previous templateXLabel[0] .
generated: This statistic shows the available ASMs of Southwest billions in the Southwest Airlines from 2011 to 2019 . In 2019 , the Southwest ASMs of billions was at 159.8 percent , up from 159.8 percent in the previous Year .

Example 314:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2001', '2018']}
title: Total recalls of children 's products in the U.S. 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Number', 'of', 'recalls']: ['52', '93', '76', '68', '75', '114', '97', '124', '148', '143', '209', '232', '111', '122', '87', '66', '90', '118']

gold: The statistic depicts the number of children 's products that were recalled in the United States from 2001 to 2018 . In 2018 , 52 children 's products were the subject of a recall .
gold_template: The statistic depicts the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] that were recalled in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[min] templateTitle[2] templateTitle[3] templateTitle[4] were the subject of a recall .

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] templateTitle[5] templateTitle[6] in templateTitle[7] in templateTitle[7] . Since templateXValue[min] , this figure had been steadily increasing up until the templateXLabel[0] templateXValue[10] in which it also reached its peak with around 213 thousand templateTitle[1] templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[7] . From templateXValue[10] on , the templateYLabel[0] of casino templateTitle[5] templateTitle[6] in templateTitleSubject[0] has been going down continuously .
generated: In the U.S. state of U.S. , there were approximately 52 thousand recalls children and 's products U.S. 2001 in 2018 in 2018 . Since 2001 , this figure had been steadily increasing up until the Year 2008 in which it also reached its peak with around 213 thousand recalls children and 's products U.S. 2001 in 2018 . From 2008 on , the Number of casino U.S. 2001 in U.S. has been going down continuously .

Example 315:
titleEntities: {'Subject': ['Botswana'], 'Date': ['2018']}
title: Urbanization in Botswana 2018
X_Axis['urban', 'population', '(', 'of', 'total)']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Unnamed:', '1']: ['69.45', '68.7', '67.93', '67.16', '66.37', '65.57', '64.77', '63.87', '62.41', '60.94', '59.44']

gold: This statistic shows the percentage of the total population living in urban areas in Botswana from 2008 to 2018 . In 2018 , 69.45 percent of the total population of Botswana was living in urban areas .
gold_template: This statistic shows the percentage of the total templateXLabel[1] living in templateXLabel[0] areas in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of the total templateXLabel[1] of templateTitleSubject[0] was living in templateXLabel[0] areas .

generated_template: While nearly two-thirds of templateTitleSubject[0] remains rural , templateTitle[0] has grown by seven percent from templateXValue[min] to templateXValue[max] . Political reforms in the 1980s created greater economic stability in templateTitleSubject[0] in the decade that followed and led to higher templateTitle[0] . The growing templateXLabel[2] of templateTitleSubject[0] , as well as people migrating from rural areas , contributes to an escalating number of inhabitants in the country 's already crowded cities .
generated: While nearly two-thirds of Botswana remains rural , Urbanization has grown by seven percent from 2008 to 2018 . Political reforms in the 1980s created greater economic stability in Botswana in the decade that followed and led to higher Urbanization . The growing total) of Botswana , as well as people migrating from rural areas , contributes to an escalating number of inhabitants in the country 's already crowded cities .

Example 316:
titleEntities: {'Subject': ['Sudan'], 'Date': ['2019']}
title: Unemployment rate in Sudan 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['13', '12.88', '12.77', '13.01', '13.16', '13.21', '13.26', '13.18', '13.04', '13.35', '13', '14.8', '14.9', '15.26', '16.06', '16.34', '16.61', '16.49', '16.62', '16.94', '16.71']

gold: This statistic shows the unemployment rate in Sudan from 1999 to 2019 . In 2019 , the estimated unemployment rate in Sudan was at approximately 13 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Sudan from 1999 to 2019 . In 2019 , the Unemployment rate in Sudan was at approximately 13 percent .

Example 317:
titleEntities: {'Subject': ['Tanzania'], 'Date': ['2019']}
title: Youth unemployment rate in Tanzania 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']: ['3.43', '3.45', '3.43', '3.66', '3.7', '3.71', '5.69', '6.34', '6.76', '5.9', '4.88', '4.58', '5.04', '6.07', '6.29', '6.19', '6.04', '5.81', '5.36', '5.64', '5.88']

gold: The statistic shows the youth unemployment rate in Tanzania from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated youth unemployment rate in Tanzania was at 3.43 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[min] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[7] percent .
generated: The statistic shows the Youth unemployment rate in Tanzania from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated Youth unemployment rate in Tanzania was at 6.34 percent .

Example 318:
titleEntities: {'Subject': ['Google'], 'Date': ['2017']}
title: Price of selected acquisitions by Google 2017
X_Axis['Company']: ['Motorola_Mobility_(2012)', 'Nest_Labs_(2014)', 'DoubleClick_(2008)', 'YouTube_(2006)', 'Part_of_HTC_mobile_division_and_licenses_(2017)', 'Waze_(2013)', 'AdMob_(2009)', 'ITA_Software_(2012)', 'Postini_(2007)', 'Apigee_(2016)', 'DeepMind_(2014)', 'Skybox_Imaging_(2014)', 'Admeld_(2011)', 'Bebop_(2015)', 'Wildfire_(2012)', 'Slide_(2010)', 'Widevine_Technologies_(2010)', 'Zagat_(2011)', 'On2_Technologies_(2010)', 'Channel_Intelligence_(2013)', 'Divide_(2014)', 'dMarc_Broadcasting_(2006)', 'Applied_Semantics_(2003)', 'Meebo_(2012)', 'FeedBurner_(2007)', 'Invite_Media_(2010)', 'Global_IP_Solutions_(2010)', 'Android_(2012)']
Y_Axis['Price', 'in', 'million', 'U.S.', 'dollars']: ['12500.0', '3200.0', '3100.0', '1650.0', '1100.0', '1100.0', '750.0', '700.0', '625.0', '625.0', '500.0', '500.0', '391.08', '380.2', '350.0', '182.0', '160.0', '151.0', '130.0', '125.0', '120.0', '102.0', '102.0', '100.0', '100.0', '80.0', '68.2', '50.0']

gold: This statistic shows a selection of companies Google , Inc. has acquired since 2003 , and their respective price . In June 2013 , Google acquired social traffic app Waze for 1.1 billion U.S. dollars . The internet company 's most expensive acquisition was Motorola Mobility in August 2011 , tallying 12.5 billion U.S. dollars .
gold_template: This statistic shows a selection of companies templateTitleSubject[0] , Inc. has acquired since 2003 , and their respective templateYLabel[0] . In June 2013 , templateTitleSubject[0] acquired social traffic app templateXValue[5] for templateYValue[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] . The internet templateXLabel[0] 's most expensive acquisition was templateXValue[0] in August 2011 , tallying templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitleSubject[0] templateTitleSubject[0] on templateTitle[2] in templateTitle[5] , split templateTitle[6] templateXLabel[0] . In the templateXValue[0] templateXValue[0] , the highest templateYLabel[1] templateYLabel[2] amounted to approximately templateYValue[max] templateYLabel[3] templateYLabel[4] in templateTitle[4] . The templateXLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] - additional information The templateTitleSubject[0] templateTitle[1] templateTitle[2] amounted to about 87,242 people in 2015 .
generated: The statistic shows the Price of million U.S. dollars in the Google on acquisitions in 2017 , split 2017 Company . In the Motorola Mobility (2012) , the highest million U.S. amounted to approximately 12500.0 dollars in Google . The Company of the Google - additional information The Google selected acquisitions amounted to about 87,242 people in 2015 .

Example 319:
titleEntities: {'Subject': ['Canada'], 'Date': ['2010', '2019']}
title: Ice hockey players in Canada 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']: ['621026', '637000', '631295', '639500', '721504', '721504', '625152', '617107', '572411']

gold: The statistics depicts the number of registered ice hockey players in Canada from 2010/11 to 2017/18 . In the 2018/19 season , there were a total of 621,026 registered ice hockey players in Canada 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[1] . In the templateXValue[0] season , there were a total of templateYValue[0] registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] according to the International templateTitle[0] templateTitle[1] Federation .

generated_template: The statistics depicts the templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , there were a total of templateYValue[max] registered templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] according to the International templateTitle[0] templateTitle[1] Federation .
generated: The statistics depicts the Number of registered Ice hockey players in the Canada from 2010/11 to 2018/19 . In the 2018/19 season , there were a total of 721504 registered Ice hockey players in the Canada according to the International Ice hockey Federation .

Example 320:
titleEntities: {'Subject': ['Malawi'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Malawi 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['10.48', '9.77', '9.12', '8.54', '8.05', '7.52', '6.9', '6.23', '5.49', '6.4', '6.06', '5.43', '5.98', '7.98', '6.96', '6.2', '5.32', '4.43', '4.0', '3.66', '3.48', '3.21', '3.5', '2.97', '3.02', '3.08', '3.04', '4.62', '3.96', '2.42', '2.08', '3.59', '3.12', '3.82', '3.0', '2.64', '2.31', '2.01', '2.05', '1.96', '2.1']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateTitle[3] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .
generated: The statistic shows the Gross domestic product ( GDP ) in Malawi from 1984 to 2018 , with projections up until 2024 . GDP refers to the total market value of all goods and services that are produced within a country per Year . It is an important indicator of the economic strength of a country .

Example 321:
titleEntities: {'Subject': ['UK'], 'Date': ['100', '2019']}
title: Number of female directors in FTSE 100 companies UK 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'female', 'directors']: ['292', '264', '259', '244', '233', '205', '169', '141']

gold: This statistic shows the total number of female directors working on the board of FTSE 100 companies in the United Kingdom ( UK ) from 2012 to 2019 . During the observed period the number of female directors ( both executive and non-executive ) in FTSE 100 companies increased by 151 to reach a total of 292 females holding a director position , as of June 2019 .
gold_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] working on the board of templateTitle[3] templateTitle[4] templateTitle[5] in the United Kingdom ( templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . During the observed period the templateYLabel[0] of templateYLabel[1] templateYLabel[2] ( both executive and non-executive ) in templateTitle[3] templateTitle[4] templateTitle[5] increased by 151 to reach a total of templateYValue[max] females holding a director position , as of June templateXValue[max] .

generated_template: This statistic illustrates the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , a total of around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] were of templateTitle[2] . In templateXValue[max] , this figure is projected to be some templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic illustrates the Number of directors FTSE 100 from 2012 to 2019 . In 2019 , a total of around 292 directors were of directors . In 2019 , this figure is projected to be some 292 directors .

Example 322:
titleEntities: {'Subject': ['Louis Cardinals'], 'Date': ['2002', '2019']}
title: Franchise value of the St. Louis Cardinals 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']: ['2100', '1900', '1800', '1600', '1400', '820', '716', '591', '518', '488', '486', '484', '460', '429', '370', '314', '308', '271']

gold: This graph depicts the value of the St. Louis Cardinals franchise of Major League Baseball from 2002 to 2019 . In 2019 , the franchise had an estimated value of 2.1 billion U.S. dollars . The St. Louis Cardinals are owned by William DeWitt Jr. , who bought the franchise for 150 million U.S. dollars in 1996 .
gold_template: This graph depicts the templateYLabel[1] of the templateTitle[2] templateTitleSubject[0] franchise of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitleSubject[0] are owned by William DeWitt Jr. , who bought the templateYLabel[0] for 150 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1996 .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] are owned by the Lamar Hunt family , who bought the templateYLabel[0] in 1960 .
generated: This graph depicts the Franchise value of the Louis Cardinals of the National Football League from 2002 to 2019 . In 2019 , the Franchise value came to 2100 million U.S. dollars . The Louis Cardinals are owned by the Lamar Hunt family , who bought the Franchise in 1960 .

Example 323:
titleEntities: {'Subject': ['Americans'], 'Date': ['2018']}
title: Americans ' moral stance towards abortion in 2018
X_Axis['Response']: ['Morally_acceptable', 'Morally_wrong', 'Depends_on_situation', 'No_opinion']
Y_Axis['Share', 'of', 'respondents']: ['43', '49', '7', '1']

gold: This statistic shows the results of a survey among Americans regarding their moral stance towards abortion in 2018 . In 2018 , 43 percent of respondents stated that they think having an abortion is morally acceptable , while 48 percent considered it morally wrong .
gold_template: This statistic shows the results of a survey among templateTitleSubject[0] regarding their templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[0] . In templateTitleDate[0] , templateYValue[0] percent of templateYLabel[1] stated that they think having an templateTitle[5] is templateXValue[0] , while 48 percent considered it templateXValue[0] templateXValue[1] .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitleSubject[0] regarding templateTitle[5] or templateTitle[6] templateTitle[7] in templateTitleDate[0] . During this survey , templateYValue[max] percent of templateYLabel[1] stated they think templateTitle[5] or templateTitle[6] templateTitle[7] are templateXValue[0] templateXValue[0] , while templateYValue[min] percent said it templateXValue[2] on the situation .
generated: This statistic shows the moral stance of Americans regarding abortion or 2018 in 2018 . During this survey , 49 percent of respondents stated they think abortion or 2018 are Morally acceptable , while 1 percent said it Depends on situation on the situation .

Example 324:
titleEntities: {'Subject': ['EU'], 'Date': ['2017']}
title: EU operating budgetary balances in 2017 , by member state
X_Axis['Country']: ['Poland', 'Greece', 'Romania', 'Hungary', 'Czech_Republic', 'Portugal', 'Bulgaria', 'Lithuania', 'Slowakia', 'Spain', 'Latvia', 'Estonia', 'Croatia', 'Slovenia', 'Malta', 'Cyprus', 'Luxembourg', 'Ireland', 'Finland', 'Denmark', 'Belgium', 'Austria', 'Netherlands', 'Sweden', 'Italy', 'France', 'United_Kingdom', 'Germany']
Y_Axis['Operating', 'budgetary', 'balances', 'in', 'billion', 'euros']: ['8.57', '3.74', '3.38', '3.14', '2.48', '2.44', '1.47', '1.27', '0.98', '0.73', '0.53', '0.47', '0.26', '0.15', '0.1', '0.05', '0.01', '-0.17', '-0.28', '-0.7', '-0.72', '-0.93', '-1.39', '-1.4', '-3.58', '-4.57', '-5.35', '-10.68']

gold: This statistic shows the operating budgetary balances of the EU member states in 2017 . A negative budgetary balance means that a country contributes more to the EU budget than it receives from it , a positive balance means the country contributes less than it receives . In 2017 , Germany contributed the most with approximately 10.68 billion euros .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitleSubject[0] templateTitle[6] states in templateTitleDate[0] . A negative templateYLabel[1] balance means that a templateXLabel[0] contributes more to the templateTitleSubject[0] budget than it receives from it , a positive balance means the templateXLabel[0] contributes less than it receives . In templateTitleDate[0] , templateXValue[last] contributed the most with approximately 10.68 templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateYLabel[2] templateYLabel[3] templateYLabel[4] of templateYLabel[5] templateTitle[2] to templateYLabel[5] in templateTitle[4] templateTitleSubject[0] templateTitle[6] as of templateTitle[7] templateTitle[8] . templateXValue[0] had the highest rate of templateYLabel[0] templateYLabel[1] valued at templateYValue[max] percent in templateTitle[4] templateTitle[5] templateTitle[6] , followed by templateXValue[1] with templateYLabel[0] templateYLabel[1] valued at templateYValue[1] percent . templateYLabel[0] templateYLabel[1] rates vary across the templateTitleSubject[0] Union and often serve political , as well as financial ends , such as for environmental protection or public safety and health .
generated: This statistic shows the Operating budgetary in balances billion euros of euros budgetary to euros in 2017 EU member as of state . Poland had the highest rate of Operating budgetary valued at 8.57 percent in 2017 by member , followed by Greece with Operating budgetary valued at 3.74 percent . Operating budgetary rates vary across the EU Union and often serve political , as well as financial ends , such as for environmental protection or public safety and health .

Example 325:
titleEntities: {'Subject': ['Waitrose', 'Great Britain'], 'Date': ['2015', '2019']}
title: Waitrose sales growth year-on-year in Great Britain 2015 to 2019
X_Axis['12', 'week', 'period', 'ending']: ['14_Jul_19', '16_Jun_19', '21_Apr_19', '24_Mar_19', '24_Feb_19', '28_Jan_19', '30_Dec_18', '2_Dec_18', '6_Nov_18', '8_Oct_18', '10_Sep_18', '13_Aug_18', '15_Jul_18', '17_Jun_18', '22_Apr_18', '25_Mar_18', '25_Feb_18', '28_Jan_18', '31_Dec_17', '3_Dec_17', '8_Oct_17', '10_Sep_17', '13_Aug_17', '16_Jul_17', '18_Jun_17', '21_May_17', '23_Apr_17', '26_Mar_17', '26_Feb_17', '29_Jan_17', '1_Jan_17', '4_Dec_16', '6_Nov_16', '9_Oct_16', '11_Sep_16', '14_Aug_16', '17_Jul_16', '19_Jun_16', '22_May_16', '24_Apr_16', '27_Mar_16', '31_Jan_16', '3_Jan_16', '6_Dec_15', '8_Nov_15', '11_Oct_15', '13_Sept_15', '16_Aug_15', '19_Jul_15']
Y_Axis['Percentage', 'growth', '(year-on-year)']: ['-1.9', '-', '0.7', '1.3', '1', '0.2', '-1.7', '-0.7', '-0.1', '0.1', '0.8', '2.4', '2.8', '0.1', '0.2', '1.5', '2.3', '1.5', '2.3', '1.6', '2.3', '2.4', '2.8', '2.8', '5.3', '3.3', '3.1', '0.3', '2.9', '3.4', '3', '1.1', '3', '3.5', '3.4', '1.4', '1.6', '1.3', '2.1', '1.5', '1.7', '0.1', '1.5', '2.7', '2.7', '2.1', '2.9', '3.7', '3']

gold: Waitrose sales have decreased by 1.9 percent in Great Britain over a 12-week period ending July 12 , 2019 compared to the same time period in 2018 . Waitrose has seen its sales grow during the last three and a half years . The second quarter of 2017 saw the highest growth , with sales going up over five percent .
gold_template: templateTitle[0] templateTitle[1] have decreased by 1.9 percent in templateTitleSubject[0] over a 12-week templateXLabel[2] templateXLabel[3] July templateXLabel[0] , templateTitleDate[1] compared to the same time templateXLabel[2] in 2018 . templateTitle[0] has seen its templateTitle[1] grow during the last templateXValue[19] and a half years . The second quarter of 2017 saw the highest templateYLabel[1] , with templateTitle[1] going up over five percent .

generated_template: templateXValue[0] templateXValue[0] led the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] in templateTitle[0] , with an templateYLabel[0] score of templateYValue[max] out of 100 . templateXValue[1] , templateXValue[2] templateXValue[2] , templateXValue[3] , and templateXValue[4] rounded out the top five . templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] In order to calculate the templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] , the source takes 12 different factors into account , including the rule of law , government size , regulatory efficiency , and open markets .
generated: 14 Jul 19 led the Percentage of Waitrose in Waitrose , with an Percentage score of -1.9 out of 100 . 16 Jun 19 , 21 Apr 19 , 24 Mar 19 , and 24 Feb 19 rounded out the top five . Waitrose Percentage In order to calculate the Waitrose Percentage , the source takes 12 different factors into account , including the rule of law , government size , regulatory efficiency , and open markets .

Example 326:
titleEntities: {'Subject': ['Louisiana'], 'Date': ['2000', '2018']}
title: Louisiana - 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']: ['18.6', '19.7', '20.2', '19.6', '19.8', '19.8', '19.9', '20.4', '18.7', '17.3', '17.3', '18.6', '19', '19.8', '19.4', '20.3', '18.8', '19.1', '20']

gold: This statistic shows the poverty rate in Louisiana from 2000 to 2018 . In 2018 , about 18.6 percent of Louisiana 's population lived below the poverty line .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] in templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitleSubject[0] templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[2] line .
generated: This statistic shows the rate 2000 in Louisiana from 2000 to 2018 . In 2018 , 18.6 percent of Louisiana 's population lived below the rate line .

Example 327:
titleEntities: {'Subject': ['June'], 'Date': ['2019', '2019']}
title: Business climate index of June 2019
X_Axis['Months', 'from', 'June', '2018', 'to', 'June', '2019']: ["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"]
Y_Axis['Index', 'points', '(50', '=', 'neutral)']: ['56.9', '56.5', '58.4', '61.9', '64.5', '64.7', '63.8', '63.2', '53.7', '52.8', '53.3', '50.2', '49.6']

gold: This statistic shows the business climate index for Brazil from June 2018 to June 2019 . The index is based on a survey of approximately 2,500 companies . Figures above 50 represent an optimistic outlook , while figures below 50 show a pessimistic business climate .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] for Brazil templateXLabel[1] templateXLabel[2] templateXLabel[3] to templateXLabel[2] templateXLabel[5] . The templateYLabel[0] is based on a survey of approximately 2,500 companies . Figures above templateYValue[11] represent an optimistic outlook , while figures below templateYValue[11] show a pessimistic templateTitle[0] templateTitle[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] ( templateTitleSubject[0] ) of the templateTitle[5] templateTitle[6] templateXLabel[1] templateTitle[7] templateXLabel[3] to templateTitle[7] templateXLabel[5] . In templateTitle[7] templateXLabel[5] , the templateTitle[0] EMI was at templateYValue[12] points .
generated: This statistic shows the Business climate index ( June ) of the 2019 from 2019 2018 to 2019 . In 2019 , the Business EMI was at 49.6 points .

Example 328:
titleEntities: {'Subject': ['Bible U.S.'], 'Date': ['2017']}
title: Preferred Bible version in the U.S. 2017
X_Axis['Response']: ['King_James_Version', 'New_International_Version', 'English_Standard_Version', 'New_King_James_Version', 'Amplified', 'Christian_Community', 'New_American_Standard', 'New_Living_Translation', 'Revised_Standard', 'Contemporary_English_Version', 'New_American_Bible', 'All_others_(1_or_less_combined)', 'Not_sure']
Y_Axis['Share', 'of', 'respondents']: ['31', '13', '9', '7', '7', '4', '3', '2', '2', '2', '2', '9', '8']

gold: The graph presents data on the popularity of the versions of the Bible read in the United States as of January 2017 . During the survey , 31 percent of the respondents stated they most often read the King James Version of the Bible . During the same survey , 32 percent of respondents stated that they had never read the Bible , whilst 16 percent stated that they read the Bible every day .
gold_template: The graph presents data on the popularity of the versions of the templateXValue[10] read in the templateTitle[3] as of January templateTitleDate[0] . During the survey , templateYValue[max] percent of the templateYLabel[1] stated they most often read the templateXValue[0] Version of the templateXValue[10] . During the same survey , 32 percent of templateYLabel[1] stated that they had never read the templateXValue[10] , whilst 16 percent stated that they read the templateXValue[10] every day .

generated_template: Roman asked what they think are the most templateTitle[2] templateXValue[0] the templateTitleSubject[0] is currently templateTitle[4] , templateYValue[max] percent of survey templateYLabel[1] stated to worry about the templateXValue[0] and templateXValue[0] . Another templateYValue[1] percent was concerned about templateXValue[1] and templateXValue[1] templateXValue[1] . By comparison , relatively few people named the templateXValue[last] templateXValue[last] or templateXValue[11] .
generated: Roman asked what they think are the most version King James Version the Bible U.S. is currently 2017 , 31 percent of survey respondents stated to worry about the King James Version and King James Version . Another 13 percent was concerned about New International Version and New International Version . By comparison , relatively few people named the Not sure or All others (1 or less combined) .

Example 329:
titleEntities: {'Subject': ['June'], 'Date': ['2019', '2019']}
title: Consumer confidence index of June 2019
X_Axis['Months', 'from', 'June', '2018', 'to', 'June', '2019']: ["Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Apr_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17"]
Y_Axis['Index', 'points', '(2001', '=', '100)']: ['114.3', '113.6', '110.6', '-', '104.7', '101.6', '98.3', '102.2', '102.2', '101.9', '102.7', '102.9', '100.5']

gold: This statistic shows the consumer confidence index for Brazil from December 2017 to December 2018 . The index is composed of several different indices , including an assessment of one 's personal financial situation . In December 2018 , Brazil 's consumer confidence was at 114.3 points .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] for Brazil templateXLabel[1] December 2017 to December templateXLabel[3] . The templateYLabel[0] is composed of several different indices , including an assessment of one 's personal financial situation . In December templateXLabel[3] , Brazil 's templateTitle[0] templateTitle[1] was at templateYValue[0] templateYLabel[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] ( templateTitleSubject[0] ) of the templateTitle[5] templateTitle[6] templateXLabel[1] templateTitle[7] templateXLabel[3] to templateTitle[7] templateXLabel[5] . In templateTitle[7] templateXLabel[5] , the templateTitle[0] EMI was at templateYValue[12] points .
generated: This statistic shows the Consumer confidence index ( June ) of the 2019 from 2019 2018 to 2019 . In 2019 , the Consumer EMI was at 100.5 points .

Example 330:
titleEntities: {'Subject': ['Canada'], 'Date': ['2019']}
title: Canada - average weekly hours worked at the main job , by industry 2019
X_Axis['Industry']: ['Forestry_fishing_mining_quarrying_oil_and_gas', 'Agriculture', 'Goods-producing_sector', 'Construction', 'Transportation_and_warehousing', 'Manufacturing', 'Utilities', 'Professional_scientific_and_technical_services', 'Public_administration', 'Finance_insurance_real_estate_rental_and_leasing', 'Total_employed_all_industries', 'Other_services_(except_public_administration)', 'Services-producing_sector', 'Health_care_and_social_assistance', 'Business_building_and_other_support_services', 'Wholesale_and_retail_trade', 'Information_culture_and_recreation', 'Educational_services', 'Accommodation_and_food_services']
Y_Axis['Average', 'usual', 'weekly', 'hours']: ['45.0', '43.5', '40.4', '40.3', '40.2', '39.3', '38.6', '37.0', '36.7', '36.7', '35.7', '34.9', '34.5', '34.2', '33.8', '33.5', '32.5', '31.8', '29.8']

gold: This statistic shows the average usual weekly hours worked in Canada in 2019 , distinguished by industry . In 2019 , Canadian employees in agriculture were working about 43.5 hours a week , which is above the national average of 35.7 hours .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[4] in templateTitleSubject[0] in templateTitleDate[0] , distinguished templateTitle[7] templateXLabel[0] . In templateTitleDate[0] , Canadian employees in templateXValue[1] were working about templateYValue[1] templateYLabel[3] a week , which is above the national templateYLabel[0] of templateYValue[10] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] in templateTitle[5] in the templateTitle[6] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[5] , with a templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[max] percent .
generated: This statistic shows the Average usual weekly of hours worked in main in the job in 2019 . In 2019 , Forestry fishing mining quarrying oil and gas had the highest Average usual weekly in the main , with a Average usual weekly of 45.0 percent .

Example 331:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2004', '2008']}
title: Global market share of the U.S. athletic and non-athletic footwear retail market 2004 to 2008
X_Axis['Year']: ['2004', '2005', '2006', '2007', '2008']
Y_Axis['Global', 'market', 'share']: ['27.3', '26.6', '25.7', '24.6', '24.3']

gold: The timeline shows the global market share of the U.S. athletic and non-athletic footwear retail market from 2004 to 2008 . In 2007 , the global market share of the U.S. athletic and non-athletic footwear retail market was 24.6 percent . Between 2004 and 2008 , the global market share of the U.S. footwear retail market decreased by 3.0 percent .
gold_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitleSubject[0] templateTitle[4] and templateTitle[5] templateTitle[6] templateTitle[7] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitleSubject[0] templateTitle[4] and templateTitle[5] templateTitle[6] templateTitle[7] templateYLabel[1] was templateYValue[3] percent . Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitleSubject[0] templateTitle[6] templateTitle[7] templateYLabel[1] decreased by 3.0 percent .

generated_template: This statistic illustrates the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per day in the templateTitleSubject[0] templateTitleSubject[0] between templateTitle[6] and templateTitle[7] , templateTitle[8] templateTitle[9] . In this four templateXLabel[0] period , individuals aged between 4 and 10 templateXValue[0] old templateTitle[3] templateYValue[max] templateYLabel[1] of templateTitle[1] templateTitle[2] per day .
generated: This statistic illustrates the Global of market share U.S. per day in the U.S. between footwear and retail , market 2004 . In this four Year period , individuals aged between 4 and 10 2004 old U.S. 27.3 market of market share per day .

Example 332:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018', '2018']}
title: Italy : volume of crude oil imported Q1 2018 , by country of origin
X_Axis['Country']: ['Azarbaijan', 'Iran', 'Iraq', 'Libya', 'Saudi_Arabia', 'Russia', 'Nigeria', 'Kazakhstan', 'USA', 'Angola', 'Canada', 'Kuwait', 'Egypt', 'Cameroon', 'Algeria', 'Equatorial_Guinea', 'Tunisia', 'Mauritania', 'Albania']
Y_Axis['Import', 'volume', 'in', 'tons']: ['12298989', '9324007', '2041664', '1840713', '1825182', '1052134', '767828', '703232', '504954', '322012', '317132', '312218', '204085', '185753', '155279', '89845', '56891', '33791', '123']

gold: During the first quarter of 2018 , Azerbaijan exported roughly 12.3 million tons of crude oil to Italy , establishing itself as the major crude oil supplier for the country . Iran followed with 9.3 million tons . The amount of crude oil imported from other suppliers was lower , during the first quarter of 2018 .
gold_template: During the first quarter of templateTitle[6] , Azerbaijan exported roughly templateYValue[max] million templateYLabel[2] of templateTitle[2] templateTitle[3] to templateTitleSubject[0] , establishing itself as the major templateTitle[2] templateTitle[3] supplier for the templateXLabel[0] . templateXValue[1] followed with templateYValue[1] million templateYLabel[2] . The amount of templateTitle[2] templateTitle[3] templateTitle[4] from other suppliers was lower , during the first quarter of templateTitle[6] .

generated_template: As of templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] worldwide in the United Kingdom . As of templateTitleDate[0] , templateXValue[0] had the largest templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateTitle[4] templateTitle[5] , followed templateTitle[6] templateXValue[1] at templateYValue[1] templateYLabel[2] . This was a decrease in the previous year .
generated: As of 2018 , the Import volume of imported Q1 worldwide in the United Kingdom . As of 2018 , Azarbaijan had the largest Import volume of tons imported Q1 , followed 2018 Iran at 9324007 tons . This was a decrease in the previous year .

Example 333:
titleEntities: {'Subject': ['HSBC'], 'Date': ['2009', '2019']}
title: Return on average ordinary shareholders ' equity at HSBC 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Return', 'on', 'equity']: ['3.6', '7.7', '5.9', '0.8', '7.2', '7.3', '9.2', '8.4', '10.9', '9.5', '5.1']

gold: The statistic shows the return on average ordinary shareholders ' equity at HSBC from 2009 to 2019 . The return on average ordinary shareholders ' equity at HSBC amounted to 3.6 percent in 2019 .
gold_template: The statistic shows the templateYLabel[0] on templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] on templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] at templateTitleSubject[0] amounted to templateYValue[0] percent in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] has been fluctuating in this period . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] amounted to approximately templateYValue[0] templateYLabel[2] .
generated: This statistic shows the Return of Return equity in HSBC from 2009 to 2019 . The Return of equity has been fluctuating in this period . In 2019 , the Return of equity amounted to approximately 3.6 equity .

Example 334:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2006', '2016']}
title: Readership of FourFourTwo magazine in the United Kingdom ( UK ) 2006 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['AIR', 'in', 'thousands']: ['483', '469', '541', '647', '631', '633', '664', '639', '589', '619', '554']

gold: This statistic displays the readership trend of FourFourTwo magazine in the United Kingdom from 2006 to 2016 . In 2015 , the magazine was read by an average 469 thousand readers per issue .
gold_template: This statistic displays the templateTitle[0] trend of templateTitleSubject[0] templateTitle[2] in the templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateTitle[2] was read by an average templateYValue[min] thousand readers per issue .

generated_template: This statistic shows the development of the templateTitle[0] of young people between the ages of templateTitle[9] to templateTitle[10] in the templateTitle[1] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[0] templateYLabel[3] young people templateTitle[4] that templateTitle[7] templateTitle[8] lived in the templateTitle[1] .
generated: This statistic shows the development of the Readership of young people between the ages of 2016 to 2016 in the FourFourTwo Kingdom 2006 to 2016 . In 2016 , approximately 483 thousands young people Kingdom that 2016 lived in the FourFourTwo .

Example 335:
titleEntities: {'Subject': ['Manchester Metrolink', 'United Kingdom'], 'Date': ['2019']}
title: Passenger journeys on the Manchester Metrolink in the United Kingdom 1992 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05', '2003/04', '2002/03', '2001/02', '2000/01', '1999/00', '1998/99', '1997/98', '1996/97', '1995/96', '1994/95', '1993/94', '1992/93']
Y_Axis['Passenger', 'journeys', 'in', 'millions']: ['43.7', '41.2', '37.8', '34.3', '31.2', '29.2', '25.0', '22.3', '19.2', '19.6', '21.1', '20.0', '19.8', '19.9', '19.7', '18.9', '18.8', '18.2', '17.2', '14.2', '13.2', '13.8', '13.4', '12.6', '12.3', '11.3', '8.1']

gold: The Manchester Metrolink recorded 43.7 million passenger journeys in 2018/19 . Since beginning its operation in April 1992 as the United Kingdom 's first modern tram system , the Metrolink has grown to become an integral part of public transportation within the city . The Metrolink is one of currently nine light rail systems in the country , next to those in London 's docklands , Croydon , Birmingham , Sheffield , Newcastle , Nottingham , Blackpool and Edinburgh .
gold_template: The templateTitleSubject[0] recorded templateYValue[max] templateYLabel[2] templateYLabel[0] templateYLabel[1] in templateXValue[0] . Since beginning its operation in April templateTitleDate[0] as the templateTitleSubject[1] 's first modern tram system , the templateTitleSubject[0] has grown to become an integral part of public transportation within the city . The templateTitleSubject[0] is one of currently nine light rail systems in the country , next to those in London 's docklands , Croydon , Birmingham , Sheffield , Newcastle , Nottingham , Blackpool and Edinburgh .

generated_template: As of templateTitleDate[1] , the Danish templateTitle[4] fotball templateTitle[6] , controlled by the Danish templateTitle[5] Association ( Dansk Boldspil-Union ) , made the 10th templateYLabel[3] in the templateYLabel[0] templateYLabel[1] templateYLabel[2] . The highest templateYLabel[3] ever reached by the templateTitle[6] was templateYValue[min] in the years templateXValue[min] and templateXValue[3] . So far , they already participated in one friendly match against Kosovo and three qualifiers for the UEFA Euro 2020 .
generated: As of 2019 , the Danish United fotball 1992 , controlled by the Danish Kingdom Association ( Dansk Boldspil-Union ) , made the 10th millions in the Passenger journeys millions . The highest millions ever reached by the 1992 was 8.1 in the years 2018/19 and 2015/16 . So far , they already participated in one friendly match against Kosovo and three qualifiers for the UEFA Euro 2020 .

Example 336:
titleEntities: {'Subject': ['Thailand'], 'Date': ['2024']}
title: Inflation rate in Thailand 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2', '1.8', '1.6', '1.2', '0.92', '0.86', '1.06', '0.67', '0.19', '-0.9', '1.9', '2.19', '3.01', '3.81', '3.29', '-0.85', '5.46', '2.2', '4.66', '4.52', '2.76']

gold: In 2018 , the average inflation rate in Thailand amounted to about 1.06 percent compared to the previous year , when it was just recovering from a slump below the 0-percent-mark in 2015 . Political turmoil begets economic turmoil In 2014 , after a coup d'etat following months of political crisis , the Thai military took over the country , and the senate and government were dissolved . As a result , Thailand 's economy experienced a sudden downturn , GDP growth and inflation slumped , while unemployment , which is usually delayed in reflecting economic struggles , has been increasing ever since .
gold_template: In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] , when it was just recovering from a slump below the 0-percent-mark in templateXValue[9] . Political turmoil begets economic turmoil In templateXValue[10] , after a coup d'etat following months of political crisis , the Thai military took over the country , and the senate and government were dissolved . As a result , templateTitleSubject[0] 's economy experienced a sudden downturn , GDP growth and templateYLabel[0] slumped , while unemployment , which is usually delayed in reflecting economic struggles , has been increasing ever since .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Thailand from 2004 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Thailand amounted to about 1.06 percent compared to the previous Year .

Example 337:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Denmark 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['72325.94', '69413.32', '66656.96', '64106.78', '61732.57', '59795.27', '60897.23', '57380.2', '54665.22', '53478.5', '62729.5', '61325.58', '58623.41', '61864.09', '58177.16', '58286.54', '64531.12', '58641.19', '52121.25', '48872.1', '46571.28', '40512.05', '33275.56', '30806.61', '30798.72', '33492.35', '33426.97', '32897.57', '35732.69', '35471.26', '30050.88', '27640.5', '29622.47', '27052.65', '26920.58', '21913.16', '22528.11', '21349.95', '17215.43', '12259.28', '11561.77']

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

Example 338:
titleEntities: {'Subject': ['Portugal'], 'Date': ['2006', '2018']}
title: Volume of wine produced in Portugal 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Thousands', 'of', 'hectoliters']: ['6.1', '6.7', '6.0', '7.0', '6.2', '6.2', '6.3', '5.6', '7.13', '5.87', '5.69', '6.07', '7.54']

gold: The volume of wine produced in Portugal was forecast to reach approximately 6.1 million hectoliters in 2018 . The production output does seem to be stagnating , with occasional spikes in production , which are likely due to better weather conditions . The data does not include juice and must .
gold_template: The templateTitle[0] of templateTitle[1] templateTitle[2] in templateTitleSubject[0] was forecast to reach approximately templateYValue[0] templateYLabel[0] templateYLabel[1] in templateXValue[max] . The production output does seem to be stagnating , with occasional spikes in production , which are likely due to better weather conditions . The data does not include juice and must .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around templateYValue[0] templateYLabel[1] templateYLabel[2] were recorded in templateTitleSubject[0] .
generated: The statistic shows the Thousands of Portugal wine from 2006 to 2018 . In 2018 , around 6.1 hectoliters were recorded in Portugal .

Example 339:
titleEntities: {'Subject': ['Lithuania'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Lithuania 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['25936.96', '24437.92', '23066.81', '21708.62', '20355.0', '19266.79', '18994.38', '16845.33', '14988.57', '14299.1', '16571.43', '15695.74', '14354.29', '14386.61', '12010.68', '11866.63', '15047.25', '12313.17', '9246.51', '7880.35', '6706.03', '5505.59', '4146.11', '3530.2', '3297.45', '3113.64', '3166.96', '2830.75', '2328.22', '1845.67']

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

generated_template: This statistic shows the Ukrainian templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to approximately templateYValue[6] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Ukrainian Gross domestic product ( GDP ) per capita from 1995 to 2018 , with projections up until 2024 . In 2018 , Lithuania 's GDP per capita amounted to approximately 18994.38 dollars .

Example 340:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Leading crowdfunding platforms in the U.S. 2017 , by number of offerings
X_Axis['Country']: ['Wefunder', 'Start_Engine', 'Seed_Invest', 'uFunding', 'Dream_Funded', 'TruCrowd', 'Nextseed', 'Net_Capital', 'Microventures', 'Jumpstart_Micro', 'Flashfunders', 'Republic', 'GridShare', 'Growth_Fountain', 'Venture.co', 'Crowd_Source_Funded', 'FundingWonder', 'ibankers', 'Local_Stake', 'Open_Night_Capital']
Y_Axis['Amount', 'of', 'offerings']: ['95', '52', '29', '18', '14', '13', '13', '12', '11', '10', '9', '9', '7', '5', '4', '3', '2', '2', '1', '1']

gold: This statistic shows the leading crowdfunding platforms in the United States as of May 2017 , by number of offerings . Wefunder had 95 offerings , which made it the largest platform in terms of offerings as of May 2017 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] as of May templateTitleDate[0] , templateTitle[5] templateTitle[6] of templateYLabel[1] . templateXValue[0] had templateYValue[max] templateYLabel[1] , which made it the largest platform in terms of templateYLabel[1] as of May templateTitleDate[0] .

generated_template: As of January templateTitleDate[0] , templateXValue[0] was ranked first among the templateTitleSubject[0] templateTitle[1] the most templateYLabel[1] templateYLabel[2] . templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] , with templateYValue[max] percent , followed by templateXValue[1] at templateYValue[1] percent of all templateYLabel[2] . Overall , the second and third most populous provinces , an templateYLabel[0] of more than 7 of the eight templateTitleSubject[0] templateTitle[1] more than 100 British pounds in 2017 .
generated: As of January 2017 , Wefunder was ranked first among the U.S. crowdfunding the most offerings . Wefunder had the highest Amount of offerings , with 95 percent , followed by Start Engine at 52 percent of all offerings . Overall , the second and third most populous provinces , an Amount of more than 7 of the eight U.S. crowdfunding more than 100 British pounds in 2017 .

Example 341:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2001', '2017']}
title: Head value of sheep and lambs in the U.S. 2001 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Value', 'per', 'head', 'in', 'U.S.', 'dollars']: ['203', '202', '214', '188', '177', '221', '170', '135', '133', '138', '134', '141', '130', '119', '104', '92', '100']

gold: This statistic shows the average value per head of sheep and lambs in the United States from 2001 to 2017 . In 2001 , this figure stood at 100 U.S. dollars and rose to 203 U.S. dollars by 2017 .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[2] and templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , this figure stood at templateYValue[16] templateYLabel[3] templateYLabel[4] and rose to templateYValue[0] templateYLabel[3] templateYLabel[4] by templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: The statistic shows the Value of U.S. value sheep lambs U.S. from 2001 to 2017 . In 2017 , U.S. value sheep lambs U.S. amounted to 221 per head .

Example 342:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2019']}
title: United Kingdom ( UK ) : National debt as a percentage of GDP 2000 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05', '2003/04', '2002/03', '2001/02', '2001/01']
Y_Axis['Percentage', 'of', 'GDP']: ['85.2', '85.3', '86.5', '86.4', '86.5', '85.5', '83.3', '81.8', '75.6', '69.6', '52.6', '40.9', '40', '39.2', '38', '35.5', '33.8', '33.7', '35.2']

gold: This statistic shows the general government gross consolidated debt ( national debt ) as a percentage of gross domestic product ( GDP ) in the United Kingdom ( UK ) from fiscal year 2000/01 to 2018/19 . After 2002/03 , national debt as a percentage of GDP rose continuously over the remainder of the period to a peak in 2016/17 .
gold_template: This statistic shows the general government gross consolidated templateTitle[4] ( templateTitle[3] templateTitle[4] ) as a templateYLabel[0] of gross domestic product ( templateYLabel[1] ) in the templateTitleSubject[0] ( templateTitleSubject[1] ) from fiscal templateXLabel[0] 2000/01 to templateXValue[0] . After templateXValue[16] , templateTitle[3] templateTitle[4] as a templateYLabel[0] of templateYLabel[1] rose continuously over the remainder of the period to a peak in templateXValue[2] .

generated_template: This statistic shows the total templateTitleSubject[0] templateTitleSubject[0] ( UK ) templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] templateXValue[0] . The overall trend was one of increase , seeing a peak in templateXValue[0] with 27.99 templateYLabel[3] British pounds and a total rise of over 5 templateYLabel[3] British pounds throughout this period .
generated: This statistic shows the total United Kingdom ( UK ) debt Percentage GDP from fiscal Year 2001/01 to fiscal Year 2018/19 . The overall trend was one of increase , seeing a peak in 2018/19 with 27.99 GDP British pounds and a total rise of over 5 GDP British pounds throughout this period .

Example 343:
titleEntities: {'Subject': ['Portugal'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Portugal 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']: ['27920.05', '26788.05', '25744.8', '24727.89', '23731.13', '23030.79', '23437.39', '21482.86', '19986.36', '19252.01', '22109.32', '21625.48', '20588.87', '23217.34', '22580.68', '23122.56', '24933.17', '22811.57', '19837.97', '18815.44', '18064.47', '15799.89', '12922.15', '11737.17', '11533.83', '12490.93', '12220.18', '11597.7', '12187.56', '11788.47', '9978.59', '9548.58', '10864.55', '9027.18', '7958.02', '5978.16', '5533.16', '4724.91', '3774.95', '2716.91', '2596.33']

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

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

Example 344:
titleEntities: {'Subject': ['Information Technology', 'Western Europe'], 'Date': ['2019']}
title: Information Technology ( IT ) : revenue in Western Europe Q4 2015-Q3 2019
X_Axis['Quarter']: ['Q3_2019', 'Q2_2019', 'Q1_2019', 'Q4_2018', 'Q3_2018', 'Q2_2018', 'Q1_2018', 'Q4_2017', 'Q3_2017', 'Q2_2017', 'Q1_2017', 'Q4_2016', 'Q3_2016', 'Q2_2016', 'Q1_2016', 'Q4_2015']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['13.54', '12.83', '16.56', '17.06', '13.6', '12.44', '14.24', '16.3', '13.22', '11.81', '13.46', '16.48', '12.66', '12.21', '13.7', '18.0']

gold: The statistic shows trends in Information Technology ( IT ) revenues in the technical consumer goods market in Western Europe from the fourth quarter of 2015 to the third quarter of 2019 . Revenue reached 13.54 billion euros by the end of Q3 2019 .
gold_template: The statistic shows trends in templateTitleSubject[0] ( IT ) revenues in the technical consumer goods market in templateTitleSubject[1] from the fourth templateXLabel[0] of templateXValue[last] to the third templateXLabel[0] of templateXValue[0] . templateYLabel[0] reached templateYValue[0] templateYLabel[1] templateYLabel[2] by the end of templateXValue[0] .

generated_template: During the fourth templateXLabel[0] of templateXValue[0] , generated a templateYLabel[0] of approximately templateYValue[max] templateYLabel[1] templateYLabel[2] , up from just under templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the preceding templateXLabel[0] . templateTitleSubject[0] is one of the most popular social networks in the preceding fiscal year . templateTitleSubject[0] is one of the most popular tourist destinations in the country , it is defined as having annual free service .
generated: During the fourth Quarter of Q3 2019 , generated a Revenue of approximately 18.0 billion euros , up from just under 12.83 billion euros in the preceding Quarter . Information Technology is one of the most popular social networks in the preceding fiscal year . Information Technology is one of the most popular tourist destinations in the country , it is defined as having annual free service .

Example 345:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015', '2016']}
title: Forecast of office rent growth in the U.S. 2015 to 2016
X_Axis['Quarter']: ['Q4_2016', 'Q3_2016', 'Q2_2016', 'Q1_2016', 'Q4_2015', 'Q3_2015', 'Q2_2015']
Y_Axis['Rent', 'growth']: ['1', '0.9', '0.9', '0.9', '0.9', '0.9', '0.8']

gold: This statistic presents a forecast of office rent growth in the United States from second quarter of 2015 to fourth quarter of 2016 . It was expected that office rent would grow by one percent in the fourth quarter of 2016 in the United States . Coworking worldwide – additional information Coworking is an alternative to the traditional office space , wherein independent workers , such as freelancers and remote workers , share a working environment .
gold_template: This statistic presents a templateTitle[0] of templateTitle[1] templateYLabel[0] templateYLabel[1] in the templateTitle[4] from second templateXLabel[0] of templateXValue[4] to fourth templateXLabel[0] of templateXValue[0] . It was expected that templateTitle[1] templateYLabel[0] would grow by templateYValue[max] percent in the fourth templateXLabel[0] of templateXValue[0] in the templateTitle[4] . Coworking worldwide – additional information Coworking is an alternative to the traditional templateTitle[1] space , wherein independent workers , such as freelancers and remote workers , share a working environment .

generated_template: This statistic illustrates the average templateTitle[1] templateYLabel[0] of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[0] to the second templateXLabel[0] of templateTitleDate[1] . In the second templateXLabel[0] of templateTitleDate[0] , templateTitleSubject[0] 's templateTitle[1] templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic illustrates the average office Rent of U.S. from the first Quarter of 2015 to the second Quarter of 2016 . In the second Quarter of 2015 , U.S. 's office Rent growth amounted to 1 growth .

Example 346:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2018']}
title: Twitter user share in selected countries 2018
X_Axis['Country']: ['United_States', 'Japan', 'Venezuela', 'United_Kingdom', 'Saudi_Arabia', 'Turkey', 'Brazil', 'Mexico', 'India', 'Spain']
Y_Axis['Share', 'of', 'Twitter', 'users']: ['18.9', '14.6', '5.8', '5.5', '4', '3.3', '3', '2.8', '2.6', '2.6']

gold: This statistic represents a ranking of the countries with the largest Twitter audiences as of July 2018 . During the measured period , the United States accounted for 18.9 percent of Twitter audiences . Japan was ranked second with a 14.6 percent share .
gold_template: This statistic represents a ranking of the templateTitle[4] with the largest templateYLabel[1] audiences as of July templateTitleDate[0] . During the measured period , the templateXValue[0] accounted for templateYValue[max] percent of templateYLabel[1] audiences . templateXValue[1] was ranked second with a templateYValue[1] percent templateYLabel[0] .

generated_template: This statistic displays the global templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , templateXValue[0] was the biggest producer of templateTitle[1] templateTitle[4] , templateTitle[2] approximately templateYValue[max] percent of templateYLabel[1] templateYLabel[2] of templateTitle[1] . The U.S. templateYLabel[0] of templateTitle[1] amounted to about 685 thousand cwt in 2015 .
generated: This statistic displays the global Twitter user share selected countries in 2018 . In that year , United States was the biggest producer of user countries , share approximately 18.9 percent of Twitter users of user . The U.S. Share of user amounted to about 685 thousand cwt in 2015 .

Example 347:
titleEntities: {'Subject': ['India'], 'Date': ['2018']}
title: Market share of passengers carried in India 2018 by domestic airlines
X_Axis['Airline', 'Brand']: ['Indigo', 'Jet_Airways', 'Spicejet', 'Air_India', 'Go_Air', 'Air_Asia', 'Vistara', 'Jetlite', 'Alliance_Air', 'Truejet', 'Air_India_Express', 'Others']
Y_Axis['Domestic', 'market', 'share']: ['39.7', '15', '13.1', '12', '8.8', '4', '3.6', '2.2', '1', '0.4', '0.1', '0.02']

gold: India 's aviation sector had increasingly emerged as a fast-growing industry . The sector had established itself as an affordable and credible alternative to the tedious and long journeys via road or rail . With a visible growth trend , it was estimated that by 2034 , India would become one of the largest aviation markets in the world .
gold_template: templateXValue[3] 's aviation sector had increasingly emerged as a fast-growing industry . The sector had established itself as an affordable and credible alternative to the tedious and long journeys via road or rail . With a visible growth trend , it was estimated that templateTitle[6] 2034 , templateXValue[3] would become templateYValue[8] of the largest aviation markets in the world .

generated_template: templateTitleSubject[0] had a templateYLabel[0] of templateYValue[max] templateYLabel[2] in operation around the world as of templateTitleDate[0] , templateTitle[3] templateXLabel[0] . During that period , the templateXValue[10] templateXValue[1] had the fewest templateYLabel[0] of the mobile retailer had a templateYLabel[0] of around templateYValue[max] percent .
generated: India had a Domestic of 39.7 share in operation around the world as of 2018 , carried Airline . During that period , the Air India Express Jet Airways had the fewest Domestic of the mobile retailer had a Domestic of around 39.7 percent .

Example 348:
titleEntities: {'Subject': ['China'], 'Date': ['2018']}
title: Share of ethnic minorities in the China 's minority autonomous regions 2018
X_Axis['Province']: ['Tibet', 'Hunan', 'Chongqing', 'Qinghai', 'Hebei', 'Sichuan', 'Gansu', 'Xinjiang', 'Guizhou', 'Yunnan', 'Hubei', 'Liaoning', 'Hainan', 'National_total', 'Guangxi', 'Guangdong', 'Ningxia', 'Jilin', 'Inner_Mongolia', 'Heilongjiang', 'Zhejiang']
Y_Axis['Share', 'of', 'ethnic', 'minorities']: ['90.05', '83.5', '74.39', '67.57', '63.75', '63.03', '62.69', '60.22', '60.14', '58.87', '56.78', '54.49', '51.69', '51.07', '44.75', '38.7', '37.39', '34.49', '22.16', '21.87', '11.81']

gold: The graph shows the share of ethnic minorities in the population of China 's minority autonomous regions by province . In 2018 , about 60.22 percent of the population in minority areas in Xinjiang belonged to ethnic minorities .
gold_template: The graph shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the population of templateTitleSubject[0] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] by templateXLabel[0] . In templateTitleDate[0] , about templateYValue[7] percent of the population in templateTitle[5] areas in templateXValue[7] belonged to templateYLabel[1] templateYLabel[2] .

generated_template: As of January templateTitleDate[0] , templateXValue[0] templateXValue[0] is the templateTitle[8] templateTitle[9] leader in templateYLabel[0] templateYLabel[1] for the templateTitle[0] templateTitle[1] templateTitle[2] team of templateTitleSubject[0] templateTitle[4] a total of templateYValue[max] templateYLabel[0] templateYLabel[1] , followed by templateXValue[1] templateXValue[1] templateTitle[4] templateYValue[1] templateYLabel[0] . templateTitleSubject[0] 3rd at World Cup 2018 Even though there was some political controversial about the 2018 World Cup being held in Russia , when asked , the majority of Belgians agreed that Russia would be a successful venue host for the games . After templateTitleSubject[0] was defeated by France at the semi-finals templateTitle[4] 1:0 , France went on to win the second and become World Champions .
generated: As of January 2018 , Tibet is the 2018 leader in Share ethnic for the Share ethnic minorities team of China 's a total of 90.05 Share ethnic , followed by Hunan 's 83.5 Share . China 3rd at World Cup 2018 Even though there was some political controversial about the 2018 World Cup being held in Russia , when asked , the majority of Belgians agreed that Russia would be a successful venue host for the games . After China was defeated by France at the semi-finals 's 1:0 , France went on to win the second and become World Champions .

Example 349:
titleEntities: {'Subject': ['Canada'], 'Date': ['2016']}
title: Top 5 origin countries of refugees admitted to Canada in 2016
X_Axis['Country']: ['Syria', 'Eritrea', 'Iraq', 'Congo', 'Afghanistan']
Y_Axis['Number', 'of', 'refugees', 'admitted']: ['33266', '3934', '1650', '1644', '1354']

gold: This statistic shows the top five origin counties of refugees that were admitted to Canada in 2016 . Syria topped the list in 2016 with 33,266 refugees from the country admitted into Canada .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] counties of templateYLabel[1] that were templateYLabel[2] to templateTitleSubject[0] in templateTitleDate[0] . templateXValue[0] topped the list in templateTitleDate[0] with templateYValue[max] templateYLabel[1] from the templateXLabel[0] templateYLabel[2] into templateTitleSubject[0] .

generated_template: This statistic illustrates the templateTitle[5] five importing templateTitle[7] of templateTitle[3] into the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] , ranked templateTitle[9] templateYLabel[0] templateYLabel[1] . templateXValue[0] ranked highest , with templateTitle[3] templateTitle[4] valuing approximately 697 million British pounds , followed templateTitle[9] templateXValue[1] accounting for 680 million British pounds worth of templateTitleSubject[1] templateTitle[4] . templateTitle[4] of templateTitle[3] to the templateTitleSubject[1] predominantly come from the European Union , with the majority of consumers drinking templateTitle[3] on a weekly basis .
generated: This statistic illustrates the admitted five importing 2016 of countries into the Canada ( Canada ) in 2016 , ranked 2016 Number refugees . Syria ranked highest , with countries refugees valuing approximately 697 million British pounds , followed 2016 Eritrea accounting for 680 million British pounds worth of Canada refugees . refugees of countries to the Canada predominantly come from the European Union , with the majority of consumers drinking countries on a weekly basis .

Example 350:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2016']}
title: Estimated food waste from major supermarkets in the United Kingdom ( UK ) 2016
X_Axis['Month']: ['Tesco', "Sainsbury's", 'Asda', 'Aldi', 'Waitrose', 'Co-op', 'Marks_and_Spencer', 'Iceland']
Y_Axis['Volume', 'in', 'tonnes']: ['59400', '35832', '32020', '13377', '12529', '12411', '10152', '2080']

gold: This statistic shows estimates of wasted food from major supermarkets in the United Kingdom ( UK ) in 2016 . In this year Tesco was found to generate the highest volume of food waste at 59.4 thousand tonnes . This was followed by Sainsbury 's with a waste generation of approximately 35.8 thousand tonnes and Asda with 32 thousand tonnes of food waste generated .
gold_template: This statistic shows estimates of wasted templateTitle[1] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] . In this year templateXValue[0] was found to generate the highest templateYLabel[0] of templateTitle[1] templateTitle[2] at templateYValue[max] thousand templateYLabel[1] . This was followed by Sainsbury 's with a templateTitle[2] generation of approximately templateYValue[1] thousand templateYLabel[1] and templateXValue[2] with templateYValue[2] thousand templateYLabel[1] of templateTitle[1] templateTitle[2] generated .

generated_template: templateTitleSubject[0] is a free-to-play online collectible card game which reached a milestone templateYValue[max] templateYLabel[2] templateYLabel[1] templateTitle[5] in templateXValue[4] templateXValue[last] , thereby doubling the templateYLabel[0] of templateYLabel[1] since templateXValue[5] templateXValue[5] . The game originally bore the subtitle `` templateTitleSubject[0] of templateTitleSubject[0] '' as it is based in the same universe as Blizzard 's extremely popular templateTitleSubject[0] series . templateTitleSubject[0] still going strong after templateYValue[2] years templateTitleSubject[0] is seen as one of the jewels in the crown of Blizzard Entertainment .
generated: United Kingdom is a free-to-play online collectible card game which reached a milestone 59400 tonnes supermarkets in Waitrose Iceland , thereby doubling the Volume of tonnes since Co-op . The game originally bore the subtitle `` United Kingdom of United Kingdom '' as it is based in the same universe as Blizzard 's extremely popular United Kingdom series . United Kingdom still going strong after 32020 years United Kingdom is seen as one of the jewels in the crown of Blizzard Entertainment .

Example 351:
titleEntities: {'Subject': ['North Carolina'], 'Date': ['1990', '2018']}
title: North Carolina - 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']: ['53369', '50343', '53764', '50797', '46784', '41208', '41553', '45206', '43830', '41906', '42930', '43513', '39797', '42056', '40238', '37279', '36515', '38162', '38317', '37254', '35838', '35840', '35601', '31979', '30114', '28820', '27771', '26853', '26329']

gold: This statistic shows the median household income in North Carolina from 1990 to 2018 . In 2018 , the median household income in North Carolina amounted to 53,369 U.S. dollars .
gold_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] parent families with a female householder and no spouse present in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[28] percent of all templateTitleSubject[0] families with a templateTitle[3] mother in the templateTitle[5] lived below the templateYLabel[0] level . In templateXValue[max] , that figure was at templateYValue[min] percent .
generated: This statistic shows the Household income of North Carolina household parent families with a female householder and no spouse present in the North Carolina from 1990 to 2018 . In 1990 , 26329 percent of all North Carolina families with a household mother in the 1990 lived below the Household level . In 2018 , that figure was at 26329 percent .

Example 352:
titleEntities: {'Subject': ['Michelin'], 'Date': ['2018']}
title: Michelin - worldwide number of employees by region 2018
X_Axis['Region']: ['Europe', 'North_America', 'Asia_(excl._India)', 'South_America', 'Africa_India_Middle-East']
Y_Axis['Number', 'of', 'employees']: ['70599', '21541', '15259', '8166', '1848']

gold: The statistic shows the worldwide number of Michelin 's employees in 2018 , by region . Michelin had 21,541 employees in North America in 2018 . In that same year , some 17.5 percent of their employees worldwide were women .
gold_template: The statistic shows the templateTitle[1] templateYLabel[0] of templateTitleSubject[0] 's templateYLabel[1] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . templateTitleSubject[0] had templateYValue[1] templateYLabel[1] in templateXValue[1] in templateTitleDate[0] . In that same year , some 17.5 percent of their templateYLabel[1] templateTitle[1] were women .

generated_template: This statistic represents the regional templateTitle[0] of templateTitleSubject[0] templateYLabel[1] in the last three months . As of October templateTitleDate[0] , the templateXValue[0] templateXValue[1] accounted for templateYValue[1] percent of templateYLabel[1] to the website and templateXValue[2] accounted for templateYValue[2] percent of templateYLabel[1] .
generated: This statistic represents the regional Michelin of Michelin employees in the last three months . As of October 2018 , the Europe North America accounted for 21541 percent of employees to the website and Asia (excl. India) accounted for 15259 percent of employees .

Example 353:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2017', '2024']}
title: United Kingdom ( UK ) oil price forecast in U.S. dollars 2017 to 2024
X_Axis['Year']: ['2023/24', '2022/23', '2021/22', '2020/21', '2019/20', '2018/19', '2017/18']
Y_Axis['U.S.', 'dollars', 'per', 'barrel']: ['64.5', '63.3', '62.0', '61.6', '62.1', '71.3', '54.6']

gold: This statistic shows the forecasted price of oil in the United Kingdom ( UK ) from 2017 to 2024 , in U.S. dollars per barrel . The price of oil is expected to increase to 64.5 U.S. dollars in 2023/24 .
gold_template: This statistic shows the forecasted templateTitle[4] of templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[0] to templateTitleDate[1] , in templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . The templateTitle[4] of templateTitle[3] is expected to increase to templateYValue[0] templateYLabel[0] templateYLabel[1] in templateXValue[0] .

generated_template: This statistic shows the predicted templateTitle[4] of templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[0] to templateTitleDate[1] in British pounds ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] . The templateTitle[4] of templateTitle[3] is expected to increase to templateYValue[0] British pounds in templateXValue[0] .
generated: This statistic shows the predicted price of oil in the United Kingdom ( UK ) from 2017 to 2024 in British pounds ( U.S. ) dollars per . The price of oil is expected to increase to 64.5 British pounds in 2023/24 .

Example 354:
titleEntities: {'Subject': ['Slovakia'], 'Date': ['2006', '2018']}
title: Number of arrivals in tourist accommodation Slovakia 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'arrivals', 'in', 'millions']: ['5.49', '5.29', '4.94', '4.27', '3.69', '4.0', '3.73', '3.54', '3.36', '3.34', '4.03', '3.74', '3.55']

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Slovakia from 2006 to 2018 . In 2018 there were around 5.49 million arrivals at accommodation establishments in Slovakia .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] there were around templateYValue[max] templateYLabel[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[2] templateYLabel[1] in short-stay templateTitle[3] in templateTitleSubject[0] have generally increased over this period , from around templateYValue[9] templateYLabel[2] in templateXValue[min] to approximately templateYValue[max] templateYLabel[2] by templateXValue[max] .
generated: This statistic shows the Number of tourist arrivals in Slovakia from 2006 to 2018 . tourist arrivals in short-stay accommodation in Slovakia have generally increased over this period , from around 3.34 millions in 2006 to approximately 5.49 millions by 2018 .

Example 355:
titleEntities: {'Subject': ['Great Britain', 'GB'], 'Date': ['2013', '2018']}
title: Annual levels of brick production in Great Britain ( GB ) 2013 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Production', 'level', 'in', 'million', 'bricks']: ['2025', '1877', '1800', '1915', '1824', '1555']

gold: British producers had manufactured nearly 2.03 billion bricks in 2018 . This was the peak since the beginning of the reporting period in 2013 and the first time figures exceeded two billion units . Following increased demand , the Brick Development Association expects production volumes to increase further in the coming years , with companies within the industry seeking to invest in greater production capacity .
gold_template: British producers had manufactured nearly templateYValue[max] templateYLabel[2] templateYLabel[3] in templateXValue[max] . This was the peak since the beginning of the reporting period in templateXValue[min] and the first time figures exceeded templateYValue[max] templateYLabel[2] units . Following increased demand , the templateTitle[2] Development Association expects templateYLabel[0] volumes to increase further in the coming years , with companies within the industry seeking to invest in greater templateYLabel[0] capacity .

generated_template: This statistic presents the average templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[max] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] were sold worldwide .
generated: This statistic presents the average Production of level of Great Britain from 2013 to 2018 . In 2018 , approximately 2025 million bricks of Great Britain were sold worldwide .

Example 356:
titleEntities: {'Subject': ['Ecuador'], 'Date': ['2005', '2017']}
title: Ecuador : poverty headcount ratio at 3.20 U.S. dollars a day 2005 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Percentage', 'of', 'population']: ['8.7', '9.4', '9.4', '8.6', '10.1', '11.4', '12.2', '14.7', '17.2', '18.1', '19.2', '19.8', '25.7']

gold: The poverty rate in Ecuador has been decreasing lately . In 2017 , approximately 8.7 percent of the Ecuadorian population was living on less than 3.20 U.S. dollars per day , down from 25.7 percent in 2005.Still , social inequality remains a challenge in Ecuador and Latin America as a whole .
gold_template: The templateTitle[1] rate in templateTitleSubject[0] has been decreasing lately . In templateXValue[max] , approximately templateYValue[0] percent of the Ecuadorian templateYLabel[1] was living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] percent in 2005.Still , social inequality remains a challenge in templateTitleSubject[0] and Latin America as a whole .

generated_template: The templateTitle[2] rate in templateTitleSubject[0] has been decreasing since recent years . In templateXValue[max] , approximately templateYValue[min] percent of the Mexicans were living on less than in less than living . 80 percent of the European templateYLabel[2] was living in cities in templateXValue[max] , but this figure is expected to decrease by 2050 .
generated: The headcount rate in Ecuador has been decreasing since recent years . In 2017 , approximately 8.6 percent of the Mexicans were living on less than in less than living . 80 percent of the European population was living in cities in 2017 , but this figure is expected to decrease by 2050 .

Example 357:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2050']}
title: U.S. - seniors as a percentage of the population 1950 to 2050
X_Axis['Year']: ['2050', '2040', '2030', '2020', '2018', '2010', '2000', '1990', '1980', '1970', '1960', '1950']
Y_Axis['Percentage', 'of', 'total', 'population']: ['22', '21.6', '20.6', '16.9', '16', '13.1', '12.4', '12.5', '11.3', '10', '9', '8']

gold: In 2017 , about 16 percent of the American population was 65 years old or over ; a figure which is expected to reach 22 percent by 2050 . This is a significant increase from 1950 , when only eight percent of the population was 65 or over . A rapidly aging population In recent years , the aging population of the United States has come into focus as a cause for concern , as the nature of work and retirement is expected to change in order to keep up .
gold_template: In 2017 , about templateYValue[4] percent of the American templateYLabel[2] was 65 years old or over ; a figure which is expected to reach templateYValue[max] percent by templateXValue[max] . This is a significant increase from templateXValue[min] , when only templateYValue[min] percent of the templateYLabel[2] was 65 or over . A rapidly aging templateYLabel[2] In recent years , the aging templateYLabel[2] of the templateTitle[0] has come into focus as a cause for concern , as the nature of work and retirement is expected to change in order to keep up .

generated_template: This statistic shows the templateTitle[0] of templateYLabel[0] templateYLabel[1] in templateTitle[3] templateTitleSubject[0] from templateXValue[10] to templateXValue[max] . In templateXValue[3] , approximately templateYValue[min] templateYLabel[2] templateYLabel[1] in templateTitle[3] templateTitleSubject[0] had been templateYLabel[0] . Employment and labor force in templateTitleSubject[0] – additional information According to the graph at hand , absolute unemployment in templateTitleSubject[0] has gradually risen over the past decade .
generated: This statistic shows the U.S. of Percentage total in population U.S. from 1960 to 2050 . In 2020 , approximately 8 population total in population U.S. had been Percentage . Employment and labor force in U.S. – additional information According to the graph at hand , absolute unemployment in U.S. has gradually risen over the past decade .

Example 358:
titleEntities: {'Subject': ['NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Buffalo Bills ( NFL ) 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1900', '1600', '1600', '1500', '1400', '935', '870', '805', '792', '799', '909', '885', '821', '756', '708', '637', '564', '458']

gold: This graph depicts the franchise value of the Buffalo Bills of the National Football League from 2002 to 2019 . In 2019 , the franchise value came to 1.9 billion U.S. dollars . The Buffalo Bills are owned by Terry and Kim Pegula .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Terry and Kim Pegula .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] are owned by the Lamar Hunt family , who bought the templateYLabel[0] in 1960 .
generated: This graph depicts the Franchise value of the NFL of the National Football League from 2002 to 2019 . In 2019 , the Franchise value came to 1900 million U.S. dollars . The NFL are owned by the Lamar Hunt family , who bought the Franchise in 1960 .

Example 359:
titleEntities: {'Subject': ['Uruguay'], 'Date': ['2006', '2017']}
title: Uruguay : poverty headcount ratio at 3.20 U.S. dollars a day 2006 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Percentage', 'of', 'population']: ['0.4', '0.5', '0.6', '0.7', '0.8', '1.1', '1', '1.3', '1.8', '1.8', '2.9', '3.7']

gold: The poverty rate in Uruguay has been decreasing recently . In 2017 , approximately 0.4 percent of Uruguayans was living on less than 3.20 U.S. dollars per day , down from 3.7 percent of the country 's population in 2006.Still , social inequality remains a challenge in Latin America as a whole .
gold_template: The templateTitle[1] rate in templateTitleSubject[0] has been decreasing recently . In templateXValue[max] , approximately templateYValue[min] percent of Uruguayans was living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] percent of the country 's templateYLabel[1] in 2006.Still , social inequality remains a challenge in Latin America as a whole .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] percent of templateYLabel[1] were living in templateTitle[3] establishments .
generated: This statistic shows the Percentage of headcount population at ratio establishments in the Uruguay from 2006 to 2017 . In 2017 , about 0.4 percent of population were living in ratio establishments .

Example 360:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. consumer business cyber security budget share 2017
X_Axis['Response']: ['3_to_4', '4_to_5', '5_to_6', '6_to_8', '8_to_10', 'More_than_10']
Y_Axis['Share', 'of', 'respondents']: ['14', '20', '21', '21', '10', '14']

gold: This statistic illustrates the share of cyber security budget as percentage of annual IT budget of consumer businesses in the United States . During the February 2017 survey period , 14 percent of C-level respondents stated that cyber security accounted for more than 10 percent of their annual IT budget .
gold_template: This statistic illustrates the templateYLabel[0] of templateTitle[3] templateTitle[4] templateTitle[5] as percentage of annual IT templateTitle[5] of templateTitle[1] businesses in the templateTitle[0] . During the February templateTitle[7] survey period , templateYValue[0] percent of C-level templateYLabel[1] stated that templateTitle[3] templateTitle[4] accounted for templateXValue[last] 10 percent of their annual IT templateTitle[5] .

generated_template: According to a templateTitleDate[0] survey , templateXValue[0] for the templateTitle[1] of templateTitle[2] templateTitle[3] for electricity generation in the templateTitleSubject[1] outweighed opposition . Of templateYLabel[1] , 35 percent supported its templateTitle[1] , compared to 23 percent who opposed it . Despite templateTitle[2] disasters occurring as recently as 2011 , when a tsunami hit a power plant in Fukushima , Japan , approximately 34 percent of templateTitleSubject[1] residents believe it provides a safe source of templateTitle[3] .
generated: According to a 2017 survey , 3 to 4 for the consumer of business cyber for electricity generation in the U.S. outweighed opposition . Of respondents , 35 percent supported its consumer , compared to 23 percent who opposed it . Despite business disasters occurring as recently as 2011 , when a tsunami hit a power plant in Fukushima , Japan , approximately 34 percent of U.S. residents believe it provides a safe source of cyber .

Example 361:
titleEntities: {'Subject': ['Denver Broncos'], 'Date': ['2019']}
title: Regular season home attendance of the Denver Broncos 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Home', 'attendance']: ['607497', '611571', '610846', '614193', '615381', '615517', '614977', '613062', '602618', '599264', '600928', '604074', '612888', '610776']

gold: This graph depicts the total regular season home attendance of the Denver Broncos franchise of the National Football League from 2006 to 2019 . In 2019 , the regular season home attendance of the franchise was 607,497 .
gold_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[0] .

generated_template: The graph shows the templateTitle[2] templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitleSubject[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In its templateXValue[max] fiscal templateXLabel[0] , templateTitleSubject[0] 's paid out a templateYLabel[0] of approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The graph shows the home attendance of Regular Denver Broncos from the fiscal Year of 2006 to the fiscal Year of 2019 . In its 2019 fiscal Year , Denver Broncos 's paid out a Home of approximately 607497 attendance .

Example 362:
titleEntities: {'Subject': ['GDP'], 'Date': ['2020']}
title: Forecast of the gross domestic product ( GDP ) growth in the euro countries 2020
X_Axis['Country']: ['Malta', 'Slovak_Republic', 'Ireland', 'Cyprus', 'Latvia', 'Estonia', 'Slovenia', 'Luxembourg', 'Lithuania', 'Greece', 'Spain', 'Finland', 'Austria', 'Netherlands', 'Portugal', 'Germany', 'France', 'Belgium', 'Italy']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['4.44', '3.46', '3.45', '3.34', '3.15', '2.9', '2.83', '2.75', '2.63', '2.16', '1.88', '1.73', '1.7', '1.69', '1.5', '1.44', '1.41', '1.39', '0.91']

gold: This statistic shows a forecast of the gross domestic product ( GDP ) growth in the euro countries in 2020 . In 2020 , the gross domestic product in Germany is forecasted to grow by 1.44 percent over the previous year .
gold_template: This statistic shows a templateTitle[0] of the templateTitle[1] templateTitle[2] templateTitle[3] ( templateYLabel[0] ) templateYLabel[1] in the templateTitle[6] templateTitle[7] in templateTitleDate[0] . In templateTitleDate[0] , the templateTitle[1] templateTitle[2] templateTitle[3] in templateXValue[15] is forecasted to grow by templateYValue[15] percent over the templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitleDate[0] . In templateXValue[0] templateXValue[0] , the templateYLabel[0] grew by about templateYValue[max] percent templateYLabel[2] to the templateYLabel[3] templateYLabel[4] , making it the templateXLabel[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitleDate[0] . The global templateYLabel[0] Today , the global templateYLabel[0] amounts to around 7 billion people , i.e .
generated: This statistic shows the 20 GDP gross the domestic GDP growth in 2020 . In Malta , the GDP grew by about 4.44 percent compared to the previous year , making it the Country gross the domestic GDP growth in 2020 . The global GDP Today , the global GDP amounts to around 7 billion people , i.e .

Example 363:
titleEntities: {'Subject': ['France'], 'Date': ['2024']}
title: Ratio of government expenditure to gross domestic product ( GDP ) in France 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Ratio', 'of', 'government', 'expenditure', 'to', 'GDP']: ['53.94', '53.96', '54', '54.19', '54.5', '55.65', '56.04', '56.38', '56.59', '56.8', '57.21']

gold: The statistic shows the ratio of government expenditure to the gross domestic product ( GDP ) in France from 2014 to 2018 , with projections up until 2024 . In 2018 , the government expenditure in France amounted to about 56.04 percent of the gross domestic product .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to the templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to about templateYValue[6] percent of the templateTitle[3] templateTitle[4] templateTitle[5] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to about templateYValue[6] percent of the country 's templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The statistic shows the Ratio of government expenditure to gross domestic product ( GDP ) in France from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure in France amounted to about 56.04 percent of the country 's gross domestic product .

Example 364:
titleEntities: {'Subject': ['Ireland'], 'Date': ['2024']}
title: Total population of Ireland 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['5.2', '5.15', '5.11', '5.06', '5.01', '4.95', '4.89', '4.83', '4.77', '4.71', '4.67']

gold: This statistic shows the total population of Ireland from 2014 to 2018 , with projections up to 2024 . In 2018 , the total population of Ireland was at approximately 4.89 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was at approximately templateYValue[6] templateYLabel[1] templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateYLabel[1] templateYLabel[0] .
generated: This statistic shows the Total population of Ireland from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Ireland amounted to approximately 4.89 millions Inhabitants .

Example 365:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2018']}
title: Leading brands in the Netherlands 2018 , ranked by Buzz score
X_Axis['Platform']: ['Samsung', 'Albert_Heijn', 'Philips', 'Google', 'Lidl', 'Jumbo', 'YouTube', 'Sony', 'Wikipedia', 'Bose']
Y_Axis['Buzz', 'score']: ['47.7', '46.8', '38.6', '37.9', '34.8', '33.8', '33.5', '28.9', '25.4', '21.9']

gold: In 2018 , Samsung was the brand with the highest Buzz score in the Netherlands , followed by two Dutch brands : food retailer Albert Heijn and Philips . A brand 's Buzz score indicates how positive a brand is perceived in a certain country . The survey tracks how many people have heard anything about a certain brand in the last two weeks , through advertising , news or word of mouth .
gold_template: In templateTitleDate[0] , templateXValue[0] was the brand with the highest templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] , followed templateTitle[5] two Dutch templateTitle[1] : food retailer templateXValue[1] and templateXValue[2] . A brand 's templateYLabel[0] templateYLabel[1] indicates how positive a brand is perceived in a certain country . The survey tracks how many people have heard anything about a certain brand in the last two weeks , through advertising , news or word of mouth .

generated_template: This statistic gives information on the most popular multi-platform templateTitle[1] templateTitle[2] in the templateTitle[3] as of October templateTitleDate[0] . During that month , templateXValue[0] was ranked first with almost templateYValue[max] monthly active templateTitle[3] users . templateXValue[0] templateXValue[2] was ranked third in terms of templateTitleSubject[0] but came first in regards of mobile-only users .
generated: This statistic gives information on the most popular multi-platform brands Netherlands in the 2018 as of October 2018 . During that month , Samsung was ranked first with almost 47.7 monthly active 2018 users . Samsung Philips was ranked third in terms of Netherlands but came first in regards of mobile-only users .

Example 366:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2005']}
title: Total retail sales of U.S. shopping malls 2005 , by size
X_Axis['Gross', 'leasable', 'area', 'in', 'square', 'feet']: ['Less_than_100001', '100001_to_200000', '200001_to_400000', '400001_to_800000', '800001_to_1000000', 'More_than_one_million']
Y_Axis['Total', 'retail', 'sales', 'in', 'billion', 'U.S.', 'dollars']: ['443.8', '388.6', '234.2', '197.6', '97.3', '168.9']

gold: This statistic shows of the total retail sales of all retail shopping malls in the United States , sorted by mall size in square feet of gross leasable area . In 2005 , shopping malls sized between 200,001 and 400,000 square feet made a total of 234.2 billion U.S. dollars of retail sales .
gold_template: This statistic shows of the templateYLabel[0] templateYLabel[1] templateYLabel[2] of all templateYLabel[1] templateTitle[4] templateTitle[5] in the templateTitle[3] , sorted templateTitle[7] mall templateTitle[8] in templateXLabel[3] templateXLabel[4] of templateXLabel[0] templateXLabel[1] templateXLabel[2] . In templateTitleDate[0] , templateTitle[4] templateTitle[5] sized between templateXValue[2] and templateXValue[2] templateXLabel[3] templateXLabel[4] made a templateYLabel[0] of templateYValue[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] of templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] templateTitleSubject[0] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] templateXLabel[1] . In templateTitleDate[0] , templateXValue[0] couples had a templateYLabel[0] of templateYValue[max] percent of the templateTitleSubject[0] templateTitleSubject[0] .
generated: This statistic shows the Total of Total retail sales in the U.S. in 2005 , by Gross leasable . In 2005 , Less than 100001 couples had a Total of 443.8 percent of the U.S. .

Example 367:
titleEntities: {'Subject': ['Russia'], 'Date': ['2024']}
title: Total population of Russia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['145.74', '146.02', '146.27', '146.47', '146.62', '146.73', '146.8', '146.9', '146.8', '146.5', '146.3']

gold: This statistic shows the total population of Russia from 2014 to 2018 , with projections up until 2024 . In 2018 , the total population of Russia was around 146.8 million people . Only a fraction of them live in the major Russian cities .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was around templateYValue[6] templateYLabel[1] people . Only a fraction of them live in the major Russian cities .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] is the second largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .
generated: This statistic shows the Total population of Russia from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Russia amounted to approximately 146.8 millions Inhabitants . population of Russia is the second largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .

Example 368:
titleEntities: {'Subject': ['YouTube YouTube Red U.S.'], 'Date': ['2017']}
title: Consumers with a YouTube or YouTube Red subscription in the U.S. 2017 , by age group
X_Axis['Response']: ['Total', 'Millennials', 'Gen_X', 'Boomers', 'Retirees']
Y_Axis['Share', 'of', 'respondents']: ['16', '27', '17', '6', '3']

gold: This statistic provides information on the share of consumers with an active YouTube or YouTube Red subscription in the United States as of January 2017 , sorted by age . According to the source , 27 percent of Millennials who subscribe to online video or music subscriptions had a YouTube or YouTube Red subscription as of January 2017 .
gold_template: This statistic provides information on the templateYLabel[0] of templateTitle[0] templateTitle[1] an active templateTitleSubject[0] or templateTitleSubject[0] subscription in the templateTitle[6] as of January templateTitleDate[0] , sorted templateTitle[8] templateTitle[9] . According to the source , templateYValue[max] percent of templateXValue[1] who subscribe to online video or music subscriptions had a templateTitleSubject[0] or templateTitleSubject[0] subscription as of January templateTitleDate[0] .

generated_template: At the beginning of templateTitleDate[0] , templateYValue[0] percent of templateTitle[0] templateTitle[1] an online video or music templateTitle[3] confirmed that they had an active templateTitleSubject[0] templateTitle[3] at the time of survey . templateXValue[1] and Gen-Xers were more likely to have a templateTitleSubject[0] templateTitle[3] than their older peers , which comes as no surprise given that templateTitleSubject[0] is considered the most indispensable TV and video source among templateTitle[0] aged between 16 to 34 years old . One of the most appealing aspects of templateTitleSubject[0] for viewers is the availability of original content , which has fast become one of the keys to the company 's success .
generated: At the beginning of 2017 , 16 percent of Consumers YouTube an online video or music Red confirmed that they had an active YouTube YouTube Red U.S. Red at the time of survey . Millennials and Gen-Xers were more likely to have a YouTube YouTube Red U.S. Red than their older peers , which comes as no surprise given that YouTube YouTube Red U.S. is considered the most indispensable TV and video source among Consumers aged between 16 to 34 years old . One of the most appealing aspects of YouTube YouTube Red U.S. for viewers is the availability of original content , which has fast become one of the keys to the company 's success .

Example 369:
titleEntities: {'Subject': ['Italy'], 'Date': ['2019']}
title: Youth unemployment rate in Italy 2004 to 2019
X_Axis['Year']: ['2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019']
Y_Axis['Unemployment', 'rate']: ['23.5', '24.1', '21.8', '20.4', '21.2', '25.3', '27.9', '29.2', '35.3', '40', '42.7', '40.3', '37.8', '34.7', '32.2', '28.9']

gold: The problem of unemployment in Italy became critical in the first years of the financial crisis which started in 2008 . Although the labor market crisis seriously affected the entire Italian working population , it particularly impacted the youngest part of the labor force . Between 2008 and 2014 alone , the share of unemployed individuals aged between 15 and 24 years increased by more than 15 percent .
gold_template: The problem of templateYLabel[0] in templateTitleSubject[0] became critical in the first years of the financial crisis which started in templateXValue[4] . Although the labor market crisis seriously affected the entire Italian working population , it particularly impacted the youngest part of the labor force . Between templateXValue[4] and templateXValue[10] alone , the share of unemployed individuals aged between 15 and templateYValue[0] years increased by more than 15 percent .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] reached its lowest level since templateXValue[min] . templateTitle[3] templateXValue[min] to templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] has been fluctuating between more than templateYValue[min] births and almost templateYValue[6] births for 1,000 templateYLabel[4] . The highest templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] during this period was recorded in templateXValue[12] .
generated: In 2019 , the Unemployment rate in Italy reached its lowest level since 2004 . Italy 2004 to 2019 , the Unemployment rate in Italy has been fluctuating between more than 20.4 births and almost 27.9 births for 1,000 rate . The highest Unemployment rate in Italy during this period was recorded in 2016 .

Example 370:
titleEntities: {'Subject': ['Nordic'], 'Date': ['2017']}
title: Surface area of the Nordic countries 2017
X_Axis['Country']: ['Sweden', 'Greenland', 'Norway', 'Finland', 'Iceland', 'Denmark', 'Faroe_Islands']
Y_Axis['Surface', 'area', 'in', 'square', 'kilometers']: ['447420', '410450', '385178', '338420', '103000', '42922', '1396']

gold: This statistic shows the surface area of the Nordic countries in 2017 . The largest of all Nordic countries is Sweden , with a surface of roughly 447 thousand square kilometers . Its neighboring country Norway has a size of approximately 385 thousand square kilometers , which includes the arctic islands of Svalbard and Jan Mayen .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] in templateTitleDate[0] . The largest of all templateTitleSubject[0] templateTitle[3] is templateXValue[0] , with a templateYLabel[0] of roughly templateYValue[max] thousand templateYLabel[2] templateYLabel[3] . Its neighboring templateXLabel[0] templateXValue[2] has a size of approximately templateYValue[2] thousand templateYLabel[2] templateYLabel[3] , which includes the arctic templateXValue[last] of Svalbard and Jan Mayen .

generated_template: As of January templateTitleDate[0] , templateXValue[0] was home to the largest templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitleSubject[1] , followed by templateXValue[1] templateXValue[1] and templateXValue[2] . At that time , templateXValue[0] templateXValue[0] and the templateXValue[2] templateXValue[2] in the templateTitle[2] number of templateTitle[3] is estimated to reach a templateYLabel[1] of approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . What was templateXValue[0] 's lowest templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] per month .
generated: As of January 2017 , Sweden was home to the largest Surface area of 2017 in the Nordic , followed by Greenland and Norway . At that time , Sweden and the Norway in the Nordic number of countries is estimated to reach a area of approximately 447420 square kilometers . What was Sweden 's lowest Surface area of Nordic square per month .

Example 371:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2017', '2023']}
title: Brazil : mobile phone internet user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['51', '50', '49', '47', '45', '43', '41']

gold: This statistic gives information on the mobile internet penetration in Brazil from 2017 to 2023 . In 2017 , 41 percent of the Brazilian population accessed internet from their mobile device . This figure is expected to grow to 51 percent in 2023 .
gold_template: This statistic gives information on the templateTitle[1] templateTitle[3] templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the Brazilian templateYLabel[1] accessed templateTitle[3] from their templateTitle[1] device . This figure is expected to grow to templateYValue[max] percent in templateXValue[max] .

generated_template: This statistic presents the templateTitle[0] networking reach in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the templateYLabel[1] in the country accessed templateTitle[0] templateTitle[1] . In templateXValue[4] , this templateYLabel[0] is projected to reach templateYValue[4] percent .
generated: This statistic presents the Brazil networking reach in Brazil from 2017 to 2023 . In 2017 , 41 percent of the population in the country accessed Brazil mobile . In 2019 , this Share is projected to reach 45 percent .

Example 372:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. per capita consumption of fresh peaches and nectarines 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']: ['2.2', '2.66', '2.73', '2.91', '3.15', '3.02', '3.86', '4.47', '4.73', '4.41', '5.08', '4.46', '4.58', '4.83', '5.15', '5.17', '5.23', '5.16', '5.3']

gold: This statistic depicts the per capita consumption of fresh peaches in the United States from 2000 to 2018 . According to the report , the U.S. per capita consumption of fresh peaches amounted to approximately 2.2 pounds in 2018 .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[min] templateYLabel[3] in templateXValue[max] .

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] templateYLabel[3] in templateXValue[max] .
generated: This statistic presents the Per capita consumption of fresh peaches in the U.S. from 2000 to 2018 . According to the report , the U.S. Per capita consumption of fresh peaches amounted to approximately 2.2 pounds in 2018 .

Example 373:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. per capita consumption of fresh carrots 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['8.5', '7.4', '7.8', '8.8', '8.5', '8.0', '7.9', '7.5', '7.8', '7.4', '8.1', '8.0', '8.1', '8.7', '8.7', '8.8', '8.4', '9.4', '9.2']

gold: The timeline shows the per capita consumption of fresh carrots in the United States from 2000 to 2018 . According to the report , the U.S. per capita consumption of fresh carrots amounted to approximately 8.5 pounds in 2018 .
gold_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] templateYLabel[3] in templateXValue[max] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] amounted to templateYValue[max] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of consumption fresh in the carrots from 2000 to 2018 . The U.S. Per capita consumption of consumption fresh amounted to 9.4 pounds in 2018 .

Example 374:
titleEntities: {'Subject': ['IMF'], 'Date': ['2011']}
title: IMF - biggest debtor nations 2011
X_Axis['Country']: ['Romania', 'Ukraine', 'Greece', 'Hungary', 'Pakistan', 'Ireland', 'Turkey', 'Belarus']
Y_Axis['Debt', 'in', 'billion', 'euros']: ['11.8', '10.3', '10.2', '8.5', '6.3', '5.6', '4.1', '2.5']

gold: The statistic shows IMF 's biggest debtor states in May 2011 . Belarus reported a debt of 2.5 billion euros .
gold_template: The statistic shows templateTitleSubject[0] 's templateTitle[1] templateTitle[2] states in May templateTitleDate[0] . templateXValue[last] reported a templateYLabel[0] of templateYValue[min] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] in the templateYLabel[2] of templateYLabel[1] per liter in templateTitle[3] templateTitle[4] for the period between December templateTitleDate[0] and December templateTitleDate[1] . The templateYLabel[2] changes concern unleaded premium ( 95 RON ) for templateXValue[3] , templateXValue[last] , templateXValue[4] , templateXValue[1] , and the templateXValue[0] templateXValue[5] , and regular unleaded for templateXValue[2] , templateXValue[6] , and the templateXValue[0] templateXValue[0] . In December templateTitleDate[1] , the templateYLabel[1] templateYLabel[2] per liter in the U.S. was templateYValue[max] percent higher than it was in December templateTitleDate[0] .
generated: This statistic shows the Debt in the euros of billion per liter in nations 2011 for the period between December 2011 and December 2011 . The euros changes concern unleaded premium ( 95 RON ) for Hungary , Belarus , Pakistan , Ukraine , and the Romania Ireland , and regular unleaded for Greece , Turkey , and the Romania . In December 2011 , the billion euros per liter in the U.S. was 11.8 percent higher than it was in December 2011 .

Example 375:
titleEntities: {'Subject': ['Syria'], 'Date': ['2010']}
title: Gross domestic product ( GDP ) in Syria 2010
X_Axis['Year']: ['2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['60.04', '53.94', '52.63', '40.49', '33.82', '28.88', '25.2', '21.7', '22.76', '20.98', '19.86', '16.79', '16.14', '16.57', '17.76', '16.56', '15.11', '13.8', '13.26', '12.74', '12.3', '9.85', '16.54', '32.5', '25.43', '21.18', '19.17']

gold: The statistic shows gross domestic product ( GDP ) in Syria from 1984 to 2010 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given year . GDP is an important indicator of a country 's economic power .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Syria from 1984 to 2010 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year . GDP is an important indicator of a country 's economic power .

Example 376:
titleEntities: {'Subject': ['Spain'], 'Date': ['2007', '2018']}
title: Annual average housing prices Spain 2007 to 2018
X_Axis['Year']: ['2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['Price', 'in', 'euros', 'per', 'square', 'meter', 'built']: ['2246', '2285', '2185', '2060', '1907', '1768', '1602', '1477', '1431', '1447', '1532', '1613']

gold: In December 2018 , a house in Spain would cost around 1.699 thousand euros per square meter built . After a long period of time in which Spain 's real estate prices increased sharply , the market was hit by the global financial crisis of 2007 , making the Spanish property bubble collapse and damaging home value . House prices have picked up ever since in the Mediterranean country .
gold_template: In December templateXValue[max] , a house in templateTitleSubject[0] would cost around 1.699 thousand templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] . After a long period of time in which templateTitleSubject[0] 's real estate templateTitle[3] increased sharply , the market was hit by the global financial crisis of templateXValue[min] , making the Spanish property bubble collapse and damaging home value . House templateTitle[3] have picked up ever since in the Mediterranean country .

generated_template: The statistic shows the templateTitle[1] of templateTitle[2] templateTitleSubject[0] as templateYLabel[0] of total e-mail templateYLabel[2] from templateXValue[min] to templateXValue[max] . In the most recently reported period , templateTitleSubject[0] messages accounted for templateYValue[11] percent of e-mail templateYLabel[2] worldwide , down from templateYValue[9] percent in templateXValue[9] .
generated: The statistic shows the average of housing Spain as Price of total e-mail per from 2007 to 2018 . In the most recently reported period , Spain messages accounted for 1613 percent of e-mail per worldwide , down from 1447 percent in 2016 .

Example 377:
titleEntities: {'Subject': ['Brunswick Corporation'], 'Date': ['2007', '2019']}
title: Global revenue of the Brunswick Corporation 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['4108.4', '4120.9', '3802.2', '3508.1', '3311.1', '3838.7', '3599.7', '3416.8', '3367.0', '3039.6', '2776.1', '4708.7', '5671.2']

gold: The statistic depicts the net sales of the Brunswick Corporation worldwide from 2007 to 2019 . In 2019 , Brunswick 's net sales was at about 4.11 billion U.S. dollars.The Brunswick Corporation is a U.S.-based company that manufactures and sells boats and marine engines . The company previously made and sold sporting and fitness equipment and bowling & billiards equipment .
gold_template: The statistic depicts the net sales of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's net sales was at about templateYValue[0] templateYLabel[1] templateYLabel[2] dollars.The templateTitleSubject[0] is a U.S.-based company that manufactures and sells boats and marine engines . The company previously made and sold sporting and fitness equipment and bowling & billiards equipment .

generated_template: templateTitleSubject[0] experienced a templateYLabel[0] of templateYValue[max] templateYLabel[2] open throughout templateTitleSubject[1] templateTitleSubject[1] as of templateXValue[max] . templateTitleSubject[0] Corporation operates a chain of general merchandise templateYLabel[2] templateYLabel[3] in templateXValue[max] , compared to templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] .
generated: Brunswick Corporation experienced a Revenue of 5671.2 U.S. open throughout Brunswick Corporation as of 2019 . Brunswick Corporation Corporation operates a chain of general merchandise U.S. dollars in 2019 , compared to 2776.1 million U.S. dollars in the previous Year .

Example 378:
titleEntities: {'Subject': ['Russia'], 'Date': ['2010', '2019']}
title: Ice hockey players in Russia 2010 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11']
Y_Axis['Number', 'of', 'players']: ['112236', '110624', '105059', '102179', '99172', '84270', '66551', '64326', '63580']

gold: The statistics depicts the number of registered ice hockey players in Russia from 2010/11 to 2018/19 . In the 2018/19 season , there were a total of 112,236 registered ice hockey players in Russia according to the International Ice Hockey Federation .
gold_template: The statistics depicts the templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , there were a total of templateYValue[max] registered templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] according to the International templateTitle[0] templateTitle[1] Federation .

generated_template: The statistics depicts the templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , there were a total of templateYValue[max] registered templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] according to the International templateTitle[0] templateTitle[1] Federation .
generated: The statistics depicts the Number of registered Ice hockey players in the Russia from 2010/11 to 2018/19 . In the 2018/19 season , there were a total of 112236 registered Ice hockey players in the Russia according to the International Ice hockey Federation .

Example 379:
titleEntities: {'Subject': ['Southwest Airlines'], 'Date': ['2010', '2019']}
title: Net income of Southwest Airlines 2010 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['2300', '2465', '3357', '2183', '2181', '1136', '754', '421', '178', '459']

gold: This statistic shows Southwest Airlines Co. 's net income from the fiscal year of 2010 to the fiscal year of 2019 . In the fiscal year of 2019 , the low-cost carrier 's net income amounted to 2.3 billion U.S. dollars .
gold_template: This statistic shows templateTitleSubject[0] Co. '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] , the low-cost carrier 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: templateTitleSubject[0] templateTitleSubject[0] reported templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] , down from templateYValue[1] templateYLabel[2] templateXLabel[0] in the previous templateXLabel[0] . templateTitleSubject[0] templateTitleSubject[0] is an aircraft manufacturing manufacturing subsidiary of the templateTitleSubject[0] Group , which is known as EADS ( European Aeronautic Defence and Space Company ) .
generated: Southwest Airlines reported Net income of 3357 million U.S. dollars in 2019 , down from 2465 million Year in the previous Year . Southwest Airlines is an aircraft manufacturing subsidiary of the Southwest Airlines Group , which is known as EADS ( European Aeronautic Defence and Space Company ) .

Example 380:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2008', '2018']}
title: Number of births in Belgium 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'births']: ['117800', '119102', '121713', '121713', '124415', '124862', '126993', '127655', '129173', '127297', '128049']

gold: In 2018 , nearly 118,000 babies were born in Belgium . This was the lowest number of births in the last decade . The number of children born in the country peaked in 2010 , at just over 129,000 .
gold_template: In templateXValue[max] , nearly 118,000 babies were born in templateTitleSubject[0] . This was the lowest templateYLabel[0] of templateYLabel[1] in the last decade . The templateYLabel[0] of children born in the country peaked in templateXValue[8] , at just over 129,000 .

generated_template: During the period from templateXValue[min] to templateXValue[max] , the highest templateYLabel[0] of babies born in templateTitleSubject[0] was in templateXValue[3] , when over templateYValue[max] thousand templateYLabel[1] occurred . In templateXValue[max] , about templateYValue[0] thousand babies were born in templateTitleSubject[0] . Within the last decade , there were yearly more baby boys than baby girls being born .
generated: During the period from 2008 to 2018 , the highest Number of babies born in Belgium was in 2015 , when over 129173 thousand births occurred . In 2018 , about 117800 thousand babies were born in Belgium . Within the last decade , there were yearly more baby boys than baby girls being born .

Example 381:
titleEntities: {'Subject': ['Faroe Islands'], 'Date': ['1993', '2019']}
title: World ranking of Faroe Islands ' national football team 1993 to 2019
X_Axis['Year']: ['1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019']
Y_Axis['FIFA', 'World', 'Ranking', 'position']: ['115', '133', '120', '135', '117', '125', '112', '117', '117', '114', '126', '131', '132', '181', '194', '184', '117', '136', '116', '153', '170', '104', '97', '83', '95', '98', '102']

gold: In 2016 , the Faroe Island 's national football team , controlled by the Football Association of the Faroe Islands , reached its highest position in the FIFA World Ranking . The team took part in the qualifying for the UEFA European Championship 2016 . Out of the ten qualifying matches , the Faroe Island 's national football team won both matches against Greece .
gold_template: In templateXValue[23] , the templateTitleSubject[0] Island 's templateTitle[5] templateTitle[6] templateTitle[7] , controlled by the templateTitle[6] Association of the templateTitleSubject[0] , reached its highest templateYLabel[3] in the templateYLabel[0] templateYLabel[1] templateYLabel[2] . The templateTitle[7] took part in the qualifying for the UEFA European Championship templateXValue[23] . Out of the ten qualifying matches , the templateTitleSubject[0] Island 's templateTitle[5] templateTitle[6] templateTitle[7] won both matches against Greece .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . Until templateXValue[20] , the templateTitle[5] did not reach a templateYLabel[3] better than templateYValue[20] . Rank templateYValue[max] was the lowest result of the templateTitle[5] , which was reached in templateXValue[16] .
generated: This statistic shows the FIFA World Ranking of the Faroe Islands Islands ' national from 1993 to 2019 . Until 2013 , the national did not reach a position better than 170 . Rank 194 was the lowest result of the national , which was reached in 2009 .

Example 382:
titleEntities: {'Subject': ['Germany'], 'Date': ['2000', '2018']}
title: Consumption of wine in Germany 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2004', '2003', '2001', '2000']
Y_Axis['Consumption', 'in', 'million', 'hectoliters']: ['20.0', '19.7', '20.1', '19.6', '20.2', '20.4', '20.3', '19.7', '20.2', '20.2', '20.7', '20.8', '19.6', '20.2', '20.0', '20.2']

gold: Over 20 million hectoliters of wine a year are consumed on average in Germany . Consumption levels have so far mostly been steady during the last decade . Meanwhile , per capita wine drinking has also remained largely unchanged during the same time .
gold_template: Over templateYValue[0] templateYLabel[1] templateYLabel[2] of templateTitle[1] a templateXLabel[0] are consumed on average in templateTitleSubject[0] . templateYLabel[0] levels have so far mostly been steady during the last decade . Meanwhile , per capita templateTitle[1] drinking has also remained largely unchanged during the same time .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] generated by templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] reported some templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[0] templateYLabel[1] . templateTitleSubject[0] is one of the 15 largest automakers templateTitle[4] .
generated: This statistic shows the Consumption million generated by Germany from 2000 to 2018 . In 2018 , Germany reported some 20.0 hectoliters Consumption million . Germany is one of the 15 largest automakers 2018 .

Example 383:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Consumers with a newspaper or magazine subscriptions in the U.S. 2017 , by age group
X_Axis['Response']: ['Total', 'Millennials', 'Gen_X', 'Boomers', 'Retirees']
Y_Axis['Share', 'of', 'respondents']: ['41', '33', '35', '45', '54']

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

generated_template: At the beginning of templateTitleDate[0] , templateYValue[0] percent of templateTitle[0] templateTitle[1] an online video or music templateTitle[3] confirmed that they had an active templateTitleSubject[0] templateTitle[3] at the time of survey . templateXValue[1] and Gen-Xers were more likely to have a templateTitleSubject[0] templateTitle[3] than their older peers , which comes as no surprise given that templateTitleSubject[0] is considered the most indispensable TV and video source among templateTitle[0] aged between 16 to 34 years old . One of the most appealing aspects of templateTitleSubject[0] for viewers is the availability of original content , which has fast become one of the keys to the company 's success .
generated: At the beginning of 2017 , 41 percent of Consumers newspaper an online video or music subscriptions confirmed that they had an active U.S. subscriptions at the time of survey . Millennials and Gen-Xers were more likely to have a U.S. subscriptions than their older peers , which comes as no surprise given that U.S. is considered the most indispensable TV and video source among Consumers aged between 16 to 34 years old . One of the most appealing aspects of U.S. for viewers is the availability of original content , which has fast become one of the keys to the company 's success .

Example 384:
titleEntities: {'Subject': ['Thailand'], 'Date': ['2015', '2035']}
title: Share of aging population Thailand 2015 to 2035
X_Axis['Year']: ['2035', '2030', '2025', '2020', '2015']
Y_Axis['Share', 'of', 'population', 'older', 'than', '65', 'years', 'old']: ['22.8', '19.4', '16', '12.9', '10.6']

gold: The statistic shows the share of population older than 65 in Thailand in 2015 , with a projection from 2020 to 2035 . In 2015 , the share of population older than 65 amounted to about 10.6 percent . In 2035 , the percentage of the population above the age of 65 was forecasted to reach 22.8 percent .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] in templateXValue[min] , with a projection from templateXValue[3] to templateXValue[max] . In templateXValue[min] , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] amounted to about templateYValue[min] percent . In templateXValue[max] , the percentage of the templateYLabel[1] above the age of templateYLabel[4] was forecasted to reach templateYValue[max] percent .

generated_template: This statistic illustrates the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[0] to templateTitleDate[1] . Global templateTitle[2] templateTitle[3] templateTitle[4] is expected to increase by templateYValue[max] percent in templateXValue[min] .
generated: This statistic illustrates the Share of aging population Thailand in the Thailand ( Thailand ) from 2015 to 2035 . Global population Thailand 2015 is expected to increase by 22.8 percent in 2015 .

Example 385:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2002', '2013']}
title: Number of books published in the U.S. in the category 'fiction ' 2002 to 2013
X_Axis['Year']: ['2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013_(projected)']
Y_Axis['Number', 'of', 'new', 'books', '/', 'editions']: ['25102', '24666', '38832', '34927', '42777', '53590', '53058', '48738', '46641', '43016', '49853', '50498']

gold: This statistic contains data on the U.S. book publishing in the category 'fiction ' from 2002 to 2013 . In 2006 , 42,777 books of fiction were published in the United States .
gold_template: This statistic contains data on the templateTitleSubject[0] book publishing in the templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[0] to templateXValue[last] . In templateXValue[4] , templateYValue[4] templateYLabel[2] of fiction were templateTitle[2] in the templateTitle[3] .

generated_template: Between templateXValue[min] and templateXValue[max] , each templateXLabel[0] between templateYValue[min] and templateYValue[max] people died because of templateTitle[2] use in the templateTitleSubject[0] . The templateYLabel[1] of casualties was lowest in templateXValue[7] and peaked in templateXValue[max] . In four out of the last five years , a new record in templateTitle[2] templateYLabel[2] was reached .
generated: Between 2002 and 2012 , each Year between 24666 and 53590 people died because of published use in the U.S. . The new of casualties was lowest in 2009 and peaked in 2012 . In four out of the last five years , a new record in published books was reached .

Example 386:
titleEntities: {'Subject': ['YouTube'], 'Date': ['2019']}
title: Most viewed YouTube videos of all time 2019
X_Axis['Month']: ['Luis_Fonsi_-_Despacito_ft._Daddy_Yankee', 'Ed_Sheeran_-_Shape_of_You', 'Wiz_Khalifa_-_See_You_Again_ft._Charlie_Puth_[Official_Video]_Furious_7_Soundtrack', 'Masha_and_the_Bear:_Recipe_for_Disaster', "Pinkfong_Kids'_Songs_&_Stories_-_Baby_Shark_Dance", 'Mark_Ronson_ft._Bruno_Mars_-_Uptown_Funk', 'PSY_-_GANGNAM_STYLE', 'Justin_Bieber_-_Sorry', 'Maroon_5_-_Sugar', 'Katy_Perry_-_Roar']
Y_Axis['Number', 'of', 'views', 'in', 'billions']: ['6.55', '4.51', '4.31', '4.18', '4.06', '3.73', '3.47', '3.22', '3.08', '2.97']

gold: On January 12 , 2017 , Puerto Rican singer Luis Fonsi released his Spanish-language music video `` Despacito '' featuring Daddy Yankee , and the rest is history . In August of the same year , the video became the most-viewed YouTube video of all time and as of December 2019 , the video still holds the top spot with over 6.55 billion lifetime views on the video platform . Music videos on YouTube `` Descpacito '' might be the current record-holder in terms of total views , but Korean artist Psy 's `` Gangnam Style '' video remained on the top spot for longest ( 1,689 days or 4.6 years ) before ceding its spot to its successor .
gold_template: On January 12 , 2017 , Puerto Rican singer templateXValue[0] released his Spanish-language music video `` templateXValue[0] '' featuring templateXValue[0] , and the rest is history . In August of the same year , the video became the most-viewed templateTitleSubject[0] video of templateTitle[4] templateTitle[5] and as of December templateTitleDate[0] , the video still holds the top spot with over templateYValue[max] templateYLabel[2] lifetime templateYLabel[1] on the video platform . Music templateTitle[3] on templateTitleSubject[0] `` Descpacito '' might be the current record-holder in terms of total templateYLabel[1] , but Korean artist templateXValue[6] 's `` templateXValue[6] '' video remained on the top spot templateXValue[3] longest ( 1,689 days or 4.6 years ) before ceding its spot to its successor .

generated_template: templateXValue[0] templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitle[2] page on templateTitle[4] in the templateTitleSubject[0] templateTitleSubject[0] ( templateXValue[0] ) as of templateTitle[8] templateTitle[9] , attracting templateYValue[max] templateYLabel[2] templateYLabel[0] templateYLabel[1] . This was more than double that of templateXValue[1] , the company with the second highest number of templateYLabel[1] . templateXValue[0] templateXValue[0] : more than just e-commerce templateXValue[0] has enjoyed significant growth in the templateXValue[0] market in recent years .
generated: Luis Fonsi - Despacito ft. Daddy Yankee was the Most viewed YouTube page on all in the YouTube ( Luis Fonsi - Despacito ft. Daddy Yankee ) as of 2019 , attracting 6.55 billions Number views . This was more than double that of Ed Sheeran - Shape of You , the company with the second highest number of views . Luis Fonsi - Despacito ft. Daddy Yankee : more than just e-commerce Luis Fonsi - Despacito ft. Daddy Yankee has enjoyed significant growth in the Luis Fonsi - Despacito ft. Daddy Yankee market in recent years .

Example 387:
titleEntities: {'Subject': ['Canada'], 'Date': ['2019', '2019']}
title: Canada : Gross Domestic Product ( GDP ) by industry December 2019
X_Axis['Industry']: ['Real_estate_and_rental_and_leasing', 'Manufacturing', 'Mining_quarrying_and_oil_and_gas_extraction', 'Construction', 'Health_care_and_social_assistance', 'Public_administration', 'Finance_and_insurance', 'Professional_scientific_and_technical_services', 'Educational_services', 'Wholesale_trade', 'Retail_trade', 'Transportation_and_warehousing', 'Information_and_cultural_industries', 'Administrative_and_support_waste_management_and_remediation_services', 'Accommodation_and_food_services', 'Utilities', 'Agriculture_forestry_fishing_and_hunting', 'Other_services_(except_public_administration)', 'Arts_entertainment_and_recreation', 'Management_of_companies_and_enterprises']
Y_Axis['GDP', 'in', 'million', 'chained', '2012', 'Canadian', 'dollars']: ['254294', '199234', '145909', '142530', '142028', '134370', '133599', '120820', '104771', '103082', '102619', '89599', '64323', '52649', '45160', '43668', '40058', '38127', '15699', '9303']

gold: This statistic shows the Gross Domestic Product ( GDP ) of Canada in December 2019 , distinguished by major industry . In December 2019 , the construction industry of Canada contributed about 142.5 billion Canadian dollars to the total Canadian GDP .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] ( templateYLabel[0] ) of templateTitleSubject[0] in templateTitle[7] templateTitle[8] , distinguished templateTitle[5] major templateXLabel[0] . In templateTitle[7] templateTitle[8] , the templateXValue[3] templateXLabel[0] of templateTitleSubject[0] contributed about templateYValue[3] templateYLabel[1] templateYLabel[4] templateYLabel[5] to the total templateYLabel[4] templateYLabel[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] in templateTitle[3] templateTitleSubject[0] templateTitle[5] in templateTitleDate[0] . In templateXValue[0] templateXValue[0] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[5] was about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the GDP million of Domestic in Product Canada by in 2019 . In Real estate and rental and leasing , the GDP million of the Canada by was about 254294 chained 2012 Canadian .

Example 388:
titleEntities: {'Subject': ['Japan'], 'Date': ['2024']}
title: Budget balance in Japan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'balance', 'in', 'trillion', 'yen']: ['-11.67', '-10.46', '-10.24', '-11.12', '-12.25', '-16.48', '-17.64', '-17.27', '-19.8', '-20.23', '-28.96']

gold: The statistic shows the budget balance of Japan from 2014 to 2017 , with projections up until 2024 . In 2017 , the state deficit of Japan was at about 17.27 trillion yen .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the state deficit of templateTitleSubject[0] was at about 17.27 templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] is the second largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .
generated: This statistic shows the Budget balance of Japan from 2014 to 2018 , with projections up until 2024 . In 2018 , the Budget balance of Japan amounted to approximately -17.64 balance Budget . balance of Japan is the second largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .

Example 389:
titleEntities: {'Subject': ['Global'], 'Date': ['2010', '2016']}
title: Global spending on golf sponsorships 2010 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Spending', 'in', 'billion', 'U.S.', 'dollars']: ['1.82', '1.73', '1.65', '1.6', '1.51', '1.44', '1.36']

gold: This statistic shows the worldwide spending for golf sponsorship from 2010 to 2016 . In 2013 , global spendings on golf sponsorships amounted to 1.6 billion U.S. dollars .
gold_template: This statistic shows the worldwide templateYLabel[0] for templateTitle[2] sponsorship from templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateTitleSubject[0] spendings on templateTitle[2] templateTitle[3] amounted to templateYValue[3] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the trade volume of templateYLabel[0] imported to the templateTitle[0] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] templateYLabel[1] to the templateTitleSubject[0] came to templateYValue[max] thousand templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the trade volume of Spending imported to the Global sponsorships 2010 to 2016 . In 2016 , Spending billion to the Global came to 1.82 thousand U.S. dollars .

Example 390:
titleEntities: {'Subject': ['Angola'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Angola 2024*
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['3.78', '4.13', '3.33', '2.87', '1.15', '-0.27', '-1.2', '-0.15', '-2.58', '0.94', '4.82']

gold: The statistic shows the growth in real GDP in Angola from 2014 to 2018 , with projections up until 2024 . In 2018 , Angola 's real gross domestic product fell by around 1.2 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] fell by around templateYValue[4] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Angola from 2014 to 2018 , with projections up until 2024 . In 2018 , Angola 's real Gross domestic product increased by around -1.2 percent compared to the previous Year .

Example 391:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1980', '2016']}
title: U.S. public schools - average expenditure per pupil 1980 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1980']
Y_Axis['Expenditures', 'per', 'pupil', 'in', 'U.S.', 'dollars']: ['12617', '12224', '11819', '11509', '11362', '11433', '11427', '11239', '10982', '10336', '9778', '9316', '8900', '8610', '8259', '7904', '7394', '7013', '6676', '6393', '6147', '5989', '5767', '5584', '5421', '5258', '4980', '4645', '4240', '3970', '3756', '3470', '2272']

gold: This graph shows the average expenditure per pupil in daily attendance in public elementary and secondary schools in the United States from the academic year of 1980 to 2016 . An average of 12,617 U.S. dollars was spent on each pupil in public elementary and secondary schools in the academic year of 2016 .
gold_template: This graph shows the templateTitle[3] templateTitle[4] templateYLabel[1] templateYLabel[2] in daily attendance in templateTitle[1] elementary and secondary templateTitle[2] in the templateTitle[0] from the academic templateXLabel[0] of templateXValue[min] to templateXValue[max] . An templateTitle[3] of templateYValue[max] templateYLabel[3] templateYLabel[4] was spent on each templateYLabel[2] in templateTitle[1] elementary and secondary templateTitle[2] in the academic templateXLabel[0] of templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[0] million people were living in templateTitle[1] templateYLabel[1] in the templateTitle[3] .
generated: This statistic shows the Expenditures of schools average in the expenditure from 1980 to 2016 . In 2016 , approximately 12617 million people were living in public per in the average .

Example 392:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: U.S. exports of trade goods to the world 1987 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987']
Y_Axis['Exports', 'in', 'billion', 'U.S.', 'dollars']: ['1645.53', '1665.99', '1546.47', '1451.46', '1503.33', '1621.87', '1578.52', '1545.82', '1482.51', '1278.49', '1056.04', '1287.44', '1148.2', '1025.97', '901.08', '814.87', '724.77', '693.1', '729.1', '781.92', '695.8', '682.14', '689.18', '625.07', '584.74', '512.63', '465.09', '448.16', '421.73', '393.59', '363.81', '322.43', '254.12']

gold: This statistic shows the growth in the U.S. export volume of trade goods to the world from 1987 to 2019 . In 2019 , U.S. exports amounted to about 1.65 trillion U.S. dollars .
gold_template: This statistic shows the growth in the templateYLabel[2] export volume of templateTitle[2] templateTitle[3] to the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[2] templateYLabel[0] amounted to about templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] was templateYValue[max] templateYLabel[1] .
generated: This statistic shows the U.S. Exports of trade goods in the U.S. from 1987 to 2019 . In 2019 , the U.S. Exports of trade goods was 1665.99 billion .

Example 393:
titleEntities: {'Subject': ['UEFA European Championships'], 'Date': ['2016']}
title: Revenue of the UEFA European Championships 1992 to 2016
X_Axis['Year']: ['2016_France', '2012_Poland_&_Ukraine', '2008_Switzerland_&_Austria', '2004_Portugal', '2000_Belgium_&_the_Netherlands', '1996_England', '1992_Sweden']
Y_Axis['Revenue', 'in', 'million', 'euros']: ['1916.0', '1390.9', '1350.9', '855.2', '229.9', '147.3', '40.9']

gold: This statistic shows the total revenue of the four UEFA European Championships held since 1992 . The EURO 2008 generated around 1.35 billion euros in revenue .
gold_template: This statistic shows the total templateYLabel[0] of the four templateTitleSubject[0] Championships held since templateXValue[last] . The EURO templateXValue[2] generated around templateYValue[2] templateYLabel[1] templateYLabel[2] in templateYLabel[0] .

generated_template: As of the fourth quarter of templateTitleDate[0] , it was found that templateYValue[max] percent of templateTitle[4] internet users aged between 25 and 34 years . This figure was based on the assumption that the continuing declining of mobile device prices will support the increase of smartphones .
generated: As of the fourth quarter of 2016 , it was found that 1916.0 percent of 1992 internet users aged between 25 and 34 years . This figure was based on the assumption that the continuing declining of mobile device prices will support the increase of smartphones .

Example 394:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2005', '2015']}
title: Comic book reading by young people in the United Kingdom ( UK ) 2005 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2005']
Y_Axis['Share', 'of', 'respondents']: ['26.3', '28.6', '27.3', '25.1', '27.9', '26.8', '50.6']

gold: This statistic illustrates the proportion of young people who read comics outside of school in the United Kingdom from 2005 to 2015 . In 2015 , 26.3 percent of school children aged eight to 18 years reported reading comic books , which was a considerable decline from 2005 . Reading comics was less common than reading magazines , fiction and newspapers in 2014 .
gold_template: This statistic illustrates the proportion of templateTitle[4] templateTitle[5] who read comics outside of school in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of school children aged eight to 18 years reported templateTitle[2] templateTitle[0] books , which was a considerable decline from templateXValue[min] . templateTitle[2] comics was less common than templateTitle[2] magazines , fiction and newspapers in templateXValue[1] .

generated_template: In the first quarter of templateXValue[max] , it was found that templateYValue[max] percent of templateTitle[4] in the templateTitleSubject[0] templateTitleSubject[0] were living to the social platform templateXValue[0] templateXValue[0] , up from templateYValue[max] percent in the previous templateXLabel[0] . During the third quarter of templateXValue[max] , more than 30 percent of the total population in the templateTitleSubject[0] templateTitleSubject[0] was founded in recent years .
generated: In the first quarter of 2015 , it was found that 50.6 percent of young in the United Kingdom were living to the social platform 2015 , up from 50.6 percent in the previous Year . During the third quarter of 2015 , more than 30 percent of the total population in the United Kingdom was founded in recent years .

Example 395:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1999', '2013']}
title: Consumer expenditure on musical instruments in the U.S. 1999 to 2013
X_Axis['Year']: ['1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013']
Y_Axis['Expenditure', 'in', 'billion', 'U.S.', 'dollars']: ['3.93', '4.75', '4.93', '5.18', '5.06', '5.33', '5.32', '5.52', '5.39', '5.13', '4.57', '4.58', '4.67', '5.14', '5.2']

gold: This statistic shows consumer expenditure on musical instruments in the United States from 1999 to 2013 . In 2013 , consumer expenditure on musical instruments reached approximately 5.2 billion U.S. dollars .
gold_template: This statistic shows templateTitle[0] templateYLabel[0] on templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateYLabel[0] on templateTitle[2] templateTitle[3] reached approximately templateYValue[3] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: According to templateYLabel[3] Census data , the templateTitle[2] and public relations templateTitle[3] saw slightly increasing revenues in since templateTitleDate[0] . At approximately 1118 templateYLabel[2] templateYLabel[3] templateYLabel[4] , the templateTitle[3] reached a record high in templateXValue[max] . The significant drops in earnings in templateXValue[min] and templateXValue[9] can be tied to the general economic situation in the country at that time .
generated: According to dollars Census data , the musical and public relations instruments saw slightly increasing revenues in since 1999 . At approximately 1118 U.S. dollars , the instruments reached a record high in 2013 . The significant drops in earnings in 1999 and 2008 can be tied to the general economic situation in the country at that time .

Example 396:
titleEntities: {'Subject': ['Retail'], 'Date': ['2014', '2019']}
title: Retail revenue from smart wearable devices worldwide 2014 and 2019
X_Axis['Year']: ['2019', '2014']
Y_Axis['Retail', 'revenue', 'in', 'billion', 'U.S.', 'dollars']: ['53.2', '4.5']

gold: The statistic depicts the expected retail revenue from smart wearable devices worldwide in 2014 and 2019 . For 2019 , the global retail revenue from smart wearable devices is expected to reach 53.2 billion U.S. dollars .
gold_template: The statistic depicts the expected templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateXValue[min] and templateXValue[max] . For templateXValue[max] , the global templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] is expected to reach templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic gives information on the templateTitle[1] templateTitle[2] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] as of October templateTitle[5] . During this period of time , templateYValue[min] percent of Indian templateYLabel[1] templateYLabel[2] were templateXValue[0] . The majority of templateYLabel[1] templateYLabel[2] were templateXValue[last] .
generated: This statistic gives information on the revenue from of revenue billion in Retail as of October devices . During this period of time , 4.5 percent of Indian revenue billion were 2019 . The majority of revenue billion were 2014 .

Example 397:
titleEntities: {'Subject': ['China'], 'Date': ['2018']}
title: Population in China in 2018 , by region
X_Axis['Province']: ['Guangdong', 'Shandong', 'Henan', 'Sichuan', 'Jiangsu', 'Hebei', 'Hunan', 'Anhui', 'Hubei', 'Zhejiang', 'Guangxi', 'Yunnan', 'Jiangxi', 'Liaoning', 'Fujian', 'Shaanxi', 'Heilongjiang', 'Shanxi', 'Guizhou', 'Chongqing', 'Jilin', 'Gansu', 'Inner_Mongolia', 'Xinjiang', 'Shanghai', 'Beijing', 'Tianjin', 'Hainan', 'Ningxia', 'Qinghai', 'Tibet']
Y_Axis['Population', 'in', 'million', 'inhabitants']: ['113.46', '100.47', '96.05', '83.41', '80.51', '75.56', '68.99', '63.24', '59.17', '57.37', '49.26', '48.3', '46.48', '43.59', '39.41', '38.64', '37.73', '37.18', '36.0', '31.02', '27.04', '26.37', '25.34', '24.87', '24.24', '21.54', '15.6', '9.34', '6.88', '6.03', '3.44']

gold: This statistic shows the regional distribution of the population in China in 2018 . That year , approximately 75.6 million people lived in Hebei province in China . Regional differences in China China is the world 's most populous country , with an exceptional economic growth momentum .
gold_template: This statistic shows the regional distribution of the templateYLabel[0] in templateTitleSubject[0] in templateTitleDate[0] . That year , approximately templateYValue[5] templateYLabel[1] people lived in templateXValue[5] templateXLabel[0] in templateTitleSubject[0] . Regional differences in templateTitleSubject[0] is the world 's most populous country , with an exceptional economic growth momentum .

generated_template: How many templateTitle[0] templateTitle[1] templateTitle[2] does have ? In January templateTitleDate[1] , the German templateTitle[4] templateXValue[3] templateXValue[3] on the templateXLabel[0] templateTitle[3] templateTitle[4] list , templateTitle[4] 29 templateYLabel[1] to in a regular season game against the Philadelphia 76ers . If templateXValue[2] continues to produce at about his career average , he has a shot to move up even further and pass templateXValue[1] templateXValue[1] with about two more full seasons of games , and even catch templateXValue[0] templateXValue[0] , the templateXLabel[0] 's current templateTitle[3] leading scorer . templateXValue[2] ' current contract with the Los Angeles Lakers runs through the 2021/22 season .
generated: How many Population China 2018 does have ? In January 2018 , the German region Sichuan on the Province by region list , region 29 million to in a regular season game against the Philadelphia 76ers . If Henan continues to produce at about his career average , he has a shot to move up even further and pass Shandong with about two more full seasons of games , and even catch Guangdong , the Province 's current by leading scorer . Henan ' current contract with the Los Angeles Lakers runs through the 2021/22 season .

Example 398:
titleEntities: {'Subject': ['Global'], 'Date': ['2016']}
title: Global tobacco production value 2016 , by country
X_Axis['Country']: ['China_mainland', 'United_States_of_America', 'Brazil', 'India', 'Japan', 'Indonesia', 'Turkey', 'Republic_of_Korea', 'Italy', 'Mozambique']
Y_Axis['Production', 'value', 'in', 'million', 'U.S.', 'dollars']: ['3434.02', '1108.88', '873.93', '616.44', '304.35', '264.49', '232.77', '212.25', '177.31', '146.08']

gold: This statistic shows the leading tobacco producing countries worldwide in 2016 , based on gross production value . In that year , China 's produced tobacco was worth approximately 3.43 billion U.S. dollars .
gold_template: This statistic shows the leading templateTitle[1] producing countries worldwide in templateTitleDate[0] , based on gross templateYLabel[0] templateYLabel[1] . In that year , templateXValue[0] 's produced templateTitle[1] was worth approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: During the year templateTitleDate[0] , the total templateTitle[3] exports templateTitle[4] templateTitleSubject[0] to the templateXValue[0] templateXValue[0] amounted to about templateYValue[max] templateYLabel[2] of 75 cl . templateYLabel[1] . The UK is the first templateTitle[1] market for French templateTitle[3] .
generated: During the year 2016 , the total value exports 2016 Global to the China mainland amounted to about 3434.02 million of 75 cl . value . The UK is the first tobacco market for French value .

Example 399:
titleEntities: {'Subject': ['Google'], 'Date': ['2001', '2019']}
title: Google network sites : advertising revenue 2001 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['21.55', '20.01', '17.62', '15.6', '15.03', '14.54', '13.65', '12.47', '10.39', '8.79', '7.17', '6.71', '5.79', '4.16', '2.69', '1.55', '0.63', '0.1', '0.0']

gold: This statistic gives information on the advertising revenue of Google network websites from 2002 to 2019 . As of the most recently reported period , the advertising revenue of Google network sites amounted to 21.54 billion U.S. dollars . That year , Alphabet 's total Google segment revenue amounted to over 160.74 billion US dollars .
gold_template: This statistic gives information on the templateTitle[3] templateYLabel[0] of templateTitleSubject[0] templateTitle[1] websites from templateXValue[17] to templateXValue[max] . As of the most recently reported period , the templateTitle[3] templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[2] amounted to 21.54 templateYLabel[1] templateYLabel[2] templateYLabel[3] . That templateXLabel[0] , Alphabet 's total templateTitleSubject[0] segment templateYLabel[0] amounted to over 160.74 templateYLabel[1] US templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] of templateTitle[2] amounted to over templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Google Revenue of sites from 2001 to 2019 . In 2019 , the Google Revenue of sites amounted to over 21.55 billion U.S. dollars .

Example 400:
titleEntities: {'Subject': ['Arsenal FC'], 'Date': ['2009', '2020']}
title: Value of Arsenal FC 's jersey sponsorship 2009 to 2020
X_Axis['Year']: ['2019/20_(Fly_Emirates)', '2018/19_(Fly_Emirates)', '2017/18_(Fly_Emirates)', '2016/17_(Fly_Emirates)', '2015/16_(Fly_Emirates)', '2014/15_(Fly_Emirates)', '2013/14_(Fly_Emirates)', '2012/13_(Fly_Emirates)', '2011/12_(Fly_Emirates)', '2010/11_(Fly_Emirates)', '2009/10_(Fly_Emirates)']
Y_Axis['Jersey', 'sponsorship', 'revenue', 'in', 'million', 'GBP']: ['40.0', '40.0', '30.0', '30.0', '30.0', '30.0', '30.0', '5.5', '5.5', '5.5', '5.5']

gold: The statistic shows the revenue Arsenal FC generated from its jersey sponsorship deal from the 2009/10 season to the 2019/20 season . In the 2019/20 season , Arsenal FC received 40 million GBP from its jersey sponsor Fly Emirates .
gold_template: The statistic shows the templateYLabel[2] templateTitleSubject[0] generated from its templateYLabel[0] templateYLabel[1] deal from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[0] season , templateTitleSubject[0] received templateYValue[max] templateYLabel[3] templateYLabel[4] from its templateYLabel[0] sponsor Fly Emirates .

generated_template: The statistic shows the revenue templateTitleSubject[0] templateTitleSubject[0] generated from its templateTitle[4] templateYLabel[1] deal from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[0] templateTitleSubject[0] templateTitleSubject[0] season received templateYValue[max] templateYLabel[2] templateYLabel[3] from its templateTitle[4] sponsor Chevrolet .
generated: The statistic shows the revenue Arsenal FC generated from its jersey sponsorship deal from the 2009/10 (Fly Emirates) season to the 2019/20 (Fly Emirates) season . In the 2019/20 (Fly Emirates) Arsenal FC season received 40.0 revenue million from its jersey sponsor Chevrolet .

Example 401:
titleEntities: {'Subject': ['United States'], 'Date': ['2017']}
title: Mechanical engineering in the United States - market size 2017
X_Axis['Year']: ['2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['340.0', '331.37', '353.55', '293.19', '316.59', '347.39', '394.2', '370.79', '380.65', '423.76', '390.5', '401.59']

gold: The statistic portrays the revenue of the mechanical engineering industry in the United States from 2006 through 2017 . In 2017 , the U.S. market for mechanical engineering was sized at around 401.6 billion U.S. dollars ( or about 326 billion euros ) .
gold_template: The statistic portrays the templateYLabel[0] of the templateTitle[0] templateTitle[1] industry in the templateTitleSubject[0] from templateXValue[min] through templateXValue[max] . In templateXValue[max] , the templateYLabel[2] templateTitle[4] for templateTitle[0] templateTitle[1] was sized at around templateYValue[11] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( or about 326 templateYLabel[1] euros ) .

generated_template: templateTitleSubject[0] had a templateYLabel[0] of templateYValue[max] templateYLabel[2] in operation around the world as of templateXValue[max] , up from templateYValue[min] in templateXValue[min] . templateTitleSubject[0] , an amalgamation of Louis Vuitton , Moët , and Hennessy , is the top selling luxury personal goods companies in the world . The company operates globally selling a diverse range of products .
generated: United States had a Revenue of 423.76 U.S. in operation around the world as of 2017 , up from 293.19 in 2006 . United States , an amalgamation of Louis Vuitton , Moët , and Hennessy , is the top selling luxury personal goods companies in the world . The company operates globally selling a diverse range of products .

Example 402:
titleEntities: {'Subject': ['Rome'], 'Date': ['2011', '2019']}
title: Hotel occupancy rate in Rome 2011 to 2019
X_Axis['Year']: ['2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019']
Y_Axis['Occupancy', 'rate']: ['67', '66', '67', '69', '69', '70', '70', '70', '71']

gold: This statistic illustrates the hotel occupancy rate in Rome from 2011 to 2019 . The occupancy rate of hotels in the city was measured at 70 percent in 2017 . Rates are forecast to remain stable in 2018 and rise by one percentage point in 2019 .
gold_template: This statistic illustrates the templateTitle[0] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of hotels in the city was measured at templateYValue[5] percent in templateXValue[6] . Rates are forecast to remain stable in templateXValue[7] and rise by one percentage point in templateXValue[max] .

generated_template: The statistic shows the estimated percentage of templateYLabel[0] templateYLabel[1] a templateTitle[0] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[0] to templateXValue[max] . Since templateXValue[4] , the share of household templateYLabel[1] templateYLabel[2] in the templateTitleSubject[1] has increased , with an estimated templateYValue[0] percent templateYLabel[1] one in templateXValue[7] . However , the share of household templateYLabel[1] templateYLabel[2] decreased to templateYValue[min] percent in templateXValue[max] .
generated: The statistic shows the estimated percentage of Occupancy rate a Hotel in the Rome ( Rome ) from 2011 to 2019 . Since 2015 , the share of household rate in the Rome has increased , with an estimated 67 percent rate one in 2018 . However , the share of household rate decreased to 66 percent in 2019 .

Example 403:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2018']}
title: Music industry employment in the United Kingdom ( UK ) 2018 , by sector
X_Axis['Industry']: ['Music_creators', 'Music_retail', 'Recorded_music', 'Music_representatives', 'Music_publishing', 'Live_music']
Y_Axis['Number', 'of', 'workers']: ['139352', '11688', '5379', '2624', '1363', '30529']

gold: This statistic shows employment in the UK music industry in 2018 , by thematic grouping . In 2018 , it was estimated that there were over 30 thousand workers in live music . In the same year , there were 139 thousand people working as music creators .
gold_template: This statistic shows templateTitle[2] in the templateTitleSubject[1] templateXValue[0] templateXLabel[0] in templateTitleDate[0] , templateTitle[7] thematic grouping . In templateTitleDate[0] , it was estimated that there were over 30 thousand templateYLabel[1] in templateXValue[last] templateXValue[0] . In the same year , there were templateYValue[max] thousand people working as templateXValue[0] .

generated_template: This graph shows the templateYLabel[0] of templateTitle[0] templateTitleSubject[0] templateTitleSubject[0] ( PACs ) active in the country in templateTitleDate[0] , distinguished templateTitle[5] templateXLabel[0] templateXLabel[1] . In templateTitleDate[0] , there were templateTitle[3] templateYValue[1] active templateXValue[1] templateTitle[0] active templateTitleSubject[0] in the country .
generated: This graph shows the Number of Music United Kingdom ( PACs ) active in the country in 2018 , distinguished UK Industry . In 2018 , there were United 11688 active Music retail Music active United Kingdom in the country .

Example 404:
titleEntities: {'Subject': ['Google'], 'Date': ['2019', '2019']}
title: Google Play : number of available apps as of Q4 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15"]
Y_Axis['Number', 'of', 'available', 'apps']: ['2570520', '2469894', '2327628', '2134302', '1977776', '2108450', '2977833', '3849865', '3662276', '3364880', '3172310', '2956763', '2811106', '2781508', '2539526', '2294798', '2012040', '1811532', '1670113', '1605359']

gold: This statistic gives information on the number of available apps in the Google Play app store . As of the fourth quarter of 2019 , over 2.57 million mobile apps were available , representing a 4.07 percent increase compared to the previous quarter .
gold_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[0] templateTitleSubject[0] app store . As of the fourth templateXLabel[0] of templateTitleDate[0] , over templateYValue[0] million mobile templateYLabel[2] were templateYLabel[1] , representing a 4.07 percent increase compared to the previous templateXLabel[0] .

generated_template: As of the fourth templateXLabel[0] of templateTitleDate[1] , templateYValue[max] mobile templateYLabel[2] were templateYLabel[1] in the templateTitle[0] templateTitleSubject[0] . This represents a 0.4 percent growth in templateYLabel[1] templateYLabel[2] compared to the previous templateXLabel[0] . The templateTitle[0] templateTitleSubject[0] does not only offer Android templateYLabel[2] but also templateYLabel[2] specifically optimized for its range of Kindle Fire tablets as well as the Fire TV and Fire Phone .
generated: As of the fourth Quarter of 2019 , 3849865 mobile apps were available in the Google . This represents a 0.4 percent growth in available apps compared to the previous Quarter . The Google does not only offer Android apps but also apps specifically optimized for its range of Kindle Fire tablets as well as the Fire TV and Fire Phone .

Example 405:
titleEntities: {'Subject': ['Lithuania'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Lithuania 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.29', '2.3', '2.39', '2.46', '2.72', '3.41', '3.49', '4.14', '2.35', '2.02', '3.54']

gold: The statistic shows the growth in real GDP in Lithuania from 2014 to 2018 , with projections up until 2024 . In 2018 , Lithuania 's real gross domestic product grew by around 3.49 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Lithuania from 2014 to 2018 , with projections up until 2024 . In 2018 , Lithuania 's real Gross domestic product increased by around 3.49 percent compared to the previous Year .

Example 406:
titleEntities: {'Subject': ['Wisconsin'], 'Date': ['1990', '2018']}
title: Wisconsin - 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']: ['62629', '63451', '59817', '55425', '58080', '55258', '53079', '52058', '50351', '51237', '51200', '51277', '51692', '44650', '45732', '46269', '45903', '45346', '45088', '45667', '41327', '39595', '40001', '40955', '35388', '31766', '33308', '31133', '30711']

gold: This statistic shows the median household income in Wisconsin from 1990 to 2018 . In 2018 , the median household income in Wisconsin amounted to 62,629 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[0] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] parent families with a female householder and no spouse present in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[28] percent of all templateTitleSubject[0] families with a templateTitle[3] mother in the templateTitle[5] lived below the templateYLabel[0] level . In templateXValue[max] , that figure was at templateYValue[min] percent .
generated: This statistic shows the Household income of Wisconsin income parent families with a female householder and no spouse present in the Wisconsin from 1990 to 2018 . In 1990 , 30711 percent of all Wisconsin families with a income mother in the 2018 lived below the Household level . In 2018 , that figure was at 30711 percent .

Example 407:
titleEntities: {'Subject': ['Royal Dutch Shell'], 'Date': ['2010', '2018']}
title: Royal Dutch Shell 's exploration costs 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Costs', 'in', 'million', 'U.S.', 'dollars']: ['208', '141', '494', '1290', '1439', '5278', '3104', '2266', '2036']

gold: This statistic shows Royal Dutch Shell 's exploration costs from 2010 through to 2018 . In 2018 , the company spent some 208 million U.S. dollars for such purposes . Royal Dutch Shell is one of the top oil and gas companies worldwide , operating in every segment of the oil and gas industry .
gold_template: This statistic shows templateTitleSubject[0] Shell templateTitle[3] templateTitle[4] templateYLabel[0] from templateXValue[min] through to templateXValue[max] . In templateXValue[max] , the company spent some templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] for such purposes . templateTitleSubject[0] Shell is one of the top oil and gas companies worldwide , operating in every segment of the oil and gas industry .

generated_template: This statistic depicts the templateTitle[0] templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] employed a templateTitle[0] of templateYValue[max] people throughout the world . templateTitleSubject[0] is the world 's largest cosmetics and beauty company , concentrating on hair color , skin care , sun protection , make-up , perfumes , and hair care .
generated: This statistic depicts the Royal Costs of million of Royal Dutch Shell exploration from 2010 to 2018 . In 2018 , Royal Dutch Shell employed a Royal of 5278 people throughout the world . Royal Dutch Shell is the world 's largest cosmetics and beauty company , concentrating on hair color , skin care , sun protection , make-up , perfumes , and hair care .

Example 408:
titleEntities: {'Subject': ['Pakistan'], 'Date': ['2024']}
title: Total population of Pakistan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['224.66', '220.53', '216.47', '212.48', '208.57', '204.73', '200.96', '197.26', '193.56', '189.87', '186.19']

gold: This statistic shows the total population of Pakistan from 2014 to 2018 with forecasts up to 2024 . In 2018 , the total population of Pakistan amounted to approximately 200.96 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] with forecasts up to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateYLabel[1] templateYLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to around templateYValue[7] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] is the second largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .
generated: The statistic shows the Total population of Pakistan from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Pakistan amounted to around 197.26 millions Inhabitants . population of Pakistan is the second largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .

Example 409:
titleEntities: {'Subject': ['Global'], 'Date': ['2017', '2022']}
title: Global sexual wellness market size 2017 to 2022
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Market', 'size', 'in', 'billion', 'U.S.', 'dollars']: ['37.19', '35.07', '33.08', '31.2', '29.42', '27.75', '26.17']

gold: This timeline depicts the size of the sexual wellness market worldwide from 2017 to 2022 . In 2017 , the size of the global sexual wellness market was over 26 billion U.S. dollars , and is forecasted to reach to about 37.2 billion U.S. dollars by 2025 .
gold_template: This timeline depicts the templateYLabel[1] of the templateTitle[1] templateTitle[2] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[1] . In templateXValue[min] , the templateYLabel[1] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] was over templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] , and is forecasted to reach to about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] by 2025 .

generated_template: The statistic depicts the total number of templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . It is expected that the number of templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] will reach templateYValue[4] templateYLabel[2] in templateXValue[max] .
generated: The statistic depicts the total number of Market size in Global from 2017 to 2023 . It is expected that the number of Market size in Global will reach 29.42 billion in 2023 .

Example 410:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Canada : number of individual firearms licenses held , by province or territory 2018
X_Axis['Month']: ['Ontario', 'Quebec', 'Alberta', 'British_Columbia', 'Saskatchewan', 'Manitoba', 'Newfoundland_and_Labrador', 'Nova_Scotia', 'New_Brunswick', 'Yukon', 'Prince_Edward_Island', 'Northwest_Territories', 'Nunavut']
Y_Axis['Number', 'of', 'firearms', 'licenses']: ['616489', '500058', '316791', '301775', '110573', '91107', '76802', '76180', '70111', '7711', '6363', '5955', '3912']

gold: This graph shows the number of individual firearms licenses held in Canada in 2018 , by province or territory . In Ontario , 616,489 firearms licenses were held in 2018 .
gold_template: This graph shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] templateYLabel[2] templateTitle[5] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] templateTitle[7] or templateTitle[8] . In templateXValue[0] , templateYValue[max] templateYLabel[1] templateYLabel[2] were templateTitle[5] in templateTitleDate[0] .

generated_template: Global Canadian provinces , templateXValue[0] had the largest templateYLabel[0] of templateTitle[1] with around 500 templateYLabel[1] , as of templateTitleDate[0] . templateTitleSubject[0] has a publicly funded health care system based on a system of taxation , fees and private funding . Current reports estimate that health care expenditures account for over 11 percent of templateTitleSubject[0] 's gross domestic product ( GDP ) .
generated: Global Canadian provinces , Ontario had the largest Number of number with around 500 firearms , as of 2018 . Canada has a publicly funded health care system based on a system of taxation , fees and private funding . Current reports estimate that health care expenditures account for over 11 percent of Canada 's gross domestic product ( GDP ) .

Example 411:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2018']}
title: Unemployment rate in Belgium 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Unemployment', 'rate']: ['6', '7.1', '7.9', '8.6', '8.6', '8.5', '7.6', '7.2', '8.4', '8', '7']

gold: In 2019 , the unemployment rate in Belgium was six percent . This is a decrease in comparison to the previous year , and the lowest unemployment rate in the last ten years . Unemployment peaked in 2014 and 2015 , at 8.6 percent .
gold_template: In 2019 , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[min] percent . This is a decrease in comparison to the previous templateXLabel[0] , and the lowest templateYLabel[0] templateYLabel[1] in the last ten years . templateYLabel[0] peaked in templateXValue[4] and templateXValue[3] , at templateYValue[max] percent .

generated_template: The templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] decreased since templateXValue[6] , when it was templateYValue[5] percent , down to templateYValue[min] percent in templateXValue[1] . However , the employment templateYLabel[1] increased in templateXValue[max] , when it was measured at templateYValue[0] percent . The templateYLabel[0] templateYLabel[1] among men has been higher than for women for many years , but in templateXValue[max] it was templateYValue[2] percent , which was 0.2 percentage point lower than the templateYValue[3] percent among women .
generated: The Unemployment rate in Belgium decreased since 2012 , when it was 8.5 percent , down to 6 percent in 2017 . However , the employment rate increased in 2018 , when it was measured at 6 percent . The Unemployment rate among men has been higher than for women for many years , but in 2018 it was 7.9 percent , which was 0.2 percentage point lower than the 8.6 percent among women .

Example 412:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. user reasons for using online dating sites or apps 2017
X_Axis['Response']: ['To_meet_people_who_share_my_interests_or_hobbies', 'To_meet_people_who_share_my_beliefs_or_values', 'To_find_someone_for_a_long-term_relationship_or_marriage', 'I_have_a_schedule_that_makes_it_hard_to_meet_interesting_people_in_other_ways', 'To_meet_people_who_just_want_to_have_fun_without_being_in_a_serious_relationship', 'None_of_the_above']
Y_Axis['Share', 'of', 'respondents']: ['61', '44', '42', '21', '26', '7']

gold: This statistic presents the reasons why users in the United States use online dating sites or apps . During the April 2017 survey , 61 percent of responding current or former dating website or app users said they used dating websites and apps to meet people who share their interests or hobbies .
gold_template: This statistic presents the templateTitle[2] why users in the templateTitle[0] use templateTitle[5] templateTitle[6] templateTitle[7] or templateTitle[8] . During the April templateTitleDate[0] survey , templateYValue[max] percent of responding current or former templateTitle[6] website or app users said they used templateTitle[6] websites and templateTitle[8] to templateXValue[0] who templateXValue[0] their templateXValue[0] or templateXValue[0] .

generated_template: As of templateTitleDate[0] , right-wing American news website templateTitle[0] was rated as templateXValue[2] at templateXValue[2] templateXValue[2] by templateYValue[2] percent of templateYLabel[1] from a survey of over two thousand templateTitleSubject[0] adults . Additionally , over half of templateYLabel[1] had either templateXValue[0] templateXValue[0] of the publication or had templateXValue[1] templateXValue[1] about templateTitle[0] 's templateTitle[1] . Conservative view of news media While right-wing websites and online publications have grown in number and popularity in recent years , Fox News remains the largest and perhaps most influential conservative news source in the templateTitle[2] .
generated: As of 2017 , right-wing American news website U.S. was rated as To find someone for a long-term relationship or marriage at To find someone for a long-term relationship or marriage by 42 percent of respondents from a survey of over two thousand U.S. adults . Additionally , over half of respondents had either To meet people who share my interests or hobbies of the publication or had To meet people who share my beliefs or values about U.S. 's user . Conservative view of news media While right-wing websites and online publications have grown in number and popularity in recent years , Fox News remains the largest and perhaps most influential conservative news source in the reasons .

Example 413:
titleEntities: {'Subject': ['Share'], 'Date': ['2012']}
title: Share of global seeds market value by country 2012
X_Axis['Country']: ['United_States', 'China', 'France', 'Brazil', 'Canada', 'India', 'Japan', 'Germany', 'Argentina', 'Italy']
Y_Axis['Market', 'value', 'share']: ['26.71', '22.15', '6.23', '5.84', '4.72', '4.45', '3.01', '2.6', '2.2', '1.71']

gold: This graph depicts the shares of the global seeds market value in 2012 , by country . The United States and China both held more than 20 percent of the market value worldwide in that year .
gold_template: This graph depicts the shares of the templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . The templateXValue[0] and templateXValue[1] both held more than 20 percent of the templateYLabel[0] templateYLabel[1] worldwide in that year .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from templateTitleDate[0] to templateTitleDate[1] . In the measured period , templateXValue[0] had the biggest templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] of templateYLabel[6] templateYLabel[1] templateYLabel[8] templateYLabel[9] - templateYValue[max] percent .
generated: This statistic shows the Share global seeds market value by from 2012 to 2012 . In the measured period , United States had the biggest Market of value share of share value share - 26.71 percent .

Example 414:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2018']}
title: Expenditure on beer in the United Kingdom 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Expenditure', 'in', 'million', 'GBP']: ['5631', '5091', '4640', '4571', '4573', '4435', '4188', '4119', '3994', '4142', '3677', '3802', '3976', '3714']

gold: In 2018 , consumers spent 5.6 billion British pounds on beer in the United Kingdom ( UK ) . This is the highest consumer spending recorded in the past thirteen years and the second time spending surpassed five billion pounds . Spending has generally grown since 2010 .
gold_template: In templateXValue[max] , consumers spent templateYValue[max] templateYLabel[1] British pounds on templateTitle[1] in the templateTitleSubject[0] ( UK ) . This is the highest consumer spending recorded in the past thirteen years and the second time spending surpassed templateYValue[1] templateYLabel[1] pounds . Spending has generally grown since templateXValue[8] .

generated_template: This statistic shows the total templateTitle[0] templateYLabel[0] on templateTitle[2] and other templateTitle[4] for pets in the United Kingdom from templateXValue[min] to templateXValue[max] , based on templateTitle[7] . In templateXValue[max] , households purchased over templateYValue[max] templateYLabel[1] British pounds worth of templateTitle[2] and other templateTitle[4] for their pets .
generated: This statistic shows the total Expenditure on United and other 2005 for pets in the United Kingdom from 2005 to 2018 , based on 2018 . In 2018 , households purchased over 5631 million British pounds worth of United and other 2005 for their pets .

Example 415:
titleEntities: {'Subject': ['European'], 'Date': ['2005', '2018']}
title: European ATM numbers 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Total', 'number', 'of', 'ATMs']: ['406532', '413414', '420200', '411243', '409136', '407001', '412799', '403996', '398040', '391175', '383951', '362244', '335083', '324797']

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

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] in the templateTitle[4] of templateTitleSubject[1] from templateXValue[min] to templateXValue[max] GE templateTitleSubject[1] is a division of GE Technology Infrastructure , which is itself a division of templateTitleSubject[0] templateTitleSubject[0] ( GE ) . In templateXValue[max] , the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[1] templateTitle[4] generated a templateYLabel[0] of templateYValue[12] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Total of European in the 2018 of European from 2005 to 2018 GE European is a division of GE Technology Infrastructure , which is itself a division of European ( GE ) . In 2018 , the European 2018 generated a Total of 335083 number ATMs .

Example 416:
titleEntities: {'Subject': ['Bosnia-Herzegovina'], 'Date': ['2019']}
title: Unemployment rate in Bosnia-Herzegovina 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']: ['21.22', '20.84', '20.47', '25.41', '27.65', '27.52', '27.45', '28.01', '27.58', '27.31', '24.07', '23.41', '28.98', '31.11', '30.49', '29.87', '29.03', '28.22', '27.13', '26.19', '25.31']

gold: This statistic shows the unemployment rate in Bosnia & Herzegovina from 1999 to 2019 . In 2019 , the unemployment rate in Bosnia & Herzegovina was at 21.22 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in Bosnia & Herzegovina from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in Bosnia & Herzegovina was at templateYValue[0] percent .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent of the total labor force . The templateYLabel[0] templateYLabel[1] is the percentage of a country 's labor force that are without jobs but are available to work and actively seeking employment . templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] is above the worldwide templateYLabel[0] templateYLabel[1] , and compared to other Sub-Saharan African countries and other regions , templateTitleSubject[0] has a relatively average templateYLabel[1] of templateYLabel[0] .
generated: In 2019 , the Unemployment rate in Bosnia-Herzegovina was at approximately 21.22 percent of the total labor force . The Unemployment rate is the percentage of a country 's labor force that are without jobs but are available to work and actively seeking employment . Bosnia-Herzegovina 's Unemployment rate is above the worldwide Unemployment rate , and compared to other Sub-Saharan African countries and other regions , Bosnia-Herzegovina has a relatively average rate of Unemployment .

Example 417:
titleEntities: {'Subject': ['North Macedonia'], 'Date': ['2024']}
title: Inflation rate in North Macedonia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2.2', '2.2', '2.2', '2.2', '1.7', '1.26', '1.46', '1.35', '-0.24', '-0.3', '-0.28', '2.79', '3.32', '3.91', '1.51', '-0.74', '7.52', '2.79', '3.27', '-0.57', '-0.66', '1.11', '2.31', '5.2', '6.61', '-1.28', '0.54', '1.29', '2.47', '16.37', '126.58']

gold: This statistic shows the average inflation rate in North Macedonia from 1994 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in North Macedonia amounted to about 1.46 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the estimated average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in North Macedonia from 1994 to 2024 . In 2018 , the estimated average Inflation rate in North Macedonia amounted to about 1.46 percent compared to the previous Year .

Example 418:
titleEntities: {'Subject': ['Bing'], 'Date': ['2017']}
title: Bing global search market share 2017 , by country
X_Axis['Country']: ['Worldwide', 'United_States', 'Brazil', 'Canada', 'Latin_America', 'Asia_Pacific', 'Australia', 'Hong_Kong', 'India', 'Indonesia', 'Malaysia', 'New_Zealand', 'Philippines', 'Singapore', 'Taiwan', 'Vietnam', 'Europe', 'Austria', 'Belgium', 'Denmark', 'Finland', 'France', 'Germany', 'Ireland', 'Italy', 'Netherlands', 'Norway', 'Spain', 'Sweden', 'Switzerland', 'United_Kingdom']
Y_Axis['Share', 'of', 'search', 'traffic']: ['9', '33', '3', '17', '5', '3', '12', '19', '7', '7', '8', '6', '5', '8', '24', '8', '9', '12', '12', '9', '7', '19', '12', '8', '9', '9', '17', '9', '12', '12', '26']

gold: This statistic shows the worldwide search market share of Bing as of August 2017 in leading online markets . During the measured period , Bing accounted for 17 percent of search traffic in Canada . The Microsoft-owned platform accounted for nine percent of search traffic worldwide .
gold_template: This statistic shows the templateXValue[0] templateYLabel[1] templateTitle[3] templateYLabel[0] of templateTitleSubject[0] as of August templateTitleDate[0] in leading online markets . During the measured period , templateTitleSubject[0] accounted for templateYValue[3] percent of templateYLabel[1] templateYLabel[2] in templateXValue[3] . The Microsoft-owned platform accounted for templateYValue[0] percent of templateYLabel[1] templateYLabel[2] templateXValue[0] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateTitle[4] templateYLabel[5] in the European Union in templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[1] was templateYValue[2] percent .
generated: The statistic shows the Share of search traffic share traffic in the European Union in 2017 . The Share search traffic in United States was 3 percent .

Example 419:
titleEntities: {'Subject': ['Bahrain'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Bahrain 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['46.4', '44.28', '42.4', '40.76', '39.31', '38.18', '37.75', '35.43', '32.25', '31.13', '33.39', '32.54', '30.75', '28.78', '25.71', '22.94', '25.71', '21.73', '18.51', '15.97', '13.15', '11.08', '9.59', '9.19', '9.06', '7.58', '7.0', '7.32', '7.06', '6.79', '6.41', '5.99', '5.44', '5.21', '4.97', '4.68', '4.48', '3.63', '3.35', '4.28', '4.53']

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

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

Example 420:
titleEntities: {'Subject': ['Average'], 'Date': ['2014', '2025']}
title: Average prices for zinc worldwide from 2014 to 2025
X_Axis['Year']: ['2025', '2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Price', 'in', 'nominal', 'U.S.', 'dollars', 'per', 'metric', 'ton']: ['2475', '2470', '2465', '2460', '2455', '2450', '2570', '2922', '2891', '2090', '1932', '2161']

gold: This statistic depicts the average annual prices for zinc from 2014 through 2025* . In 2018 , the average price for zinc stood at 2,922 nominal U.S. dollars per metric ton .
gold_template: This statistic depicts the templateTitleSubject[0] annual templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[5] templateXValue[min] through 2025* . In templateXValue[7] , the templateTitleSubject[0] templateYLabel[0] templateTitle[2] templateTitle[3] stood at templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] .

generated_template: This statistic depicts the templateTitleSubject[0] annual templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[5] templateXValue[min] through 2025* . In templateXValue[7] , the templateTitleSubject[0] templateYLabel[0] templateTitle[2] templateTitle[3] stood at templateYValue[7] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic depicts the Average annual prices for zinc from 2014 through 2025* . In 2018 , the Average Price for zinc stood at 2922 nominal U.S. dollars per metric .

Example 421:
titleEntities: {'Subject': ['Georgia'], 'Date': ['1992', '2018']}
title: Georgia - unemployment rate 1992 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92"]
Y_Axis['Unemployment', 'rate']: ['3.9', '4.7', '5.4', '6', '7.1', '8.2', '9.2', '10.2', '10.5', '9.9', '6.2', '4.5', '4.7', '5.3', '4.8', '4.8', '5', '4', '3.6', '3.9', '4.3', '4.6', '4.7', '4.8', '5.2', '6', '6.9']

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

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[0] percent . You can access the monthly templateYLabel[0] templateYLabel[1] for the country here .
generated: This statistic displays the Unemployment rate in Georgia from 1992 to 2018 . In 2018 , Unemployment rate in Georgia was 3.9 percent . You can access the monthly Unemployment rate for the country here .

Example 422:
titleEntities: {'Subject': ['European'], 'Date': ['2020']}
title: Revenue of the top European soccer leagues ( Big Five ) 2006 to 2020
X_Axis['Year']: ['2019/20', '2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['17.95', '17.0', '15.59', '14.66', '13.42', '12.1', '11.3', '9.8', '9.3', '8.6', '8.4', '7.9', '7.7', '7.16']

gold: The statistic shows the total revenue of the top European professional soccer leagues ( Big Five ) from 2006/07 to 2019/20 . In the 2017/18 season , the total revenue of the `` Big Five '' leagues was estimated at 15.59 billion euros .
gold_template: The statistic shows the total templateYLabel[0] of the templateTitle[1] templateTitleSubject[0] professional templateTitle[3] templateTitle[4] ( templateTitle[5] Five ) from templateXValue[last] to templateXValue[0] . In the templateXValue[2] season , the total templateYLabel[0] of the `` templateTitle[5] Five '' templateTitle[4] was estimated at templateYValue[2] templateYLabel[1] templateYLabel[2] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[1] in the templateXValue[0] season .
generated: The statistic shows the Revenue of the European from the 2006/07 season to the 2019/20 season . The Revenue of the European amounted to 17.95 billion in the 2019/20 season .

Example 423:
titleEntities: {'Subject': ['Jordan'], 'Date': ['2015']}
title: Median age of the population in Jordan 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']: ['34.9', '33.1', '31.2', '29.3', '27.5', '25.6', '23.8', '22.1', '21.3', '20.6', '19.6', '19.1', '16.8', '16.3', '15.5', '16.4', '17.1', '17.4', '18.0', '17.6', '17.2']

gold: This statistic shows the median age of the population in Jordan 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 depicts the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of a templateTitle[2] is an index that divides the templateTitle[2] into two equal groups : half of the templateTitle[2] is older than the templateYLabel[0] templateYLabel[1] and the other half younger . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] 's templateTitle[2] was templateYValue[7] templateYLabel[2] .
generated: The statistic depicts the Median age in Jordan from 1950 to 2050 . The Median age of a population is an index that divides the population into two equal groups : half of the population is older than the Median age and the other half younger . In 2015 , the Median age of Jordan 's population was 22.1 years .

Example 424:
titleEntities: {'Subject': ['Saudi Arabia'], 'Date': ['2012', '2028']}
title: Total contribution of travel and tourism to GDP in Saudi Arabia 2012 to 2028
X_Axis['Year']: ['2028', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Value', 'in', 'billion', 'Saudi', 'Riyal']: ['573.1', '258.1', '240.9', '228.1', '232.3', '215.4', '21.7', '209.2']

gold: This statistic describes the total economic contribution of tourism and travel to Saudi Arabia from 2012 to 2017 , with an estimate for 2018 and a forecast for 2028 . According to forecasts , the total economic contribution of tourism and travel to the GDP of Saudi Arabia in 2028 will be around 573 billion Saudi Riyal in 2028 .
gold_template: This statistic describes the templateTitle[0] economic templateTitle[1] of templateTitle[3] and templateTitle[2] to templateYLabel[2] templateTitleSubject[0] from templateXValue[min] to templateXValue[2] , with an estimate for templateXValue[1] and a forecast for templateXValue[max] . According to forecasts , the templateTitle[0] economic templateTitle[1] of templateTitle[3] and templateTitle[2] to the templateTitle[4] of templateYLabel[2] templateTitleSubject[0] in templateXValue[max] will be around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] .

generated_template: In templateXValue[max] , the annual templateYLabel[0] of visitor exports is expected to reach templateYValue[max] templateYLabel[1] templateYLabel[2] . The spending of templateTitle[0] tourists in the country slightly decreased in templateXValue[3] , after the terrorist attacks which took place in Paris and Nice respectively in templateXValue[4] and templateXValue[3] . However , in templateXValue[2] , templateTitleSubject[0] had the fifth largest templateTitle[0] tourism templateTitle[3] worldwide .
generated: In 2028 , the annual Value of visitor exports is expected to reach 573.1 billion Saudi . The spending of Total tourists in the country slightly decreased in 2016 , after the terrorist attacks which took place in Paris and Nice respectively in 2015 and 2016 . However , in 2017 , Saudi Arabia had the fifth largest Total tourism worldwide .

Example 425:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Revenue of the fastest-growing private security companies in the U.S. 2018
X_Axis['Company']: ['Netizen', 'Cisoshare', 'Exabeam', 'KnowBe4', 'Transcend_Security_Solutions', 'Perimeter_Security_Partners', 'Tomahawk_Strategic_Solutions', 'Kisi_Security', 'Aysco_Technology_Integration', 'Kenna_Security', 'Point3_Security', 'BOS_Security', 'Satelles', 'Skynet_Integrations', 'Home_View_Technologies']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['6.3', '3.9', '38.8', '72.3', '8.3', '15.3', '2.8', '2.8', '20.4', '13.2', '5.8', '7.3', '5.0', '2.0', '16.5']

gold: This statistic shows the revenue of the fastest-growing private security companies in the United States in 2018 . The fastest growing security company in the United States was Netizen , which generated revenue of 6.3 million U.S. dollars in 2018 .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] templateXValue[4] templateTitle[4] in the templateTitle[5] in templateTitleDate[0] . The fastest growing templateXValue[4] templateXLabel[0] in the templateTitle[5] was templateXValue[0] , which generated templateYLabel[0] of templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitleDate[0] .

generated_template: The statistic presents the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] in templateTitleDate[0] . In that year , the templateYLabel[0] of the was the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[5] with a templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic presents the Revenue of the Revenue fastest-growing private security companies in the U.S. in 2018 . In that year , the Revenue of the was the Revenue fastest-growing private security in the U.S. with a Revenue of 72.3 million U.S. dollars .

Example 426:
titleEntities: {'Subject': ['Norway'], 'Date': ['2024']}
title: Norway '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']: ['8.6', '8.21', '7.84', '7.75', '7.82', '7.57', '7.25', '4.92', '4.04', '6.07', '8.77']

gold: The statistic shows Norway '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 , Norway 's surplus amounted to around 7.25 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] surplus amounted to around templateYValue[6] percent of templateYLabel[3] .

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a trade deficit . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] trade deficit amounted to around 7.23 percent of templateYLabel[3] .
generated: The statistic shows Norway '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 trade deficit . In 2018 , Norway 's trade deficit amounted to around 7.23 percent of GDP .

Example 427:
titleEntities: {'Subject': ['U.S'], 'Date': ['1990', '2010']}
title: U.S active duty military suicides 1990 to 2010
X_Axis['Year']: ['2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Number', 'of', 'suicides']: ['289', '302', '259', '211', '213', '182', '197', '190', '174', '153', '153', '150', '165', '159', '188', '250', '232', '236', '238', '256', '232']

gold: This graph shows the total number of self-inflicted deaths of active military personnel in the United States from 1990 to 2010 . In 2010 , 289 active duty U.S. military personnel committed suicide .
gold_template: This graph shows the total templateYLabel[0] of self-inflicted deaths of templateTitle[1] templateTitle[3] personnel in the country from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] templateTitle[1] templateTitle[2] U.S. templateTitle[3] personnel committed suicide .

generated_template: In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] made up almost templateYValue[0] percent of templateTitle[0] templateYLabel[1] in the templateTitleSubject[0] . This is probably due to the templateTitle[1] insurance system . In the templateTitleSubject[0] , every resident or employee is obliged to take a basic templateTitle[1] insurance to cover medical costs from , for example , visits to a general practitioner or the hospital .
generated: In 2010 , the U.S active duty made up almost 289 percent of U.S suicides in the U.S . This is probably due to the active insurance system . In the U.S , every resident or employee is obliged to take a basic active insurance to cover medical costs from , for example , visits to a general practitioner or the hospital .

Example 428:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2012', '2014']}
title: PC online games revenue in Malaysia 2012 to 2014
X_Axis['Year']: ['2014', '2013', '2012']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['66.5', '54.1', '44.1']

gold: The statistic presents a forecast of the PC online games revenue in Malaysia from 2012 to 2014 . It was estimated that the 2014 PC online games revenue for Malaysia would be 66.5 million U.S. dollars .
gold_template: The statistic presents a forecast of the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . It was estimated that the templateXValue[max] templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] for templateTitleSubject[0] would be templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic illustrates the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] company from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the health templateTitle[1] chain is expected to generate a templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . In templateXValue[1] , the templateTitleSubject[0] based company operated 307 clubs .
generated: The statistic illustrates the Revenue of the Malaysia company from 2012 to 2014 . In 2014 , the health online chain is expected to generate a Revenue of 66.5 million U.S. dollars . In 2013 , the Malaysia based company operated 307 clubs .

Example 429:
titleEntities: {'Subject': ['LINE'], 'Date': ['2014', '2016']}
title: LINE : number of monthly active users 2014 to 2016
X_Axis['Quarter']: ["Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14"]
Y_Axis['Number', 'of', 'monthly', 'users', 'in', 'millions']: ['217.0', '220.0', '220.0', '218.4', '215.0', '212.0', '211.0', '205.0', '190.0', '179.0', '170.0']

gold: This statistic shows a timeline with the amount of monthly active LINE users worldwide as of the fourth quarter of 2016 . As of that period , the mobile messaging app announced more than 217 million monthly active users . In October 2014 , LINE had also reported 560 million registered users worldwide .
gold_template: This statistic shows a timeline with the amount of templateYLabel[1] templateTitle[3] templateTitleSubject[0] templateYLabel[2] worldwide as of the fourth templateXLabel[0] of templateTitleDate[1] . As of that period , the mobile messaging app announced more than templateYValue[0] templateYLabel[3] templateYLabel[1] templateTitle[3] templateYLabel[2] . In October templateTitleDate[0] , templateTitleSubject[0] had also reported 560 templateYLabel[3] registered templateYLabel[2] worldwide .

generated_template: During the fourth templateXLabel[0] of templateTitleDate[0] , the Chinese app company templateTitleSubject[0] amounted to templateYValue[0] thousand templateYLabel[2] templateYLabel[3] . This represents an increase of the previous year , it was founded in June 2017 .
generated: During the fourth Quarter of 2014 , the Chinese app company LINE amounted to 217.0 thousand users millions . This represents an increase of the previous year , it was founded in June 2017 .

Example 430:
titleEntities: {'Subject': ['Liberia'], 'Date': ['2019']}
title: Unemployment rate in Liberia 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']: ['2.03', '2.03', '2.03', '2.15', '2.18', '2.21', '2.29', '2.26', '2.28', '2.27', '2.25', '1.96', '2.03', '2.23', '2.42', '2.53', '2.61', '2.66', '2.61', '2.77', '2.78']

gold: This statistic shows the unemployment rate in Liberia from 1999 to 2019 . In 2019 , the unemployment rate in Liberia was at 2.03 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at templateYValue[0] percent .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent of the total labor force . The templateYLabel[0] templateYLabel[1] is the percentage of a country 's labor force that are without jobs but are available to work and actively seeking employment . templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] is above the worldwide templateYLabel[0] templateYLabel[1] , and compared to other Sub-Saharan African countries and other regions , templateTitleSubject[0] has a relatively average templateYLabel[1] of templateYLabel[0] .
generated: In 2019 , the Unemployment rate in Liberia was at approximately 2.03 percent of the total labor force . The Unemployment rate is the percentage of a country 's labor force that are without jobs but are available to work and actively seeking employment . Liberia 's Unemployment rate is above the worldwide Unemployment rate , and compared to other Sub-Saharan African countries and other regions , Liberia has a relatively average rate of Unemployment .

Example 431:
titleEntities: {'Subject': ['Zimbabwe'], 'Date': ['2019']}
title: Youth unemployment rate in Zimbabwe 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']: ['8.13', '8.16', '8.18', '8.56', '8.79', '8.86', '8.96', '8.86', '8.47', '8.15', '8.06', '7.39', '6.96', '6.85', '7.21', '7.41', '8.54', '9.5', '10.11', '11.02', '11.95']

gold: The statistic shows the youth unemployment rate in Zimbabwe from 1999 and 2019 . The data are ILO estimates . In 2019 , the estimated youth unemployment rate in Zimbabwe was at 8.13 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . The data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[0] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[0] percent .
generated: The statistic shows the Youth unemployment rate in Zimbabwe from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated Youth unemployment rate in Zimbabwe was at 8.13 percent .

Example 432:
titleEntities: {'Subject': ['El Pais', 'Spain'], 'Date': ['1999', '2018']}
title: El Pais ( Spain ) : circulation 1999 to 2018
X_Axis['Month']: ['July_2017-June_2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Circulation']: ['163759', '194005', '221390', '259775', '292227', '324814', '365118', '370080', '391816', '431034', '435083', '432204', '453602', '469183', '440226', '435299', '433617', '436302', '435433']

gold: This timeline shows the average circulation of the Spanish daily El Pais from 1999 to 2018 . In the period between July 2017 and June 2018 , the Spanish newspaper sold on average 163.8 thousand copies daily .
gold_template: This timeline shows the average templateYLabel[0] of the Spanish daily templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In the period between templateXValue[0] 2017 and June templateXValue[0] , the Spanish newspaper sold on average templateYValue[min] thousand copies daily .

generated_template: As of 23rd January templateTitleDate[0] , templateXValue[0] templateXValue[0] is the templateTitle[8] templateTitle[9] leader in templateYLabel[0] templateYLabel[1] for the international templateTitle[2] team of templateTitleSubject[0] templateTitle[4] a total of templateYValue[max] thousand templateYLabel[0] templateYLabel[1] , followed by templateXValue[1] templateXValue[1] templateXValue[1] templateTitle[4] templateYValue[1] thousand . templateXValue[1] has passed away back in 1979 and templateXValue[0] retired from the templateTitle[1] already back in 1974 so these records are exceptionally old . templateTitleSubject[0] not at World Cup 2018 As templateTitleSubject[0] is generally a very prominent country for templateTitle[2] and even part of the Big Five , which are the biggest templateTitle[2] league market countries ( England , Germany , Spain , templateTitleSubject[0] and France ) .
generated: As of 23rd January 1999 , July 2017-June 2018 is the 2018 leader in Circulation for the international Spain team of El Pais 1999 a total of 469183 thousand Circulation , followed by 2016 1999 194005 thousand . 2016 has passed away back in 1979 and July 2017-June 2018 retired from the Pais already back in 1974 so these records are exceptionally old . El Pais not at World Cup 2018 As El Pais is generally a very prominent country for Spain and even part of the Big Five , which are the biggest Spain league market countries ( England , Germany , Spain , El Pais and France ) .

Example 433:
titleEntities: {'Subject': ['Great Britain', 'UK'], 'Date': ['2016']}
title: Best cars to own based on ride quality in Great Britain ( UK ) 2016
X_Axis['Car', 'Model']: ['Tesla_Model_S_MkI', 'Land_Rover_Discovery_MkIV', 'Renault_Kadjar_MkI', 'Lexus_IS_MkIII', 'Renault_ZOE_MkI', 'Lexus_GS_MkIV', 'Jaguar_XF_MkI', 'Citroen_C5_MkII', 'Skoda_Citigo_MkI', 'Jeep_Grand_Cherokee_MKIV', 'Toyota_Land_Cruiser_MkVII', 'Lexus_RX_MkII', 'MG_MG6_MkI', 'Lexus_RX_MkIII', 'Subaru_Forester_MkIV']
Y_Axis['Percentage']: ['97.19', '94.63', '93.72', '93.3', '92.99', '92.8', '92.68', '92.62', '92.41', '92.38', '92.26', '92.07', '91.7', '91.35', '91.32']

gold: This statistic shows the leading 15 car models according to the Auto Express Driver Power 2016 survey responses based on ride quality . The survey was carried out by the British automotive magazine online between 2015 and 2016 . Lexus had four models in the top 15 based on ride quality .
gold_template: This statistic shows the leading 15 templateXLabel[0] models according to the Auto Express Driver Power templateTitleDate[0] survey responses templateTitle[3] on templateTitle[4] templateTitle[5] . The survey was carried out by the British automotive magazine online between 2015 and templateTitleDate[0] . templateXValue[3] had four models in the top 15 templateTitle[3] on templateTitle[4] templateTitle[5] .

generated_template: As of the third quarter of templateTitle[10] , The British Broadcasting Corporation ( templateXValue[0] ) made up templateXValue[1] of the top templateXValue[4] templateXValue[0] templateTitle[2] in the templateTitleSubject[0] templateTitleSubject[0] for weekly templateTitle[5] . templateXValue[0] templateXValue[0] templateXValue[0] , the templateTitle[0] templateXValue[0] templateXLabel[0] in the templateTitleSubject[1] saw its average consumer listen to over ten hours per week . The templateTitleSubject[1] templateXValue[0] industry has seen revenues remain stagnant between 2014 and 2018 .
generated: As of the third quarter of 2016 , The British Broadcasting Corporation ( Tesla Model S MkI ) made up Land Rover Discovery MkIV of the top Renault ZOE MkI Tesla Model S MkI own in the Great Britain for weekly quality . Tesla Model S MkI , the Best Tesla Model S MkI Car in the UK saw its average consumer listen to over ten hours per week . The UK Tesla Model S MkI industry has seen revenues remain stagnant between 2014 and 2018 .

Example 434:
titleEntities: {'Subject': ['EU'], 'Date': ['2009', '2018']}
title: Number of illegal entries between BCPs to the EU 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'illegal', 'entries', 'in', 'thousands']: ['150.1', '204.72', '511.05', '1822.18', '282.93', '107.37', '72.44', '141.05', '104.06', '104.6']

gold: This statistic shows the total number of individuals detected entering the European Union ( EU ) illegally between border-crossing points ( BCPs ) from 2009 to 2018 . In 2013 , there was a total of approximately 107 thousand illegal entries between BCPs , making it a 48 percent increase on the previous year . By 2015 the number of individuals had increased to almost two million illegal entries .
gold_template: This statistic shows the total templateYLabel[0] of individuals detected entering the European Union ( templateTitleSubject[0] ) illegally templateTitle[3] border-crossing points ( templateTitle[4] ) from templateXValue[min] to templateXValue[max] . In templateXValue[5] , there was a total of approximately templateYValue[5] thousand templateYLabel[1] templateYLabel[2] templateTitle[3] templateTitle[4] , making it a 48 percent increase on the previous templateXLabel[0] . By templateXValue[3] the templateYLabel[0] of individuals had increased to almost templateYValue[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitleSubject[0] amounted to templateYValue[0] thousand , up from templateYValue[min] templateYLabel[1] in the previous templateXLabel[0] .
generated: The statistic shows the Number of illegal at EU from 2009 to 2018 . In 2018 , the Number of illegal entries EU amounted to 150.1 thousand , up from 72.44 illegal in the previous Year .

Example 435:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016', '2021']}
title: Self-paced e-learning industry revenue in the U.S. 2016 to 2021
X_Axis['Year']: ['2021', '2020', '2019', '2018', '2017', '2016']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['15.86', '17.35', '18.57', '19.53', '20.33', '20.85']

gold: This statistic presents the self-paced e-learning market revenue in the United States from 2016 to 2021 . The self-paced online learning market , in the U.S. , reached $ 20.85 billion in revenues in 2016 . The figure is estimated to decrease to $ 15.86 billion by 2021 .
gold_template: This statistic presents the templateTitle[0] templateTitle[1] market templateYLabel[0] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateTitle[0] online learning market , in the templateYLabel[2] , reached $ templateYValue[max] templateYLabel[1] in revenues in templateXValue[min] . The figure is estimated to decrease to $ templateYValue[min] templateYLabel[1] by templateXValue[max] .

generated_template: The graph presents data on the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] sales templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . The source estimates that the templateTitleSubject[0] VR templateTitle[4] market size in templateXValue[4] will be worth templateYValue[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] . This number is expected to grow to templateYValue[max] templateYLabel[1] by the end of templateXValue[max] .
generated: The graph presents data on the e-learning industry revenue U.S. sales Revenue worldwide from 2016 to 2021 . The source estimates that the U.S. VR U.S. market size in 2017 will be worth 20.33 billion U.S. dollars . This number is expected to grow to 20.85 billion by the end of 2021 .

Example 436:
titleEntities: {'Subject': ['North America'], 'Date': ['2018']}
title: Leading cinema circuits in North America in 2018 , by number of screens
X_Axis['Month']: ['AMC_Theatres', 'Regal_Entertainment_Group', 'Cinemark_USA_Inc.', 'Cineplex_Entertainment_LP', 'Marcus_Theatres_Corp.', 'Harkins_Theatres', 'Southern_Theatres_LLC', 'B_&_B_Theatres', 'National_Amusements_Inc.', 'Malco_Theatres_Inc.']
Y_Axis['Number', 'of', 'screens']: ['8218', '7350', '4544', '1683', '895', '515', '499', '400', '392', '353']

gold: The graph shows leading cinema circuits in North America as of July 2018 , ranked by number of screens . AMC Theatres ranked first with 8,218 screens . Total attendance at AMC Theatres worldwide reached record levels in 2017 , with over 346 million attendees .
gold_template: The graph shows templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] as of July templateTitleDate[0] , ranked templateTitle[6] templateYLabel[0] of templateYLabel[1] . templateXValue[0] ranked first with templateYValue[max] templateYLabel[1] . Total attendance at templateXValue[0] worldwide reached record levels in 2017 , with over 346 million attendees .

generated_template: templateTitleSubject[0] Inc. is a Canadian owned retail chain headquartered in Montreal . There were 1,225 templateTitleSubject[0] templateYLabel[1] in templateTitleSubject[1] as of February templateTitleDate[0] . templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] per templateTitle[5] , templateYValue[max] , and templateXValue[last] templateXValue[last] templateXValue[last] had just templateYValue[min] .
generated: North America Inc. is a Canadian owned retail chain headquartered in Montreal . There were 1,225 North America screens in North America as of February 2018 . AMC Theatres had the highest Number of screens per 2018 , 8218 , and Malco Theatres Inc. had just 353 .

Example 437:
titleEntities: {'Subject': ['North America'], 'Date': ['2017']}
title: Reasons for cutting the cord in North America 2017
X_Axis['Response']: ['Price_-_too_expensive', 'I_use_an_internet_streaming_service_such_as_Netflix_Hulu_Amazon_Video_etc.', 'I_use_an_antenna_to_get_the_basic_channels_on_my_TV', 'I_like_to_binge_watch_an_entire_season_of_a_TV_series_through_my_streaming_service', 'I_moved/relocated_and_I_do_not_plan_to_sign-up_for_cable/satellite_service_again', 'The_bulk_of_my_TV_viewing_was_the_original_series_on_streaming_services', "I_share_a_friend/family_member's_login_to_watch_shows_on_their_cable/satellite_provider's_app"]
Y_Axis['Share', 'of', 'respondents']: ['86.7', '39.7', '23', '15.9', '13', '7.7', '0.9']

gold: The graph shows reasons for cutting the cord named by respondents from North America in the fourth quarter of 2017 . During a a survey , it was found that 86.7 percent of respondents cut off their cable or satellite service because it was too expensive .
gold_template: The graph templateXValue[last] templateTitle[0] templateXValue[4] templateTitle[2] the templateTitle[3] named by templateYLabel[1] from templateTitleSubject[0] in the fourth quarter of templateTitleDate[0] . During a survey , it templateXValue[5] found that templateYValue[max] percent of templateYLabel[1] cut off templateXValue[last] cable or satellite templateXValue[1] because it templateXValue[5] templateXValue[0] .

generated_template: This statistic illustrates the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] users in the templateTitle[5] in templateTitleDate[0] . During the survey , templateYValue[max] percent of the templateYLabel[1] stated that they found the website templateXValue[last] .
generated: This statistic illustrates the Reasons for cutting of cord users in the America in 2017 . During the survey , 86.7 percent of the respondents stated that they found the website I share a friend/family member's login to watch shows on their cable/satellite provider's app .

Example 438:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Dietary supplement usage in U.S. adults by gender 2018
X_Axis['Sex']: ['Female', 'Male']
Y_Axis['Percentage', 'of', 'U.S.', 'adults']: ['77', '73']

gold: This statistic indicates the percentage of U.S. adults that take dietary supplements , distributed by gender . The statistic is based on a survey conducted in August 2018 . Among U.S. adult males , some 73 percent reported taking dietary supplements .
gold_template: This statistic indicates the templateYLabel[0] of templateYLabel[1] templateYLabel[2] that take templateTitle[0] supplements , distributed templateTitle[5] templateTitle[6] . The statistic is based on a survey conducted in August templateTitleDate[0] . Among templateYLabel[1] adult males , some templateYValue[min] percent reported taking templateTitle[0] supplements .

generated_template: This statistic gives information on the templateTitle[1] templateTitle[2] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] as of October templateTitle[5] . During this period of time , templateYValue[min] percent of Indian templateYLabel[1] templateYLabel[2] were templateXValue[0] . The majority of templateYLabel[1] templateYLabel[2] were templateXValue[last] .
generated: This statistic gives information on the supplement usage of U.S. adults in U.S. as of October by . During this period of time , 73 percent of Indian U.S. adults were Female . The majority of U.S. adults were Male .

Example 439:
titleEntities: {'Subject': ['Boston Bruins'], 'Date': ['2005', '2019']}
title: Boston Bruins ' revenue 2005 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['228', '191', '176', '169', '158', '164', '114', '129', '125', '110', '108', '97', '87', '86']

gold: The statistic shows the revenue of the Boston Bruins from the 2005/06 season to the 2018/19 season . The revenue of the Boston Bruins amounted to 228 million U.S. dollars in the 2018/19 season .
gold_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .

generated_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .
generated: This graph depicts the annual National Hockey League Revenue of the Boston Bruins from the 2005/06 season to the 2018/19 season . The Revenue of the Boston Bruins amounted to 228 million U.S. dollars in the 2018/19 season .

Example 440:
titleEntities: {'Subject': ['Rwanda'], 'Date': ['2018']}
title: Urbanization in Rwanda 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['17.21', '17.13', '17.06', '17', '16.97', '16.95', '16.94', '16.94', '16.93', '16.93', '16.93']

gold: This statistic shows the degree of urbanization in Rwanda from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 17.21 percent of Rwanda 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Rwanda from 2008 to 2018 . Urbanization means the Share of urban population in the total population of a country . In 2018 , 17.21 percent of Rwanda 's total population lived in urban areas and cities .

Example 441:
titleEntities: {'Subject': ['Production'], 'Date': ['2013', '2020']}
title: Production of pork worldwide 2013 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Thousand', 'tons', 'carcass', 'weight']: ['96.38', '106.0', '112.94', '112.07', '111.39', '112.01', '110.65', '108.85']

gold: This statistic depicts the production of pork worldwide from 2013 to 2020 . The net production of pork worldwide amounted to about 113 million tons carcass weight in 2018 , and forecasted to decrease to 96.4 million metric tones by 2020 .
gold_template: This statistic depicts the templateTitleSubject[0] of templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . The net templateTitleSubject[0] of templateTitle[1] templateTitle[2] amounted to about templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[2] , and forecasted to decrease to templateYValue[min] templateYLabel[0] metric tones by templateXValue[max] .

generated_template: This statistic illustrates the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateYLabel[0] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] was approximately 0.99 templateYLabel[1] templateYLabel[2] , up from templateYValue[5] templateYLabel[1] templateYLabel[2] in the previous templateXLabel[0] .
generated: This statistic illustrates the Thousand of worldwide 2013 2020 in the pork from 2013 to 2020 . In 2019 , the Thousand of the worldwide 2013 2020 in the pork was approximately 0.99 tons carcass , up from 112.01 tons carcass in the previous Year .

Example 442:
titleEntities: {'Subject': ['UEFA Champions League'], 'Date': ['2005', '2018']}
title: UEFA Champions League total performance and bonus payments to clubs 2005 to 2018
X_Axis['Year']: ['2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Bonus', 'payments', 'in', 'million', 'euros']: ['1412.6', '1396.13', '1349.43', '1033.43', '904.6', '910.0', '754.1', '786.3', '757.5', '583.4', '585.6', '584.9', '437.1']

gold: The statistic shows the total amount of bonus payments to the participating clubs in the UEFA Champions League from the 2005/06 season to the 2017/18 season . In the 2017/18 season , the total bonus payments to the participating clubs amounted to 1,412.6 million euros .
gold_template: The statistic shows the templateTitle[3] amount of templateYLabel[0] templateYLabel[1] to the participating templateTitle[7] in the templateTitleSubject[0] League from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[0] season , the templateTitle[3] templateYLabel[0] templateYLabel[1] to the participating templateTitle[7] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: By the period templateXValue[0] , templateYValue[min] percent of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] smoked at the time of delivery . The share of templateYLabel[1] templateYLabel[2] templateTitle[2] has decreased since templateTitleDate[0] when almost templateYValue[max] percent of templateYLabel[1] templateYLabel[2] did so . templateTitle[2] templateTitle[3] templateTitle[4] can lead to many birth complications , so it is advised that the expecting mother quits templateTitle[2] for the health of the baby .
generated: By the period 2017/18 , 437.1 percent of payments million in UEFA Champions League smoked at the time of delivery . The share of payments million League has decreased since 2005 when almost 1412.6 percent of payments million did so . League total performance can lead to many birth complications , so it is advised that the expecting mother quits League for the health of the baby .

Example 443:
titleEntities: {'Subject': ['Syngenta'], 'Date': ['2009', '2018']}
title: Syngenta 's R & D expenditure worldwide 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Expenditure', 'in', 'million', 'U.S.', 'dollars']: ['-1300', '-1273', '-1299', '1362', '1430', '1376', '1257', '1191', '1032', '952']

gold: The statistic shows Syngenta AG 's expenditure on research and development ( R & D ) worldwide from 2009 to 2018 . Syngenta is one of the largest global agribusiness companies . It is based in Basel , Switzerland , and notably markets seeds and pesticides .
gold_template: The statistic shows templateTitleSubject[0] AG templateTitle[1] templateYLabel[0] on research and development ( templateTitle[2] templateTitle[3] templateTitle[4] ) templateTitle[6] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] is one of the largest global agribusiness companies . It is based in Basel , Switzerland , and notably markets seeds and pesticides .

generated_template: templateTitleSubject[0] is an international pharmaceutical company based out of Germany . As of templateXValue[max] , the company had a total of templateYValue[0] templateYLabel[1] globally . This is a significant increase from the previous years .
generated: Syngenta is an international pharmaceutical company based out of Germany . As of 2018 , the company had a total of -1300 million globally . This is a significant increase from the previous years .

Example 444:
titleEntities: {'Subject': ['The Cheesecake Factory'], 'Date': ['2018']}
title: The Cheesecake Factory 's number of establishments 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'restaurants']: ['201', '199', '194', '188', '177', '168', '162', '156', '149', '160']

gold: This statistic shows the number of The Cheesecake Factory restaurants from 2009 to 2018 . In 2018 , U.S.-based restaurant chain The Cheesecake Factory had 201 restaurants , up from 199 the previous year .
gold_template: This statistic shows the templateYLabel[0] of The templateTitleSubject[0] restaurants from templateXValue[min] to templateXValue[max] . In templateXValue[max] , U.S.-based restaurant chain The templateTitleSubject[0] had templateYValue[max] templateYLabel[1] , up from templateYValue[1] the previous templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . templateTitleSubject[0] Inc. is a leading company in cleaning , sanitizing , pest elimination services , food safety and infection prevention products and services worldwide . In templateTitleDate[0] , the corporation had 26,568 templateYLabel[1] .
generated: This statistic shows the Number of restaurants at The Cheesecake Factory between 2009 and 2018 . The Cheesecake Factory Inc. is a leading company in cleaning , sanitizing , pest elimination services , food safety and infection prevention products and services worldwide . In 2018 , the corporation had 26,568 restaurants .

Example 445:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2017']}
title: Life expectancy at birth in Vietnam 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Life', 'expectancy', 'at', 'birth', 'in', 'years']: ['75.24', '75.17', '75.11', '75.06', '75.01', '74.96', '74.9', '74.84', '74.75', '74.63', '74.47']

gold: This statistic shows the life expectancy at birth in Vietnam from 2007 to 2017 . In 2017 , the average life expectancy at birth in Vietnam was 75.24 years .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] was templateYValue[max] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] had reached about templateYValue[max] years.Demographic development in templateTitleSubject[0] – additional information templateYLabel[0] templateYLabel[1] at templateYLabel[2] refers to the average number of years a group of people born in the same templateXLabel[0] would live , assuming constant mortality rates . The country with the highest templateYLabel[0] templateYLabel[1] at templateYLabel[2] was Japan , while templateTitleSubject[0] had reached a templateYLabel[0] templateYLabel[1] above global average .
generated: The statistic shows the Life expectancy at birth in Vietnam from 2007 to 2017 . In 2017 , the average Life expectancy at birth in Vietnam had reached about 75.24 years.Demographic development in Vietnam – additional information Life expectancy at birth refers to the average number of years a group of people born in the same Year would live , assuming constant mortality rates . The country with the highest Life expectancy at birth was Japan , while Vietnam had reached a Life expectancy above global average .

Example 446:
titleEntities: {'Subject': ['Major League Soccer'], 'Date': ['2019']}
title: Major League Soccer teams ranked by operating income 2019
X_Axis['Team', 'Name']: ['Atlanta_United', 'LA_Galaxy', 'Portland_Timbers', 'Real_Salt_Lake', 'Seattle_Sounders', 'D.C._United', 'Sporting_Kansas_City', 'Orlando_City_SC', 'New_England_Revolution', 'Philadelphia_Union', 'Los_Angeles_FC', 'Vancouver_Whitecaps', 'Colorado_Rapids', 'San_Jose_Earthquakes', 'New_York_Red_Bulls', 'Houston_Dynamo', 'FC_Dallas', 'Columbus_Crew', 'Minnesota_United', 'Montreal_Impact', 'Chicago_Fire', 'New_York_City_FC', 'Toronto_FC']
Y_Axis['Operating', 'income/loss', 'in', 'million', 'U.S.', 'dollars']: ['7', '5', '4', '2', '1', '1', '1', '-1', '-2', '-5', '-5', '-5', '-5', '-5', '-6', '-6', '-7', '-8', '-8', '-12', '-16', '-16', '-19']

gold: The statistic shows a ranking of Major League Soccer teams according to their operating income/loss . Atlanta United had an operating income of seven million U.S. dollars in the 2019 MLS season .
gold_template: The statistic shows a ranking of templateTitleSubject[0] Soccer templateTitle[3] according to their templateYLabel[0] templateYLabel[1] . templateXValue[0] had an templateYLabel[0] templateTitle[7] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in the templateTitleDate[0] MLS season .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[5] templateYLabel[4] templateYLabel[5] templateTitle[6] top templateTitleSubject[0] templateTitle[7] . During the first quarter of templateTitle[8] , an templateXValue[10] templateYLabel[1] templateYLabel[2] templateYLabel[5] templateTitle[6] templateXValue[0] templateTitle[5] templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . In constrast , top templateXValue[0] influencer Cara Delevigne templateTitle[5] an templateXValue[10] user templateTitle[4] of 662,894 interactions templateYLabel[4] templateYLabel[5] .
generated: The statistic shows the Operating of income/loss million U.S. by dollars operating top Major League Soccer income . During the first quarter of 2019 , an Los Angeles FC income/loss million dollars operating Atlanta United by 7 income/loss million U.S. . In constrast , top Atlanta United influencer Cara Delevigne by an Los Angeles FC user ranked of 662,894 interactions dollars .

Example 447:
titleEntities: {'Subject': ['Eritrea'], 'Date': ['2024']}
title: Total population of Eritrea 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['6.68', '6.58', '6.48', '6.38', '6.27', '6.16', '6.05', '5.93', '5.82', '5.7', '5.58']

gold: This statistic shows the total population of Eritrea from 2014 to 2024 . All figures are estimates . In 2018 , the total population of Eritrea was estimated to amount to approximately 6.05 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . All figures are estimates . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was estimated to amount to approximately templateYValue[6] templateYLabel[1] templateYLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateYLabel[1] templateYLabel[0] . See figures for Switzerland 's templateTitle[1] and the templateTitle[1] of Italy for comparison .
generated: The statistic shows the Total population of Eritrea from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Eritrea amounted to around 6.05 millions Inhabitants . See figures for Switzerland 's population and the population of Italy for comparison .

Example 448:
titleEntities: {'Subject': ['Europe'], 'Date': ['2020']}
title: Europe : forecasted distribution of golfers in 2020 , by age group
X_Axis['Year']: ['20_or_younger', '20_to_40_years', '40_to_50_years', '50_to_60_years', '60_or_older']
Y_Axis['Share', 'of', 'average', 'increase']: ['6', '11', '18', '24', '42']

gold: The statistic displays the forecast of a golf player distribution in Europe in 2020 , by age group . With data from five European countries it was forecasted that in 2020 approximately 24 percent of golf players will be between 50 and 60 years old .
gold_template: The statistic displays the forecast of a golf player templateTitle[2] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateTitle[6] templateTitle[7] . With data from five European countries it was templateTitle[1] that in templateTitleDate[0] approximately templateYValue[3] percent of golf players will be between templateXValue[2] and templateXValue[3] templateXValue[1] old .

generated_template: This statistic illustrates the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per day in the templateTitleSubject[0] templateTitleSubject[0] between templateTitle[6] and templateTitle[7] , templateTitle[8] templateTitle[9] . In this four templateXLabel[0] period , individuals aged between 4 and 10 templateXValue[0] old templateTitle[3] templateYValue[max] templateYLabel[1] of templateTitle[1] templateTitle[2] per day .
generated: This statistic illustrates the Europe of forecasted distribution golfers per day in the Europe between age and group , group . In this four Year period , individuals aged between 4 and 10 20 or younger old golfers 42 average of forecasted distribution per day .

Example 449:
titleEntities: {'Subject': ['European'], 'Date': ['2015']}
title: Number of people with hearing loss global vs European countries 2015
X_Axis['Country']: ['Global', 'Europe', 'European_Union', 'United_Kingdom', 'France', 'Spain', 'Netherlands', 'Austria', 'Sweden', 'Belgium', 'Poland', 'Denmark', 'Ireland']
Y_Axis['Estimated', 'number', 'of', 'people', 'with', 'hearing', 'loss']: ['328.0', '119.0', '51.0', '10.0', '6.0', '3.5', '1.6', '1.6', '1.4', '1.3', '1.0', '0.8', '0.8']

gold: This statistic shows the estimated number of people with hearing loss worldwide and in Europe as of 2015 , by country , in millions . As of this time an estimated 119 million people in the whole of Europe were hard of hearing , with 3.5 million of these people located in Spain .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] worldwide and in templateXValue[1] as of templateTitleDate[0] , by templateXLabel[0] , in millions . As of this time an templateYLabel[0] templateYValue[1] million templateYLabel[2] in the whole of templateXValue[1] were hard of templateYLabel[4] , templateYLabel[3] templateYValue[5] million of these templateYLabel[2] located in templateXValue[5] .

generated_template: This statistic shows the templateYLabel[0] of the chemical element templateTitleSubject[0] ( also known as wolfram ) templateTitle[2] in templateTitleDate[0] , templateTitle[3] templateXLabel[0] . In that year , templateXValue[0] had the templateXValue[last] 's largest templateTitleSubject[0] templateYLabel[0] , at some 1.9 million templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Estimated of the chemical element European ( also known as wolfram ) hearing in 2015 , loss Country . In that year , Global had the Ireland 's largest European Estimated , at some 1.9 million number people .

Example 450:
titleEntities: {'Subject': ['Forecast'], 'Date': ['2010', '2025']}
title: Forecast of rare earth oxide holmium oxide price globally 2010 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', 'kilogram']: ['41', '303', '107', '66', '63', '55', '51', '49', '46', '38', '20', '20', '20', '21', '21', '22']

gold: This statistic displays the price development of rare earth oxide holmium oxide globally from 2009 to 2025 . It expected that the price of holmium oxide will reach some 49 U.S. dollars per kilogram in 2017 .
gold_template: This statistic displays the templateYLabel[0] development of templateTitle[1] templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[3] templateTitle[7] from 2009 to templateXValue[max] . It expected that the templateYLabel[0] of templateTitle[4] templateTitle[3] will reach some templateYValue[7] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[7] .

generated_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] between templateXValue[min] and templateXValue[9] , with projections until templateXValue[max] . In templateXValue[max] , the templateTitle[0] is expected to produce around templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows Forecast rare earth Price between 2010 and 2019 , with projections until 2025 . In 2025 , the Forecast is expected to produce around 303 U.S. dollars .

Example 451:
titleEntities: {'Subject': ['Michael Kors'], 'Date': ['2020']}
title: Number of followers of Michael Kors on social media 2020
X_Axis['Platform']: ['Facebook', 'Instagram', 'Twitter']
Y_Axis['Number', 'of', 'followers', 'in', 'millions']: ['17.91', '16.0', '3.5']

gold: This statistic depicts the number of followers of Michael Kors on social media as of January 2020 . During the measured period , the largest social media presence of the brand was on Facebook with 17.91 million followers , as opposed to its 3.5 million follower base on Twitter .
gold_template: This statistic depicts the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] on templateTitle[4] templateTitle[5] as of January templateTitleDate[0] . During the measured period , the largest templateTitle[4] templateTitle[5] presence of the brand was on templateXValue[0] with templateYValue[max] templateYLabel[2] templateYLabel[1] , as opposed to its templateYValue[min] templateYLabel[2] follower base on templateXValue[last] .

generated_template: This statistic gives information on the templateTitle[1] of templateTitleSubject[0] templateYLabel[2] worldwide as of January templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[max] percent of templateTitleSubject[0] templateTitle[3] were templateXValue[0] and templateYValue[min] percent were templateXValue[last] .
generated: This statistic gives information on the followers of Michael Kors millions worldwide as of January 2020 , sorted media 2020 . During the survey period , 17.91 percent of Michael Kors Kors were Facebook and 3.5 percent were Twitter .

Example 452:
titleEntities: {'Subject': ['Foursquare'], 'Date': ['10', '14']}
title: Number of registered members on Foursquare 2010 to 2014
X_Axis['Month']: ["Dec_'10", "Jan_'11", "Mar_'12", "May_'12", "Jan_'13", "Jan_'14", "May_'14", "Oct_'14"]
Y_Axis['Number', 'of', 'registered', 'members', 'in', 'millions']: ['5', '6', '15', '20', '30', '45', '50', '55']

gold: This statistic gives information on the number of registered members on Foursquare between December 2010 and October 2014 . As of that month , the social check-in app community had accumulated over 55 million members worldwide .
gold_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] on templateTitleSubject[0] between December templateTitleDate[0] and October templateTitleDate[1] . As of that templateXLabel[0] , the social check-in app community had accumulated over templateYValue[max] templateYLabel[3] templateYLabel[2] worldwide .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitleSubject[0] templateTitle[4] from templateXValue[last] to December templateTitleDate[0] . In that year , the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] templateTitle[4] amounted to approximately templateYValue[max] templateYLabel[1] templateYLabel[2] , up from templateYValue[5] templateYLabel[1] templateYLabel[2] in July templateTitle[6] .
generated: This statistic shows the Number registered Number of Foursquare 2010 from Oct '14 to December 10 . In that year , the Number of Foursquare 2010 amounted to approximately 55 registered members , up from 45 registered members in July 2014 .

Example 453:
titleEntities: {'Subject': ['United States'], 'Date': ['1998', '2018']}
title: Natural gas production - United States 1998 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1998']
Y_Axis['Production', 'in', 'billion', 'cubic', 'meters']: ['831.8', '745.8', '727.4', '740.3', '704.7', '655.7', '649.1', '617.4', '575.2', '557.6', '546.1', '521.9', '524.0', '511.1', '526.4', '540.8', '536.0', '555.5', '543.2', '538.7']

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

generated_template: The statistic shows the templateYLabel[0] of lives lost due to templateTitle[0] and flash templateTitle[0] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were a total of templateYValue[0] templateYLabel[1] reported due to flash templateTitle[0] and river templateTitle[0] in the templateTitle[1] .
generated: The statistic shows the Production of lives lost due to Natural and flash Natural in the gas from 1998 to 2018 . In 2018 , there were a total of 831.8 billion reported due to flash Natural and river Natural in the gas .

Example 454:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Dating website or app usage among U.S. online users 2019
X_Axis['Response']: ["Yes_I'm_doing_so_currently", "Yes_I've_done_so_in_the_past", 'No_never', "Don't_know"]
Y_Axis['Share', 'of', 'respondents']: ['7', '24', '65', '3']

gold: This statistic presents the percentage of adult online users in the United States who have used a dating website or app as of January 2019 . According to the findings , only seven percent of respondents stated that they were currently using a dating website or app , while in comparison 65 percent of respondents reported to have never used a dating app or website before .
gold_template: This statistic presents the percentage of adult templateTitle[6] templateTitle[7] in the templateTitle[5] who have used a templateTitle[0] templateTitle[1] or templateTitle[2] as of January templateTitleDate[0] . According to the findings , only templateYValue[0] percent of templateYLabel[1] stated that they were templateXValue[0] using a templateTitle[0] templateTitle[1] or templateTitle[2] , while in comparison templateYValue[max] percent of templateYLabel[1] reported to have templateXValue[2] used a templateTitle[0] templateTitle[2] or templateTitle[1] before .

generated_template: This statistic shows the results of a templateTitleSubject[0] , conducted in templateTitleDate[0] among adult Americans , on whether they believe the glass is half full or half empty . templateYValue[max] percent of templateYLabel[1] said they consider themselves optimists . The templateTitle[1] and templateTitle[2] of the American people templateTitle[1] is defined as a mental attitude or worldview that favors a positive outcome , while templateTitle[2] favors a negative outcome or prediction .
generated: This statistic shows the results of a U.S. , conducted in 2019 among adult Americans , on whether they believe the glass is half full or half empty . 65 percent of respondents said they consider themselves optimists . The website and app of the American people website is defined as a mental attitude or worldview that favors a positive outcome , while app favors a negative outcome or prediction .

Example 455:
titleEntities: {'Subject': ['Atlanta Falcons'], 'Date': ['2019']}
title: Regular season home attendance of the Atlanta Falcons 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Home', 'attendance']: ['572811', '583184', '575681', '559998', '562845', '493515', '561795', '560773', '551892', '542800', '545384', '512520', '547610', '563456']

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

generated_template: The graph shows the templateTitle[2] templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitleSubject[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In its templateXValue[max] fiscal templateXLabel[0] , templateTitleSubject[0] 's paid out a templateYLabel[0] of approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The graph shows the home attendance of Regular Atlanta Falcons from the fiscal Year of 2006 to the fiscal Year of 2019 . In its 2019 fiscal Year , Atlanta Falcons 's paid out a Home of approximately 572811 attendance .

Example 456:
titleEntities: {'Subject': ['Inflation'], 'Date': ['2019']}
title: Inflation rate of the main industrialized and emerging countries 2019
X_Axis['Country']: ['Russia', 'Brazil', 'India', 'China', 'USA', 'United_Kingdom', 'Germany', 'France', 'Japan']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['4.68', '3.79', '3.44', '2.31', '1.82', '1.81', '1.49', '1.17', '0.99']

gold: This statistic shows the inflation rate of the main industrialized and emerging countries in 2019 . In 2019 , the inflation rate in China was estimated to amount to approximately 2.31 percent compared to the previous year .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] and templateTitle[4] templateTitle[5] in templateTitleDate[0] . In templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] in templateXValue[3] was estimated to amount to approximately templateYValue[3] percent templateYLabel[2] to the templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] templateTitle[1] in templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , the estimated templateYLabel[0] templateYLabel[1] of templateXValue[0] was at templateYValue[3] percent .
generated: The statistic shows the Inflation rate in main industrialized emerging in 2019 . In that year , the estimated Inflation rate of Russia was at 2.31 percent .

Example 457:
titleEntities: {'Subject': ['U.S.'], 'Date': ['14', '20']}
title: Monthly car loan rates in the U.S. 2017 to 2020
X_Axis['Month']: ['Jan_20', 'Dec_19', 'Nov_19', 'Oct_19', 'Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16', 'Jun_16', 'May_16', 'Apr_16', 'Mar_16', 'Feb_16', 'Jan_16', 'Dec_15', 'Nov_15', 'Oct_15', 'Sep_15', 'Aug_15', 'Jul_15', 'Jun_15', 'May_15', 'Apr_15', 'Mar_15', 'Feb_15', 'Jan_15', 'Dec_14', 'Nov_14', 'Oct_14', 'Sep_14', 'Aug_14', 'Jul_14', 'Jun_14', 'May_14', 'Apr_14', 'Mar_14', 'Feb_14', 'Jan_14']
Y_Axis['Interest', 'rate']: ['4.56', '4.61', '4.59', '4.61', '4.61', '4.63', '4.66', '4.74', '4.72', '4.77', '4.77', '4.78', '4.77', '4.96', '4.93', '4.93', '4.79', '4.8', '4.83', '4.82', '4.64', '4.59', '4.52', '4.53', '4.51', '4.43', '4.29', '4.3', '4.29', '4.25', '4.51', '4.44', '4.38', '4.35', '4.38', '4.36', '4.35', '4.32', '4.27', '4.26', '4.23', '4.18', '4.17', '4.17', '4.25', '4.28', '4.33', '4.29', '4.33', '4.4', '4.33', '4.3', '4.32', '4.38', '4.37', '4.39', '4.37', '4.37', '4.31', '4.07', '4.07', '4.12', '4.06', '4.04', '4.02', '4.03', '4.03', '4.13', '4.13', '4.18', '4.23', '4.21', '4.25']

gold: This statistic presents the average interest rate on 60-month new car loans in the United States from January 2014 to January 2020 . Car loan interest rates amounted to 4.56 percent as of January 30 , 2020 . The smaller the car loan interest rates , the cheaper the loan is .
gold_template: This statistic presents the average templateYLabel[0] templateYLabel[1] on 60-month new templateTitle[1] loans in the templateTitle[4] from January 2014 to January templateTitleDate[1] . templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[3] amounted to templateYValue[0] percent as of January 30 , templateTitleDate[1] . The smaller the templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[3] , the cheaper the templateTitle[2] is .

generated_template: The templateYLabel[1] of the DJIA templateYLabel[0] amounted to templateYValue[0] on January 31 , templateTitleDate[1] . templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] – additional information The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] is a price-weighted templateTitleSubject[0] of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ , including Goldman Sachs , IBM and Walt Disney . This templateYLabel[0] is considered to be a barometer of the state of the American economy .
generated: The rate of the DJIA Interest amounted to 4.56 on January 31 , 20 . U.S. Interest – additional information The U.S. Interest is a price-weighted U.S. of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ , including Goldman Sachs , IBM and Walt Disney . This Interest is considered to be a barometer of the state of the American economy .

Example 458:
titleEntities: {'Subject': ['Europe'], 'Date': ['2016']}
title: Number of natural mineral waters in Europe 2016 , by country
X_Axis['Country']: ['Germany', 'Italy', 'Hungary', 'Spain', 'Poland', 'France', 'Romania', 'United_Kingdom', 'Greece', 'Austria', 'Belgium', 'Bulgaria', 'Portugal', 'Slovakia', 'Lithuania', 'Netherlands', 'Czech_Republic', 'Denmark', 'Sweden', 'Slovenia', 'Latvia', 'Estonia', 'Croatia', 'Ireland', 'Finland']
Y_Axis['Litres', 'consumed', 'per', 'capita']: ['821', '322', '214', '165', '119', '90', '69', '67', '44', '33', '27', '22', '21', '20', '17', '13', '11', '11', '11', '9', '5', '4', '4', '2', '1']

gold: This statistic represents the number of natural mineral waters in Europe in 2016 . Germany had the highest number of natural mineral waters with 821 certified natural mineral water sources .
gold_template: This statistic represents the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateTitleDate[0] . templateXValue[0] had the highest templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] with templateYValue[max] certified templateTitle[1] templateTitle[2] water sources .

generated_template: This statistic shows the templateYLabel[2] templateYLabel[3] templateTitle[2] of templateTitle[0] templateTitle[1] in templateTitleSubject[0] in templateTitleDate[0] . In this year , templateXValue[0] was the leading market for the templateTitle[2] of templateTitle[0] templateTitle[1] with templateYValue[max] liters of templateTitle[0] templateTitle[1] templateYLabel[1] templateYLabel[2] person and templateXValue[1] was the second largest consumer of templateTitle[0] templateTitle[1] with templateYValue[1] liters templateYLabel[1] templateYLabel[2] person . templateTitle[0] templateTitle[1] in the European Union is predominantly made up of the natural mineral templateTitle[1] category.Germany is the market with the largest amount of different mineral templateTitle[1] brands .
generated: This statistic shows the per capita mineral of Number natural in Europe in 2016 . In this year , Germany was the leading market for the mineral of Number natural with 821 liters of Number natural consumed per person and Italy was the second largest consumer of Number natural with 322 liters consumed per person . Number natural in the European Union is predominantly made up of the natural mineral natural category.Germany is the market with the largest amount of different mineral natural brands .

Example 459:
titleEntities: {'Subject': ['Luxury'], 'Date': ['2016']}
title: Luxury destinations with the largest growth in travel worldwide 2016
X_Axis['Country']: ['Kenya', 'Iceland', 'Saint_Martin', 'China', 'Ecuador', 'Japan', 'South_Africa', 'Tanzania', 'Croatia', 'Jamaica']
Y_Axis['Year-over-year', 'travel', 'growth']: ['59', '56', '39', '35', '34', '32', '28', '27', '25', '23']

gold: This statistic shows the luxury travel destinations with the largest growth in travel worldwide as of August 2016 . Luxury travel to Kenya grew by 59 percent in 2016 compared with the previous year . In 2017 , the Caribbean was the leading international destination for a spring vacation according to U.S. respondents .
gold_template: This statistic shows the templateTitleSubject[0] templateYLabel[1] templateTitle[1] templateTitle[2] the templateTitle[3] templateYLabel[2] in templateYLabel[1] templateTitle[6] as of August templateTitleDate[0] . templateTitleSubject[0] templateYLabel[1] to templateXValue[0] grew by templateYValue[max] percent in templateTitleDate[0] compared templateTitle[2] the previous year . In 2017 , the Caribbean was the leading international destination for a spring vacation according to U.S. respondents .

generated_template: This statistic shows the leading templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] in the world templateTitle[5] templateTitleDate[0] to templateTitleDate[1] . Over the past decade , templateXValue[0] has demonstrated the templateTitle[2] economic templateYLabel[3] rate templateTitle[1] templateYLabel[1] templateYLabel[0] templateYLabel[2] templateYLabel[3] sitting as high as templateYValue[max] templateYLabel[4] . The overall quarterly templateYLabel[2] templateYLabel[3] in the country can be found here .
generated: This statistic shows the leading Luxury destinations the largest growth travel in the world worldwide 2016 to 2016 . Over the past decade , Kenya has demonstrated the largest economic growth rate destinations travel Year-over-year growth sitting as high as 59 growth . The overall quarterly growth in the country can be found here .

Example 460:
titleEntities: {'Subject': ['Europe'], 'Date': ['2012', '2016']}
title: Forecast for the number of new hotel rooms opening in Europe from 2012 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'hotel', 'rooms']: ['34060', '34451', '39178', '30982', '37818']

gold: This statistic shows a forecast for the number of new hotel rooms opening in Europe from 2012 to 2016 . In 2013 , 30,982 new hotel rooms opened in the European hotel market . It was forecasted that 34,060 new hotel rooms would open in 2016 .
gold_template: This statistic shows a templateTitle[0] templateTitle[1] the templateYLabel[0] of templateTitle[3] templateYLabel[1] templateYLabel[2] templateTitle[6] in templateTitleSubject[0] templateTitle[8] templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateYValue[min] templateTitle[3] templateYLabel[1] templateYLabel[2] opened in the European templateYLabel[1] market . It was forecasted that templateYValue[0] templateTitle[3] templateYLabel[1] templateYLabel[2] would open in templateXValue[max] .

generated_template: This statistic illustrates the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per day in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateTitle[2] and templateTitle[3] templateTitle[4] templateYLabel[1] was expected to increase by templateYValue[max] percent .
generated: This statistic illustrates the Forecast of for number new per day in the Europe ( Europe ) from 2012 to 2016 . In 2014 , the number and new hotel was expected to increase by 39178 percent .

Example 461:
titleEntities: {'Subject': ['Finland'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Finland 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['330.94', '317.53', '305.24', '292.81', '280.71', '269.65', '274.21', '252.87', '239.11', '232.97', '273.04', '270.07', '256.85', '273.93', '248.26', '252.14', '285.09', '255.74', '216.73', '204.77', '196.98', '171.37', '139.98', '129.34', '125.88', '135.4', '134.11', '127.0', '132.15', '134.35', '103.76', '89.32', '113.23', '128.29', '141.8', '119.11', '109.26', '91.78', '73.65', '56.22', '53.03']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateTitle[3] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .
generated: The statistic shows the Gross domestic product ( GDP ) in Finland from 1984 to 2018 , with projections up until 2024 . GDP refers to the total market value of all goods and services that are produced within a country per Year . It is an important indicator of the economic strength of a country .

Example 462:
titleEntities: {'Subject': ['Switzerland'], 'Date': ['2024']}
title: National debt of Switzerland 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'in', 'billion', 'U.S.', 'dollars']: ['258.2', '261.41', '264.78', '267.72', '270.63', '273.8', '280.14', '286.05', '276.79', '281.84', '279.83']

gold: The statistic shows the national debt of Switzerland from 2014 to 2017 , with projections up until 2024 . In 2017 , the national debt of Switzerland amounted to around 286.05 billion U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was around templateYValue[6] templateYLabel[2] templateYLabel[3] templateYLabel[4] . For comparison , the Greek templateYLabel[1] amounted to approximately 375 templateYLabel[2] templateYLabel[3] templateYLabel[4] that same templateXLabel[0] .
generated: This statistic shows the National debt of Switzerland from 2014 to 2018 , with projections up until 2024 . In 2018 , the National debt in Switzerland was around 280.14 billion U.S. dollars . For comparison , the Greek debt amounted to approximately 375 billion U.S. dollars that same Year .

Example 463:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2017', '2023']}
title: Mexico : mobile phone internet user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['59', '58', '56', '53', '50', '47', '43']

gold: The statistic shows the mobile phone internet user penetration in Mexico from 2017 to 2023 . In 2017 , 43 percent of the population users accessed the internet through their mobile device . This figure is projected to grow to 59percent in 2023 .
gold_template: The statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the templateYLabel[1] users accessed the templateTitle[3] through their templateTitle[1] device . This figure is projected to grow to 59percent in templateXValue[max] .

generated_template: This statistic provides information on the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it was estimated that templateYValue[max] percent of the Indian templateYLabel[1] will be accessing templateTitle[1] networks , up from templateYValue[5] percent in templateXValue[5] .
generated: This statistic provides information on the mobile phone internet in Mexico from 2017 to 2023 . In 2023 , it was estimated that 59 percent of the Indian population will be accessing mobile networks , up from 47 percent in 2018 .

Example 464:
titleEntities: {'Subject': ['Stryker'], 'Date': ['2011', '2019']}
title: Stryker 's annual research , development and engineering expenses 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Expenses', 'in', 'million', 'U.S.', 'dollars']: ['971', '862', '787', '715', '625', '614', '536', '471', '462']

gold: The statistic shows the annual research , development and engineering expenses of Stryker from 2011 to 2019 . Stryker 's research , development and engineering expenses have gradually increased since 2011 , reaching 971 million U.S. dollars in 2019 . The Stryker Corporation is a U.S. medical technology company headquartered in Kalamazoo , Michigan .
gold_template: The statistic shows the templateTitle[2] templateTitle[3] , templateTitle[4] and templateTitle[5] templateYLabel[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] templateTitle[1] templateTitle[3] , templateTitle[4] and templateTitle[5] templateYLabel[0] have gradually increased since templateXValue[min] , reaching templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . The templateTitleSubject[0] Corporation is a templateYLabel[2] medical technology company headquartered in Kalamazoo , Michigan .

generated_template: This statistic presents the templateTitle[0] templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[3] had a templateTitle[0] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] , up from templateYValue[1] a templateXLabel[0] earlier .
generated: This statistic presents the Stryker Expenses of million of Stryker development from 2011 to 2019 . In 2019 , Stryker research had a Stryker of 971 U.S. dollars , up from 862 a Year earlier .

Example 465:
titleEntities: {'Subject': ['European Union'], 'Date': []}
title: Fresh orange production volume in the European Union 2016/17 , by country
X_Axis['Country']: ['Spain', 'Italy', 'Greece', 'Portugal', 'Cyprus']
Y_Axis['Volume', 'in', '1,000', 'tons']: ['3731', '1500', '920', '344', '30']

gold: In 2018/2019 , Spain was the leading producer of fresh oranges in the European Union ( EU28 ) , with over 3.7 million tons of fresh oranges produced . The Spanish production was more than two times the production of Italy , the second largest producer of oranges . The other three producers in the EU produced less than one million tons during this year .
gold_template: In 2018/2019 , templateXValue[0] was the leading producer of templateTitle[0] oranges in the templateTitleSubject[0] ( EU28 ) , with over 3.7 million templateYLabel[2] of templateTitle[0] oranges produced . The Spanish templateTitle[2] was more than two times the templateTitle[2] of templateXValue[1] , the second largest producer of oranges . The other three producers in the EU produced less than one million templateYLabel[2] during this year .

generated_template: This statistic shows the templateTitle[2] expensive templateTitle[0] templateTitle[3] a monthly templateTitleSubject[0] subscription templateTitle[6] as of April templateTitleDate[0] . The data reveals that templateXValue[0] was the templateTitle[2] expensive place in the world to get templateTitleSubject[0] , with a monthly subscription costing templateYValue[min] templateYLabel[1] templateYLabel[2] . In templateXValue[2] , the monthly price templateTitle[3] a templateTitleSubject[0] subscription amounted to templateYValue[1] templateYLabel[1] templateYLabel[2] , and Mexican subscribers paid templateYValue[3] templateYLabel[1] templateYLabel[2] per month to the streaming service .
generated: This statistic shows the production expensive Fresh volume a monthly European Union subscription 2016/17 as of April . The data reveals that Spain was the production expensive place in the world to get European Union , with a monthly subscription costing 30 1,000 tons . In Greece , the monthly price volume a European Union subscription amounted to 1500 1,000 tons , and Mexican subscribers paid 344 1,000 tons per month to the streaming service .

Example 466:
titleEntities: {'Subject': ['China'], 'Date': ['1990', '2018']}
title: Average size of households in China 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2000', '1995', '1990']
Y_Axis['Number', 'of', 'persons']: ['3.03', '3.17', '3.11', '3.1', '2.97', '2.98', '3.02', '2.87', '2.88', '2.89', '3.13', '3.23', '3.5']

gold: This graph shows the average size of households in China from 1990 to 2018 . That year , approximately three people were living in an average Chinese household.Average number of people per household in China – additional information A household is commonly defined as one person living alone or a group of people living together and sharing certain living accommodations . The average number of people living in one household in China dropped from 3.5 in 1990 to 2.87 in 2011 .
gold_template: This graph shows the templateTitle[0] templateTitle[1] of templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . That templateXLabel[0] , approximately templateYValue[0] people were living in an templateTitle[0] Chinese household.Average templateYLabel[0] of people per household in templateTitleSubject[0] – additional information A household is commonly defined as one person living alone or a group of people living together and sharing certain living accommodations . The templateTitle[0] templateYLabel[0] of people living in one household in templateTitleSubject[0] dropped from templateYValue[max] in templateXValue[min] to templateYValue[min] in templateXValue[7] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] templateYLabel[1] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the cruise company templateTitleSubject[0] templateTitleSubject[0] carried approximately templateYValue[max] templateYLabel[2] templateYLabel[1] on its ships ..
generated: This statistic shows the Number of China persons 2018 from 1990 to 2018 . In 2018 , the cruise company China carried approximately 3.5 persons on its ships ..

Example 467:
titleEntities: {'Subject': ['Phoenix Suns'], 'Date': ['2001', '2019']}
title: Phoenix Suns ' 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']: ['246', '235', '218', '173', '154', '145', '137', '121', '136', '147', '148', '148', '145', '132', '132', '111', '109', '107']

gold: The statistic shows the revenue of the Phoenix Suns 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 246 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] 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] . templateTitleSubject[0] templateTitleSubject[0] The templateTitleSubject[0] templateTitleSubject[0] are a professional basketball franchise of the National Basketball Association ( NBA ) , entering the league as an expansion team in 1968 .
generated: The statistic shows the Revenue of the Phoenix Suns 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 246 million U.S. dollars . Phoenix Suns The Phoenix Suns are a professional basketball franchise of the National Basketball Association ( NBA ) , entering the league as an expansion team in 1968 .

Example 468:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2017']}
title: Most important export partner countries for Brazil in 2017
X_Axis['Country']: ['China', 'United_States', 'Argentina', 'Netherlands']
Y_Axis['Share', 'in', 'total', 'export']: ['21.8', '12.5', '8.1', '4.3']

gold: This statistic shows the most important export partner countries for Brazil in 2017 . In 20167 the main export partner country of Brazil was China with a share of 21.8 percent in exports .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitleSubject[0] in templateTitleDate[0] . In 20167 the main templateYLabel[2] templateTitle[3] templateXLabel[0] of templateTitleSubject[0] was templateXValue[0] with a templateYLabel[0] of templateYValue[max] percent in exports .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in the templateTitle[2] templateTitle[3] templateTitle[4] as of October templateTitleDate[0] . If the granted loans amount to more than templateYValue[max] percent in relation to customer deposits at the templateXValue[0] , the deposits are not sufficient for a refinancing of the loans . Thus , the templateXValue[0] depend on other means of refinancing .
generated: The statistic shows the Share total in the export partner countries as of October 2017 . If the granted loans amount to more than 21.8 percent in relation to customer deposits at the China , the deposits are not sufficient for a refinancing of the loans . Thus , the China depend on other means of refinancing .

Example 469:
titleEntities: {'Subject': ['Stuxnet'], 'Date': []}
title: Stuxnet - percentage of infected hosts by country
X_Axis['Country']: ['Iran', 'Indonesia', 'India', 'Azerbaijan', 'Pakistan', 'Malaysia', 'U.S.', 'Uzbekistan', 'Russia', 'Great_Britain', 'Other']
Y_Axis['Percentage', 'of', 'infected', 'hosts']: ['58.31', '17.83', '9.96', '3.4', '1.4', '1.16', '0.89', '0.71', '0.61', '0.57', '5.15']

gold: The statistic shows the percentage of Stuxnet infected hosts by country in 2010 . 58.31 percent of infected hosts were located in Iran .
gold_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] templateTitle[4] templateXLabel[0] in 2010 . templateYValue[max] percent of templateYLabel[1] templateYLabel[2] were located in templateXValue[0] .

generated_template: This statistic shows the number of templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] of templateYLabel[2] caused templateTitle[5] terrorist activities in templateTitleDate[0] . The templateTitle[2] templateTitle[3] of templateYLabel[2] from templateYLabel[1] in templateTitleDate[0] occurred in templateXValue[0] , accounting for templateYValue[max] percent of all templateYLabel[2] . In that year alone , terrorist incidents worldwide resulted in approximately 15,952 fatalities .
generated: This statistic shows the number of Stuxnet percentage the infected hosts of hosts caused country terrorist activities in . The infected hosts of hosts from infected in occurred in Iran , accounting for 58.31 percent of all hosts . In that year alone , terrorist incidents worldwide resulted in approximately 15,952 fatalities .

Example 470:
titleEntities: {'Subject': ['U.S. January TV'], 'Date': ['2020', '2020']}
title: Leading trailers in the U.S. January 2020 , by weekly TV ad spend
X_Axis['Year']: ['1917', 'Dolittle', 'Bad_Boys_for_Life', 'Like_a_Boss', 'Just_Mercy']
Y_Axis['Spending', 'in', 'million', 'U.S.', 'dollars']: ['10.41', '5.77', '4.18', '3.9', '3.72']

gold: The leading movie commercial in the United States based on weekly television advertising spending for the week ending January 5 , 2020 was for war drama film ' 1917 ' _ , with a 10.41 million U.S. dollar spend by studio Universal Pictures . Universal also spent 5.77 million U.S. dollars on TV promotion of 'Dolittle ' _ .
gold_template: The templateTitle[0] movie commercial in the templateTitle[2] based on templateTitle[6] television advertising templateYLabel[0] templateXValue[2] the week ending templateTitleSubject[0] 5 , templateTitle[4] was templateXValue[2] war drama film ' templateXValue[0] ' _ , with a templateYValue[max] templateYLabel[1] templateYLabel[2] dollar templateTitle[9] templateTitle[5] studio Universal Pictures . Universal also spent templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] on templateTitleSubject[0] promotion of 'Dolittle ' _ .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] from the templateTitle[1] from 2014 to templateTitleDate[1] . According to the report , approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[2] were exported from the templateTitleSubject[0] during the fiscal templateXLabel[0] templateXValue[2] .
generated: This statistic shows the Leading Spending of U.S. from the trailers from 2014 to 2020 . According to the report , approximately 10.41 million U.S. dollars of U.S. were exported from the U.S. January TV during the fiscal Year Bad Boys for Life .

Example 471:
titleEntities: {'Subject': ['LinkedIn'], 'Date': ['2020']}
title: LinkedIn : distribution of global audiences 2020 , by gender
X_Axis['Sex']: ['Female', 'Male']
Y_Axis['Percentage', 'of', 'users']: ['43', '57']

gold: This statistic gives information on the distribution of LinkedIn users worldwide as of January 2020 , sorted by gender . During the survey period , 43 percent of LinkedIn audiences were female and 57 percent were male .
gold_template: This statistic gives information on the templateTitle[1] of templateTitleSubject[0] templateYLabel[1] worldwide as of January templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[min] percent of templateTitleSubject[0] templateTitle[3] were templateXValue[0] and templateYValue[max] percent were templateXValue[last] .

generated_template: This statistic gives information on the templateTitle[1] of templateTitleSubject[0] templateYLabel[1] worldwide as of January templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[max] percent of templateTitleSubject[0] templateTitle[3] were templateXValue[0] and templateYValue[max] percent were templateXValue[last] .
generated: This statistic gives information on the distribution of LinkedIn users worldwide as of January 2020 , sorted by gender . During the survey period , 57 percent of LinkedIn audiences were Female and 57 percent were Male .

Example 472:
titleEntities: {'Subject': ['Premier League'], 'Date': ['2010', '2019']}
title: Premier League total broadcasting payments to clubs 2010 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11']
Y_Axis['Broadcasting', 'payments', 'in', 'million', 'GBP']: ['2456.01', '2419.6', '2398.5', '1633.9', '1605.3', '1563.0', '1061.0', '1055.0', '953.0']

gold: The statistic depicts the broadcasting payments to Premier League clubs from 2010/11 to 2018/19 . In the 2018/19 season , all Premier League clubs combined received a total of 2.46 billion British Pounds in broadcasting payments .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] to templateTitleSubject[0] clubs from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , all templateTitleSubject[0] clubs combined received a templateTitle[2] of templateYValue[max] templateYLabel[2] British Pounds in templateYLabel[0] templateYLabel[1] .

generated_template: In the academic templateXLabel[0] templateXValue[0] there were approximately templateYValue[0] thousand templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] , around 655 templateYLabel[1] less than there were in the templateXValue[last] academic templateXLabel[0] . Throughout most of this period there has been a steady decline in the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] .
generated: In the academic Year 2018/19 there were approximately 2456.01 thousand payments in the Premier League , around 655 payments less than there were in the 2010/11 academic Year . Throughout most of this period there has been a steady decline in the Broadcasting of payments in the Premier League .

Example 473:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Canada : reported arson rate 2000 to 2018
X_Axis['Year']: ['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['Rate', 'of', 'arson', 'per', '100,000', 'residents']: ['44.5', '46.27', '41.36', '43.12', '40.22', '40.48', '40.54', '39.57', '40.22', '39.86', '35.98', '30.29', '31.96', '25.41', '24.06', '25.12', '23.67', '23.4', '21.59']

gold: This statistic shows the reported arson rate in Canada from 2000 to 2018 . There were about 21.59 arsons per 100,000 residents in Canada in 2018 .
gold_template: This statistic shows the templateTitle[1] templateYLabel[1] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . There were about templateYValue[min] arsons templateYLabel[2] 100,000 templateYLabel[4] in templateTitleSubject[0] in templateXValue[max] .

generated_template: There were over templateYValue[0] thousand templateYLabel[1] in templateTitleSubject[0] during templateXValue[0] , a decrease of around 900 templateYLabel[1] when compared with the previous templateXLabel[0] . Same sex marriage was legalized in templateTitleSubject[0] on December 16 , templateXValue[4] following the Marriage and Civil Partnership Act . Compared with templateXValue[min] , there was a net decrease of around 2.77 thousand templateYLabel[1] by templateXValue[max] .
generated: There were over 44.5 thousand arson in Canada during 2000 , a decrease of around 900 arson when compared with the previous Year . Same sex marriage was legalized in Canada on December 16 , 2004 following the Marriage and Civil Partnership Act . Compared with 2000 , there was a net decrease of around 2.77 thousand arson by 2018 .

Example 474:
titleEntities: {'Subject': ['Marathon Oil'], 'Date': ['2010', '2018']}
title: Marathon Oil 's number of employees 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Number', 'of', 'employees']: ['2400', '2300', '2117', '2611', '3330', '3359', '3367', '3322', '29677']

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

generated_template: This statistic represents the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] templateYLabel[1] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In its templateXValue[max] fiscal templateXLabel[0] , the Switzerland-based electronics company employed templateYValue[0] people worldwide .
generated: This statistic represents the Number of Marathon Oil employees from the fiscal Year of 2010 to the fiscal Year of 2018 . In its 2018 fiscal Year , the Switzerland-based electronics company employed 2400 people worldwide .

Example 475:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Most popular social networks of U.S. teens 2016
X_Axis['Platform']: ['YouTube', 'Gmail', 'Snapchat', 'Instagram', 'Facebook', 'Kik_Messenger', 'Skype', 'Twitter', 'Vine', 'Tumblr']
Y_Axis['Percentage', 'of', 'teenagers']: ['91', '75', '66', '65', '61', '52', '43', '40', '31', '24']

gold: This statistic provides information about the most popular websites visited by teenagers in the United States as of June 2016 . During the survey period , video sharing platform YouTube was most popular among U.S. teens with a 91 percent usage rate . Snapchat was ranked third with 66 percent reporting that they accessed the photo sharing app .
gold_template: This statistic provides information about the templateTitle[0] templateTitle[1] websites visited by templateYLabel[1] in the templateTitle[4] as of June templateTitleDate[0] . During the survey period , video sharing templateXLabel[0] templateXValue[0] was templateTitle[0] templateTitle[1] among templateTitleSubject[0] templateTitle[5] with a templateYValue[max] percent usage rate . templateXValue[2] was ranked third with templateYValue[2] percent reporting that they accessed the photo sharing app .

generated_template: This statistic shows the most popular templateXValue[5] templateTitle[3] templateTitle[4] in the templateTitle[1] as of July templateTitleDate[0] , ranked templateTitle[6] templateYLabel[0] . As of July templateTitleDate[0] , templateXValue[0] was the most popular templateXValue[5] templateTitle[3] service , with a templateYLabel[0] of templateYValue[max] percent , whereas templateXValue[1] had a templateYLabel[0] of templateYValue[1] percent .
generated: This statistic shows the most popular Kik Messenger networks U.S. in the popular as of July 2016 , ranked 2016 Percentage . As of July 2016 , YouTube was the most popular Kik Messenger networks service , with a Percentage of 91 percent , whereas Gmail had a Percentage of 75 percent .

Example 476:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Percentage of population volunteering in the U.S. in 2015 , by age
X_Axis['Year']: ['16_to_24_years', '25_to_34_years', '35_to_44_years', '45_to_54_years', '55_to_64_years', '65_years_and_over']
Y_Axis['Percentage', 'of', 'population', 'volunteering']: ['21.8', '22.3', '28.9', '28', '25.1', '23.5']

gold: This statistic displays the percentage of population volunteering in the U.S. in 2015 , by age . In 2015 , 21.8 percent of Americans 16 to 24 years old volunteered at least once during the year .
gold_template: This statistic displays the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . In templateTitleDate[0] , templateYValue[min] percent of Americans templateXValue[0] to templateXValue[0] old volunteered at least once during the templateXLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . The templateYLabel[0] of templateTitle[2] templateTitle[3] has continuously decreased during this period . In templateXValue[0] , this templateYLabel[0] totalled approximately templateYValue[min] templateYLabel[2] British pounds .
generated: This statistic shows the Percentage of volunteering U.S. in the U.S. from 65 years and over to 16 to 24 years . The Percentage of volunteering U.S. has continuously decreased during this period . In 16 to 24 years , this Percentage totalled approximately 21.8 volunteering British pounds .

Example 477:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. sweet cherry production 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Production', 'in', 'thousand', 'tons']: ['344.4', '437.6', '350.2', '335.5', '363.6', '332.1', '424.0', '334.4', '313.2', '442.9', '248.1', '310.7', '294.2', '250.8', '283.1', '245.7', '181.4', '230.4', '207.9']

gold: This statistic shows the total amount of sweet cherries produced in the United States from 2000 to 2018 . In 2018 , around 344 thousand tons of sweet cherries were produced in the U.S .
gold_template: This statistic shows the total amount of templateTitle[1] cherries produced in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around templateYValue[0] thousand templateYLabel[2] of templateTitle[1] cherries were produced in the templateTitle[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitleSubject[0] templateTitle[4] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . According to the report , templateTitleSubject[0] templateTitle[4] templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[max] templateYLabel[2] templateYLabel[3] in templateXValue[max] .
generated: This statistic shows the U.S. sweet Production of U.S. 2000 in the production from 2000 to 2018 . According to the report , U.S. 2000 Production thousand amounted to approximately 442.9 tons in 2018 .

Example 478:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Leading baby wipes vendors in the U.S. 2016 , based on sales
X_Axis['Company']: ['Private_label', 'Kimberly_Clark_Corp.', 'Procter_&_Gamble', 'Seventh_Generation', 'The_Honest_Co.', 'Johnson_&_Johnson', 'Nice-Pak_Products', 'Paper_Partners', 'Kas_Direct', 'Irish_Breeze']
Y_Axis['Million', 'U.S.', 'dollars']: ['494.4', '416.2', '276.6', '9.6', '8.6', '7.1', '6.3', '6.1', '5.7', '4.4']

gold: The statistic shows the leading baby wipes vendors in the United States in 2016 , based on sales . In that year , Kimberly Clark was the second largest U.S. baby wipes vendor with sales of 416.2 million U.S. dollars . Total sales of U.S. baby wipes vendors amounted to about 1.25 billion U.S. dollars in 2016 .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] in templateTitleDate[0] , templateTitle[6] on templateTitle[7] . In that year , templateXValue[1] was the second largest templateYLabel[1] templateTitle[1] templateTitle[2] vendor with templateTitle[7] of templateYValue[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] . Total templateTitle[7] of templateYLabel[1] templateTitle[1] templateTitle[2] templateTitle[3] amounted to about 1.25 templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleDate[0] .

generated_template: The statistic presents the templateYLabel[0] of the templateTitle[0] templateXValue[7] templateTitle[2] templateTitle[3] in the templateTitle[4] in templateTitleDate[0] . In that year , templateXValue[0] was the country 's top top selling templateXValue[7] templateTitle[2] templateXLabel[0] with templateYLabel[0] that amounted to about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic presents the Million of the Leading Paper Partners wipes vendors in the U.S. in 2016 . In that year , Private label was the country 's top selling Paper Partners wipes Company with Million that amounted to about 494.4 dollars .

Example 479:
titleEntities: {'Subject': ['Los Angeles Rams'], 'Date': ['2006', '2019']}
title: Regular season home attendance of the Los Angeles Rams 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Home', 'attendance']: ['498605', '579439', '507136', '665318', '419220', '456146', '455657', '396925', '451153', '423376', '441896', '479840', '514352', '522608']

gold: This graph depicts the total regular season home attendance of the St. Louis / Los Angeles Rams franchise of the National Football League from 2006 to 2019 . In 2019 , the regular season home attendance of the franchise was 498,605 . The franchise moved from St. Louis to Los Angeles before the 2016 season .
gold_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the St. Louis / templateTitleSubject[0] Rams franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[0] . The franchise moved from St. Louis to templateTitleSubject[0] before the templateXValue[3] templateTitle[1] .

generated_template: The graph shows the templateTitle[2] templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitleSubject[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In its templateXValue[max] fiscal templateXLabel[0] , templateTitleSubject[0] 's paid out a templateYLabel[0] of approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The graph shows the home attendance of Regular Los Angeles Rams from the fiscal Year of 2006 to the fiscal Year of 2019 . In its 2019 fiscal Year , Los Angeles Rams 's paid out a Home of approximately 498605 attendance .

Example 480:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Estimated U.S. clothing and clothing accessories per capita sales 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'sales', 'in', 'U.S.', 'dollars']: ['795', '804', '797', '786', '774', '763', '734', '689', '667', '709', '734', '714', '680', '650', '616', '599', '587', '594']

gold: The timeline shows the estimated clothing and clothing accessories per capita sales in the United States from 2000 to 2017 . In 2017 , clothing and clothing accessories sales amounted to 795 U.S. dollars per capita .
gold_template: The timeline shows the templateTitle[0] templateTitle[2] and templateTitle[2] templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] and templateTitle[2] templateTitle[4] templateYLabel[2] amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] templateYLabel[0] templateYLabel[1] .

generated_template: In the second half of templateXValue[0] , there were approximately templateYValue[0] thousand templateTitle[1] templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[7] in the templateTitle[7] . Since templateXValue[min] , this figure had steadily increased up until the templateXLabel[0] templateXValue[10] in which it also reached its peak with around 213 thousand templateTitle[1] templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[7] . From templateXValue[10] on , the templateYLabel[0] of casino templateTitle[5] templateTitle[6] in templateTitleSubject[0] has been going down continuously .
generated: In the second half of 2017 , there were approximately 795 thousand U.S. clothing and clothing accessories per capita in sales in the sales . Since 2000 , this figure had steadily increased up until the Year 2007 in which it also reached its peak with around 213 thousand U.S. clothing and clothing accessories per capita in sales . From 2007 on , the Per of casino per capita in U.S. has been going down continuously .

Example 481:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Number of aggravated assaults in the U.S. in 2018 , by state
X_Axis['State']: ['California', 'Texas', 'Florida', 'New_York', 'Tennessee', 'Michigan', 'Illinois', 'North_Carolina', 'Pennsylvania', 'Arizona', 'Georgia', 'Missouri', 'Alabama', 'South_Carolina', 'Louisiana', 'Ohio', 'Indiana', 'Massachusetts', 'Maryland', 'Colorado', 'Washington', 'New_Mexico', 'Oklahoma', 'Arkansas', 'Wisconsin', 'New_Jersey', 'Virginia', 'Nevada', 'Kansas', 'Oregon', 'Minnesota', 'Iowa', 'Kentucky', 'Mississippi', 'Alaska', 'Utah', 'Connecticut', 'District_of_Columbia', 'West_Virginia', 'Nebraska', 'Montana', 'Idaho', 'Delaware', 'South_Dakota', 'Hawaii', 'North_Dakota', 'New_Hampshire', 'Rhode_Island', 'Wyoming', 'Maine', 'Vermont']
Y_Axis['Number', 'of', 'aggravated', 'assaults']: ['105412', '73656', '55551', '43171', '31717', '31021', '30539', '27526', '24077', '23528', '22783', '22042', '18944', '18446', '17866', '17674', '16834', '16648', '16135', '14547', '14251', '13598', '13084', '12378', '11263', '10463', '10113', '10027', '9559', '7360', '6857', '5931', '5059', '4696', '4391', '4319', '4294', '3971', '3945', '3461', '3120', '2957', '2845', '2682', '1925', '1560', '1435', '1366', '870', '803', '710']

gold: This statistic shows the total number of aggravated assaults reported in the United States in 2018 , by state . In 2018 , the federal state of California was ranked first with 105,412 cases of aggravated assaults , followed by Texas with 73,656 reported cases of aggravated assaults .
gold_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] reported in the templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , the federal templateXLabel[0] of templateXValue[0] was ranked first with templateYValue[max] cases of templateYLabel[1] templateYLabel[2] , followed templateTitle[5] templateXValue[1] with templateYValue[1] reported cases of templateYLabel[1] templateYLabel[2] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] and templateTitle[4] in the templateTitleSubject[0] templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In that year , most templateTitle[2] templateYLabel[1] and templateTitle[4] occurred in templateXValue[0] templateXValue[0] - a total of templateYValue[max] . In templateXValue[last] , no templateTitle[2] templateYLabel[1] and templateTitle[4] occurred in templateTitleDate[0] .
generated: The statistic shows the Number of assaults aggravated and 2018 in the U.S. in 2018 , state . In that year , most assaults aggravated and 2018 occurred in California - a total of 105412 . In Vermont , no assaults aggravated and 2018 occurred in 2018 .

Example 482:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2012', '2015']}
title: Smartphone use for professional reasons among U.S. physicians 2012 to 2015
X_Axis['Month']: ['March_2015', 'March_2014', 'March_2013', 'March_2012']
Y_Axis['Percentage', 'of', 'respondents']: ['84', '79', '76', '68']

gold: This survey indicates the percentage of physicians in the United States who use smartphones for professional purposes from March 2012 to March 2015 . In March 2014 , 79 percent of surveyed physicians used smartphones for their medical practice . Use of digital health has grown in the medical industry as consumers are expressing increasing interests in mobile health technologies .
gold_template: This survey indicates the templateYLabel[0] of templateTitle[7] in the templateTitle[6] who templateTitle[1] smartphones templateTitle[2] templateTitle[3] purposes from templateXValue[0] templateXValue[last] to templateXValue[0] . In templateXValue[0] templateXValue[1] , templateYValue[1] percent of surveyed templateTitle[7] used smartphones templateTitle[2] their medical practice . templateTitle[1] of digital health has grown in the medical industry as consumers are expressing increasing interests in mobile health technologies .

generated_template: As of March templateTitleDate[0] , it was found that templateTitleSubject[0] had just templateYValue[min] thousand templateYLabel[1] in the templateTitleSubject[1] templateTitleSubject[1] templateTitleSubject[1] . templateTitleSubject[0] had a total of health templateYLabel[0] templateYLabel[1] in the templateTitle[5] and templateYValue[min] percent were templateXValue[last] . Overall , 38 percent of templateYLabel[1] stated that they had increased to templateTitleSubject[0] when templateXValue[0] templateYLabel[2] in the UK .
generated: As of March 2012 , it was found that U.S. had just 68 thousand respondents in the U.S. . U.S. had a total of health Percentage respondents in the among and 68 percent were March 2012 . Overall , 38 percent of respondents stated that they had increased to U.S. when March 2015 respondents in the UK .

Example 483:
titleEntities: {'Subject': ['Forecast'], 'Date': ['2014', '2020']}
title: Forecast of sulfur fertilizer demand worldwide 2014 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Total', 'demand', 'in', 'million', 'metric', 'tons']: ['69.0', '67.7', '70.38', '68.08', '65.65', '63.47', '61.44']

gold: This statistic displays a forecast of total global demand for sulfur fertilizer from 2014 to 2020 . By 2020 , the annual demand for sulfur fertilizer is expected to reach some 69 million metric tons . Increasing crop prices lead to increased fertilizer demands and has been especially noted in recent years in South Asia .
gold_template: This statistic displays a templateTitleSubject[0] of templateYLabel[0] global templateYLabel[1] for templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . By templateXValue[max] , the annual templateYLabel[1] for templateTitle[1] templateTitle[2] is expected to reach some templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Increasing crop prices lead to increased templateTitle[2] demands and has been especially noted in recent years in South Asia .

generated_template: This statistic shows the global templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , templateTitle[0] templateTitleSubject[0] had a templateYLabel[0] templateYLabel[1] of around templateYValue[2] templateYLabel[2] templateYLabel[3] templateYLabel[4] globally .
generated: This statistic shows the global Total demand of Forecast sulfur from 2014 to 2020 . In 2019 , Forecast had a Total demand of around 70.38 million metric tons globally .

Example 484:
titleEntities: {'Subject': ['Smartphone'], 'Date': ['2016', '2021']}
title: Smartphone users worldwide 2016 to 2021
X_Axis['Year']: ['2021', '2020', '2019', '2018', '2017', '2016']
Y_Axis['Smartphone', 'users', 'in', 'billions']: ['3.8', '3.5', '3.2', '2.9', '2.7', '2.5']

gold: How many people have smartphones worldwide ? The number of smartphone users worldwide today surpasses three billion and is forecast to further grow by several hundred million in the next few years . China , India , and the United States are the countries with the highest number of smartphone users , with each country easily surpassing the 100 million user mark . Smartphone unit sales levelling off In the past five years , about 1.4 billion smartphones were sold worldwide annually , reflecting stagnation in the smartphone market during the last few years .
gold_template: How many people have smartphones templateTitle[2] ? The number of templateYLabel[0] templateYLabel[1] templateTitle[2] today surpasses templateYValue[2] templateYLabel[2] and is forecast to further grow by several hundred templateYLabel[2] in the next few years . China , India , and the country are the countries with the highest number of templateYLabel[0] templateYLabel[1] , with each country easily surpassing the 100 templateYLabel[2] user mark . templateYLabel[0] unit sales levelling off In the past five years , about 1.4 templateYLabel[2] smartphones were sold templateTitle[2] annually , reflecting stagnation in the templateYLabel[0] market during the last few years .

generated_template: General Motors incurred around templateYValue[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] in R & D costs in templateXValue[max] . This figure puts the car manufacturer of templateYLabel[0] for five percent in templateXValue[3] and a further templateYValue[2] percent less likely to templateYValue[max] percent in templateXValue[max] . Looking at the same quarter of the population , the templateYLabel[0] of the chemical templateYLabel[1] templateYLabel[2] was forecasted to increase .
generated: General Motors incurred around 2.7 users billions in R & D costs in 2021 . This figure puts the car manufacturer of Smartphone for five percent in 2018 and a further 3.2 percent less likely to 3.8 percent in 2021 . Looking at the same quarter of the population , the Smartphone of the chemical users billions was forecasted to increase .

Example 485:
titleEntities: {'Subject': ['Instagram Stories'], 'Date': ['16', '19']}
title: Daily active users of Instagram Stories 2019
X_Axis['Month']: ["Jan_'19", "Jun_'18", "Oct_'17", "Jun_'17", "Apr_'17", "Jan_'17", "Oct_'16"]
Y_Axis['Number', 'of', 'DAU', 'in', 'millions']: ['500', '400', '300', '250', '200', '150', '100']

gold: In January 2019 , photo sharing platform Instagram reported 500 million daily active Stories users worldwide , up from 400 million global DAU in June 2018 . Stories is a feature of the app allowing users post photo and video sequences that disappear 24 hours after being posted . Instagram usageInstagram has over one billion monthly active users and is one of the most popular social networks worldwide .
gold_template: In January templateTitle[5] , photo sharing platform templateTitleSubject[0] reported templateYValue[max] templateYLabel[2] templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[2] worldwide , up from templateYValue[1] templateYLabel[2] global templateYLabel[1] in June 2018 . templateTitleSubject[0] is a feature of the app allowing templateTitle[2] post photo and video sequences that disappear 24 hours after being posted . templateTitleSubject[0] usageInstagram has over templateYValue[max] templateYLabel[2] monthly templateTitle[1] templateTitle[2] and is one of the most popular social networks worldwide .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitleSubject[0] templateYLabel[1] from templateXValue[last] to December templateTitleDate[0] . As of that templateXLabel[0] , the mobile messenger had a total of templateYValue[1] templateYLabel[2] templateYLabel[1] in the preceding year .
generated: This statistic shows the Number of active users Instagram Stories DAU from Oct '16 to December 16 . As of that Month , the mobile messenger had a total of 400 millions DAU in the preceding year .

Example 486:
titleEntities: {'Subject': ['Iran'], 'Date': ['2014', '2024']}
title: National debt of Iran 2014 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'in', 'billion', 'U.S.', 'dollars']: ['575.52', '436.78', '344.33', '256.69', '205.66', '167.57', '142.95', '139.13', '143.56', '101.62', '31.64']

gold: The statistic shows the national debt of Iran from 2014 to 2018 , with projections up until 2024 . In 2018 , the national debt of Iran amounted to around 142.95 billion U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to around templateYValue[6] templateYLabel[2] templateYLabel[3] templateYLabel[4] . For comparison , the Greek templateYLabel[1] amounted to approximately 360 templateYLabel[2] euros that same templateXLabel[0] .
generated: The statistic shows the National debt of Iran from 2014 to 2018 , with projections up until 2024 . In 2018 , the National debt in Iran amounted to around 142.95 billion U.S. dollars . For comparison , the Greek debt amounted to approximately 360 billion euros that same Year .

Example 487:
titleEntities: {'Subject': ['Latin America', 'Caribbean'], 'Date': ['2017']}
title: Latin America & the Caribbean : homicide rates 2017 , by country
X_Axis['Country']: ['El_Salvador', 'Jamaica', 'Honduras', 'Belize', 'Bahamas', 'Brazil', 'Guatemala', 'Colombia', 'Mexico', 'Puerto_Rico', 'Guyana', 'Costa_Rica', 'Dominican_Republic', 'Grenada', 'Panama', 'Uruguay', 'Peru', 'Nicaragua', 'Ecuador', 'Suriname', 'Argentina', 'Chile']
Y_Axis['Homicides', 'per', '100,000', 'inhabitants']: ['61.8', '57.0', '41.7', '37.9', '30.9', '30.5', '26.1', '24.9', '24.8', '18.5', '14.8', '12.3', '11.3', '11.1', '9.7', '8.2', '7.7', '7.4', '5.8', '5.5', '5.2', '4.3']

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

generated_template: This statistic shows the templateTitleSubject[0] templateXValue[last] templateTitle[2] templateXLabel[5] templateTitle[4] templateTitle[5] templateXValue[2] to templateXValue[1] , templateTitle[8] templateYLabel[0] templateYLabel[1] . The largest ship templateXLabel[5] was templateXValue[0] templateXValue[0] 's Oasis III with a templateYLabel[0] templateYLabel[1] of templateYValue[max] and an expected templateXLabel[6] date of August templateXValue[0] .
generated: This statistic shows the Latin America Chile & Country homicide rates Honduras to Jamaica , country Homicides per . The largest ship Country was El Salvador 's Oasis III with a Homicides per of 61.8 and an expected Country date of August El Salvador .

Example 488:
titleEntities: {'Subject': ['Moscow'], 'Date': ['2019']}
title: Prime office rental prices in Moscow Q1 2015-Q2 2019
X_Axis['Quarter']: ["Q1_'15", "Q2_'15", "Q3_'15", "Q4_'15", "Q1_'16", "Q2_'16", "Q3_'16", "Q4_'16", "Q1_'17", "Q2_'17", "Q3_'17", "Q4_'17", "Q2_'18", "Q3_'18", "Q1_'19", "Q2_'19"]
Y_Axis['Cost', 'per', 'square', 'meter', 'in', 'euros']: ['760', '697', '692', '670', '670', '720', '613', '760', '726', '684', '669', '654', '693', '693', '703', '704']

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

generated_template: This statistic illustrates and estimate of templateTitleSubject[0] 's templateYLabel[1] from the first templateXLabel[0] of templateTitleDate[0] to the fourth templateXLabel[0] of templateTitle[5] . As of the last reported templateXLabel[0] , the photo sharing app is projected to generate templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in global revenues , up from an templateYLabel[0] templateYValue[6] templateYLabel[2] templateYLabel[3] templateYLabel[4] in the second templateXLabel[0] of 2019 .
generated: This statistic illustrates and estimate of Moscow 's per from the first Quarter of 2019 to the fourth Quarter of Q1 . As of the last reported Quarter , the photo sharing app is projected to generate 760 square meter euros in global revenues , up from an Cost 613 square meter euros in the second Quarter of 2019 .

Example 489:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Average U.S. brand response rate on social media 2017 , by vertical
X_Axis['Month']: ['Utilities', 'Retail', 'Consumer_Goods', 'Banking/Finance', 'Travel/Hospitality', 'Internet/Technology', 'Marketing/Advertising', 'Automotive', 'Real_Estate', 'Healthcare', 'Professional_Services', 'Government', 'Education', 'Nonprofit', 'Media/Entertainment']
Y_Axis['Average', 'response', 'rate']: ['18', '16', '14', '13', '12', '11', '11', '11', '10', '9', '9', '8', '7', '7', '6']

gold: This statistic presents the average brand response rate on social media in the United States as of the third quarter of 2017 , by vertical . According to the findings , the retail industry had an average response rate of 16 percent to communicating back to their consumers on social media , while the consumer goods industry reported in 14 percent .
gold_template: This statistic presents the templateYLabel[0] templateTitle[2] templateYLabel[1] templateYLabel[2] on templateTitle[5] templateTitle[6] in the templateTitle[1] as of the third quarter of templateTitleDate[0] , templateTitle[8] templateTitle[9] . According to the findings , the templateXValue[1] industry had an templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[1] percent to communicating back to their consumers on templateTitle[5] templateTitle[6] , while the templateXValue[2] industry reported in templateYValue[2] percent .

generated_template: The timeline shows the current templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] templateTitle[3] in the templateTitle[5] as of December templateTitleDate[0] . As of that time , the social messaging app announced more than templateYValue[max] thousand templateYLabel[1] templateYLabel[0] . The templateXValue[1] templateXValue[1] was ranked second with templateYValue[1] thousand templateYLabel[0] templateYLabel[1] .
generated: The timeline shows the current Average U.S. brand of U.S. response in the social as of December 2017 . As of that time , the social messaging app announced more than 18 thousand response Average . The Retail was ranked second with 16 thousand Average response .

Example 490:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013/14', '2017/18']}
title: U.S. rice import volume 2013/14 - 2017/18
X_Axis['Year']: ['2013/14', '2014/15', '2015/16', '2016/17', '2017/18']
Y_Axis['Imports', 'in', 'thousand', 'metric', 'tons']: ['755', '757', '768', '787', '775']

gold: This statistic shows the volume of rice imports to the United States from 2013/2014 to 2017/2018 , measured in thousand metric tons . During the trade year 2016/17 , rice imports to the U.S. amounted to about 787 thousand metric tons .
gold_template: This statistic shows the templateTitle[3] of templateTitle[1] templateYLabel[0] to the templateTitle[0] from 2013/2014 to 2017/2018 , measured in thousand templateYLabel[2] templateYLabel[3] . During the trade templateXLabel[0] templateXValue[3] , templateTitle[1] templateYLabel[0] to the templateTitleSubject[0] amounted to about templateYValue[max] thousand templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the share of internet templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[0] to templateTitleDate[1] . templateYValue[4] percent of templateTitleSubject[1] templateYLabel[1] were aged between 18 and 24 years .
generated: This statistic shows the share of internet thousand in the U.S. ( U.S. ) from 2013/14 to 2017/18 . 775 percent of U.S. thousand were aged between 18 and 24 years .

Example 491:
titleEntities: {'Subject': ['UK'], 'Date': ['2018', '2018']}
title: UK : reach of top active social media platforms in Q3 2018
X_Axis['Platform']: ['Youtube', 'Facebook', 'FB_Messenger', 'Whatsapp', 'Instagram', 'Twitter', 'Snapchat', 'LinkedIn', 'Pinterest', 'Skype', 'Reddit', 'Tumblr', 'Twitch', 'WeChat', 'Viber', 'Imgur']
Y_Axis['Share', 'of', 'respondents']: ['80', '78', '60', '58', '47', '46', '27', '27', '27', '22', '14', '13', '12', '8', '8', '7']

gold: This statistic illustrates the results of a survey about the leading active social media platforms in the UK in 2018 . During the survey period , it was found that 80 percent of the respondents reported that they used Facebook . Facebook is a popular free social networking website that allows registered users to create profiles , upload photos and video , send messages and keep in touch with friends , family and colleagues .
gold_template: This statistic illustrates the results of a survey about the leading templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitleSubject[0] in templateTitle[8] . During the survey period , it was found that templateYValue[max] percent of the templateYLabel[1] reported that they used templateXValue[1] . templateXValue[1] is a popular free templateTitle[4] networking website that allows registered users to create profiles , upload photos and video , send messages and keep in touch with friends , family and colleagues .

generated_template: templateXValue[0] is the most active templateTitle[0] templateTitle[1] among internet users in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) , with templateYValue[max] percent of people reporting use of the service . templateXValue[1] was very close behind , with a rate of templateYValue[1] percent . templateXValue[0] 's broad audience in the templateTitleSubject[0] templateTitleSubject[0] templateXValue[0] reaches a broad audience in the templateTitleSubject[1] , making it an attractive partner for advertisers of almost any product .
generated: Youtube is the most active UK reach among internet users in the UK ( UK ) , with 80 percent of people reporting use of the service . Facebook was very close behind , with a rate of 78 percent . Youtube 's broad audience in the UK Youtube reaches a broad audience in the UK , making it an attractive partner for advertisers of almost any product .

Example 492:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1900', '2016']}
title: Earthquakes that caused the most economic damage in the U.S. 1900 to 2016
X_Axis['Date,', 'Location']: ['January_17_1994_Los_Angeles', 'October_18_1989_San_Francisco', 'February_28_2001_Seattle', 'March_28_1964_Prince_William_Sound', 'August_24_2014_San_Francisco_California', 'February_9_1971_Los_Angeles', 'April_18_1906_San_Francisco', 'October_1_1987_Los_Angeles', 'December_22_2003_San_Robbles_(California)', 'October_15_2006_Hawai_Island', 'June_28_1992_Landers_California', 'April_22_1992_South_California']
Y_Axis['Damage', 'in', 'million', 'U.S.', 'dollars']: ['30000', '5600', '2000', '1020', '700', '535', '524', '213', '200', '150', '100', '100']

gold: The statistic shows the earthquakes that resulted in the most economic damage in the United States from 1900 to 2016 . The earthquake that occurred on January 17 , 1994 in Los Angeles caused approximately 30 billion U.S. dollars worth of damage and is the costliest earthquake on record .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] resulted in the templateTitle[3] templateTitle[4] templateYLabel[0] in the templateTitle[6] from templateTitleDate[0] to templateTitleDate[1] . The earthquake templateTitle[1] occurred on templateXValue[0] , templateXValue[0] in templateXValue[0] caused approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] worth of templateYLabel[0] and is the costliest earthquake on record .

generated_template: The statistic illustrates the templateYLabel[0] of templateTitleSubject[0] internet templateTitle[2] in templateTitleSubject[1] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In that year , about templateYValue[max] million people were located in templateXValue[0] templateXValue[0] .
generated: The statistic illustrates the Damage of U.S. internet caused in U.S. in 1900 , economic Date, . In that year , about 30000 million people were located in January 17 1994 Los Angeles .

Example 493:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018']}
title: Population density in Italy 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Inhabitants', 'per', 'square', 'kilometer']: ['205.45', '205.81', '206.12', '206.47', '206.67', '204.78', '202.42', '201.87', '201.53', '200.91', '200.0']

gold: The statistic shows the population density in Italy from 2008 to 2018 . In 2018 , the population density in Italy amounted to about 205.45 inhabitants per square kilometer . See the population of Italy for comparison .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . See the templateTitle[0] of templateTitleSubject[0] for comparison .

generated_template: In templateXValue[max] , templateTitleSubject[0] had a templateTitle[0] templateTitle[1] of about templateYValue[max] people templateYLabel[1] templateYLabel[2] templateYLabel[3] . The country has one of the lowest templateTitle[0] densities in the world , as the total templateTitle[0] is very small in relation to the dimensions of the land . templateTitleSubject[0] has a relatively stable templateTitle[0] size , consistently with a growth of around one percent compared to the previous templateXLabel[0] .
generated: In 2018 , Italy had a Population density of about 206.67 people per square kilometer . The country has one of the lowest Population densities in the world , as the total Population is very small in relation to the dimensions of the land . Italy has a relatively stable Population size , consistently with a growth of around one percent compared to the previous Year .

Example 494:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: U.S. consumers ' purchase location of shampoos and conditioners 2014
X_Axis['Response']: ['"Big_Box"_retail_store_(e.g._Walmart_Target)', 'Grocery_store/Supermarket', 'Pharmacy_(e.g._CVS_Walgreens)', 'Online_(Net)', 'Online_mass_merchandiser_(e.g._Amazon_drugstore.com)', "Department_Stores_(e.g._Macy's_Nordstrom)", 'In-person_at_a_specialty_beauty_products_merchant_(e.g._Sephora)', 'Online_specialty_beauty_products_merchant_(e.g._Sephora_Ultra)', 'In-person/not_in_a_store_(e.g._Avon_Mary_Kay)', 'Online_through_a_"sampling"_membership_program_(e.g._Ipsy_Birchbox)', "Online_through_a_specific_brand's_website_(e.g._Clairol_CoverGirl)", 'Somewhere_else']
Y_Axis['Share', 'of', 'respondents']: ['62', '36', '31', '12', '8', '5', '4', '3', '2', '1', '1', '10']

gold: This statistic presents the results of a survey among U.S. adult consumers . The survey was fielded online by Harris Interactive in June 2014 , asking the respondents where they usually purchase their shampoo and/or conditioners . Some 12 percent of U.S. adults indicated that they buy their shampoo/conditioner online .
gold_template: This statistic presents the results of a survey among templateTitleSubject[0] adult templateTitle[1] . The survey was fielded templateXValue[3] by Harris Interactive in June templateTitleDate[0] , asking the templateYLabel[1] where they usually templateTitle[3] their shampoo and/or templateTitle[6] . Some templateYValue[3] percent of templateTitleSubject[0] adults indicated that they buy their shampoo/conditioner templateXValue[3] .

generated_template: A survey of online users in the templateTitle[5] revealed that templateYValue[max] percent of templateYLabel[1] felt that templateXValue[0] templateXValue[0] and messaging apps were among the three types of apps that they templateTitle[1] the most templateTitleSubject[0] on . During the third quarter templateTitleDate[0] survey , templateYValue[6] percent of templateYLabel[1] stated the same about gaming apps .
generated: A survey of online users in the shampoos revealed that 62 percent of respondents felt that "Big Box" retail store (e.g. Walmart Target) and messaging apps were among the three types of apps that they consumers the most U.S. on . During the third quarter 2014 survey , 4 percent of respondents stated the same about gaming apps .

Example 495:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Leading U.S. states in sunflower production 2019
X_Axis['State']: ['South_Dakota', 'North_Dakota', 'Minnesota', 'California', 'Colorado', 'Kansas', 'Nebraska', 'Texas']
Y_Axis['Production', 'in', 'thousand', 'pounds']: ['831600', '740700', '102630', '70680', '59400', '53925', '44850', '39650']

gold: The U.S. state with the highest production volume of sunflowers is South Dakota at 831.6 million pounds in 2019 . North Dakota came in second at 740.7 million pounds of sunflowers . Sunflower products There are several products that are derived from sunflowers .
gold_template: The templateTitleSubject[0] templateXLabel[0] with the highest templateYLabel[0] volume of sunflowers is templateXValue[0] at templateYValue[max] templateYLabel[1] templateYLabel[2] in templateTitleDate[0] . templateXValue[1] templateXValue[0] came in second at templateYValue[1] templateYLabel[1] templateYLabel[2] of sunflowers . templateTitle[3] products There are several products that are derived from sunflowers .

generated_template: This statistic provides information on the templateTitle[0] templateTitle[1] of templateTitle[3] templateTitle[4] templateYLabel[1] templateYLabel[2] as of templateTitleSubject[0] templateTitle[5] , templateTitle[6] templateTitle[7] . As of that period , templateXValue[0] templateXValue[0] had the highest active templateYLabel[3] usage rate with templateYValue[max] percent of the templateYLabel[1] templateYLabel[2] .
generated: This statistic provides information on the Leading U.S. of sunflower production thousand pounds as of U.S. 2019 , 2019 . As of that period , South Dakota had the highest active pounds usage rate with 831600 percent of the thousand pounds .

Example 496:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2016']}
title: Leading eSports pro players on Twitter worldwide 2016 , by number of followers
X_Axis['Month']: ['Søren_Bjerg_(Bjergsen)', 'Enrique_Cedeño_Martínez_(xPeke)', 'Yiliang_Peng_(Doublelift)', 'Jason_Tran_(WildTurtle)', 'Danil_Ishutin_(Dendi)', 'Hai_Du_Lam_(Hai)', 'Henrik_Hansen_(Froggen)', 'Martin_Larsson_(Rekkles)', 'Bora_Kim_(Yell0wStaR)', 'Zachary_Scuderi_(Sneaky)']
Y_Axis['Number', 'of', 'Twitter', 'followers', 'in', 'thousands']: ['604', '596', '393', '376', '334', '297', '270', '258', '256', '244']

gold: The graph shows the leading eSports professional players on Twitter worldwide as of January 2016 , ranked by the number of fans . As of the measured period , Søren Bjerg , a player from Denmark also known as Bjergsen , was the most famous on Twitter , with 604 thousand followers . He was followed by Enrique Martínez , aka xPeke , who gathered 596 thousand followers on Twitter .
gold_template: The graph shows the templateTitle[0] templateTitle[1] professional templateTitle[3] on templateYLabel[1] templateTitle[5] as of January templateTitleDate[0] , ranked templateTitle[7] the templateYLabel[0] of fans . As of the measured period , templateXValue[0] , a player from Denmark also known as Bjergsen , was the most famous on templateYLabel[1] , with templateYValue[max] thousand templateYLabel[2] . He was followed templateTitle[7] templateXValue[1] , aka xPeke , who gathered templateYValue[1] thousand templateYLabel[2] on templateYLabel[1] .

generated_template: templateTitleSubject[0] Inc. is a Canadian owned retail chain headquartered in Montreal . There were 1,225 templateTitleSubject[0] templateYLabel[1] in templateTitleSubject[1] as of February templateTitleDate[0] . templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] per templateTitle[5] , templateYValue[max] , and templateXValue[last] templateXValue[last] templateXValue[last] had just templateYValue[min] .
generated: Twitter Inc. is a Canadian owned retail chain headquartered in Montreal . There were 1,225 Twitter in Twitter as of February 2016 . Søren Bjerg (Bjergsen) had the highest Number of Twitter per worldwide , 604 , and Zachary Scuderi (Sneaky) had just 244 .

Example 497:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2009', '2019']}
title: Population in Sweden 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Inhabitants', 'in', 'millions']: ['10.33', '10.23', '10.12', '10.0', '9.85', '9.75', '9.64', '9.56', '9.48', '9.42', '9.34']

gold: This statistic shows the total population in Sweden from 2009 to 2019 . The number of inhabitants in Sweden has increased by nearly one million in this time period . In 2009 , there were approximately 9.34 million inhabitants in Sweden and by the end of 2019 the Swedish population reached 10.33 million people .
gold_template: This statistic shows the total templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The number of templateYLabel[0] in templateTitleSubject[0] has increased by nearly one templateYLabel[1] in this time period . In templateXValue[min] , there were approximately templateYValue[min] templateYLabel[1] templateYLabel[0] in templateTitleSubject[0] and by the end of templateXValue[max] the Swedish templateTitle[0] reached templateYValue[max] templateYLabel[1] people .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] of templateTitleSubject[0] was estimated at approximately templateYValue[max] templateYLabel[1] templateYLabel[0] . The Syrian refugee crisis The war in templateTitleSubject[0] has been escalating since 2011 , which has made it difficult to estimate the current templateTitle[1] of the country .
generated: This statistic shows the Population Sweden of Sweden from 2009 to 2019 . In 2019 , the Sweden of Sweden was estimated at approximately 10.33 millions Inhabitants . The Syrian refugee crisis The war in Sweden has been escalating since 2011 , which has made it difficult to estimate the current Sweden of the country .

Example 498:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018', '2027']}
title: Projected increase of the national defense budget in Canada fiscal years 2018 to 2027
X_Axis['Year']: ['2026-2027', '2025-2026', '2024-2025', '2023-2024', '2022-2023', '2021-2022', '2020-2021', '2019-2020', '2018-2019', '2017-2018']
Y_Axis['Annual', 'funding', 'increase', 'in', 'million', 'Canadian', 'dollars']: ['2300', '2020', '1751', '1495', '1250', '1016', '793', '580', '377', '184']

gold: This statistic shows the projected increase over baseline spending for the national defense budget of Canada between the fiscal years 2018 to 2027 . In fiscal year 2027 , the defense budget for Canada is projected to be 2.3 billion Canadian dollars greater than 2026 budget .
gold_template: This statistic shows the templateTitle[0] templateYLabel[2] over baseline spending for the templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleSubject[0] between the templateTitle[6] templateTitle[7] templateTitle[8] to templateTitleDate[0] . In templateTitle[6] templateXLabel[0] templateTitleDate[0] , the templateTitle[3] templateTitle[4] for templateTitleSubject[0] is templateTitle[0] to be templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] greater than 2026 templateTitle[4] .

generated_template: This statistic shows the total templateYLabel[0] of templateTitle[0] enrollments in templateTitle[3] across the templateTitle[2] from the academic templateXLabel[0] templateXValue[last] to templateXValue[0] . In templateXValue[0] , around templateYValue[max] templateYLabel[2] templateYLabel[1] enrolled for templateTitle[0] degrees in the templateTitle[2] .
generated: This statistic shows the total Annual of Projected enrollments in defense across the national from the academic Year 2017-2018 to 2026-2027 . In 2026-2027 , around 2300 increase funding enrolled for Projected degrees in the national .

Example 499:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2015', '2020']}
title: Purchasing power change in the Netherlands 2015 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Year', 'on', 'year', 'percentage', 'change']: ['1.3', '1.6', '0.3', '0.3', '2.6', '1']

gold: In 2018 , the purchasing power in the Netherlands increased for the fourth year in a row . Purchasing power growth slowed down considerably though in 2017 and 2018 in comparison to 2015 and especially 2016 . In the next two years , the purchasing power was forecast to increase further .
gold_template: In templateXValue[2] , the templateTitle[0] templateTitle[1] in the templateTitleSubject[0] increased for the fourth templateXLabel[0] in a row . templateTitle[0] templateTitle[1] growth slowed down considerably though in templateXValue[3] and templateXValue[2] in comparison to templateXValue[min] and especially templateXValue[4] . In the next templateYValue[1] years , the templateTitle[0] templateTitle[1] was forecast to increase further .

generated_template: According to quarterly data , the average templateYLabel[0] of templateTitle[2] committed in the United Kingdom ( templateTitleSubject[0] ) has significantly in recent years . This is due to increase to the previous templateXLabel[0] , the templateYLabel[0] in this figure was significantly to templateYValue[max] percent in templateXValue[max] . Telecommunications market As of templateXValue[3] , the cellular subscription growth rate in templateTitleSubject[0] was 3.8 percent , ranking it in the middle field of Asia Pacific countries .
generated: According to quarterly data , the average Year of change committed in the United Kingdom ( Netherlands ) has significantly in recent years . This is due to increase to the previous Year , the Year in this figure was significantly to 2.6 percent in 2020 . Telecommunications market As of 2017 , the cellular subscription growth rate in Netherlands was 3.8 percent , ranking it in the middle field of Asia Pacific countries .

Example 500:
titleEntities: {'Subject': ['General Motors'], 'Date': ['2014']}
title: General Motors - passenger cars produced worldwide 1999 to 2014
X_Axis['Year']: ['2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Passenger', 'cars', 'produced', '(in', 'millions)']: ['6.64', '6.73', '6.61', '6.87', '6.27', '5.0', '6.02', '6.26', '5.78', '5.66', '4.5', '4.68', '4.9', '4.66', '5.27', '5.34']

gold: The timeline shows the passenger car production of General Motors worldwide from 1999 to 2014 . In 2013 , GM produced 6.7 million passenger cars worldwide . The U.S. automaker is world 's fourth largest manufacturer of passenger cars in terms of production .
gold_template: The timeline shows the templateYLabel[0] car production of templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[1] , GM templateYLabel[2] templateYValue[1] million templateYLabel[0] templateYLabel[1] templateTitle[5] . The U.S. automaker is world 's fourth largest manufacturer of templateYLabel[0] templateYLabel[1] in terms of production .

generated_template: The timeline shows the templateYLabel[0] car production of templateTitleSubject[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateYLabel[2] around templateYValue[max] million templateYLabel[0] templateYLabel[1] templateTitle[4] . templateTitleSubject[0] is ranked among the 15 largest automakers templateTitle[4] .
generated: The timeline shows the Passenger car production of General Motors produced from 1999 to 2014 . In 2014 , General Motors produced around 6.87 million Passenger cars produced . General Motors is ranked among the 15 largest automakers produced .

Example 501:
titleEntities: {'Subject': ['ACSI'], 'Date': ['2019']}
title: ACSI - U.S. customer satisfaction with social media 2019
X_Axis['Platform']: ['Pinterest', 'YouTube', 'Wikipedia', 'Instagram', 'Snapchat', 'Twitter', 'LinkedIn', 'Tumblr', 'Facebook']
Y_Axis['ACSI', 'score', '(100-point', 'scale)']: ['80', '78', '74', '72', '71', '69', '69', '64', '63']

gold: This graph shows the American Customer Satisfaction Index ( ACSI ) of customer satisfaction with social media websites in 2019 . Overall , Pinterest scored the highest level of customer satisfaction with 80 index points . Facebook was ranked last with an index score rating of 63 / 100 index points .
gold_template: This graph shows the American templateTitle[2] templateTitle[3] Index ( templateYLabel[0] ) of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] websites in templateTitleDate[0] . Overall , templateXValue[0] scored the highest level of templateTitle[2] templateTitle[3] templateTitle[4] templateYValue[max] index points . templateXValue[last] was ranked last templateTitle[4] an index templateYLabel[1] rating of templateYValue[min] / 100 index points .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] and templateTitle[4] templateTitle[5] in the templateTitle[6] in templateTitleDate[0] . During the February templateTitleDate[0] survey period , templateYValue[1] percent of respondents stated that they had templateYLabel[5] accessed templateXValue[1] , either via desktop or mobile internet connection .
generated: The statistic shows the ACSI U.S. customer of satisfaction and social media in the 2019 in 2019 . During the February 2019 survey period , 78 percent of respondents stated that they had scale) accessed YouTube , either via desktop or mobile internet connection .

Example 502:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. mobile device owner monthly app download rate 2018 , by age group
X_Axis['Response']: ['Teens_(13-17)', 'Millennials_(18-34)', 'Gen_X_(35-54)', 'Boomers_(55-64)']
Y_Axis['Share', 'of', 'respondents']: ['83', '74', '59', '39']

gold: This statistic gives information on the percentage of mobile device owners in the United States who download apps at least once a month or more as of April 2018 , sorted by age group . During the survey period , it was found that 74 percent of responding Millennial app users downloaded apps to their mobile device on a monthly basis .
gold_template: This statistic gives information on the percentage of templateTitle[1] templateTitle[2] owners in the templateTitle[0] who templateTitle[6] apps at least once a month or more as of April templateTitleDate[0] , sorted templateTitle[9] templateTitle[10] templateTitle[11] . During the survey period , it was found that templateYValue[1] percent of responding Millennial templateTitle[5] users downloaded apps to their templateTitle[1] templateTitle[2] on a templateTitle[4] basis .

generated_template: This statistic shows the results of a survey among female templateTitleSubject[0] high school students templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . According to the source , templateYValue[max] percent of female students in the templateTitle[1] were templateTitle[5] templateTitle[6] as of 2013 .
generated: This statistic shows the results of a survey among female U.S. high school students owner monthly app download . According to the source , 83 percent of female students in the mobile were app download as of 2013 .

Example 503:
titleEntities: {'Subject': ['Boeing'], 'Date': ['2004', '2019']}
title: Boeing 737 - orders 2004 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Number', 'of', 'aircraft']: ['69', '837', '865', '701', '666', '1196', '1208', '1184', '625', '508', '197', '488', '850', '733', '574', '152']

gold: In 2019 , Boeing received gross orders for 69 units of its 737 narrow-body jet airliner series , but net orders after cancellations and changes ( under the ASC 606 revenue recognition standard ) came to a devastating negative 183 units . The slump in net orders came as a result of the jet maker 's 737 MAX crisis . Boeing delivered some 18 units of its 737 aircraft to Delta Air Lines in 2019 .
gold_template: In templateXValue[max] , templateTitleSubject[0] received gross templateTitle[2] for templateYValue[min] units of its templateTitle[1] narrow-body jet airliner series , but net templateTitle[2] after cancellations and changes ( under the ASC 606 revenue recognition standard ) came to a devastating negative 183 units . The slump in net templateTitle[2] came as a result of the jet maker 's templateTitle[1] MAX crisis . templateTitleSubject[0] delivered some 18 units of its templateTitle[1] templateYLabel[1] to Delta Air Lines in templateXValue[max] .

generated_template: templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] revenues have generally been increasing over the last several years . As of templateXValue[max] , templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] total templateYLabel[0] templateYLabel[1] was estimated to be about templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] . This is a slight decrease from the previous templateXLabel[0] when templateYLabel[0] templateYLabel[1] was at about 20 templateYLabel[2] .
generated: Boeing 737 Boeing 2004 revenues have generally been increasing over the last several years . As of 2019 , Boeing 737 Boeing 2004 total Number aircraft was estimated to be about 69 aircraft . This is a slight decrease from the previous Year when Number aircraft was at about 20 aircraft .

Example 504:
titleEntities: {'Subject': ['Leading'], 'Date': ['2017']}
title: Leading countries worldwide based on coffee area harvested 2017
X_Axis['Country']: ['Brazil', 'Indonesia', 'Côte_d’Ivoire', 'Colombia', 'Ethiopia', 'Mexico', 'Viet_Nam', 'Honduras', 'India', 'Peru']
Y_Axis['Area', 'in', 'thousand', 'hectares']: ['1800.4', '1253.8', '925.44', '798.36', '694.33', '638.6', '605.18', '505.12', '449.36', '423.55']

gold: This statistic illustrates the global leading 10 countries based on coffee area harvested in 2017 . In that year , Mexico harvested an area of 638.6 thousand hectares of green coffee and was ranked sixth among coffee-growing countries worldwide .
gold_template: This statistic illustrates the global templateTitleSubject[0] 10 templateTitle[1] templateTitle[3] on templateTitle[4] templateYLabel[0] templateTitle[6] in templateTitleDate[0] . In that year , templateXValue[5] templateTitle[6] an templateYLabel[0] of templateYValue[5] thousand templateYLabel[2] of green templateTitle[4] and was ranked sixth among coffee-growing templateTitle[1] templateTitle[2] .

generated_template: During the year templateTitleDate[0] , the total templateTitle[3] exports templateTitle[4] templateTitleSubject[0] to the templateXValue[0] templateXValue[0] amounted to about 27 templateYLabel[2] of 75 cl . templateYLabel[1] . The UK is the first templateTitle[1] market for French templateTitle[3] .
generated: During the year 2017 , the total based exports coffee Leading to the Brazil amounted to about 27 hectares of 75 cl . thousand . The UK is the first countries market for French based .

Example 505:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2011', '2019']}
title: Facebook : worldwide quarterly revenue 2011 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11"]
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['21082', '17652', '16886', '15077', '16914', '13727', '13231', '11966', '12972', '10328', '9321', '8032', '8809', '7011', '6436', '5382', '5841', '4501', '4042', '3543', '3851', '3203', '2910', '2502', '2585', '2016', '1813', '1458', '1585', '1262', '1184', '1058', '1131']

gold: In the fourth quarter of 2019 , social network Facebook 's total revenues amounted to 21.08 billion U.S. dollars , the majority of which were generated through advertising . The company announced over seven million active advertisers on Facebook during the third quarter of 2019 . During that fiscal period , the company 's net income was 7.35 billion U.S. dollars .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[1] , social network templateTitleSubject[0] 's total revenues amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , the majority of which were generated through advertising . The company announced over seven templateYLabel[1] active advertisers on templateTitleSubject[0] during the third templateXLabel[0] of templateTitleDate[1] . During that fiscal period , the company 's net income was 7.35 templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitle[5] templateTitle[6] templateYLabel[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the final templateXLabel[0] of templateXValue[0] , up templateTitle[8] 263.5 templateYLabel[1] in the corresponding templateXLabel[0] of templateXValue[4] . The templateTitleSubject[0] ' subscription templateYLabel[0] generally grows steadily over the course of each year . However , for the most part , the company sees a small templateYLabel[0] decrease in the third templateXLabel[0] , which has been an ongoing and consistent trend for the company over the last few years .
generated: The Facebook 2019 Revenue amounted to 21082 million U.S. dollars in the final Quarter of Q4 '19 , up 2019 263.5 million in the corresponding Quarter of Q4 '18 . The Facebook ' subscription Revenue generally grows steadily over the course of each year . However , for the most part , the company sees a small Revenue decrease in the third Quarter , which has been an ongoing and consistent trend for the company over the last few years .

Example 506:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Payment type preference when shopping at fast food restaurants in the U.S. 2018
X_Axis['Response']: ['Credit_card', 'Debit_card', 'Cash']
Y_Axis['Share', 'of', 'respondents']: ['18', '44', '32']

gold: This statistic shows the preferred form of payment when shopping at fast food restaurants among consumers in the United States in 2018 . In the study it was found that 32 percent of consumers preferred to use cash when making purchases at fast food restaurants .
gold_template: This statistic shows the preferred form of templateTitle[0] templateTitle[3] templateTitle[4] at templateTitle[5] templateTitle[6] templateTitle[7] among consumers in the templateTitle[8] in templateTitleDate[0] . In the study it was found that templateYValue[2] percent of consumers preferred to use templateXValue[last] templateTitle[3] making purchases at templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: This statistic shows the results of a survey conducted templateTitle[3] the Hearth , Patio & Barbecue Association ( HPBA ) in July and August templateTitleDate[0] . During the survey , templateTitleSubject[0] consumers were asked about which types of grills they owned . Approximately templateYValue[max] percent of the templateYLabel[1] indicated templateTitle[2] of a templateXValue[0] templateXValue[0] .
generated: This statistic shows the results of a survey conducted when the Hearth , Patio & Barbecue Association ( HPBA ) in July and August 2018 . During the survey , U.S. consumers were asked about which types of grills they owned . Approximately 44 percent of the respondents indicated preference of a Credit card .

Example 507:
titleEntities: {'Subject': ['Easter U.S.'], 'Date': ['2019']}
title: Planned Easter expenditure per capita in the U.S. by item 2019
X_Axis['Month']: ['Food', 'Clothing', 'Gifts', 'Candy', 'Flowers', 'Decorations', 'Greeting_cards', 'Other']
Y_Axis['Average', 'expenditure', 'in', 'U.S.', 'dollars']: ['47.97$', '27.29$', '24.01$', '20.78$', '10.79$', '8.73$', '6.52$', '5.15$']

gold: This statistic shows the results of a survey among people in the United States on the amount of money they are planning to spend on the following items for the 2019 Easter holidays . Respondents stated that they are planning to spend an average of 20.78 U.S. dollars on candy for the upcoming Easter holidays .
gold_template: This statistic shows the results of a survey among people in the templateTitle[5] on the amount of money they are planning to spend on the following items for the templateTitleDate[0] templateTitleSubject[0] holidays . Respondents stated that they are planning to spend an templateYLabel[0] of 20.78 templateYLabel[2] templateYLabel[3] on templateXValue[3] for the upcoming templateTitleSubject[0] holidays .

generated_template: How many people play templateTitleSubject[0] ? PlayerUnknown 's Battlegrounds ( templateTitleSubject[0] ) , which is available on templateTitle[1] and templateTitle[2] , crossed the templateYValue[max] templateYLabel[2] templateTitle[3] templateTitle[4] mark in templateXValue[0] templateXValue[0] - an impressive figure considering it was released little over a year earlier . The Battle Royale game developed by Bluehole was made available to the public in March templateXValue[1] . One of the biggest games on Steam While it is available on both PlayStation and Xbox One , templateTitleSubject[0] 's origins lie in templateTitle[2] and , more specifically , on Steam .
generated: How many people play Easter U.S. ? PlayerUnknown 's Battlegrounds ( Easter U.S. ) , which is available on Easter and expenditure , crossed the 47.97$ U.S. per capita mark in Food - an impressive figure considering it was released little over a year earlier . The Battle Royale game developed by Bluehole was made available to the public in March Clothing . One of the biggest games on Steam While it is available on both PlayStation and Xbox One , Easter U.S. 's origins lie in expenditure and , more specifically , on Steam .

Example 508:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2017', '2023']}
title: Argentina : internet user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['94', '93', '91', '88', '85', '82', '77']

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

generated_template: This statistic provides information on the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it was estimated that templateYValue[max] percent of the Indian templateYLabel[1] will be accessing templateTitle[1] networks , up from templateYValue[min] percent in templateXValue[min] .
generated: This statistic provides information on the internet user penetration in Argentina from 2017 to 2023 . In 2023 , it was estimated that 94 percent of the Indian population will be accessing internet networks , up from 77 percent in 2017 .

Example 509:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2010', '2018']}
title: Migration balance in Belgium 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Migration', 'balance']: ['50180', '44536', '42239', '47682', '39954', '34843', '44365', '62157', '79446']

gold: In 2018 , the migration balance in Belgium was roughly 50,000 , meaning that the number of immigrants moving to Belgium outnumbered the number of people leaving the country by about 50,000 . This was an increase in comparison to the previous year , but significantly lower than for example in 2010 and 2011 , when the migration balance was 79,446 and 62,157 respectively . It was also considerably lower than in neighboring country the Netherlands , which in 2018 had a positive migration balance of over 86,000 .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was roughly 50,000 , meaning that the number of immigrants moving to templateTitleSubject[0] outnumbered the number of people leaving the country by about 50,000 . This was an increase in comparison to the previous templateXLabel[0] , but significantly lower than for example in templateXValue[min] and templateXValue[7] , when the templateYLabel[0] templateYLabel[1] was templateYValue[max] and templateYValue[7] respectively . It was also considerably lower than in neighboring country the Netherlands , which in templateXValue[max] had a positive templateYLabel[0] templateYLabel[1] of over 86,000 .

generated_template: templateTitle[2] templateYLabel[1] in templateTitleSubject[0] reached an all-time high as of templateXValue[max] , when templateTitleSubject[0] templateTitle[1] auto industry sold some templateYValue[max] templateYLabel[2] templateYLabel[3] . This figure is almost double the templateTitleDate[0] templateYLabel[1] , when just templateYValue[min] templateYLabel[2] templateTitle[2] templateYLabel[3] were sold in templateTitleSubject[0] . templateTitle[2] industry in templateTitleSubject[0] In templateXValue[max] , templateTitle[2] vehicles also made up the majority of vehicles in production in templateTitleSubject[0] , followed by passenger vehicles .
generated: Belgium balance in Belgium reached an all-time high as of 2018 , when Belgium balance auto industry sold some 79446 balance . This figure is almost double the 2010 balance , when just 34843 balance Belgium balance were sold in Belgium . Belgium industry in Belgium In 2018 , Belgium vehicles also made up the majority of vehicles in production in Belgium , followed by passenger vehicles .

Example 510:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2001', '2019']}
title: Employment in U.S. publishing industries 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['Employment', 'in', '1,000s']: ['733.7', '730.5', '730.8', '728.7', '725.5', '727.4', '735.7', '741.1', '751.1', '768.6', '837.8', '897.4', '902.8', '901.2', '901.5', '913.8', '942.2', '986.6', '1045.7']

gold: The statistic above presents employment data for the U.S. publishing industries from 2001 to 2019 . In January 2019 , over 733 thousand people were estimated to be working in print or software publishing companies , down from the 730.5 thousand people recorded in January of the previous year .
gold_template: The statistic above presents templateYLabel[0] data for the templateTitleSubject[0] templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In January templateXValue[max] , over 733 thousand people were estimated to be working in print or software templateTitle[2] companies , down from the templateYValue[1] thousand people recorded in January of the previous templateXLabel[0] .

generated_template: Between templateXValue[min] to templateXValue[max] , the templateYLabel[0] of people employed worldwide by templateTitleSubject[0] was templateYValue[max] . In the most recently reported templateXLabel[0] , the company had templateYLabel[0] of over templateYValue[max] thousand templateYLabel[1] , down slightly from templateYValue[1] thousand in the previous templateXLabel[0] . The level of templateTitle[2] and templateTitle[3] expenditure was highest in templateXValue[min] .
generated: Between 2001 to 2019 , the Employment of people employed worldwide by U.S. was 1045.7 . In the most recently reported Year , the company had Employment of over 1045.7 thousand 1,000s , down slightly from 730.5 thousand in the previous Year . The level of publishing and industries expenditure was highest in 2001 .

Example 511:
titleEntities: {'Subject': ['Switzerland'], 'Date': ['2020']}
title: Number of employed persons in Switzerland 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Employed', 'persons', 'in', 'millions']: ['5.02', '4.97', '5.06', '5.01', '4.96', '4.9', '4.82', '4.73', '4.67', '4.66', '4.56']

gold: The statistic shows the number of employed persons in Switzerland from 2010 to 2018 , with projections up until 2020 . In 2018 , the amount of gainfully employed persons in Switzerland amounted to 5.06 million .
gold_template: The statistic shows the templateTitle[0] of templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[2] , with projections up until templateXValue[max] . In templateXValue[2] , the amount of gainfully templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[2] .

generated_template: templateTitle[0] advertising templateTitle[2] is increasing year-on-year , with templateXValue[2] figures estimated at over 320 templateYLabel[2] templateYLabel[3] templateYLabel[4] . By templateXValue[max] , these expenditures are forecast to surpass half a templateYLabel[2] templateYLabel[4] . The growing advertising investments are most probably a response to the potential customer exposure posed by this medium , as podcasts reach more and more listeners in the templateTitle[3] each templateXLabel[0] .
generated: Number advertising persons is increasing year-on-year , with 2018 figures estimated at over 320 millions . By 2020 , these expenditures are forecast to surpass half a millions . The growing advertising investments are most probably a response to the potential customer exposure posed by this medium , as podcasts reach more and more listeners in the Switzerland each Year .

Example 512:
titleEntities: {'Subject': ['Afghanistan'], 'Date': ['2017']}
title: Fertility rate in Afghanistan 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'children', 'born', 'per', 'woman']: ['4.63', '4.8', '4.98', '5.16', '5.36', '5.56', '5.77', '5.98', '6.18', '6.37', '6.56']

gold: This timeline shows the fertility rate in Afghanistan from 2007 to 2017 . In 2017 , Afghanistan 's fertility rate amounted to 4.63 children born per woman . Today , Afghanistan is among the countries with the highest fertility rate on the world fertility rate ranking .
gold_template: This timeline shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] amounted to templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Today , templateTitleSubject[0] is among the countries with the highest templateTitle[0] templateTitle[1] on the world templateTitle[0] templateTitle[1] ranking .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[2] templateYLabel[1] by one templateYLabel[4] while being of child-bearing age . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Fertility rate in Afghanistan from 2007 to 2017 . The Fertility rate is the average Number of born children by one woman while being of child-bearing age . In 2017 , the Fertility rate in Afghanistan amounted to 4.63 born per woman .

Example 513:
titleEntities: {'Subject': ['GDP'], 'Date': ['2018']}
title: National debt of selected countries in relation to gross domestic product ( GDP ) 2018
X_Axis['Country']: ['Japan', 'United_States', 'France', 'Brazil', 'United_Kingdom', 'India', 'Germany', 'China', 'Russia']
Y_Axis['National', 'debt', 'in', 'relation', 'to', 'gross', 'domestic', 'product', '(GDP)']: ['237.69', '106.22', '99.31', '91.57', '85.55', '69.04', '58.58', '55.57', '16.49']

gold: This statistic shows the national debt of important industrial and emerging countries in 2019 in relation to the gross domestic product ( GDP ) . In 2019 , the national debt of China was at about 55.57 percent of the gross domestic product .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of important industrial and emerging templateTitle[3] in 2019 in templateYLabel[2] to the templateYLabel[3] templateYLabel[4] templateYLabel[5] ( templateTitleSubject[0] ) . In 2019 , the templateYLabel[0] templateYLabel[1] of templateXValue[7] was at about templateYValue[7] percent of the templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: templateXValue[0] was the largest templateTitle[1] of templateYLabel[0] tourists arriving to templateTitleSubject[0] in templateTitleDate[0] at over 2.25 templateYLabel[3] visitors . Bangladeshi tourists have been outnumbering the tourists that visit 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] templateXValue[1] of America , and the templateXValue[1] templateXValue[2] had the highest number of tourists visiting templateTitleSubject[0] , while about templateYValue[3] percent of all templateYLabel[2] .
generated: Japan was the largest debt of National tourists arriving to GDP in 2018 at over 2.25 gross visitors . Bangladeshi tourists have been outnumbering the tourists that visit the Country from other European and American selected for the past few years . In 2018 , citizens from Japan , the United States of America , and the United States France had the highest number of tourists visiting GDP , while about 91.57 percent of all relation .

Example 514:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': []}
title: Liver transplants in the United Kingdom ( UK ) 2018/19
X_Axis['Country', 'of', 'residence']: ['England', 'Scotland', 'Wales', 'Northern_Ireland']
Y_Axis['Number', 'of', 'transplants']: ['779', '114', '35', '34']

gold: In the period 2018/19 , 779 liver transplants were carried out in England , followed by 114 conducted in Scotland . England has by far the largest population of the countries in the United Kingdom , so it is unsurprising it has the highest number of transplants performed in a year . State of liver transplants in the UK The number of liver transplants in the United Kingdom in 2018/19 was an five percent increase from the number that took place in the preceding year .
gold_template: In the period templateTitle[5] , templateYValue[max] templateTitle[0] templateYLabel[1] were carried out in templateXValue[0] , followed by templateYValue[1] conducted in templateXValue[1] . templateXValue[0] has by far the largest population of the countries in the templateTitleSubject[0] , so it is unsurprising it has the highest templateYLabel[0] of templateYLabel[1] performed in a year . State of templateTitle[0] templateYLabel[1] in the templateTitleSubject[1] The templateYLabel[0] of templateTitle[0] templateYLabel[1] in the templateTitleSubject[0] in templateTitle[5] was an five percent increase from the templateYLabel[0] that took place in the preceding year .

generated_template: This statistic illustrates the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[2] in the templateTitle[0] in templateTitleDate[0] , based on sales value . In that year , Coca Cola 's templateYLabel[1] templateYLabel[2] amounted to templateYValue[max] percent .
generated: This statistic illustrates the Number of transplants United in the Liver in , based on sales value . In that year , Coca Cola 's transplants amounted to 779 percent .

Example 515:
titleEntities: {'Subject': ['Countries'], 'Date': ['2017']}
title: Countries with the highest infant mortality rate 2017
X_Axis['Country']: ['Afghanistan', 'Somalia', 'Central_African_Republic', 'Guinea-Bissau', 'Chad', 'Niger', 'Burkina_Faso', 'Nigeria', 'Mali', 'Sierra_Leone', 'Democratic_Republic_of_Congo', 'Angola', 'Mozambique', 'Equatorial_Guinea', 'South_Sudan', 'Zambia', 'Gambia', 'Comoros', 'Burundi', 'Uganda']
Y_Axis['Child', 'deaths', 'in', 'the', 'first', 'year', 'of', 'life', 'per', '1,000', 'live', 'births']: ['110.6', '94.8', '86.3', '85.7', '85.4', '81.1', '72.2', '69.8', '69.5', '68.4', '68.2', '67.6', '65.9', '65.2', '62.8', '61.1', '60.2', '60.0', '58.8', '56.1']

gold: This statistic shows the 20 countries* with the highest infant mortality rate in 2017 . An estimated 110.6 infants per 1,000 live births died in the first year of life in Afghanistan in 2017 . Infant and child mortality Infant mortality usually refers to the death of children younger than one year .
gold_template: This statistic shows the 20 countries* templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[0] . An estimated templateYValue[max] infants templateYLabel[5] 1,000 templateYLabel[7] templateYLabel[8] died in the templateYLabel[2] templateYLabel[3] of templateYLabel[4] in templateXValue[0] in templateTitleDate[0] . templateTitle[3] and templateYLabel[0] templateTitle[4] templateTitle[3] templateTitle[4] usually refers to the death of children younger than one templateYLabel[3] .

generated_template: The statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] as of January templateTitleDate[0] . As of the measured period , the social network had an templateYLabel[0] templateYLabel[1] of templateYValue[2] percent in templateYLabel[2] and templateTitle[2] 's templateYLabel[4] templateYLabel[5] to templateYLabel[6] projected templateTitle[5] templateTitleDate[0] to templateTitleDate[1] .
generated: The statistic shows the 20 Countries highest the infant mortality rate 2017 as of January 2017 . As of the measured period , the social network had an Child deaths of 86.3 percent in first and infant 's life per to 1,000 projected 2017 to 2017 .

Example 516:
titleEntities: {'Subject': ['Hollywood'], 'Date': ['2016']}
title: Stereotyping of ethnic minorities in Hollywood movies 2016
X_Axis['Response']: ['Do_a_good_job_of_portraying_racial_minorities', 'Give_into_stereotypes_when_portraying_racial_minorities', 'Not_sure']
Y_Axis['Share', 'of', 'respondents']: ['38', '37', '24']

gold: The survey shows result of survey on stereotyping of racial minorities in Hollywood movies in the United States as of February 2016 . Durign the survey , 38 of respondents stated Hollywood movies did a good job of potraying racial minorities .
gold_template: The survey shows result of survey on templateTitle[0] of templateXValue[0] in templateTitleSubject[0] templateTitle[4] in the country as of February templateTitleDate[0] . Durign the survey , templateYValue[max] of templateYLabel[1] stated templateTitleSubject[0] templateTitle[4] did a templateXValue[0] of potraying templateXValue[0] .

generated_template: During a survey in June templateTitleDate[0] , it was found that just templateYValue[min] percent of templateTitle[3] adults visited a movie theater one or templateXValue[0] templateXValue[0] templateXValue[0] month , but templateYValue[max] percent stated that they went to the cinema to watch a movie templateXValue[1] or templateXValue[last] or less . Another survey earlier in the same templateXValue[last] revealed that visits to the cinema can vary according to generation . On average , 34 percent of Gen Xers had seen one movie within the last month , compared to 43 percent of those classed as being members of Generation Z .
generated: During a survey in June 2016 , it was found that just 24 percent of Hollywood adults visited a movie theater one or Do a good job of portraying racial minorities month , but 38 percent stated that they went to the cinema to watch a movie Give into stereotypes when portraying racial minorities or Not sure or less . Another survey earlier in the same Not sure revealed that visits to the cinema can vary according to generation . On average , 34 percent of Gen Xers had seen one movie within the last month , compared to 43 percent of those classed as being members of Generation Z .

Example 517:
titleEntities: {'Subject': ['Russia'], 'Date': ['2017']}
title: Fertility rate in Russia 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'children', 'born', 'per', 'woman']: ['1.76', '1.76', '1.78', '1.75', '1.71', '1.69', '1.58', '1.57', '1.54', '1.5', '1.42']

gold: This statistic shows the fertility rate of Russia from 2007 to 2017 . The fertility rate is the average number of children a woman will have during her child-bearing years . In 2017 , the fertility rate of Russia 's population was 1.76 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] of templateTitleSubject[0] 's population was templateYValue[0] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] to one templateYLabel[4] while being of child-bearing age . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Fertility rate in Russia from 2007 to 2017 . The Fertility rate is the average Number of children born to one woman while being of child-bearing age . In 2017 , the Fertility rate in Russia amounted to 1.42 children born per woman .

Example 518:
titleEntities: {'Subject': ['King'], 'Date': ['2010', '2018']}
title: King annual income 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Operating', 'income', 'in', 'million', 'U.S.', 'dollars']: ['750.0', '700.0', '537.0', '516.78', '574.85', '567.59', '7.85', '-1.32', '1.74']

gold: This statistic shows a timeline with the global annual operating income of King.com from 2010 to 2018 . In 2018 , the company reported an income of 750 million U.S. dollars . Popular King titles include Candy Crush Saga , Farm Ville Saga as well as Bubble Witch Saga .
gold_template: This statistic shows a timeline with the global templateTitle[1] templateYLabel[0] templateYLabel[1] of King.com from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company reported an templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Popular templateTitleSubject[0] titles include Candy Crush Saga , Farm Ville Saga as well as Bubble Witch Saga .

generated_template: The timeline presents the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] of templateTitleSubject[0] generated templateYLabel[0] templateYLabel[1] worth approximately templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: The timeline presents the Operating income of the King of King from 2010 to 2018 . In 2018 , the King of King generated Operating income worth approximately 750.0 million U.S. .

Example 519:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017', '2018']}
title: Total number of U.S. children enrolled in pre-K , by state 2017 to 2018
X_Axis['State']: ['United_States_', 'California', 'Texas', 'Florida', 'New_York', 'Georgia', 'Illinois', 'New_Jersey', 'Wisconsin', 'Oklahoma', 'Michigan', 'Massachusetts', 'Maryland', 'Pennsylvania', 'North_Carolina', 'South_Carolina', 'Iowa', 'Kentucky', 'Colorado', 'Arkansas', 'Louisiana', 'Tennessee', 'Virginia', 'Ohio', 'Alabama', 'West_Virginia', 'Connecticut', 'Kansas', 'District_of_Columbia', 'Nebraska', 'Washington', 'Oregon', 'New_Mexico', 'Vermont', 'Minnesota', 'Maine', 'Arizona', 'Missouri', 'Nevada', 'Mississippi', 'Rhode_Island', 'North_Dakota', 'Delaware', 'Hawaii', 'Alaska', 'Montana', 'Guam']
Y_Axis['Number', 'of', 'children', 'enrolled']: ['1565168', '241859', '231485', '173645', '121572', '80536', '74940', '50684', '46736', '39807', '37325', '34130', '31162', '29710', '28385', '27443', '27195', '21270', '21037', '19498', '18911', '18354', '17959', '17913', '16051', '14629', '14449', '14022', '13332', '12950', '12491', '9464', '9119', '8449', '7672', '5551', '5256', '2378', '2102', '1840', '1080', '965', '845', '373', '315', '279', '71']

gold: The statistic above provides information on the number of the 3- and 4-year-old children enrolled in pre-kindergarten programs in the United States for the 2017/2018 school year , by state . Between 2017 and 2018 , about 50,684 children in New Jersey were enrolled in pre-K programs .
gold_template: The statistic above provides information on the templateYLabel[0] of the 3- and 4-year-old templateYLabel[1] templateYLabel[2] in pre-kindergarten programs in the templateXValue[0] for the 2017/2018 school year , templateTitle[6] templateXLabel[0] . Between templateTitleDate[0] and templateTitleDate[1] , about templateYValue[7] templateYLabel[1] in templateXValue[4] templateXValue[7] were templateYLabel[2] in templateTitle[5] programs .

generated_template: How much is the most popular in the templateTitleSubject[0] competition since templateTitleDate[0] , Porto Goalkeeper templateXValue[0] templateXValue[0] has the highest number of templateTitle[4] , taking the pitch templateYValue[max] times . templateTitle[3] of these templateTitle[4] have been for Real Madrid . templateXValue[0] templateXValue[0] is followed templateTitle[5] templateXValue[1] templateXValue[1] who made templateYValue[1] templateTitle[4] in the templateTitleSubject[0] .
generated: How much is the most popular in the U.S. competition since 2017 , Porto Goalkeeper United States  has the highest number of enrolled , taking the pitch 1565168 times . children of these enrolled have been for Real Madrid . United States  is followed pre-K California who made 241859 enrolled in the U.S. .

Example 520:
titleEntities: {'Subject': ['UK'], 'Date': ['2019']}
title: UK : real estate prime office rent prices in selected cities Q3 2019
X_Axis['City']: ['Reading', 'Manchester', 'Bristol', 'Edinburgh', 'Birmingham', 'Glasgow', 'Leeds', 'Cardiff', 'Newcastle']
Y_Axis['Price', 'per', 'square', 'meter', 'in', 'euros']: ['468', '444', '438', '425', '413', '389', '365', '304', '298']

gold: This statistic displays the most expensive cities for prime office rents in the United Kingdom ( UK ) as of September 2019 , excluding London . As of September 2019 , it can be seen that Reading was the most expensive location within the UK for prime office rents outside of London , with an average price reaching 468 euros per square meter per year . This was followed by Manchester , Bristol and Edinburgh .
gold_template: This statistic displays the most expensive templateTitle[8] for templateTitle[3] templateTitle[4] rents in the United Kingdom ( templateTitleSubject[0] ) as of September templateTitleDate[0] , excluding London . As of September templateTitleDate[0] , it can be seen that templateXValue[0] was the most expensive location within the templateTitleSubject[0] for templateTitle[3] templateTitle[4] rents outside of London , with an average templateYLabel[0] reaching templateYValue[max] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[1] year . This was followed by templateXValue[1] , templateXValue[2] and templateXValue[3] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitle[3] in leading countries in templateTitleSubject[0] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] with a total of approximately templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the UK real Price of prime in leading countries in UK in 2019 . In that year , Reading was the UK real UK prime with a total of approximately 468 per square .

Example 521:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1995', '2019']}
title: Number of drive-in cinema sites in the U.S. 1995 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995']
Y_Axis['Number', 'of', 'drive-in', 'cinema', 'sites']: ['321', '321', '349', '349', '349', '393', '393', '366', '366', '374', '381', '383', '383', '396', '401', '402', '400', '432', '440', '442', '446', '524', '577', '583', '593']

gold: The number of drive-in cinema sites in the United States remained at 321 in 2019 , the same as in the previous year . The figure tends to remain the same for years at a time , and is always far lower than the number of indoor sites , which make up the vast majority of cinemas in the country .
gold_template: The templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[4] remained at templateYValue[min] in templateXValue[max] , the same as in the previous templateXLabel[0] . The figure tends to remain the same for years at a time , and is always far lower than the templateYLabel[0] of indoor templateYLabel[3] , which make up the vast majority of cinemas in the country .

generated_template: As of templateXValue[max] , around templateYValue[0] percent of templateTitle[5] in the templateTitle[4] stated they felt it was `` fairly easy '' or `` very easy '' to obtain templateTitleSubject[0] . This is a significant decrease from templateYValue[max] percent of templateTitle[5] templateTitle[6] who templateTitle[2] the drug to be easy to obtain in the templateXLabel[0] templateXValue[20] . templateTitleSubject[0] is a hallucinogenic drug that can alter the user 's thoughts , feelings , and perception of reality .
generated: As of 2019 , around 321 percent of 1995 in the U.S. stated they felt it was `` fairly easy '' or `` very easy '' to obtain U.S. . This is a significant decrease from 593 percent of 1995 2019 who cinema the drug to be easy to obtain in the Year 1999 . U.S. is a hallucinogenic drug that can alter the user 's thoughts , feelings , and perception of reality .

Example 522:
titleEntities: {'Subject': ['Hispanics'], 'Date': ['1990', '2018']}
title: Birth rate of Hispanics in the U.S. 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Births', 'per', '1,000', 'of', 'Hispanic', 'population']: ['14.8', '15.2', '16.0', '16.3', '16.5', '16.7', '17.1', '17.6', '18.7', '20.3', '21.8', '23.0', '23.3', '22.9', '22.8', '22.8', '22.7', '22.9', '23.1', '22.5', '22.7', '23.0', '23.8', '24.1', '24.7', '25.4', '26.1', '26.5', '26.7']

gold: This graph displays the birth rate of Hispanics in the United States from 1990 to 2018 . In 2018 , about 14.8 children were born per 1,000 of Hispanic population .
gold_template: This graph displays the templateTitle[0] templateTitle[1] of templateTitleSubject[0] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[min] children were born templateYLabel[1] 1,000 of templateYLabel[3] templateYLabel[4] .

generated_template: Over the past 30 years , the templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] has been steadily declining , and in templateXValue[max] , there were templateYValue[min] births templateYLabel[2] 1,000 of the templateYLabel[4] . In templateXValue[min] , this figure stood at templateYValue[max] births templateYLabel[2] 1,000 of the templateYLabel[4] . Demographics have an impact The average templateYLabel[0] templateYLabel[1] in the templateTitle[1] may be falling , but when broken down along ethnic and economic lines , a different picture is painted : Native Hawaiian and other Pacific Islander women saw the highest templateYLabel[0] templateYLabel[1] in templateXValue[max] among all ethnicities , and White and Asian women both saw the lowest templateYLabel[0] templateYLabel[1] .
generated: Over the past 30 years , the Births per in the Hispanics has been steadily declining , and in 2018 , there were 14.8 births 1,000 of the population . In 1990 , this figure stood at 26.7 births 1,000 of the population . Demographics have an impact The average Births per in the rate may be falling , but when broken down along ethnic and economic lines , a different picture is painted : Native Hawaiian and other Pacific Islander women saw the highest Births per in 2018 among all ethnicities , and White and Asian women both saw the lowest Births per .

Example 523:
titleEntities: {'Subject': ['Greece'], 'Date': ['2007', '2018']}
title: Household internet access in Greece 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Share', 'of', 'households']: ['76', '71', '69', '68', '66', '56', '54', '50', '46', '38', '31', '25']

gold: This statistic shows the share of households in Greece that had access to the internet from 2007 to 2018 . Internet penetration grew in Greece during this period . In 2018 , 76 percent of Greek households had internet access .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] that had templateTitle[2] to the templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[1] penetration grew in templateTitleSubject[0] during this period . In templateXValue[max] , templateYValue[max] percent of Greek templateYLabel[1] had templateTitle[1] templateTitle[2] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] due to templateTitle[0] between templateXLabel[0] templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of templateYLabel[1] were committed in templateTitleSubject[0] . templateTitleSubject[0] suffered from 1,294 terrorist attacks that templateXLabel[0] .
generated: This statistic shows the Share of households in Greece due to Household between Year 2007 and 2018 . In 2018 , 76 percent of households were committed in Greece . Greece suffered from 1,294 terrorist attacks that Year .

Example 524:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2011', '2018']}
title: Number of enrolled university students in South Korea 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Enrolled', 'university', 'students', 'in', 'millions']: ['2.03', '2.05', '2.08', '2.11', '2.13', '2.13', '2.1', '2.07']

gold: This statistic illustrates the number of students enrolled in universities in South Korea from 2011 to 2018 . In 2018 , there were approximately 2.03 million students enrolled in universities in South Korea .
gold_template: This statistic illustrates the templateTitle[0] of templateYLabel[2] templateYLabel[0] in universities in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[min] templateYLabel[3] templateYLabel[2] templateYLabel[0] in universities in templateTitleSubject[0] .

generated_template: In templateXValue[max] , the templateTitle[0] total templateYLabel[0] of templateTitle[5] templateTitle[6] traded per day was over 547 templateYLabel[1] templateYLabel[2] templateYLabel[3] . This means that every day the market was open , the templateTitle[0] amount of templateYLabel[2] government templateTitle[6] bought and sold amounted to half a templateYLabel[1] templateYLabel[2] templateYLabel[3] in that templateXLabel[0] . What are templateTitle[5] templateTitle[6] ? templateTitle[5] templateTitle[6] are templateYLabel[2] government debt , bonds sold to finance the templateTitle[4] government .
generated: In 2018 , the Number total Enrolled of Korea 2011 traded per day was over 547 university students millions . This means that every day the market was open , the Number amount of students government 2011 bought and sold amounted to half a university students millions in that Year . What are Korea 2011 ? Korea 2011 are students government debt , bonds sold to finance the South government .

Example 525:
titleEntities: {'Subject': ['Worlds'], 'Date': ['2018']}
title: Worlds ' most dangerous cities , by murder rate 2018
X_Axis['City']: ['Tijuana_-_Mexico', 'Acapulco_-_Mexico', 'Caracas_-_Venezuela', 'Ciudad_Victoria_-_Mexico', 'Ciudad_Juarez_-_Mexico', 'Irapuato_-_Mexico', 'Ciudad_Guayana_-_Venezuela', 'Natal_-_Brazil', 'Fortaleza_-_Brazil', 'Ciudad_Bolivar_-_Venezuela', 'Cape_Town_-_South_Africa', 'Belem_-_Brazil', 'Cancun_-_Mexico', 'Feira_de_Santana_-_Brazil', 'St._Louis_Missouri_-_U.S.', 'Culiacan_-_Mexico', 'Barquisimeto_-_Venezuela', 'Uruapan_-_Mexico', 'Kingston_-_Jamaica', 'Ciudad_Obregón_-_Mexico', 'Maceio_-_Brazil', 'Vitoria_da_Conquista_-_Brazil', 'Baltimore_Maryland_-_U.S.', 'San_Salvador_-_El_Salvador', 'Aracaju_-_Brazil', 'Coatzacoalcos_-_Mexico', 'Palmira_-_Colombia', 'Maturin_-_Venezuela', 'Salvador_-_Brazil', 'Macapa_-_Brazil', 'Cali_-_Colombia', 'Celaya_-_Mexico', 'San_Pedro_Sula_-_Honduras', 'Ensenada_-_Mexico', 'Campos_dos_Goytacazes_-_Brazil', 'Tepic_-_Mexico', 'Manaus_-_Brazil', 'Guatemala_City_-_Guatemala', 'Recife_-_Brazil', 'Distrito_Central_-_Honduras', 'San_Juan_-_Puerto_Rico', 'Valencia_-_Venezuela', 'Reynosa_-_Mexico', 'João_Pessoa_-_Brazil', 'Nelson_Mandela_Bay_-_South_Africa', 'Detroit_Michigan_-_U.S.', 'Durban_-_South_Africa', 'Teresina_-_Brazil', 'Chihuahua_-_Mexico', 'New_Orleans_Louisiana_-_U.S.']
Y_Axis['Murder', 'rate', 'per', '100,000', 'inhabitants']: ['138.26', '110.5', '99.98', '86.01', '85.56', '81.44', '78.3', '74.67', '69.15', '69.09', '66.36', '65.31', '64.46', '63.29', '60.59', '60.52', '56.67', '54.52', '54.12', '52.09', '51.46', '50.75', '50.52', '50.32', '48.77', '48.35', '47.97', '47.24', '47.23', '47.2', '47.03', '46.99', '46.67', '46.6', '46.28', '44.89', '44.0', '43.73', '43.72', '43.3', '42.4', '42.36', '41.48', '41.36', '39.16', '38.78', '38.51', '37.61', '37.5', '36.87']

gold: This statistic ranks the 50 most dangerous cities of 2018 , by murder rate per 100,000 inhabitants . Tijuana 's murder rate was 138.26 for every 100,000 people living in the city . The world 's most dangerous cities The Citizens ' Council for Public Security and Criminal Justice published a ranking of the world 's most dangerous cities in 2018 , ranking cities according to the number of murders per 100,000 inhabitants that year .
gold_template: This statistic ranks the templateYValue[23] templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleDate[0] , templateTitle[5] templateYLabel[0] templateYLabel[1] templateYLabel[2] 100,000 templateYLabel[4] . templateXValue[0] 's templateYLabel[0] templateYLabel[1] was templateYValue[max] for every 100,000 people living in the templateXValue[37] . The world 's templateTitle[2] templateTitle[3] templateTitle[4] The Citizens templateTitle[1] Council for Public Security and Criminal Justice published a ranking of the world 's templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] , ranking templateTitle[4] according to the number of murders templateYLabel[2] 100,000 templateYLabel[4] that year .

generated_template: The statistic shows the templateTitle[0] of templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[6] through the Supplemental Nutrition Assistance Program ( templateTitleSubject[0] , formerly called templateTitleSubject[0] templateTitleSubject[0] ) in templateTitleDate[0] , by templateXLabel[0] . About templateYValue[2] percent of the templateTitle[3] in templateXValue[5] is templateTitle[4] templateTitle[6] through the Supplemental Nutrition Assistance Program .
generated: The statistic shows the Worlds of Worlds dangerous cities murder through the Supplemental Nutrition Assistance Program ( Worlds , formerly called Worlds ) in 2018 , by City . About 99.98 percent of the dangerous in Irapuato - Mexico is cities murder through the Supplemental Nutrition Assistance Program .

Example 526:
titleEntities: {'Subject': ['Leading'], 'Date': ['2018']}
title: Leading internet traffic categories worldwide 2018
X_Axis['Category']: ['Video', 'Web', 'Gaming', 'Social_media', 'Content_marketplaces', 'File_sharing', 'Audio_streaming']
Y_Axis['Share', 'of', 'downstream', 'internet', 'traffic']: ['57.7', '17', '7.8', '5.1', '4.6', '2.8', '1']

gold: This statistic presents the distribution of global downstream internet traffic as of October 2018 , by category . During the measured period , video accounted for over half of downstream internet traffic volume . Within that category , Netflix was by far the market leader in terms of global video traffic .
gold_template: This statistic presents the distribution of global templateYLabel[1] templateYLabel[2] templateYLabel[3] as of October templateTitleDate[0] , by templateXLabel[0] . During the measured period , templateXValue[0] accounted for over half of templateYLabel[1] templateYLabel[2] templateYLabel[3] volume . Within that templateXLabel[0] , Netflix was by far the market leader in terms of global templateXValue[0] templateYLabel[3] .

generated_template: This statistic illustrates the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] of templateTitle[4] in the templateTitle[5] in templateTitleDate[0] templateTitle[7] templateXLabel[0] and ethnicity . In templateTitleDate[0] , templateYValue[max] percent of templateTitleSubject[0] templateTitle[0] templateTitle[2] income were women .
generated: This statistic illustrates the Leading internet traffic Share of worldwide in the 2018 in 2018 Category and ethnicity . In 2018 , 57.7 percent of Leading traffic income were women .

Example 527:
titleEntities: {'Subject': ['Golf-Association'], 'Date': ['2012', '2013']}
title: Golf-Association executives ' compensation 2012 to 2013
X_Axis['Month']: ['Tim_Finchem_(PGA_Tour_commissioner_&_CEO)_2013', 'Joe_Steranka_(Former_of_America_CEO)_2013', 'Dick_Rugge_(Former_USGA_senior_director_equipment_standards)_2012', 'Tom_Wade_(PGA_Tour_global_commercial_officer)_2013', 'Charles_Zink_(PGA_Tour_co-chief_operating_officer)_2013', 'Ed_Moorhouse_(PGA_Tour_co-chief_operating_officer)_2013', 'Ron_Price_(PGA_Tour_executive_VP_CFO)_2013', 'David_Pillsbury_(PGA_Tour_executive_VP_championship_managment_&_tournament_business_affairs)_2013', 'Mike_Whan_(LPGA_Tour_commissioner)_2012', 'Ty_Votaw_(PGA_Tour_executive_VP_&_chief_of_global_communications)_2013', 'Mike_Davis_(USGA_executive_director)_2012', 'Joseph_Monahan_(PGA_Tour_executive_VP_&_chief_marketing_officer)_2013', 'David_Fay_(Former_USGA_executive_director)_2012', 'Michael_Butz_(USGA_senior_managing_director_Open_championships_&_association_relations)', 'Joe_Louis_Barrow_Jr._(World_Golf_Foundation_executive_VP_The_First_Tee_CEO)_2013', 'Bill_Calfee_(PGA_Tour_president_Web.com_Tour)_2013', 'Darrell_Crall_(PGA_of_America_COO)_2013', 'Kerry_Haigh_(PGA_of_America_chief_championships_officer)_2013', 'Rick_Anderson_(PGA_Tour_executive_VP_television_and_digital)_2013', 'James_Pazder_(PGA_Tour_executive_VP_&_chief_of_operations)_2013', 'Mike_Stevens_(PGA_Tour_president_Champions_Tour)_2013', 'Stephen_Mona_(World_Golf_Foundation_CEO)_2013', 'Mark_Russell_(PGA_Tour_VP_rules_and_competitions)_2013', 'Stephen_Hamblin_(American_Junior_Golf_Assosiation_executive_director)_2012', 'Joseph_Beditz_(National_Golf_Foundation_president/CEO)_2012']
Y_Axis['Compensations', 'in', 'million', 'U.S.', 'dollars']: ['4.58', '2.59', '1.8', '1.17', '1.16', '1.13', '1.06', '0.97', '0.89', '0.79', '0.77', '0.73', '0.65', '0.64', '0.62', '0.55', '0.54', '0.54', '0.54', '0.51', '0.49', '0.45', '0.45', '0.43', '0.25']

gold: The graph depicts the earnings of 25 golf association executives in 2012 and 2013 . Tim Finchem , PGA Tour commissioner and CEO , tops the earnings with an amount of 4.58 million U.S. dollars .
gold_template: The graph depicts the earnings of 25 templateXValue[14] templateXValue[13] templateTitle[1] in templateXValue[2] and templateXValue[0] . templateXValue[0] , PGA templateXValue[0] and CEO , tops the earnings with an amount of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic presents the templateYLabel[0] of templateTitle[2] templateTitleSubject[0] chat app users from June templateTitleDate[0] to March templateTitleDate[1] . As of that templateXLabel[0] , the mobile messenger had almost templateYValue[max] thousand templateTitle[2] users worldwide . The chat app had been acquired by Japanese e-commerce company Rakuten in February 2014 for 900 templateYLabel[2] U.S. dollars , making it the second-most expensive acquisition of the e-commerce platform up to date .
generated: This statistic presents the Compensations of ' Golf-Association chat app users from June 2012 to March 2013 . As of that Month , the mobile messenger had almost 4.58 thousand ' users worldwide . The chat app had been acquired by Japanese e-commerce company Rakuten in February 2014 for 900 U.S. dollars , making it the second-most expensive acquisition of the e-commerce platform up to date .

Example 528:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2019', '2029']}
title: Forecast of population growth in Denmark 2019 to 2029
X_Axis['Year']: ['2029', '2028', '2027', '2026', '2025', '2024', '2023', '2022', '2021', '2020', '2019']
Y_Axis['Number', 'of', 'inhabitants', '(in', 'millions)']: ['6.09', '6.07', '6.04', '6.02', '6.0', '5.97', '5.95', '5.92', '5.89', '5.87', '5.83']

gold: The statistic shows a forecast of the Danish population growth from 2019 to 2029 . The total number of inhabitants will keep on increasing . According to the forecast there will be roughly over 6 million of people living in Denmark by 2029 .
gold_template: The statistic shows a templateTitle[0] of the Danish templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . The total templateYLabel[0] of templateYLabel[1] will keep on increasing . According to the templateTitle[0] there will be roughly over templateYValue[max] million of people living in templateTitleSubject[0] by templateXValue[max] .

generated_template: templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) of templateTitle[3] and templateTitle[4] countries in templateTitleSubject[0] is expected to reach over 30.2 templateYLabel[1] templateYLabel[2] templateYLabel[3] by templateXValue[max] . This would be double the templateYLabel[0] from a decade earlier , which was about templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitle[3] and templateTitle[4] economies are the second-largest source of global templateYLabel[0] .
generated: Forecast population growth ( Number ) of Denmark and 2019 countries in Denmark is expected to reach over 30.2 inhabitants (in millions) by 2029 . This would be double the Number from a decade earlier , which was about 5.83 inhabitants (in millions) . Denmark and 2019 economies are the second-largest source of global Number .

Example 529:
titleEntities: {'Subject': ['Piracy'], 'Date': ['2019']}
title: Piracy - actual and attempted attacks worldwide by country 2019
X_Axis['Country']: ['Nigeria', 'Indonesia', 'Singapore_Staits', 'Malaysia', 'Peru', 'Venezuela', 'Cameroon']
Y_Axis['Number', 'of', 'incidents']: ['35', '25', '12', '11', '10', '6', '6']

gold: The statistic represents the total number of actual and attempted piracy attacks in the world 's most perilous territorial waters in 2019 . That year , there were six actual and attempted piracy attacks off the Venezuelan coast .
gold_template: The statistic represents the total templateYLabel[0] of templateTitle[1] and templateTitle[2] templateTitleSubject[0] templateTitle[3] in the world 's most perilous territorial waters in templateTitleDate[0] . That year , there were templateYValue[min] templateTitle[1] and templateTitle[2] templateTitleSubject[0] templateTitle[3] off the Venezuelan coast .

generated_template: As of January templateTitleDate[0] , templateXValue[0] was home to the largest templateYLabel[0] of templateYLabel[1] in the templateTitle[2] , with templateYValue[max] thousand templateYLabel[2] . templateXValue[1] , the templateXValue[4] templateXValue[1] and templateXValue[2] templateXValue[2] were second and third in the list , with over 148 and templateYValue[2] percent of all templateYLabel[1] . Global overview templateYLabel[0] of templateTitleSubject[0] – additional information in the European Union .
generated: As of January 2019 , Nigeria was home to the largest Number of incidents in the attempted , with 35 thousand incidents . Indonesia , the Peru Indonesia and Singapore Staits were second and third in the list , with over 148 and 12 percent of all incidents . Global overview Number of Piracy – additional information in the European Union .

Example 530:
titleEntities: {'Subject': ['Frozen'], 'Date': ['2006', '2018']}
title: Frozen yogurt production 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['Production', 'in', 'million', 'gallons']: ['50.2', '62.5', '66.93', '74.23', '66.76', '74.48', '74.0', '62.7', '50.1', '46.0', '78.6', '74.7', '66.0']

gold: This statistic shows the frozen yogurt production in the United States from 2006 to 2018 . In 2018 , about 50.2 million gallons of frozen yogurt were produced . Frozen yogurt is a frozen , low-calorie dessert , which is often served in a large variety of flavors .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] templateYLabel[1] templateYLabel[2] of templateTitle[0] templateTitle[1] were produced . templateTitle[0] templateTitle[1] is a templateTitle[0] , low-calorie dessert , which is often served in a large variety of flavors .

generated_template: The statistic represents templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . The company generated around templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] in the fiscal templateXLabel[0] of templateXValue[max] . templateTitleSubject[0] Founded by Warren A. templateTitleSubject[0] in 1898 , the templateTitleSubject[0] Corporation is the largest construction and engineering company in the country and the twelfth largest construction contractor worldwide .
generated: The statistic represents Frozen yogurt production from the fiscal Year of 2006 to the fiscal Year of 2018 . The company generated around 50.2 million gallons in Production in the fiscal Year of 2018 . Frozen Founded by Warren A. Frozen in 1898 , the Frozen Corporation is the largest construction and engineering company in the country and the twelfth largest construction contractor worldwide .

Example 531:
titleEntities: {'Subject': ['Welsh Assembly'], 'Date': ['1999', '2011']}
title: Welsh Assembly elections : turnout rates 1999 to 2011
X_Axis['Year']: ['1999', '2003', '2007', '2011']
Y_Axis['Turnout', 'rate']: ['46.4', '38.2', '43.5', '41.8']

gold: This statistic shows the voter turnout rates for constituency votes in the Welsh Assembly elections from 1999 to 2011 . Over the last four elections there was a variation in voter turnout of 8.2 percent . The peak , in 1999 , was followed in 2003 by the lowest turnout rate of this period .
gold_template: This statistic shows the voter templateYLabel[0] templateTitle[4] for constituency votes in the templateTitleSubject[0] elections from templateXValue[min] to templateXValue[max] . Over the last four templateTitle[2] there was a variation in voter templateYLabel[0] of 8.2 percent . The peak , in templateXValue[min] , was followed in templateXValue[1] by the lowest templateYLabel[0] templateYLabel[1] of this period .

generated_template: Singaporeans consumed on average around templateYValue[0] percent of templateYLabel[2] each in templateXValue[max] , which was among the lowest in Asia-Pacific . This was partly due to the heavy taxation of alcoholic beverages in templateTitleSubject[0] . Even so , health officials were concerned about a rise in unhealthy drinking habits among the young .
generated: Singaporeans consumed on average around 46.4 percent of rate each in 2011 , which was among the lowest in Asia-Pacific . This was partly due to the heavy taxation of alcoholic beverages in Welsh Assembly . Even so , health officials were concerned about a rise in unhealthy drinking habits among the young .

Example 532:
titleEntities: {'Subject': ['U.S.'], 'Date': []}
title: U.S. women who have given oral sex to a male in their lifetime , by age group
X_Axis['Age', 'group']: ['14-15', '16-17', '18-19', '20-24', '25-29', '30-39', '40-49', '50-59', '60-69', '70_and_older']
Y_Axis['Share', 'of', 'respondents']: ['13', '29', '61', '78', '89', '80', '83', '80', '73', '43']

gold: This statistic shows the share of American women who have ever given oral sex to a male in their lifetime , sorted by age group . The findings were acquired in early 2009 and published in the Journal of Sexual Medicine , a publication on sexual behavior in the United States , in 2010 . 89 percent of respondents aged 25 to 29 stated they have given oral sex to a male at some time during their life .
gold_template: This statistic shows the templateYLabel[0] of American templateTitle[1] templateTitle[2] templateTitle[3] ever templateTitle[4] templateTitle[5] templateTitle[6] to a templateTitle[7] in templateTitle[8] templateTitle[9] , sorted templateTitle[10] templateXLabel[0] templateXLabel[1] . The findings were acquired in early 2009 and published in the Journal of Sexual Medicine , a publication on sexual behavior in the templateTitle[0] , in 2010 . templateYValue[max] percent of templateYLabel[1] aged 25 to templateYValue[1] stated they templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] to a templateTitle[7] at some time during templateTitle[8] life .

generated_template: This statistic shows the templateYLabel[0] of American templateTitle[1] templateTitle[2] templateTitle[3] ever templateTitle[4] templateTitle[5] in templateTitle[6] templateTitle[7] , sorted templateTitle[8] templateXLabel[0] templateXLabel[1] . The findings were acquired in early templateTitleDate[0] and published in the Journal of Sexual Medicine , a publication on sexual behavior in the templateTitle[0] , in 2010 . templateYValue[max] percent of templateYLabel[1] aged 25 to 29 stated they templateTitle[3] templateTitle[4] templateTitle[5] at some time during templateTitle[6] life .
generated: This statistic shows the Share of American women who have ever given oral in sex male , sorted their Age group . The findings were acquired in early and published in the Journal of Sexual Medicine , a publication on sexual behavior in the U.S. , in 2010 . 89 percent of respondents aged 25 to 29 stated they have given oral at some time during sex life .

Example 533:
titleEntities: {'Subject': ['Germany'], 'Date': ['2001', '2018']}
title: Share of internet users in Germany 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['Share', 'of', 'internet', 'users']: ['84', '81', '79', '77.6', '76.8', '76.5', '75.6', '74.7', '72', '69.1', '65.1', '60.2', '58.2', '55.1', '52.7', '50.1', '41.7', '37']

gold: In 2018 , the share of German internet users amounted to 84 percent , an increase compared to the previous year at 81 percent . This share has only been growing in recent years . Considering current German population numbers stand at almost 83 million , such a high share of internet users is significant in itself and also for predicting future trends on digitalization and online connectivity in the country .
gold_template: In templateXValue[max] , the templateYLabel[0] of German templateYLabel[1] templateYLabel[2] amounted to templateYValue[max] percent , an increase compared to the previous templateXLabel[0] at templateYValue[1] percent . This templateYLabel[0] has only been growing in recent years . Considering current German population numbers stand at almost 83 million , such a high templateYLabel[0] of templateYLabel[1] templateYLabel[2] is significant in itself and also for predicting future trends on digitalization and online connectivity in the country .

generated_template: The statistic shows the templateYLabel[0] of damages templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] templateTitle[3] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , around templateYValue[0] percent of the templateYLabel[1] templateYLabel[2] were templateYLabel[2] in the templateTitleSubject[0] .
generated: The statistic shows the Share of damages internet users in the Germany between 2001 and 2018 . In 2018 , around 84 percent of the internet users were users in the Germany .

Example 534:
titleEntities: {'Subject': ['GDP'], 'Date': ['2017']}
title: U.S. exports , as a percentage of GDP 1990 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']
Y_Axis['Exports', 'as', 'a', 'percentage', 'of', 'GDP']: ['12.06', '11.85', '12.43', '13.53', '13.54', '13.53', '13.53', '12.32', '11.01', '12.51', '11.5', '10.65', '10', '9.63', '9.04', '9.13', '9.67', '10.66', '10.27', '10.48', '11.08', '10.71', '10.61', '9.86', '9.52', '9.68', '9.64', '9.23']

gold: In 2017 , exports of goods and services from the United States made up just over 12 percent of its gross domestic product ( GDP ) . This is an increase from 9.23 percent of the GDP of the United States in 1990 . Trade and foreign relations The United States ' GDP is the largest in the world , clocking in at around 18.57 trillion U.S. dollars in 2018. International trade is a huge boon to the U.S. economy , both financially and regarding foreign relations .
gold_template: In templateXValue[max] , templateYLabel[0] of goods and services from the templateTitle[0] made up just over templateYValue[0] percent of its gross domestic product ( templateYLabel[2] ) . This is an increase from templateYValue[27] percent of the templateYLabel[2] of the templateTitle[0] in templateXValue[min] . Trade and foreign relations The templateTitle[0] ' templateYLabel[2] is the largest in the world , clocking in at around 18.57 trillion templateTitle[0] dollars in 2018. International trade is a huge boon to the templateTitle[0] economy , both financially and regarding foreign relations .

generated_template: In templateXValue[max] , according to the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] templateTitleSubject[0] reached templateYValue[min] percent . This is an increase from templateYValue[min] in templateXValue[min] , when there were templateYValue[min] templateYLabel[1] templateYLabel[2] in templateXValue[min] . What is the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] templateTitleSubject[0] has decreased by two percent .
generated: In 2017 , according to the Exports percentage GDP in the GDP reached 9.04 percent . This is an increase from 9.04 in 1990 , when there were 9.04 percentage GDP in 1990 . What is the highest Exports percentage GDP in the GDP has decreased by two percent .

Example 535:
titleEntities: {'Subject': ['Europe'], 'Date': ['2014']}
title: Millionaire households number in Europe in 2014 , by country
X_Axis['Country']: ['Germany', 'France', 'Italy', 'United_Kingdom', 'Netherlands', 'Switzerland', 'Belgium', 'Austria', 'Spain', 'Luxembourg', 'Portugal', 'Greece', 'Finland', 'Slovakia', 'Cyprus', 'Slovenia']
Y_Axis['Number', 'of', 'millionaire', 'households']: ['1433985', '1334066', '818538', '796646', '703108', '555483', '415117', '200298', '168134', '50612', '46416', '34723', '25995', '9532', '7269', '6784']

gold: The statistic displays the number of households that own net private wealth of at least one million euros in Europe as of 2014 . The countries with the largest number of millionaire households include Germany ( 1.4 million of ultra-rich households ) and France ( 1.3 million households ) .
gold_template: The statistic displays the templateYLabel[0] of templateYLabel[2] that own net private wealth of at least one million euros in templateTitleSubject[0] as of templateTitleDate[0] . The countries with the largest templateYLabel[0] of templateYLabel[1] templateYLabel[2] include templateXValue[0] ( 1.4 million of ultra-rich templateYLabel[2] ) and templateXValue[1] ( 1.3 million templateYLabel[2] ) .

generated_template: The templateTitleSubject[0] producer of templateTitle[2] templateTitle[3] templateTitle[4] was the European Union in templateTitleDate[0] . In that year , the 28 countries of the European Union collectively produced about templateYValue[max] million templateYLabel[2] templateYLabel[3] templateYLabel[4] of templateTitle[2] templateTitle[3] . The templateXValue[1] templateXValue[1] came in second at around templateYValue[1] million templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The Europe producer of number Europe 2014 was the European Union in 2014 . In that year , the 28 countries of the European Union collectively produced about 1433985 million households of number Europe . The France came in second at around 1334066 million households .

Example 536:
titleEntities: {'Subject': ['CVS Health'], 'Date': ['2012', '2025']}
title: CVS Health 's share of retail prescriptions filled in the U.S. 2012 to 2025
X_Axis['Year']: ['2025', '2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Share', 'of', 'retail', 'prescriptions', 'filled']: ['46.55', '44.55', '42.55', '40.55', '38.55', '36.55', '34.55', '33.55', '32.72', '30.1', '23.45', '21.63', '20.99', '17.25']

gold: This statistic depicts CVS Caremark 's share of retail prescriptions filled in the United States from 2012 to 2025 . The CVS Caremark Corporation is a U.S. drug retailing company , with three major segments : CVS/pharmacy , Caremark Pharmacy Services and MinuteClinic . CVS Caremark is headquartered in Woonsocket , Rhode Island .
gold_template: This statistic depicts templateTitleSubject[0] Caremark templateTitle[2] templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[7] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] Caremark Corporation is a templateTitle[7] drug retailing company , with three major segments : CVS/pharmacy , Caremark Pharmacy Services and MinuteClinic . templateTitleSubject[0] Caremark is headquartered in Woonsocket , Rhode Island .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the number of templateTitle[0] in templateTitleSubject[0] was around templateYValue[13] templateYLabel[2] .
generated: This statistic shows the Health Share retail in CVS Health from 2012 to 2018 , with projections up until 2025 . In 2018 , the number of CVS in CVS Health was around 17.25 prescriptions .

Example 537:
titleEntities: {'Subject': ['Zimbabwe'], 'Date': ['2015']}
title: Median age of the population in Zimbabwe 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']: ['26.9', '25.7', '24.4', '22.8', '21.1', '19.6', '18.7', '18.4', '18.6', '18.3', '18.2', '17.6', '16.9', '16.0', '15.1', '15.4', '15.6', '16.0', '17.2', '18.1', '19.0']

gold: This statistic shows the median age of the population in Zimbabwe 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 depicts 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: The statistic depicts the Median age of the population in Zimbabwe from 1950 to 2050 . The Median age is the age that divides a population into two numerically equal groups ; that is , half the people are younger than this age and half are older . It is a single index that summarizes the age distribution of a population .

Example 538:
titleEntities: {'Subject': ['GlaxoSmithKline'], 'Date': ['2011', '2018']}
title: GlaxoSmithKline 's advertising spending 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Profit', 'in', 'million', 'British', 'pounds']: ['1376', '1351', '1265', '1059', '671', '808', '839', '910']

gold: This statistic describes the advertising spending of GlaxoSmithKline from 2011 to 2018 . In 2018 , the company reported ad spending of some 1.38 billion British pounds . GlaxoSmithKline plc is a global pharmaceutical and biotech company , headquartered in London .
gold_template: This statistic describes the templateTitle[2] templateTitle[3] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company reported ad templateTitle[3] of some templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] plc is a global pharmaceutical and biotech company , headquartered in London .

generated_template: In templateXValue[max] , the templateTitle[0] total templateYLabel[0] of templateTitle[5] templateTitle[6] traded per day was over 547 templateYLabel[1] templateYLabel[2] templateYLabel[3] . This means that every day the market was open , the templateTitle[0] amount of templateYLabel[2] government templateTitle[6] bought and sold amounted to half a templateYLabel[1] templateYLabel[2] templateYLabel[3] in that templateXLabel[0] . What are templateTitle[5] templateTitle[6] ? templateTitle[5] templateTitle[6] are templateYLabel[2] government debt , bonds sold to finance the templateTitle[4] government .
generated: In 2018 , the GlaxoSmithKline total Profit of 2018 traded per day was over 547 million British pounds . This means that every day the market was open , the GlaxoSmithKline amount of British government 2018 bought and sold amounted to half a million British pounds in that Year . What are 2018 ? 2018 are British government debt , bonds sold to finance the 2011 government .

Example 539:
titleEntities: {'Subject': ['Births'], 'Date': ['2018', '2018']}
title: Births - number by age of mother 2018
X_Axis['Year']: ['15_to_19_years', '20_to_24_years', '25_to_29_years', '30_to_34_years', '35_to_39_years', '40_to_44_years', '45_to_50_years']
Y_Axis['Number', 'of', 'children', 'born', 'in', 'thousands']: ['469', '3268', '9668', '15269', '19902', '20145', '25038']

gold: This statistic displays the total number of births in the United States as of June 2018 , by age of mother . In 2018 , women aged between 15 and 19 years gave birth to 469,000 children in the United States .
gold_template: This statistic displays the total templateYLabel[0] of templateTitleSubject[0] in the country as of June templateTitleDate[0] , templateTitle[2] templateTitle[3] of templateTitle[4] . In templateTitleDate[0] , women aged between templateXValue[0] and templateXValue[0] gave birth to templateYValue[min] templateYLabel[1] in the country .

generated_template: In templateXValue[0] , the templateTitle[0] templateTitleDate[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to templateYValue[max] percent . The templateYLabel[1] of football events is the live coverage of football matches on television or radio . In recent years , the global templateTitle[5] of the French top football league is increasing , whether it concerns the market templateTitle[5] of the teams or the transfer fee spending of templateTitle[0] templateTitleDate[0] football clubs .
generated: In 15 to 19 years , the Births 2018 Number children born in Births amounted to 25038 percent . The children of football events is the live coverage of football matches on television or radio . In recent years , the global 2018 of the French top football league is increasing , whether it concerns the market 2018 of the teams or the transfer fee spending of Births 2018 football clubs .

Example 540:
titleEntities: {'Subject': ['Orlando Magic'], 'Date': ['2001', '2019']}
title: Orlando Magic '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']: ['244', '223', '211', '166', '143', '143', '139', '126', '140', '108', '107', '100', '92', '89', '82', '78', '80', '82']

gold: The statistic shows the revenue of the Orlando Magic 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 244 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] templateTitleSubject[0] 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: The statistic shows the Revenue of the Orlando Magic 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 244 million U.S. dollars .

Example 541:
titleEntities: {'Subject': ['Kickstarter'], 'Date': ['2019']}
title: Distribution of Kickstarter funding amounts raised 2019
X_Axis['Money', 'raised', '(in', 'U.S.', 'dollars)']: ['Less_than_1000', '1000_to_9999', '10000_to_19999', '20000_to_99999', '100K_to_999999', 'More_than_1M+']
Y_Axis['Number', 'of', 'projects']: ['21945', '92970', '24579', '24804', '6063', '385']

gold: The statistic shows the number of successfully funded projects on the crowdfunding platform Kickstarter as of October 2 , 2018 . It shows the number of total successfully funded projects by funds raised . As of that time , the number of successfully funded projects at Kickstarter which raised more than one million U.S. dollars amounted to 385 projects .
gold_template: The statistic shows the templateYLabel[0] of successfully funded templateYLabel[1] on the crowdfunding platform templateTitleSubject[0] as of October 2 , 2018 . It shows the templateYLabel[0] of total successfully funded templateYLabel[1] by funds templateXLabel[1] . As of that time , the templateYLabel[0] of successfully funded templateYLabel[1] at templateTitleSubject[0] which templateXLabel[1] templateXValue[last] templateXValue[0] one million templateXLabel[3] dollars amounted to templateYValue[min] templateYLabel[1] .

generated_template: How many people play templateTitleSubject[0] ? Having burst onto the scene in templateXValue[4] , templateTitleSubject[0] has since become a templateTitle[4] phenomenon , amassing almost templateYValue[max] templateYLabel[2] templateYLabel[1] across the globe as of templateXValue[0] templateXValue[0] . How did templateTitleSubject[0] become so big ? The reasons why templateTitleSubject[0] has become such a global hit are clear to see . Not only is the game free , but it is also available on most gaming platforms .
generated: How many people play Kickstarter ? Having burst onto the scene in 100K to 999999 , Kickstarter has since become a raised phenomenon , amassing almost 92970 projects across the globe as of Less than 1000 . How did Kickstarter become so big ? The reasons why Kickstarter has become such a global hit are clear to see . Not only is the game free , but it is also available on most gaming platforms .

Example 542:
titleEntities: {'Subject': ['North Carolina'], 'Date': ['1960', '2018']}
title: Population density in North Carolina 1960 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2010', '2000', '1990', '1980', '1970', '1960']
Y_Axis['Number', 'of', 'residents', 'per', 'square', 'mile']: ['213.6', '211.3', '208.7', '206.6', '204.5', '202.6', '200.6', '196.1', '165.6', '136.4', '120.9', '104.6', '93.5']

gold: This graph shows the population density in the federal state of North Carolina from 1960 to 2018 . In 2018 , the population density of North Carolina stood at 213.6 residents per square mile of land area .
gold_template: This graph shows the templateTitle[0] templateTitle[1] in the federal state of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] stood at templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] of land area .

generated_template: This graph shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] templateTitle[3] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] .
generated: This graph shows the Population density in North Carolina Carolina 1960 to 2018 . In 2018 , the Population density of North Carolina Carolina amounted to 213.6 residents per square mile .

Example 543:
titleEntities: {'Subject': ['Indonesia'], 'Date': ['2000', '2016']}
title: Household electricity consumption per capita in Indonesia 2000 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Consumption', 'in', 'kilowatt', 'hours', 'per', 'capita']: ['348.3', '333.3', '319.9', '296.5', '281.9', '258.8', '238.2', '222.7', '203.3', '195.2', '183.5', '174.5', '171.9', '153.9', '148.0', '147.2', '135.9']

gold: This statistic represents the household consumption of electricity per capita in Indonesia from the year 2000 to 2016 , in kilowatt hours . In the year 2016 , household consumption of electricity per capita in Indonesia was about 348 kilowatts per hour .
gold_template: This statistic represents the templateTitle[0] templateYLabel[0] of templateTitle[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from the templateXLabel[0] templateXValue[min] to templateXValue[max] , in templateYLabel[1] templateYLabel[2] . In the templateXLabel[0] templateXValue[max] , templateTitle[0] templateYLabel[0] of templateTitle[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] was about templateYValue[max] kilowatts templateYLabel[3] hour .

generated_template: The statistic represents the total templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . It is expected that the templateYLabel[0] of templateTitle[1] templateTitle[2] will reach some templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[8] .
generated: The statistic represents the total Consumption of Indonesia consumption per capita from 2000 to 2016 . It is expected that the Consumption of electricity consumption will reach some 348.3 kilowatt hours per capita in 2008 .

Example 544:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2019']}
title: Facebook : number of followers of popular luxury brands 2019
X_Axis['Designer', 'Brand']: ['Louis_Vuitton', 'Chanel', 'Gucci', 'Michael_Kors', 'Burberry', 'Dior', 'Dolce_&_Gabbana', 'Ralph_Lauren', 'Armani', 'Coach', 'Prada', 'Versace', 'Jimmy_Choo', 'Christian_Louboutin', 'Hermès']
Y_Axis['Followers', 'in', 'millions']: ['23.28', '21.96', '18.2', '17.94', '17.31', '16.65', '11.74', '9.16', '8.63', '7.36', '6.6', '5.37', '3.71', '3.35', '3.13']

gold: This statistic provides information on the leading luxury brands with the most followers on Facebook as of May 2019 , ranked by number of followers . According to the findings , the luxury brand Louis Vuitton had recorded in a total of 23.28 million likes on Facebook , and ranking second was Chanel with 21.96 million page likes .
gold_template: This statistic provides information on the leading templateTitle[4] templateTitle[5] with the most templateYLabel[0] on templateTitleSubject[0] as of May templateTitleDate[0] , ranked by templateTitle[1] of templateYLabel[0] . According to the findings , the templateTitle[4] templateXLabel[1] templateXValue[0] had recorded in a total of templateYValue[max] templateYLabel[1] likes on templateTitleSubject[0] , and ranking second was templateXValue[1] with templateYValue[1] templateYLabel[1] page likes .

generated_template: The statistic above presents the distribution of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , templateXValue[0] templateTitle[0] templateTitle[1] templateTitle[2] accounted for templateYValue[max] percent of all templateTitle[0] templateTitle[1] templateTitle[2] .
generated: The statistic above presents the distribution of Facebook number followers in the popular in 2019 , brands Designer . In 2019 , Louis Vuitton Facebook number followers accounted for 23.28 percent of all Facebook number followers .

Example 545:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2017']}
title: Fairtrade food and drink sales revenue in the United Kingdom 1999 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Sales', 'in', 'million', 'GBP']: ['1720', '1608', '1572', '1612', '1710', '1553', '1253', '1064', '749', '635', '458', '285', '195', '141', '92', '63', '51', '33', '22']

gold: This statistic illustrates the sales of Fairtrade food and drink products in the United Kingdom ( UK ) from 1999 to 2017 . In 2005 , 195 million British pounds was spent on Fairtrade food and drink products . Sales rose during the period under consideration to approximately 1.72 billion British pounds in sales in 2017 .
gold_template: This statistic illustrates the templateYLabel[0] of templateTitle[0] templateTitle[1] and templateTitle[2] products in the templateTitleSubject[0] ( UK ) from templateXValue[min] to templateXValue[max] . In templateXValue[12] , templateYValue[12] templateYLabel[1] British pounds was spent on templateTitle[0] templateTitle[1] and templateTitle[2] products . templateYLabel[0] rose during the period under consideration to approximately templateYValue[max] templateYLabel[1] British pounds in templateYLabel[0] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] under jurisdiction of federal or state correctional authorities from templateXValue[min] to templateXValue[max] . Around templateYValue[0] thousand people were imprisoned in the templateTitle[2] in templateXValue[max] , compared to templateYValue[17] templateYLabel[2] in the templateXLabel[0] templateXValue[17] .
generated: This statistic shows the Sales of million under jurisdiction of federal or state correctional authorities from 1999 to 2017 . Around 1720 thousand people were imprisoned in the drink in 2017 , compared to 33 GBP in the Year 2000 .

Example 546:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Leading prescriptions dispensed in the U.S. diabetes market 2014
X_Axis['Medicine']: ['Metformin_HCI', 'Glimepiride', 'Metformin_ER_(G)', 'Glipizide', 'Lantus_(long-acting_insulin)', 'Lantus_SoloStar_(long-acting_insulin)', 'Januvia_(sitagliptin)', 'Glipizide_ER', 'Glyburide', 'Pioglitazone']
Y_Axis['Rx', 'dispensed', 'in', 'million', 'units']: ['59.2', '12.7', '12.5', '10.4', '9.6', '9.5', '8.8', '7.1', '6.5', '5.5']

gold: The statistic shows the leading prescriptions dispensed in the U.S. diabetes market in 2014 . In that year , Metformin HCI was the leading diabetes prescription dispensed in the United States at 59.2 million units .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] templateTitle[4] templateTitle[5] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[0] templateTitle[4] prescription templateYLabel[1] in the templateTitle[3] at templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] mudslides in the period from 1900 to 2016* . A flood in templateXValue[1] in 1998 templateTitle[2] an templateYLabel[0] templateYLabel[1] of approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Rx dispensed U.S. mudslides in the period from 1900 to 2016* . A flood in Glimepiride in 1998 dispensed an Rx dispensed of approximately 59.2 million units .

Example 547:
titleEntities: {'Subject': ['Europe'], 'Date': ['2018']}
title: Direct investment position of the U.S. in Europe 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Direct', 'investments', 'in', 'trillion', 'U.S.', 'dollars']: ['3.61', '3.55', '3.31', '3.08', '2.9', '2.6', '2.45', '2.25', '2.03', '1.99', '1.84', '1.68', '1.4', '1.21', '1.18', '0.98', '0.86', '0.77', '0.69']

gold: This statistic shows the direct investment position of the United States in Europe from 2000 to 2018 , on a historical-cost basis . In 2018 , the U.S. investments made in Europe were valued at approximately 3.61 trillion U.S. dollars . The total direct position of the United States abroad amounted to 5.95 trillion U.S. dollars in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitleSubject[0] were valued at approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The total templateYLabel[0] templateTitle[2] of the templateTitle[3] abroad amounted to 5.95 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: In templateXValue[max] , foreign templateYLabel[0] templateTitle[1] ( FDI ) from the templateTitle[3] to other countries amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Foreign templateYLabel[0] templateTitle[1] reflects the ownership of business from templateYValue[17] country in another country . It differs from a traditional templateTitle[1] in companies located templateTitle[4] by the ownership factor in case of FDI .
generated: In 2018 , foreign Direct investment ( FDI ) from the U.S. to other countries amounted to 3.61 trillion U.S. dollars . Foreign Direct investment reflects the ownership of business from 0.77 country in another country . It differs from a traditional investment in companies located Europe by the ownership factor in case of FDI .

Example 548:
titleEntities: {'Subject': ['Carolina Panthers', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Carolina Panthers ( 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']: ['2400', '2300', '2300', '2075', '1560', '1250', '1057', '1048', '1002', '1037', '1049', '1040', '956', '936', '878', '760', '642', '609']

gold: This graph depicts the franchise value of the Carolina Panthers from the National Football League from 2002 to 2019 . In 2019 , the franchise value came to 2.4 billion U.S. dollars.The Carolina Panthers are owned by David Tepper , who bought the franchise for about 2.3 billion U.S. dollars in 2018 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateYLabel[2] templateYLabel[3] dollars.The templateTitleSubject[0] are owned by David Tepper , who bought the templateYLabel[0] for about templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[1] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] are owned by Stuart Sternberg , who bought the templateYLabel[0] for 200 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 2000 .
generated: This graph depicts the Franchise value of the Carolina Panthers of the National Football League from 2002 to 2019 . In 2019 , the Franchise value came to around 2400 million U.S. dollars . The Carolina Panthers are owned by Stuart Sternberg , who bought the Franchise for 200 million U.S. dollars in 2000 .

Example 549:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019', '2050']}
title: U.S. production of energy from biomass forecast 2019 to 2050
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2019']
Y_Axis['Production', 'in', 'quadrillion', 'Btu']: ['5.54', '5.39', '5.27', '5.2', '5.13', '4.96', '4.74', '4.82']

gold: This statistic gives outlook figures on the production of biomass energy between 2019 and 2050 . In 2050 , U.S. biomass energy production is forecast to increase to around 5.54 quadrillion British thermal units .
gold_template: This statistic gives outlook figures on the templateYLabel[0] of templateTitle[4] templateTitle[2] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[4] templateTitle[2] templateYLabel[0] is templateTitle[5] to increase to around templateYValue[max] templateYLabel[1] British thermal units .

generated_template: This statistic shows the estimated templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[min] templateYLabel[2] .
generated: This statistic shows the estimated Production of energy from biomass in U.S. from 2019 to 2050 . In 2019 , the Production of energy from biomass quadrillion in U.S. amounted to 4.74 Btu .

Example 550:
titleEntities: {'Subject': ['China'], 'Date': ['2008', '2015']}
title: Market value of honey in China based on sale price 2008 to 2015
X_Axis['Year']: ['2015', '2011', '2010', '2009', '2008']
Y_Axis['Market', 'value', 'in', 'million', 'U.S.', 'dollars']: ['553.6', '419.6', '380.8', '348.2', '328.7']

gold: The statistic shows the market value of honey in China between 2008 and 2010 , including a forecast for 2015 , based on sales price .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] in templateTitleSubject[0] between templateXValue[min] and templateXValue[2] , including a forecast for templateXValue[max] , templateTitle[4] on sales templateTitle[6] .

generated_template: This statistic illustrates the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per day in the templateTitleSubject[0] templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . Global templateTitle[2] templateTitle[3] templateTitle[4] is expected to increase by templateYValue[max] percent in templateXValue[min] compared to 2018 .
generated: This statistic illustrates the Market of value honey China per day in the China between 2008 and 2015 . Global honey China based is expected to increase by 553.6 percent in 2008 compared to 2018 .

Example 551:
titleEntities: {'Subject': ['Photo'], 'Date': ['2013']}
title: Photo sharing sites : daily upload market share 2013
X_Axis['Platform']: ['Snapchat', 'Facebook', 'Instagram', 'Flickr']
Y_Axis['Share', 'of', 'uploads']: ['49', '43', '7', '1']

gold: This statistic presents the four most popular photo sharing sites as of November 2013 , sorted by share of daily photo uploads . During that month , Instagram accounted for seven percent of daily photo uploads .
gold_template: This statistic presents the four most popular templateTitleSubject[0] templateTitle[1] templateTitle[2] as of November templateTitleDate[0] , sorted by templateYLabel[0] of templateTitle[3] templateTitleSubject[0] templateYLabel[1] . During that month , templateXValue[2] accounted for templateYValue[2] percent of templateTitle[3] templateTitleSubject[0] templateYLabel[1] .

generated_template: This statistic illustrates the templateTitle[0] templateTitle[1] templateTitle[2] networks in templateTitleSubject[0] templateTitle[5] as of November templateTitleDate[0] , based on reach . During the survey period it was found that templateYValue[max] percent templateYLabel[1] templateYLabel[2] had a active between a monthly basis .
generated: This statistic illustrates the Photo sharing sites networks in Photo market as of November 2013 , based on reach . During the survey period it was found that 49 percent uploads had a active between a monthly basis .

Example 552:
titleEntities: {'Subject': ['Nokia'], 'Date': ['2019']}
title: Nokia 's net sales 1999 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Net', 'sales', 'in', 'billion', 'euros']: ['23.32', '22.56', '23.15', '23.64', '12.5', '11.76', '12.71', '30.18', '38.66', '42.45', '40.98', '50.71', '51.06', '41.12', '34.19', '29.37', '29.53', '30.02', '31.19', '30.38', '19.77']

gold: In 2018 , Nokia had 22.5 billion euros in net sales , which is a small decrease from the year before . In the past decade , the company has made several directional shifts from handheld devices to network infrastructure in order to keep up with the quickly evolving electronics and telecommunications industry . Focus on B2B markets and products After selling its mobile and devices segment to Microsoft in 2014 , Nokia has focused on its network infrastructure business .
gold_template: In templateXValue[1] , templateTitleSubject[0] had 22.5 templateYLabel[2] templateYLabel[3] in templateYLabel[0] templateYLabel[1] , which is a small decrease from the templateXLabel[0] before . In the past decade , the company has made several directional shifts from handheld devices to network infrastructure in order to keep up with the quickly evolving electronics and telecommunications industry . Focus on B2B markets and products After selling its mobile and devices segment to Microsoft in templateXValue[5] , templateTitleSubject[0] has focused on its network infrastructure business .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] was around templateYValue[0] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Net sales of the Nokia from 1999 to 2019 . In 2019 , the Net sales in the Nokia was around 23.32 billion euros .

Example 553:
titleEntities: {'Subject': ['Europe'], 'Date': ['2017', '2017']}
title: Leading food and beverage retailers of Europe 2017 , based on revenue
X_Axis['Company', '(Country', 'of', 'origin)']: ['Schwarz_Unternehmenstreuhand_KG_(Germany)', 'Aldi_Einkauf_GmbH_&_Co._oHG_(Germany)', 'Tesco_PLC_(UK)', 'Ahold_Delhaize_(formerly_Koninklijke_Ahold_N.V._and_Delhaize_Group_SA_[Netherlands])', 'Auchan_Holding_SA_(France)', 'Edeka_Group_(Germany)', 'Rewe_Combine_(Germany)', 'Casino_Guichard-Perrachon_S.A._(France)', 'Centres_Distributeurs_E._Leclerc_(France)_', 'Metro_AG_(Germany)', 'The_IKEA_Group_(INGKA_Holding_B.V.)_(Netherlands)', 'J_Sainsbury_plc_(UK)', 'LVMH_Moët_Hennessy-Louis_Vuitton_S.A._(France)', 'ITM_Developpement_International_(Intermarche;_France)_', 'Inditex_S.A._(Spain)', 'Migros-Genossenschafts_Bund_(Switzerland)_', 'Ceconomy_AG_(Germany)', 'Mercadona_SA_(Spain)', 'Coop_Group_(Switzerland)_', 'Wm_Morrison_Supermarkets_PLC_(UK)']
Y_Axis['Billion', 'U.S.', 'dollars']: ['111.77', '98.29', '73.96', '72.31', '58.61', '57.48', '49.71', '42.6', '41.54', '40.96', '37.43', '36.6', '33.29', '31.85', '28.89', '24.53', '24.43', '23.68', '22.52', '22.43']

gold: In 2018 , the German based Schwarz Gruppe was the leading food and beverage retailer from Europe and generated 111.77 billion U.S. dollars in revenue . The second largest retailer was also German . Aldi Einkauf GmbH & Ko .
gold_template: In 2018 , the German templateTitle[6] templateXValue[0] Gruppe was the templateTitle[0] templateTitle[1] and templateTitle[2] retailer from templateTitleSubject[0] and generated templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[7] . The second largest retailer was also German . templateXValue[1] GmbH templateXValue[1] Ko .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In that year , the templateXValue[0] templateXValue[0] was the templateTitle[1] templateTitle[2] templateXLabel[0] in the world with templateYLabel[0] amounting to approximately 6.96 templateYLabel[1] Swiss Francs . In 2015 , templateXValue[2] was by far the most valuable Swiss templateTitle[2] brand .
generated: The statistic shows the Billion of Europe food beverage retailers Europe in 2017 . In that year , the Schwarz Unternehmenstreuhand KG (Germany) was the food beverage Company in the world with Billion amounting to approximately 6.96 U.S. Swiss Francs . In 2015 , Tesco PLC (UK) was by far the most valuable Swiss beverage brand .

Example 554:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1970', '2018']}
title: Pre-primary school enrollment numbers in the U.S. 1970 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2000', '1995', '1990', '1985', '1980', '1975', '1970']
Y_Axis['Number', 'of', 'children', 'enrolled', '(in', 'millions)']: ['8.74', '8.64', '8.76', '8.61', '8.76', '8.83', '8.77', '9.16', '9.01', '8.84', '8.66', '8.76', '8.73', '8.52', '8.73', '8.65', '8.04', '8.03', '8.23', '5.16', '5.14', '4.28']

gold: This graph shows the number of children enrolled in pre-primary school institutions ( kindergarten or nursery ) in the United States from 1970 to 2018 . In 2018 , around 8.74 million children were enrolled in nursery or kindergarten programs in the United States .
gold_template: This graph shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitle[0] templateTitle[1] institutions ( kindergarten or nursery ) in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around templateYValue[0] million templateYLabel[1] were templateYLabel[2] in nursery or kindergarten programs in the templateTitle[4] .

generated_template: In templateXValue[max] , the templateTitle[3] templateTitle[4] amount of individual templateTitle[1] templateTitle[2] templateTitle[6] templateTitle[7] in the templateTitle[0] was about templateYValue[0] thousand templateYLabel[2] templateYLabel[3] . templateTitle[1] templateTitle[2] in the templateTitle[0] The economic and social purpose of templateTitle[1] templateTitle[2] is directed at handling financial guarantees to individuals and families . It provides a safety net to families in case of an unforeseen death of a bread-winning family member .
generated: In 2018 , the numbers U.S. amount of individual school enrollment 2018 in the Pre-primary was about 8.74 thousand enrolled (in . school enrollment in the Pre-primary The economic and social purpose of school enrollment is directed at handling financial guarantees to individuals and families . It provides a safety net to families in case of an unforeseen death of a bread-winning family member .

Example 555:
titleEntities: {'Subject': ['North Carolina'], 'Date': ['1992', '2018']}
title: North Carolina - 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', '4.5', '5.1', '5.7', '6.3', '8', '9.3', '10.3', '10.9', '10.6', '6.1', '4.7', '4.7', '5.2', '5.5', '6.4', '6.6', '5.5', '3.7', '3.2', '3.5', '3.7', '4.3', '4.3', '4.4', '5', '6']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[min] percent . You can access the monthly templateYLabel[0] templateYLabel[1] for the country here .
generated: This statistic shows the Unemployment rate in North Carolina from 1992 to 2018 . In 2018 , the Unemployment rate in North Carolina was 3.2 percent . You can access the monthly Unemployment rate for the country here .

Example 556:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2019']}
title: U.S. unemployment level 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['Unemployed', 'in', 'millions']: ['6.0', '6.31', '6.98', '7.75', '8.3', '9.62', '11.46', '12.51', '13.75', '14.83', '14.27', '8.92', '7.08', '7.0', '7.59', '8.15', '8.77', '8.38', '6.8', '5.69', '5.88', '6.21', '6.74', '7.24', '7.4', '8.0', '8.94', '9.61', '8.63', '7.05']

gold: This statistic shows the unemployment level in the United States from 1990 to 2019 . National unemployment level decreased to an average of six million people looking for work in 2019 . See the United States unemployment rate and the monthly unemployment rate for further information .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] in the templateTitle[0] from templateTitleDate[0] to templateTitleDate[1] . National templateTitle[1] templateTitle[2] decreased to an average of templateYValue[0] templateYLabel[1] people looking for work in templateTitleDate[1] . See the templateTitle[0] templateTitle[1] rate and the monthly templateTitle[1] rate for further information .

generated_template: Since templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] in the templateTitle[3] has stayed more or less steady . In templateTitleDate[0] , the female templateYLabel[0] templateYLabel[1] was templateYValue[29] percent , and in 2018 , the templateYLabel[0] templateYLabel[1] was at templateYValue[0] percent . However , it reached a peak in 2000 at templateYValue[max] percent .
generated: Since 1990 , the Unemployed millions of level in the 1990 has stayed more or less steady . In 1990 , the female Unemployed millions was 7.05 percent , and in 2018 , the Unemployed millions was at 6.0 percent . However , it reached a peak in 2000 at 14.83 percent .

Example 557:
titleEntities: {'Subject': ['Arizona'], 'Date': ['2005', '2019']}
title: Arizona Coyotes ' revenue 2005 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['102', '96', '98', '101', '92', '80', '67', '83', '70', '67', '66', '68', '67', '63']

gold: This graph depicts the annual National Hockey League revenue of the Arizona Coyotes from the 2005/06 season to the 2018/19 season . The revenue of the Arizona Coyotes amounted to 102 million U.S. dollars in the 2018/19 season .
gold_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .

generated_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .
generated: This graph depicts the annual National Hockey League Revenue of the Arizona from the 2005/06 season to the 2018/19 season . The Revenue of the Arizona amounted to 102 million U.S. dollars in the 2018/19 season .

Example 558:
titleEntities: {'Subject': ['Europe'], 'Date': ['2014']}
title: Wealth per adult on average in Europe in 2014 , by country
X_Axis['Country']: ['Luxembourg', 'Switzerland', 'Belgium', 'Netherlands', 'Austria', 'Germany', 'United_Kingdom', 'France', 'Italy', 'Cyprus', 'Finland', 'Spain', 'Portugal', 'Slovenia', 'Greece', 'Slovakia']
Y_Axis['Average', 'wealth', 'per', 'adult']: ['432221', '394917', '240928', '213365', '188552', '185857', '183325', '178862', '163493', '137298', '124285', '92341', '84847', '67878', '58877', '33295']

gold: The statistic displays the average value of wealth per adult in selected European countries as of 2014 . The average value of wealth per adult in Luxembourg amounted to 432.2 thousand euros , while in the United Kingdom ( UK ) it reached approximately 188.6 thousand euros .
gold_template: The statistic displays the templateYLabel[0] value of templateYLabel[1] templateYLabel[2] templateYLabel[3] in selected European countries as of templateTitleDate[0] . The templateYLabel[0] value of templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[0] amounted to templateYValue[max] thousand euros , while in the templateXValue[6] ( UK ) it reached approximately templateYValue[4] thousand euros .

generated_template: This statistic illustrates the annual templateYLabel[0] templateYLabel[1] templateYLabel[2] abstractions in selected countries in templateTitleDate[0] , or the latest year available . templateXValue[1] reported templateYLabel[2] templateYLabel[3] levels of templateYValue[1] templateYLabel[4] templateYLabel[5] templateYLabel[0] templateYLabel[1] . templateYLabel[2] templateYLabel[3] templateYLabel[0] person About 70 percent of the planet 's surface is covered with templateYLabel[2] .
generated: This statistic illustrates the annual Average wealth per abstractions in selected countries in 2014 , or the latest year available . Switzerland reported per adult levels of 394917 adult Average wealth . per adult Average person About 70 percent of the planet 's surface is covered with per .

Example 559:
titleEntities: {'Subject': ['Hays'], 'Date': ['2007', '2019']}
title: Revenue of Hays worldwide 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'million', 'GBP']: ['1129.7', '1072.8', '954.6', '810.3', '764.2', '724.9', '719.0', '734.0', '672.1', '557.7', '670.8', '786.8', '633.6']

gold: This statistic shows the revenue of Hays worldwide from 2007 to 2019 . In 2019 , the UK-based recruitment specialist Hays generated over 1.1 billion British pounds in revenue worldwide , up from one billion the previous year .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the UK-based recruitment specialist templateTitleSubject[0] generated over templateYValue[max] templateYLabel[1] British pounds in templateYLabel[0] templateTitle[2] , up from templateYValue[max] templateYLabel[1] the previous templateXLabel[0] .

generated_template: Global hotel giant Marriot templateTitleSubject[0] accounted for a templateYLabel[0] of approximately templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] , down from the previous templateXLabel[0] 's total of templateYValue[max] templateYLabel[1] templateYLabel[3] . Likely due to the global recession in the hotel industry With a significant drop in the U.S. market as the company 's templateYLabel[0] has seen steady growth over the past 12 years . The majority of templateTitleSubject[0] has increased by consumers such as Hilton templateTitle[4] , Hyatt Hotel , and Host & Resorts in terms of revenue in templateXValue[1] .
generated: Global hotel giant Marriot Hays accounted for a Revenue of approximately 1129.7 million GBP in 2019 , down from the previous Year 's total of 1129.7 million GBP . Likely due to the global recession in the hotel industry With a significant drop in the U.S. market as the company 's Revenue has seen steady growth over the past 12 years . The majority of Hays has increased by consumers such as Hilton 2019 , Hyatt Hotel , and Host & Resorts in terms of revenue in 2018 .

Example 560:
titleEntities: {'Subject': ['Engie'], 'Date': ['2018']}
title: Engie - revenue by region 2018
X_Axis['Country']: ['France', 'Other_EU_countries', 'Belgium', 'Asia_Middle_East_and_Oceania', 'South_America', 'North_America', 'Other_European_countries', 'Africa']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['24.98', '15.45', '5.96', '4.94', '4.2', '3.87', '0.82', '0.39']

gold: This statistic represents Engie 's revenue in the fiscal year of 2018 , by region . The French multinational energy company generated a revenue of around six billion euros in its Belgium segment . The company was formed by the merger of Gaz de France and Suez to GDF Suez and officially changed its name to Engie in April 2015 .
gold_template: This statistic represents templateTitleSubject[0] 's templateYLabel[0] in the fiscal year of templateTitleDate[0] , templateTitle[2] templateTitle[3] . The French multinational energy company generated a templateYLabel[0] of around templateYValue[2] templateYLabel[1] templateYLabel[2] in its templateXValue[2] segment . The company was formed templateTitle[2] the merger of Gaz de templateXValue[0] and Suez to GDF Suez and officially changed its name to templateTitleSubject[0] in April 2015 .

generated_template: This statistic shows the templateYLabel[0] of templateXValue[0] templateTitleSubject[0] templateYLabel[1] templateTitle[4] as of templateTitleDate[0] , templateTitle[6] templateTitle[7] . According to the report , the templateXValue[0] templateXValue[0] account for approximately templateYValue[max] templateYLabel[2] templateYLabel[1] , followed by templateXValue[1] .
generated: This statistic shows the Revenue of France Engie billion 2018 as of 2018 , 2018 . According to the report , the France account for approximately 24.98 euros billion , followed by Other EU countries .

Example 561:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1982', '2020']}
title: Mass shootings in the U.S. 1982 to 2020
X_Axis['Year']: ['1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020']
Y_Axis['Total', 'number', 'of', 'mass', 'shootings']: ['1', '0', '2', '0', '1', '1', '1', '2', '1', '3', '2', '4', '1', '1', '1', '2', '3', '5', '1', '1', '0', '1', '1', '2', '3', '4', '3', '4', '1', '3', '7', '5', '4', '7', '6', '11', '12', '10', '1']

gold: As of February 26 , there was one mass shootings in the United States in 2020 . This is compared to one mass shooting in 1982 , one in 2000 , and 12 mass shootings in 2018 . School shootings The United States sees the most school shootings in the world .
gold_template: As of February 26 , there was templateYValue[0] templateYLabel[2] templateYLabel[3] in the templateTitle[2] in templateXValue[max] . This is compared to templateYValue[0] templateYLabel[2] shooting in templateXValue[min] , templateYValue[0] in templateXValue[18] , and templateYValue[max] templateYLabel[2] templateYLabel[3] in templateXValue[36] . School templateYLabel[3] The templateTitle[2] sees the most school templateYLabel[3] in the world .

generated_template: As of December templateXValue[max] , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] has gradually increased from templateYValue[min] percent to templateYValue[0] percent . It 's templateYLabel[0] templateYLabel[1] templateYLabel[2] was at it 's highest in templateXValue[34] when templateYValue[max] percent of the templateTitleSubject[1] 's templateYLabel[3] was spent on the military . After templateXValue[34] , templateYLabel[0] templateYLabel[1] declined gradually , and then at a much faster pace after the end of the Cold War in templateXValue[27] , with the templateTitleSubject[0] templateYLabel[0] of just reaching the templateYValue[min] percent benchmark set by NATO .
generated: As of December 2020 , the Total of number mass in the U.S. has gradually increased from 0 percent to 1 percent . It 's Total number mass was at it 's highest in 2016 when 12 percent of the U.S. 's shootings was spent on the military . After 2016 , Total number declined gradually , and then at a much faster pace after the end of the Cold War in 2009 , with the U.S. Total of just reaching the 0 percent benchmark set by NATO .

Example 562:
titleEntities: {'Subject': ['Cineplex'], 'Date': ['2010', '2018']}
title: Attendance at Cineplex cinemas 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Attendance', 'in', 'millions']: ['69.27', '70.4', '74.6', '77.0', '73.6', '72.7', '71.2', '66.1', '67.0']

gold: The timeline presents the attendance figures at Cineplex from 2010 to 2018 . In 2018 , 69.27 million people attended movies at the Canadian movie theater chain , down from 70.4 million visitors a year earlier .
gold_template: The timeline presents the templateYLabel[0] figures at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] templateYLabel[1] people attended movies at the Canadian movie theater chain , down from templateYValue[1] templateYLabel[1] visitors a templateXLabel[0] earlier .

generated_template: This statistic represents the templateTitle[0] templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] employed a templateTitle[0] of templateYValue[max] people throughout the world . templateTitleSubject[0] is the world 's largest cosmetics and beauty company , concentrating on hair color , skin care , sun protection , make-up , perfumes , and hair care .
generated: This statistic represents the Attendance of millions of Cineplex 2018 from 2010 to 2018 . In 2018 , Cineplex employed a Attendance of 77.0 people throughout the world . Cineplex is the world 's largest cosmetics and beauty company , concentrating on hair color , skin care , sun protection , make-up , perfumes , and hair care .

Example 563:
titleEntities: {'Subject': ['Angola'], 'Date': ['2019']}
title: Unemployment rate in Angola 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']: ['7.25', '7.25', '7.14', '7.28', '7.28', '7.43', '7.45', '7.36', '7.36', '9.09', '10.61', '12.04', '14.63', '17.67', '20.53', '23.64', '23.93', '23.9', '23.12', '22.89', '20.9']

gold: This statistic shows the unemployment rate in Angola from 1999 to 2019 . In 2019 , the unemployment rate in Angola was 7.25 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Angola from 1999 to 2019 . In 2019 , the Unemployment rate in Angola was at approximately 7.25 percent .

Example 564:
titleEntities: {'Subject': ['Aston Villa'], 'Date': ['2016']}
title: Value of Aston Villa 's jersey sponsorship 2016
X_Axis['Year']: ['2015/16_(Intuit_Quickbooks)', '2014/15_(Dafabet.com)', '2013/14_(Dafabet.com)', '2012/13_(Genting)', '2011/12_(Genting)', '2010/11_(FxPro)', '2009/10_(Acorns)']
Y_Axis['Jersey', 'sponsorship', 'revenue', 'in', 'million', 'GBP']: ['5', '5', '5', '8', '8', '5', '0']

gold: The statistic shows the revenue Aston Villa generated from its jersey sponsorship deal from the 2009/10 season to the 2015/16 season . In the 2012/13 season Aston Villa received 8 million GBP from its jersey sponsor Genting .
gold_template: The statistic shows the templateYLabel[2] templateTitleSubject[0] generated from its templateYLabel[0] templateYLabel[1] deal from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[3] season templateTitleSubject[0] received templateYValue[max] templateYLabel[3] templateYLabel[4] from its templateYLabel[0] sponsor Genting .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[0] templateTitleSubject[0] templateTitle[2] templateYLabel[1] from the first templateXLabel[0] of templateTitleDate[0] to the second templateXLabel[0] of templateTitleDate[1] . In the second templateXLabel[0] of templateTitleDate[0] , templateYValue[max] percent of the templateYLabel[2] between the ages of 18 and templateXValue[0] templateXValue[0] to templateYLabel[1] .
generated: The statistic shows the Jersey of Value Aston Villa Villa sponsorship from the first Year of 2016 to the second Year of 2016 . In the second Year of 2016 , 8 percent of the revenue between the ages of 18 and 2015/16 (Intuit Quickbooks) to sponsorship .

Example 565:
titleEntities: {'Subject': ['England', 'Wales'], 'Date': ['2014']}
title: Reasons for unreported vandalism against businesses in England and Wales 2014
X_Axis['Response']: ['Trivial/no_loss', 'Lack_of_police_engagement', 'Private/dealt_with_ourselves', 'Lack_of_evidence', 'Reported_to_other_authorities', 'Inconvenient_to_report', 'Police_came', 'Common_occurrence', 'Fear_of_reprisal', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['43', '40', '13', '3', '3', '3', '3', '2', '1', '6']

gold: This survey shows the reasons businesses gave for not reporting cases of vandalism on their premises to the police in England and Wales in 2014 . Of respondents , 43 percent claimed they did not report the incident as there was no loss or damage or the crime was too trivial to report to the police .
gold_template: This survey shows the templateTitle[0] templateTitle[5] gave templateTitle[1] not reporting cases of templateTitle[3] on their premises to the templateXValue[1] in templateTitleSubject[0] and templateTitleSubject[1] in templateTitleDate[0] . Of templateYLabel[1] , templateYValue[max] percent claimed they did not templateXValue[5] the incident as there was no templateXValue[0] or damage or the crime was too trivial to templateXValue[5] to the templateXValue[1] .

generated_template: This statistic shows the templateTitle[0] ten templateTitle[1] in templateTitle[2] templateTitle[3] templateXValue[1] on templateTitle[5] templateTitle[6] in the templateTitle[7] as forecasted by professional chefs for templateTitleDate[0] . During the survey , templateYValue[2] percent of the templateYLabel[1] stated that templateXValue[2] templateTitle[2] templateTitle[3] templateXValue[1] would be a `` hot trend '' for templateTitle[5] templateTitle[6] in templateTitleDate[0] .
generated: This statistic shows the Reasons ten for in unreported vandalism Lack of police engagement on businesses England in the Wales as forecasted by professional chefs for 2014 . During the survey , 13 percent of the respondents stated that Private/dealt with ourselves unreported vandalism Lack of police engagement would be a `` hot trend '' for businesses England in 2014 .

Example 566:
titleEntities: {'Subject': ['England', 'Wales'], 'Date': ['2005', '2019']}
title: Road deaths involving police pursuit in England and Wales from 2005 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05']
Y_Axis['Fatailities']: ['30', '17', '28', '13', '7', '10', '27', '12', '13', '19', '22', '17', '19', '32', '23']

gold: This statistic shows the number of road traffic fatalities related to police pursuits in England and Wales from 2004/05 to 2018/19 . During the period concerned , the number of road traffic fatalities related to police pursuits fluctuated , peaking in 2005/06 at 32 deaths .
gold_template: This statistic shows the number of templateTitle[0] traffic fatalities related to templateTitle[3] pursuits in templateTitleSubject[0] and templateTitleSubject[1] templateTitle[7] templateXValue[last] to templateXValue[0] . During the period concerned , the number of templateTitle[0] traffic fatalities related to templateTitle[3] pursuits fluctuated , peaking in templateXValue[13] at templateYValue[max] templateTitle[1] .

generated_template: This statistic shows the number of templateTitle[0] templateTitle[2] by templateTitle[1] in templateTitleSubject[0] and templateTitleSubject[1] from templateXValue[last] to templateXValue[0] . During this period , the number of templateTitle[0] templateTitle[2] by templateTitle[1] fluctuated , peaking in templateXValue[2] at templateYValue[max] templateTitle[2] . By templateXValue[0] it fell down to templateYValue[0] templateTitle[2] .
generated: This statistic shows the number of Road involving by deaths in England and Wales from 2004/05 to 2018/19 . During this period , the number of Road involving by deaths fluctuated , peaking in 2016/17 at 32 involving . By 2018/19 it fell down to 30 involving .

Example 567:
titleEntities: {'Subject': ['Burger King'], 'Date': ['2011', '2014']}
title: Burger King 's EBITDA margin worldwide 2011 to 2014
X_Axis['Year']: ['2014', '2013', '2012', '2011']
Y_Axis['EBITDA', 'margin']: ['16.71', '51.3', '27', '21.3']

gold: This statistic shows Burger King 's EBITDA margin worldwide from 2011 to 2014 . Between 2012 and 2013 fast food chain Burger King 's earnings before interest , taxes , depreciation and amortization increased by 51.3 percent .
gold_template: This statistic shows templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] templateTitle[5] from templateXValue[min] to templateXValue[max] . Between templateXValue[2] and templateXValue[1] fast food chain templateTitleSubject[0] 's earnings before interest , taxes , depreciation and amortization increased by templateYValue[max] percent .

generated_template: The statistic represents the global templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[0] in templateTitleDate[0] , templateTitle[6] templateTitle[7] . In templateTitleDate[0] , the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] is expected to increase by templateYValue[max] percent .
generated: The statistic represents the global EBITDA of 's EBITDA margin in the Burger in 2011 , 2011 2014 . In 2011 , the 's EBITDA margin worldwide is expected to increase by 51.3 percent .

Example 568:
titleEntities: {'Subject': ['NBA'], 'Date': ['2018/19', '2018/19']}
title: Gate receipts of the Golden State Warriors ( NBA ) 2018/19
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11']
Y_Axis['Gate', 'receipts', 'in', 'million', 'U.S.', 'dollars']: ['178', '164', '143', '134', '77', '55', '50', '31', '41']

gold: The statistic depicts the gate receipts/ticket sales of the Golden State Warriors , franchise of the National Basketball Association , from 2010/11 to 2018/19 . In the 2018/19 season , the gate receipts of the Golden State Warriors were at 178 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] receipts/ticket sales of the templateTitleSubject[0] Warriors , franchise of the National Basketball Association , from 2010/11 to templateTitle[6] . In the templateTitle[6] season , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Warriors were at templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistics shows the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[0] templateYLabel[3] templateYLabel[4] .
generated: The statistics shows the estimated Gate receipts million of the NBA from the 10/11 season to the 18/19 season . In the 10/11 season , the Gate receipts million was at 178 U.S. dollars .

Example 569:
titleEntities: {'Subject': ['Subaru', 'United Kingdom', 'UK'], 'Date': ['2019']}
title: Subaru car sales in the United Kingdom ( UK ) 2016 to 2019
X_Axis['Month']: ['Dec_19', 'Nov_19', 'Oct_19', 'Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16']
Y_Axis['Units', 'sold']: ['708', '157', '88', '526', '52', '103', '244', '138', '101', '632', '74', '174', '193', '145', '135', '829', '100', '112', '265', '231', '153', '761', '62', '155', '246', '216', '99', '510', '44', '152', '202', '155', '123', '706', '48', '178', '330', '219', '256', '762', '69', '148']

gold: This statistic shows the monthly amount of cars sold by Subaru in the United Kingdom ( UK ) between July 2016 and December 2019 . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December 2019 , 708 new Subaru cars had been sold .
gold_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between July templateTitleDate[0] and December templateTitleDate[1] . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December templateTitleDate[1] , templateYValue[0] new templateTitleSubject[0] cars had been templateYLabel[1] .

generated_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] in the templateTitleSubject[1] templateTitleSubject[1] ( templateTitleSubject[2] ) between July templateTitleDate[0] and December templateTitleDate[1] . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December templateTitleDate[1] , templateYValue[0] new templateTitleSubject[0] cars had been templateYLabel[1] .
generated: This statistic shows the monthly amount of cars sold by Subaru in the United Kingdom ( UK ) between July 2019 and December 2019 . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December 2019 , 708 new Subaru cars had been sold .

Example 570:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2008', '2018']}
title: Number of butcher shops and meat retailers in the United Kingdom ( UK ) 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'enterprises']: ['5690', '5864', '5929', '5874', '5940', '6056', '6134', '6220', '6283', '6399', '6633']

gold: Between 2008 and 2018 , the number of stores that specialize in the sales of meat has been shrinking In the United Kingdom . During this period , the number of meat specialty stores has seen a decrease of almost 943 . Of these stores , approximately 1.75 thousand generated a turnover between one hundred thousand and 250 thousand British pounds in 2018 .
gold_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] of stores that specialize in the sales of templateTitle[3] has been shrinking In the templateTitleSubject[0] . During this period , the templateYLabel[0] of templateTitle[3] specialty stores has seen a decrease of almost 943 . Of these stores , approximately 1.75 thousand generated a turnover between one hundred thousand and 250 thousand British pounds in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of confirmed templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] has fluctuated in this period . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] amounted to approximately templateYValue[max] million .
generated: This statistic shows the Number of confirmed enterprises in United Kingdom from 2008 to 2018 . The Number of enterprises has fluctuated in this period . In 2018 , the Number of enterprises amounted to approximately 6633 million .

Example 571:
titleEntities: {'Subject': ['Spain'], 'Date': ['2018']}
title: Average annual wages in Spain 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Average', 'annual', 'wages', 'in', 'euros']: ['27946', '28171', '28738', '28902', '28405', '28400', '28336', '29166', '29585', '30101', '28198', '27101', '26751', '26853', '26697', '26976', '27049', '26851', '26856']

gold: This statistic shows the average annual wages in Spain from 2000 to 2018 . Over this 18-year period , annual wages in Spain have fluctuated greatly , peaking at approximately 30 thousand euros in 2009 and decreasing to approximately 28 thousand euros yearly in 2012 . The average annual wage stood at approximately 28 thousand euros in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Over this 18-year period , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] have fluctuated greatly , peaking at approximately templateYValue[8] thousand templateYLabel[3] in templateXValue[9] and decreasing to approximately templateYValue[0] thousand templateYLabel[3] yearly in templateXValue[6] . The templateYLabel[0] templateYLabel[1] wage stood at approximately templateYValue[0] thousand templateYLabel[3] in templateXValue[max] .

generated_template: Between templateXValue[min] and templateXValue[max] the templateYLabel[0] templateYLabel[1] wage of templateTitleSubject[0] was templateYValue[max] thousand templateYLabel[3] per templateXLabel[0] , a growth of just over 5.8 thousand templateYLabel[3] when compared with templateXValue[min] . From templateXValue[min] until templateXValue[11] , templateYLabel[2] rose by less than a thousand templateYLabel[3] , with wage growth accelerating mainly in the period after templateXValue[8] . The 607 Euro increase recorded between templateXValue[8] and templateXValue[7] was the largest wage rise seen during this period .
generated: Between 2000 and 2018 the Average annual wage of Spain was 30101 thousand euros per Year , a growth of just over 5.8 thousand euros when compared with 2000 . From 2000 until 2007 , wages rose by less than a thousand euros , with wage growth accelerating mainly in the period after 2010 . The 607 Euro increase recorded between 2010 and 2011 was the largest wage rise seen during this period .

Example 572:
titleEntities: {'Subject': ['Countries'], 'Date': []}
title: Countries ranked by number of ice hockey players 2018/19
X_Axis['Country']: ['Canada', 'United_States', 'Czech_Republic', 'Russia', 'Finland', 'Sweden', 'Switzerland', 'France', 'Germany', 'Japan', 'Slovakia', 'Norway', 'Great_Britain', 'Austria', 'Hungary', 'Latvia', 'Kazakhstan', 'Ukraine', 'Italy', 'Belarus']
Y_Axis['Number', 'of', 'players']: ['621026', '567908', '121613', '112236', '64641', '55431', '27867', '21667', '21340', '18837', '11394', '10353', '8162', '7670', '7106', '7000', '6915', '5384', '5210', '4580']

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

generated_template: As of June templateTitleDate[0] , templateXValue[0] was ranked first among the templateTitleSubject[0] templateTitle[1] the most templateYLabel[1] templateYLabel[2] . templateXValue[0] had templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] , more than double the amount of third-ranked templateXValue[2] templateXValue[2] templateTitle[1] just over 30 thousand people .
generated: As of June , Canada was ranked first among the Countries ranked the most players . Canada had 621026 Number players , more than double the amount of third-ranked Czech Republic ranked just over 30 thousand people .

Example 573:
titleEntities: {'Subject': ['Guyana'], 'Date': ['2019']}
title: Unemployment rate in Guyana 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['12.22', '12.15', '12.12', '12.34', '12.55', '12.37', '12.28', '11.93', '11.74', '11.66', '11.4', '10.47', '10.48', '10.7', '11.09', '11.58', '11.76', '11.81', '11.76', '11.86', '12.06']

gold: This statistic shows the unemployment rate in Guyana from 1999 to 2019 . In 2019 , the unemployment rate in Guyana was 12.22 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was around templateYValue[0] percent . Economy and employment in templateTitleSubject[0] Since reaching a decade low in templateXValue[12] , templateYLabel[0] in templateTitleSubject[0] has experienced considerable surges , most notably after templateXValue[8] .
generated: This statistic shows the Unemployment rate in Guyana from 1999 to 2019 . In 2019 , the Unemployment rate in Guyana was around 12.22 percent . Economy and employment in Guyana Since reaching a decade low in 2007 , Unemployment in Guyana has experienced considerable surges , most notably after 2011 .

Example 574:
titleEntities: {'Subject': ['Vegetables'], 'Date': ['2000', '2018']}
title: Vegetables : global production volume 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Production', 'volume', 'in', 'million', 'metric', 'tons']: ['1088.9', '1094.34', '1075.2', '1051.52', '1030.32', '997.84', '978.52', '954.89', '921.52', '900.66', '876.15', '843.23', '809.33', '779.82', '760.29', '750.86', '721.42', '700.09', '682.43']

gold: This statistic depicts the total production volume of vegetables ( including melons ) worldwide from 1990 to 2018 . In 2014 , some 1169.45 million metric tons of vegetables and melons were produced worldwide .
gold_template: This statistic depicts the total templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] ( including melons ) worldwide from 1990 to templateXValue[max] . In templateXValue[4] , some 1169.45 templateYLabel[2] templateYLabel[3] templateYLabel[4] of templateTitleSubject[0] and melons were produced worldwide .

generated_template: The templateTitleSubject[0] templateTitleSubject[0] is home to some templateYValue[max] templateYLabel[2] passenger templateYLabel[1] . After slowly increasing over the past years , figures reached their peak in templateXValue[max] , with templateTitleSubject[1] roads busier than ever before . The only noticeable decline – apart from templateXValue[4] when the source changed – was seen in templateXValue[9] during the recession .
generated: The Vegetables is home to some 1094.34 million passenger volume . After slowly increasing over the past years , figures reached their peak in 2018 , with Vegetables roads busier than ever before . The only noticeable decline – apart from 2014 when the source changed – was seen in 2009 during the recession .

Example 575:
titleEntities: {'Subject': ['Latvia'], 'Date': ['2006', '2018']}
title: Number of arrivals in tourist accommodation Latvia 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'arrivals', 'in', 'millions']: ['2.8', '2.58', '2.3', '2.14', '2.1', '1.84', '1.64', '1.58', '1.31', '1.11', '1.56', '1.49', '1.33']

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Latvia from 2006 to 2018 . Since 2009 there has been an increasing trend in arrivals . In 2018 , the number of arrivals ( including both foreign and domestic ) at accommodation in Latvia amounted to approximately 2.8 million .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Since templateXValue[9] there has been an increasing trend in templateYLabel[1] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( including both foreign and domestic ) at templateTitle[3] in templateTitleSubject[0] amounted to approximately templateYValue[max] templateYLabel[2] .

generated_template: Approximately templateYValue[max] templateYLabel[2] templateYLabel[1] were recorded at travel templateTitle[3] in templateTitleSubject[0] in templateXValue[max] International tourism was the highest contributor , with around two thirds of templateYLabel[1] in visitor templateTitle[3] coming from a foreign country . templateTitleSubject[0] tourism growth Since templateXValue[9] international visitor numbers to templateTitleSubject[0] have steadily increased . Including both overnight and same-day visitors , figures reached over 30 templateYLabel[2] by templateXValue[1] .
generated: Approximately 2.8 millions arrivals were recorded at travel accommodation in Latvia in 2018 International tourism was the highest contributor , with around two thirds of arrivals in visitor accommodation coming from a foreign country . Latvia tourism growth Since 2009 international visitor numbers to Latvia have steadily increased . Including both overnight and same-day visitors , figures reached over 30 millions by 2017 .

Example 576:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013']}
title: Age distribution of mobile gamers in the U.S. 2013
X_Axis['Year']: ['18-24', '25-34', '35-44', '45-54', '55-64', '65+']
Y_Axis['Percentage', 'of', 'respondents']: ['7', '17', '19', '22', '24', '11']

gold: This statistic gives information on the age distribution of mobile gamers in the United States as of May 2013 . During the survey period , it was found that 17 percent of mobile games were 25 to 34 years old . The average age of a mobile gamer was 46.5 years .
gold_template: This statistic gives information on the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] in the templateTitle[4] as of May templateTitleDate[0] . During the survey period , it was found that templateYValue[1] percent of templateTitle[2] games were 25 to 34 years old . The average templateTitle[0] of a templateTitle[2] gamer was 46.5 years .

generated_template: This statistic displays the templateTitle[7] distribution of templateTitle[1] at templateTitleSubject[0] in the templateTitleSubject[1] templateTitleSubject[1] as of templateTitleDate[0] . According to their annual report , templateYValue[max] percent of templateTitleSubject[0] templateTitle[1] are in the templateTitle[7] templateTitle[8] 45 - 54 . templateTitleSubject[0] is a British retailer selling clothing , furniture and other household items in a department store format .
generated: This statistic displays the 2013 distribution of distribution at U.S. in the U.S. as of 2013 . According to their annual report , 24 percent of U.S. distribution are in the 2013 45 - 54 . U.S. is a British retailer selling clothing , furniture and other household items in a department store format .

Example 577:
titleEntities: {'Subject': ['NASA'], 'Date': ['2014', '2024']}
title: NASA - budget 2014 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'in', 'billion', 'U.S.', 'dollars']: ['21.87', '21.66', '21.44', '21.23', '21.02', '21.5', '20.74', '19.65', '19.29', '18.01', '17.65']

gold: This graph show NASA 's projected budget from 2014 to 2024 . NASA 's budget is projected to be at around 21 billion U.S. dollars in 2020 . The National Aeronautics and Space Administration ( NASA ) is the U.S. agency responsible for aeronautics and aerospace research .
gold_template: This graph show templateTitleSubject[0] 's projected templateYLabel[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] 's templateYLabel[0] is projected to be at around templateYValue[2] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[4] . The National Aeronautics and Space Administration ( templateTitleSubject[0] ) is the templateYLabel[2] agency responsible for aeronautics and aerospace research .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateYLabel[1] templateYLabel[0] . templateTitle[1] and economy of templateTitleSubject[0] is the sixth most populated country in the EU , and the ninth most populated one in Europe .
generated: The statistic shows the NASA budget of NASA from 2014 to 2018 , with projections up until 2024 . In 2018 , the NASA budget of NASA amounted to around 20.74 billion Budget . budget and economy of NASA is the sixth most populated country in the EU , and the ninth most populated one in Europe .

Example 578:
titleEntities: {'Subject': ['U.S. June'], 'Date': ['2014', '2014']}
title: Frequency of making online restaurant reservations in the U.S. as of June 2014
X_Axis['Response']: ['Yes_many_times', 'Yes_once_or_twice', 'Never']
Y_Axis['Share', 'of', 'respondents']: ['9.6', '37.6', '52.8']

gold: This statistic shows the frequency with which consumers made online reservations when dining out in restaurants in the United States as of June 2014 . During the survey , 37.6 percent of respondents said they had made online reservations once or twice .
gold_template: This statistic shows the templateTitleSubject[0] with which consumers made templateTitle[2] templateTitle[4] when dining out in restaurants in the templateTitle[5] as of templateTitle[6] templateTitle[7] . During the survey , templateYValue[1] percent of templateYLabel[1] said they had made templateTitle[2] templateTitle[4] templateXValue[1] or templateXValue[1] .

generated_template: This statistic shows the results of a survey , conducted by Gallup in the templateTitle[7] in May templateTitleDate[0] , on templateTitle[2] templateTitle[3] templateTitle[4] gay and lesbian templateTitle[6] . During this survey , templateYValue[max] percent of the templateYLabel[1] were in favor of templateTitle[2] templateTitle[3] templateTitle[4] gay and lesbian templateTitle[6] in the templateTitle[7] .
generated: This statistic shows the results of a survey , conducted by Gallup in the 2014 in May 2014 , on online restaurant reservations gay and lesbian June . During this survey , 52.8 percent of the respondents were in favor of online restaurant reservations gay and lesbian June in the 2014 .

Example 579:
titleEntities: {'Subject': ['Overwatch'], 'Date': ['2018']}
title: Number of Overwatch players worldwide 2018
X_Axis['Month']: ['May_2018', 'October_2017', 'April_2017', 'January_2017', 'October_2016', 'August_2016', 'May_2016']
Y_Axis['Number', 'of', 'players', 'in', 'millions']: ['40', '35', '30', '25', '20', '15', '7']

gold: How many people play Overwatch ? Overwatch , a team-based first-person shooter video game , launched in May 2016 and already a week later it was reported to have had seven million players . As of May 2018 , Overwatch had 40 million players worldwide . Overwatch 's eSports success While the number of gamers playing Overwatch has increased dramatically , so has the appeal of the game as an eSport .
gold_template: How many people play templateTitleSubject[0] ? templateTitleSubject[0] , a team-based first-person shooter video game , launched in templateXValue[0] templateXValue[4] and already a week later it was reported to have had templateYValue[min] templateYLabel[2] templateYLabel[1] . As of templateXValue[0] , templateTitleSubject[0] had templateYValue[max] templateYLabel[2] templateYLabel[1] templateTitle[3] . templateTitleSubject[0] 's eSports success While the templateYLabel[0] of gamers playing templateTitleSubject[0] has increased dramatically , so has the appeal of the game as an eSport .

generated_template: How many people play templateTitleSubject[0] ? PlayerUnknown 's Battlegrounds ( templateTitleSubject[0] ) , which is available on templateTitle[1] and templateTitle[2] , crossed the templateYValue[max] templateYLabel[2] templateTitle[3] templateTitle[4] mark in templateXValue[0] templateXValue[0] - an impressive figure considering it was released little over a year earlier . The Battle Royale game developed by Bluehole was made available to the public in March templateXValue[1] . One of the biggest games on Steam While it is available on both PlayStation and Xbox One , templateTitleSubject[0] 's origins lie in templateTitle[2] and , more specifically , on Steam .
generated: How many people play Overwatch ? PlayerUnknown 's Battlegrounds ( Overwatch ) , which is available on Overwatch and players , crossed the 40 millions worldwide 2018 mark in May 2018 - an impressive figure considering it was released little over a year earlier . The Battle Royale game developed by Bluehole was made available to the public in March October 2017 . One of the biggest games on Steam While it is available on both PlayStation and Xbox One , Overwatch 's origins lie in players and , more specifically , on Steam .

Example 580:
titleEntities: {'Subject': ['Chinese'], 'Date': ['2008/09', '2018/19']}
title: Number of Chinese students in the U.S. 2008/09 - 2018/19
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09']
Y_Axis['Number', 'of', 'students']: ['369548', '363341', '350755', '328547', '304040', '274439', '235597', '194029', '157558', '127628', '98235']

gold: Colleges and universities in the United States are becoming an increasingly popular study destination for Chinese students , with over 369 thousand choosing to take courses there in the 2018/19 academic year . This made China the leading source of international students in the U.S. education market . The education exodus Business and management courses led the field in terms of what Chinese students were studying in the United States , followed closely by engineering , mathematics and computer science programs .
gold_template: Colleges and universities in the templateTitle[3] are becoming an increasingly popular study destination for templateTitleSubject[0] templateYLabel[1] , with over 369 thousand choosing to take courses there in the templateXValue[0] academic templateXLabel[0] . This made China the leading source of international templateYLabel[1] in the templateTitle[3] education market . The education exodus Business and management courses led the field in terms of what templateTitleSubject[0] templateYLabel[1] were studying in the templateTitle[3] , followed closely by engineering , mathematics and computer science programs .

generated_template: In the fall semester templateXValue[max] , templateYValue[0] templateYLabel[1] were templateTitle[1] in universities and other higher education institutions in templateTitleSubject[0] . Since 2000 , the templateYLabel[0] of individuals in templateTitleSubject[0] with an upper secondary education increased , while the individuals without decreased . In templateXValue[max] , 21.7 percent of the female population had an upper secondary education degree of three years and 24.9 percent of the male population .
generated: In the fall semester 2018/19 , 369548 students were Chinese in universities and other higher education institutions in Chinese . Since 2000 , the Number of individuals in Chinese with an upper secondary education increased , while the individuals without decreased . In 2018/19 , 21.7 percent of the female population had an upper secondary education degree of three years and 24.9 percent of the male population .

Example 581:
titleEntities: {'Subject': ['Vale'], 'Date': ['2009', '2018']}
title: Vale 's employee number 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'employees']: ['70270', '73596', '73062', '74098', '76531', '83286', '85305', '79646', '70785', '60036']

gold: This statistic shows mining company Vale 's number of employees worldwide from 2009 to 2018 . In 2018 , the company employed some 70,300 people . Vale S.A. , formerly called by the full name Companhia Vale do Rio Doce , is a Brazilian multinational metals and mining company , also specializing in logistic operations .
gold_template: This statistic shows mining company templateTitleSubject[0] templateTitle[1] templateYLabel[0] of templateYLabel[1] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company employed some 70,300 people . templateTitleSubject[0] S.A. , formerly called by the full name Companhia templateTitleSubject[0] do Rio Doce , is a Brazilian multinational metals and mining company , also specializing in logistic operations .

generated_template: templateTitleSubject[0] is an international pharmaceutical company based out of Germany . As of templateXValue[max] , the company had a total of templateYValue[0] templateYLabel[1] globally . This is a significant increase from the previous years .
generated: Vale is an international pharmaceutical company based out of Germany . As of 2018 , the company had a total of 70270 employees globally . This is a significant increase from the previous years .

Example 582:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Production of copper in Canada by province 2018
X_Axis['Month']: ['Newfoundland_and_Labrador', 'Prince_Edward_Island', 'Nova_Scotia', 'New_Brunswick', 'Quebec', 'Ontario', 'Manitoba', 'Saskatchewan', 'Alberta', 'British_Columbia', 'Yukon', 'Northwest_Territories', 'Nunavut']
Y_Axis['Production', 'in', 'metric', 'tons']: ['27456', '0', '0', '487', '35912', '135297', '33608', '0', '0', '293468', '9282', '0', '0']

gold: This statistic displays preliminary estimates of the copper production in Canada , distributed by province , in 2018 . During that year , Quebec produced some 35,912 metric tons of this mineral . Copper is often used as part of coin currency , as well as electrical equipment due to its heat and electrical conduction properties .
gold_template: This statistic displays preliminary estimates of the templateTitle[1] templateYLabel[0] in templateTitleSubject[0] , distributed templateTitle[3] templateTitle[4] , in templateTitleDate[0] . During that year , templateXValue[4] produced some templateYValue[4] templateYLabel[1] templateYLabel[2] of this mineral . templateTitle[1] is often used as part of coin currency , as well as electrical equipment due to its heat and electrical conduction properties .

generated_template: In January templateTitleDate[0] , the templateYLabel[0] for one templateYLabel[4] of templateTitle[0] templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[0] ) templateTitle[4] templateTitle[5] stood at some templateYValue[0] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] is also known as `` templateTitleSubject[0] light sweet '' , and is a grade of templateTitle[4] templateTitle[5] often used as a benchmark for templateTitle[5] pricing . templateYLabel[0] of templateTitle[0] templateTitleSubject[0] templateTitleSubject[0] templateTitle[4] templateTitle[5] , templateTitle[7] templateTitleSubject[0] is also known as `` templateTitleSubject[0] light sweet '' and is a grade of templateTitle[4] templateTitle[5] .
generated: In January 2018 , the Production for one tons of Production Canada ( Canada ) province 2018 stood at some 27456 metric tons . Canada is also known as `` Canada light sweet '' , and is a grade of province 2018 often used as a benchmark for 2018 pricing . Production of Production Canada province 2018 , 2018 Canada is also known as `` Canada light sweet '' and is a grade of province 2018 .

Example 583:
titleEntities: {'Subject': ['Number'], 'Date': ['2014']}
title: Number of crowdfunding platforms worldwide 2014 , by region
X_Axis['Country']: ['Europe', 'North_America', 'Asia', 'South_America', 'Oceania', 'Africa']
Y_Axis['Number', 'of', 'CFPs']: ['600', '375', '169', '50', '37', '19']

gold: The statistic shows the number of crowdfunding platforms worldwide in 2014 , by region . In that year , there were 375 crowdfunding platforms in North America . Crowdfunding is a way of collecting money from various individuals interested in a given project .
gold_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . In that year , there were templateYValue[1] templateTitle[1] templateTitle[2] in templateXValue[1] . templateTitle[1] is a way of collecting money from various individuals interested in a given project .

generated_template: This statistic illustrates the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] templateTitle[5] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[0] templateTitle[2] templateXLabel[0] of templateTitleSubject[0] templateTitle[5] , with templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[0] coming from Germany.IKEAIKEA is an internationally known home furnishing retailer . It has grown rapidly since it was founded in 1943 .
generated: This statistic illustrates the Number crowdfunding platforms worldwide of Number by in 2014 . In that year , Europe was the Number platforms Country of Number by , with 600 percent of Number 's Number coming from Germany.IKEAIKEA is an internationally known home furnishing retailer . It has grown rapidly since it was founded in 1943 .

Example 584:
titleEntities: {'Subject': ['Manitoba', 'Canada'], 'Date': ['2018']}
title: GDP of Manitoba , Canada 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['GDP', 'in', 'million', 'chained', '2012', 'Canadian', 'dollars']: ['62862.5', '61932.6', '59966.8', '59082.5', '58276.3', '57169.9', '55676.4', '54057.9', '52841.8', '51522.1', '51668.8', '50017.3', '48918.3', '47127.8', '45727.8', '44494.6', '44031.4', '43301.5', '42734.1']

gold: This statistic shows the gross domestic product ( GDP ) of Manitoba from 2000 to 2018 . In 2018 , Manitoba 's GDP was 62.86 billion chained 2012 Canadian dollars .
gold_template: This statistic shows the gross domestic product ( templateYLabel[0] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateXValue[6] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the number of people employed in the templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . Once a major powerhouse of the templateTitleSubject[1] 's economy , the templateTitle[1] templateTitle[3] was the lifeblood of several regions , providing templateTitle[0] for more than templateYValue[min] templateYLabel[1] workers . Since that time , shifting attitudes towards templateTitle[1] and the emergence of alternative energy sources such as wind and solar have seen templateTitle[1] 's role in the templateTitleSubject[1] 's energy mix diminish in recent .
generated: This statistic shows the number of people employed in the Manitoba Canada 2000 in the Manitoba ( Canada ) from 2000 to 2018 . Once a major powerhouse of the Canada 's economy , the Manitoba 2000 was the lifeblood of several regions , providing GDP for more than 42734.1 million workers . Since that time , shifting attitudes towards Manitoba and the emergence of alternative energy sources such as wind and solar have seen Manitoba 's role in the Canada 's energy mix diminish in recent .

Example 585:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Average order value of online food orders in the U.S. 2017
X_Axis['Response']: ['0$_no_expenses', 'Up_to_25$', 'Up_to_50$', 'Up_to_75$', 'Up_to_100$', 'Up_to_150$', 'Up_to_300$', 'More_than_300$']
Y_Axis['Share', 'of', 'respondents']: ['1', '26', '34', '12', '14', '6', '6', '0']

gold: This statistic displays the average order value of online food orders in the United States as of April 2017 . During the survey period , 26 percent of responding online food shoppers stated that their usual online food order amounted to up to 25 U.S. dollars .
gold_template: This statistic displays the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of April templateTitleDate[0] . During the survey period , templateYValue[1] percent of responding templateTitle[3] templateTitle[4] shoppers stated that their usual templateTitle[3] templateTitle[4] templateTitle[1] amounted to templateXValue[1] to 25 templateTitleSubject[0] dollars .

generated_template: A survey conducted in templateTitleDate[0] reveals that most Italians templateXValue[6] their templateTitle[0] templateTitle[1] templateTitle[2] templateXValue[1] the templateTitle[6] of templateXValue[2] and templateXValue[2] . More specifically , about templateYValue[max] percent of people declared they templateXValue[6] templateXValue[6] for the templateTitle[0] time in that templateTitle[6] range . Approximately templateYValue[3] percent of young adults templateXValue[6] their templateTitle[0] templateTitle[1] templateTitle[2] in their early 20s .
generated: A survey conducted in 2017 reveals that most Italians Up to 300$ their Average order value Up to 25$ the U.S. of Up to 50$ and Up to 50$ . More specifically , about 34 percent of people declared they Up to 300$ for the Average time in that U.S. range . Approximately 12 percent of young adults Up to 300$ their Average order value in their early 20s .

Example 586:
titleEntities: {'Subject': ['New England Patriots'], 'Date': []}
title: Career touchdown leaders - New England Patriots 1960 to 2020
X_Axis['Month']: ['Rob_Gronkowski', 'Stanley_Morgan', 'Ben_Coates', 'Randy_Moss', 'Sam_Cunningham', 'Jim_Nance', 'Tony_Collins', 'Gino_Cappelletti', 'Irving_Fryar', 'Larry_Garron', 'Julian_Edelman', 'Jim_Colclough', 'Corey_Dillon', 'Curtis_Martin', 'Wes_Welker', 'Steve_Grogan', 'Troy_Brown', 'LeGarrette_Blount', 'Kevin_Faulk', 'James_White']
Y_Axis['Touchdowns', 'scored']: ['80', '68', '50', '50', '49', '46', '44', '42', '42', '42', '41', '39', '39', '37', '37', '36', '35', '35', '33', '32']

gold: The statistic shows New England Patriots players with the most touchdowns scored in franchise history . Rob Gronkowski is the career touchdown leader of the New England Patriots with 80 touchdowns .
gold_template: The statistic shows templateTitleSubject[0] Patriots players with the most templateYLabel[0] templateYLabel[1] in franchise history . templateXValue[0] is the templateTitle[0] templateTitle[1] leader of the templateTitleSubject[0] Patriots with templateYValue[max] templateYLabel[0] .

generated_template: The statistic shows templateTitleSubject[0] templateTitle[4] players with the most templateYLabel[0] templateYLabel[1] in franchise history . templateXValue[0] templateXValue[0] is the templateTitle[0] templateTitle[1] leader of the templateTitleSubject[0] templateTitle[4] with templateYValue[max] templateYLabel[0] .
generated: The statistic shows New England Patriots England players with the most Touchdowns scored in franchise history . Rob Gronkowski is the Career touchdown leader of the New England Patriots England with 80 Touchdowns .

Example 587:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010/11', '2018/19']}
title: Total U.S. domestic raisin consumption 2010/11 - 2018/19
X_Axis['Year']: ['2018/2019', '2017/2018', '2016/2017', '2015/2016', '2014/2015', '2013/2014', '2012/2013', '2011/2012', '2010/2011']
Y_Axis['Domestic', 'consumption', 'in', 'metric', 'tons']: ['205564', '205000', '220909', '235136', '238039', '215636', '205122', '215579', '208646']

gold: This statistic shows the total United States domestic raisin consumption from 2010/2011 to 2017/2018 , and provides a projection for 2018/2019 . In crop year 2015/2016 , the domestic raisin consumption in the United States amounted to 235,136 metric tons .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] templateTitle[3] templateYLabel[1] from templateXValue[last] to templateXValue[1] , and provides a projection for templateXValue[0] . In crop templateXLabel[0] templateXValue[3] , the templateYLabel[0] templateTitle[3] templateYLabel[1] in the templateTitle[1] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[2] templateYLabel[0] of templateTitle[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , there were a total of templateYValue[max] directory templateTitle[0] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] according to the International templateTitle[0] templateYLabel[0] .
generated: This statistic shows the domestic of Total consumption metric in the U.S. from 2010/2011 to 2018/2019 . In the 2018/2019 season , there were a total of 238039 directory Total consumption in the U.S. according to the International Total Domestic .

Example 588:
titleEntities: {'Subject': ['Texas'], 'Date': ['2018']}
title: Per capita real GDP of Texas 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'real', 'GDP', 'in', 'chained', '2012', 'U.S.', 'dollars']: ['59674', '58125', '57280', '58077', '56457', '55573', '54097', '52397', '51568', '51264', '52481', '53470', '51811', '49732', '49241', '47583', '48031', '47932', '47664']

gold: This statistic shows the per capita real Gross Domestic Product of Texas from 2000 to 2018 . In 2018 , the per capita real GDP of Texas stood at 59,674 chained 2012 U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[max] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[max] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .
generated: This statistic shows the Per capita real Gross Domestic Product of Texas from 2000 to 2018 . In 2018 , the Per capita real GDP of Texas stood at 59674 chained 2012 U.S. dollars .

Example 589:
titleEntities: {'Subject': ['Disneyland Paris'], 'Date': ['2006', '2016']}
title: Disneyland Paris visitors spending per day 2006 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Average', 'spend', 'in', 'euros', '(excluding', 'VAT)']: ['54.0', '53.7', '50.7', '48.1', '46.4', '46.2', '45.3']

gold: This statistic displays daily expenditure per person at Disneyland Paris theme parks in France between 2006 and 2016 . Visitors spending includes entrance fee , catering , merchandise and other services in the theme parks . In the fiscal year 2016 , the average spending dipped to 54 euros ( before VAT ) .
gold_template: This statistic displays daily expenditure templateTitle[4] person at templateTitleSubject[0] theme parks in France between templateTitle[6] and templateXValue[max] . templateTitle[2] templateTitle[3] includes entrance fee , catering , merchandise and other services in the theme parks . In the fiscal templateXLabel[0] templateXValue[max] , the templateYLabel[0] templateTitle[3] dipped to templateYValue[max] templateYLabel[2] ( before VAT ) .

generated_template: This statistic shows the templateYLabel[0] of adults in the country templateTitle[1] templateTitle[2] as templateTitle[4] , templateTitle[3] , templateTitle[5] , or templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYValue[min] percent of adults surveyed stated they templateTitle[2] as LGBT .
generated: This statistic shows the Average of adults in the country Paris visitors as per , spending , day , or 2006 from 2010 to 2016 . In 2014 , 45.3 percent of adults surveyed stated they visitors as LGBT .

Example 590:
titleEntities: {'Subject': ['U.S.'], 'Date': []}
title: U.S. winter heating oil prices 2005/06 - 2019/20
X_Axis['Winter', 'of']: ['2019/20', '2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Price', 'in', 'U.S.', 'dollars', 'per', 'gallon']: ['3.02', '3.07', '2.78', '2.41', '2.06', '3.04', '3.88', '3.87', '3.73', '3.38', '2.85', '2.65', '3.33', '2.42', '2.44']

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

generated_template: The statistic above presents the distribution of templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , templateXValue[2] templateTitle[0] templateTitle[1] templateYLabel[1] accounted for templateYValue[max] percent of all templateTitle[0] templateTitle[1] templateYLabel[1] .
generated: The statistic above presents the distribution of U.S. winter U.S. in the oil in , 2005/06 Winter . In , 2017/18 U.S. winter U.S. accounted for 3.88 percent of all U.S. winter U.S. .

Example 591:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015', '2018']}
title: Share of U.S. nickel imports by country 2015 to 2018
X_Axis['Country', 'of', 'origin']: ['Other', 'Finland', 'Australia', 'Norway', 'Canada']
Y_Axis['Share', 'of', 'nickel', 'imports']: ['32', '8', '8', '11', '41']

gold: This statistic shows the percentage of nickel imports to the United States over the period between 2014 and 2018 , by country of origin . In that period , some 41 percent of all nickel imports into the United States came from Canada .
gold_template: This statistic shows the percentage of templateYLabel[1] templateYLabel[2] to the templateTitle[1] over the period between 2014 and templateTitleDate[1] , templateTitle[4] templateXLabel[0] of templateXLabel[1] . In that period , some templateYValue[max] percent of all templateYLabel[1] templateYLabel[2] into the templateTitle[1] came from templateXValue[last] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] the templateXLabel[0] templateXLabel[1] of the child 's templateTitle[5] in the templateTitle[6] as of November templateTitleDate[0] . During the survey period , the templateXValue[0] templateXValue[0] accounted for templateYValue[0] percent of the templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Share U.S. nickel the Country origin of the child 's country in the 2015 as of November 2015 . During the survey period , the Other accounted for 32 percent of the nickel imports .

Example 592:
titleEntities: {'Subject': ['Under Armour'], 'Date': ['2009', '2019']}
title: Global revenue growth of Under Armour 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'growth']: ['1', '4', '3', '22', '28', '32', '27', '25', '38', '24', '18']

gold: This statistic depicts the growth of Under Armour 's revenue worldwide from 2009 to 2019 . In 2019 , Under Armour 's net revenue increased by one percent . Under Armour is an American sporting goods manufacturer , based in Baltimore , Maryland .
gold_template: This statistic depicts the templateYLabel[1] of templateTitleSubject[0] 's templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's net templateYLabel[0] increased by templateYValue[min] percent . templateTitleSubject[0] is an American sporting goods manufacturer , based in Baltimore , Maryland .

generated_template: The statistic shows the templateTitleSubject[0] of the templateYLabel[0] templateYLabel[1] since the birth of Christ . templateYValue[max] templateYLabel[2] people were living on earth in templateXValue[max] . Additional information on templateYLabel[0] templateYLabel[1] The global templateYLabel[1] has risen dramatically in the last 100 years from templateYValue[3] percent in templateXValue[3] to templateYValue[max] percent .
generated: The statistic shows the Under Armour of the Revenue growth since the birth of Christ . 38 growth people were living on earth in 2019 . Additional information on Revenue growth The global growth has risen dramatically in the last 100 years from 22 percent in 2016 to 38 percent .

Example 593:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: U.S. : reported robbery cases 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Number', 'of', 'reported', 'cases']: ['282061', '320596', '332797', '328109', '322905', '345093', '355051', '354746', '369089', '408742', '443563', '447324', '449246', '417438', '401470', '414235', '420806', '422921', '408016', '409371', '447186', '497950', '535590', '580510', '618950', '659870', '672480', '687730', '639270']

gold: This graph shows the reported number of robbery cases in the United States from 1990 to 2018 . In 2018 an estimated 282,061 cases occurred nationwide .
gold_template: This graph shows the templateYLabel[1] templateYLabel[0] of templateTitle[2] templateYLabel[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] an estimated templateYValue[min] templateYLabel[2] occurred nationwide .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] parent families with a female householder and no spouse present in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[28] percent of all templateTitleSubject[0] families with a templateTitle[3] mother in the templateTitle[5] lived below the templateYLabel[0] level . In templateXValue[max] , that figure was at templateYValue[min] percent .
generated: This statistic shows the Number reported of U.S. cases parent families with a female householder and no spouse present in the U.S. from 1990 to 2018 . In 1990 , 639270 percent of all U.S. families with a cases mother in the 2018 lived below the Number level . In 2018 , that figure was at 282061 percent .

Example 594:
titleEntities: {'Subject': ['Cincinnati Bengals', 'NFL'], 'Date': ['2001', '2018']}
title: Revenue of the Cincinnati Bengals ( 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']: ['380', '359', '355', '329', '296', '258', '250', '235', '236', '232', '222', '205', '194', '175', '171', '150', '141', '130']

gold: The statistic depicts the revenue of the Cincinnati Bengals , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Cincinnati Bengals was 380 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Cincinnati Bengals , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the Revenue of the Cincinnati Bengals was 380 million U.S. dollars .

Example 595:
titleEntities: {'Subject': ['India'], 'Date': ['2019']}
title: Highest grossing domestic movies India 2019
X_Axis['Movie', 'Name']: ['War', 'Kabir_Singh', 'Uri-_The_Surgical_Strike', 'Bharat', 'Mission_Mangal', 'Kesari', 'Total_Dhamaal', 'Saaho', 'Chhichhore', 'Super_30']
Y_Axis['Box', 'office', 'gross', 'in', 'billion', 'Indian', 'rupees']: ['2.92', '2.76', '2.44', '1.97', '1.93', '1.52', '1.5', '1.49', '1.47', '1.47']

gold: The Bollywood movie 'War ' was the highest grossing domestic movie produced in India in 2019 with an all India net collection of almost three billion Indian rupees . This was followed by 'Kabir Singh ' at around 2.8 billion rupees worth box office collection that year .
gold_template: The Bollywood templateXLabel[0] 'War ' was the templateTitle[0] templateTitle[1] templateTitle[2] templateXLabel[0] produced in templateTitleSubject[0] in templateTitleDate[0] with an all templateTitleSubject[0] net collection of almost templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] . This was followed by 'Kabir templateXValue[1] ' at around templateYValue[1] templateYLabel[3] templateYLabel[5] worth templateYLabel[0] templateYLabel[1] collection that year .

generated_template: The graph shows the templateYLabel[0] of templateTitle[0] templateTitleSubject[0] templateXLabel[0] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , around templateYValue[max] million people in the templateTitleSubject[0] spoke templateXValue[0] at templateTitle[3] . The distribution of the templateTitleSubject[0] population by ethnicity can be accessed here .
generated: The graph shows the Box of Highest India Movie in 2019 , 2019 Movie . In 2019 , around 2.92 million people in the India spoke War at movies . The distribution of the India population by ethnicity can be accessed here .

Example 596:
titleEntities: {'Subject': ['Luxembourg'], 'Date': ['2024']}
title: Inflation rate in Luxembourg 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['1.93', '1.88', '1.95', '1.87', '1.74', '1.73', '2.02', '2.11', '0.04', '0.06', '0.7', '1.7', '2.89', '3.73', '2.8', '0.01', '4.09', '2.66', '2.96', '3.76', '3.24', '2.53', '2.06', '2.4', '3.78', '1.02', '0.97', '1.37', '1.56', '1.9', '2.2', '3.6', '3.2', '3.1', '3.7', '3.4', '1.4', '-0.1', '0.3', '4.09', '5.64']

gold: This statistic shows the average inflation rate in Luxembourg from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Luxembourg amounted to about 2.02 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

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

Example 597:
titleEntities: {'Subject': ['European'], 'Date': ['2015']}
title: Average planned spend on Christmas presents in selected European countries 2015
X_Axis['Country']: ['United_Kingdom', 'Luxembourg', 'France', 'Austria', 'Spain', 'Italy', 'Germany', 'Belgium', 'Czech_Republic', 'Romania', 'Poland', 'Netherlands']
Y_Axis['Median', 'amount', 'in', 'euros']: ['420', '300', '250', '250', '200', '200', '200', '150', '150', '110', '70', '40']

gold: This statistic displays the average amount consumers plan to spend on Christmas presents in 2015 in selected European countries . The United Kingdom ( UK ) had the highest spend , with consumers expecting to budget 420 euros for Christmas gifts .
gold_template: This statistic displays the templateTitle[0] templateYLabel[1] consumers plan to templateTitle[2] on templateTitle[3] templateTitle[4] in templateTitleDate[0] in templateTitle[5] templateTitleSubject[0] templateTitle[7] . The templateXValue[0] ( UK ) had the highest templateTitle[2] , with consumers expecting to budget templateYValue[max] templateYLabel[2] for templateTitle[3] gifts .

generated_template: templateTitleSubject[0] , the warehouse shopping club , continued to grow in templateTitleDate[0] with 782 locations worldwide at year 's end . The U.S. is still home to the vast majority of the American retailer 's templateYLabel[1] , but it has expanded into templateYValue[7] other markets . With the expansion in store count comes an increase of members , and at the end of 2018 templateTitleSubject[0] could boast over 98 million cardholders .
generated: European , the warehouse shopping club , continued to grow in 2015 with 782 locations worldwide at year 's end . The U.S. is still home to the vast majority of the American retailer 's amount , but it has expanded into 150 other markets . With the expansion in store count comes an increase of members , and at the end of 2018 European could boast over 98 million cardholders .

Example 598:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2005', '2018']}
title: U.S. motion picture/video production and distribution - revenue 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Estimated', 'revenue', 'in', 'billion', 'U.S.', 'dollars']: ['69.91', '64.41', '66.86', '64.43', '62.83', '64.5', '61.89', '59.63', '59.41', '55.83', '61.14', '61.91', '59.17', '56.83']

gold: The statistic above presents estimates of the annual aggregate revenue of the U.S. motion picture and video production and distribution industry from 2005 to 2018 . In 2018 , the industry generated an estimated total revenue of 69.91 billion U.S. dollars .
gold_template: The statistic above presents estimates of the annual aggregate templateYLabel[1] of the templateYLabel[3] templateTitle[1] picture and video templateTitle[3] and templateTitle[4] industry from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the industry generated an templateYLabel[0] total templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic above presents estimates of the annual aggregate templateYLabel[1] of the templateYLabel[3] templateTitle[1] publishing industry from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[3] templateTitle[1] templateTitle[2] generated an templateYLabel[0] total templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic above presents estimates of the annual aggregate revenue of the U.S. motion publishing industry from 2005 to 2018 . In 2018 , U.S. motion picture/video generated an Estimated total revenue of 69.91 billion U.S. dollars .

Example 599:
titleEntities: {'Subject': ['Sporting Goods'], 'Date': ['2006', '2018']}
title: Dick 's Sporting Goods : gross profit 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Gross', 'profit', 'in', 'million', 'U.S.', 'dollars']: ['2437', '2489', '2366', '2183', '2087', '1944', '1837', '1595', '1449', '1217', '1184', '1158', '897']

gold: The timeline depicts the gross profit of Dick 's Sporting Goods from 2006 to 2018 . The gross profit of Dick 's Sporting Goods amounted to 2,437 million U.S. dollars in 2018 .
gold_template: The timeline depicts the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] of official templateYLabel[1] templateTitle[2] increased from approximately templateYValue[min] thousand to templateYValue[0] thousand , after which it experienced a slight decline . As of templateXValue[1] , England is the leader with 1.87 thousand official templateYLabel[1] templateTitle[2] followed by Germany and France with respectively 731 and 602 registered templateYLabel[1] templateTitle[2] . The rise in the templateYLabel[0] of templateYLabel[1] templateTitle[2] in templateTitleSubject[0] was accompanied with a rise in templateYLabel[1] popularity .
generated: Between 2006 and 2018 , the Gross of official profit Sporting increased from approximately 897 thousand to 2437 thousand , after which it experienced a slight decline . As of 2017 , England is the leader with 1.87 thousand official profit Sporting followed by Germany and France with respectively 731 and 602 registered profit Sporting . The rise in the Gross of profit Sporting in Sporting Goods was accompanied with a rise in profit popularity .

Example 600:
titleEntities: {'Subject': ['Michigan'], 'Date': ['1990', '2018']}
title: Michigan - 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']: ['60449', '57700', '57091', '54203', '52005', '48801', '50015', '48879', '46276', '45994', '49788', '49370', '48647', '45933', '42256', '45022', '42715', '45047', '45512', '46089', '41821', '38742', '39225', '36426', '35284', '32662', '32267', '32117', '29937']

gold: This statistic shows the median household income in Michigan from 1990 to 2018 . In 2018 , the median household income in Michigan amounted to 60,449 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[max] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] parent families with a female householder and no spouse present in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[28] percent of all templateTitleSubject[0] families with a templateTitle[3] mother in the templateTitle[5] lived below the templateYLabel[0] level . In templateXValue[max] , that figure was at templateYValue[min] percent .
generated: This statistic shows the Household income of Michigan income parent families with a female householder and no spouse present in the Michigan from 1990 to 2018 . In 1990 , 29937 percent of all Michigan families with a income mother in the 2018 lived below the Household level . In 2018 , that figure was at 29937 percent .

Example 601:
titleEntities: {'Subject': ['NFL'], 'Date': ['2006', '2019']}
title: Average Fan Cost Index of NFL teams 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Fan', 'Cost', 'Index', 'in', 'U.S.', 'dollars']: ['540.52', '536.04', '502.84', '480.89', '479.11', '459.73', '443.93', '427.42', '420.54', '412.64', '396.36', '367.31', '346.16']

gold: The statistic shows the average Fan Cost Index in the National Football League from 2006 to 2019 . The average Fan Cost Index was at 540.52 U.S. dollars in 2019 .
gold_template: The statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the National Football League from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[max] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: As of the company 's templateXValue[max] fiscal templateXLabel[0] , the templateTitleSubject[0] templateTitleSubject[0] employed over templateYValue[min] thousand people around the world . templateTitleSubject[0] has consistently decreased its total employment since around templateXValue[11] when it reported over 180 thousand workers . This templateXValue[max] total represents the company 's lowest employment figure in recent history .
generated: As of the company 's 2019 fiscal Year , the NFL employed over 346.16 thousand people around the world . NFL has consistently decreased its total employment since around 2007 when it reported over 180 thousand workers . This 2019 total represents the company 's lowest employment figure in recent history .

Example 602:
titleEntities: {'Subject': ['Global'], 'Date': ['2019']}
title: Global online shopping order value 2019 , by platform
X_Axis['Platform']: ['Macintosh', 'Windows', 'iOS', 'Chrome_OS', 'Linux', 'Android', 'Windows_Phone']
Y_Axis['Order', 'value', 'in', 'U.S.', 'dollars']: ['132.6', '127.77', '93.52', '87.98', '85.72', '76.21', '66.06']

gold: This statistic provides information on the average order value of online shopping orders worldwide in the second quarter of 2019 , differentiated by platform . During that period , online orders which were placed through Android devices had an average value of 76.21 U.S. dollars .
gold_template: This statistic provides information on the average templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] orders worldwide in the second quarter of templateTitleDate[0] , differentiated templateTitle[6] templateXLabel[0] . During that period , templateTitle[1] orders which were placed through templateXValue[5] devices had an average templateYLabel[1] of templateYValue[5] templateYLabel[2] templateYLabel[3] .

generated_template: templateXValue[0] was the most popular templateTitle[1] templateTitle[2] templateXLabel[0] among templateTitle[0] templateTitle[1] templateTitle[2] users in templateTitleSubject[0] in templateTitleDate[0] . templateYValue[max] percent of internet users in templateTitleSubject[0] reported to have used templateXValue[0] on a templateTitle[0] basis . The templateTitle[1] templateTitle[2] templateXLabel[0] had 2.91 million users in 2016 and was forecast to have around 3.12 million users templateTitle[6] 2022 .
generated: Macintosh was the most popular online shopping Platform among Global online shopping users in Global in 2019 . 132.6 percent of internet users in Global reported to have used Macintosh on a Global basis . The online shopping Platform had 2.91 million users in 2016 and was forecast to have around 3.12 million users by 2022 .

Example 603:
titleEntities: {'Subject': ['Russia', 'CIS January'], 'Date': []}
title: Weekend box office revenue in Russia and CIS January 2020 , by film
X_Axis['Month']: ['Kholop', 'Perfect_Man', 'Spies_in_Disguise', 'Bad_Boys_for_Life', 'Invasion', 'Marafon_Zhelaniy', 'Soyuz_Spaseniya', 'The_Grudge', 'Jumanji:_The_Next_Level', 'Richard_Jewell']
Y_Axis['Revenue', 'in', 'thousand', 'U.S.', 'dollars']: ['12530.82', '6603.13', '5899.09', '5092.75', '3106.25', '1927.94', '1856.38', '1376.52', '1098.84', '828.02']

gold: Over three weekends of January 2020 , the Russian comedy film `` Kholop , '' translated as `` Serf , '' had the largest aggregate gross box office in Armenia , Belarus , Kazakhstan , Moldova , and Russia , measuring at approximately 12.5 million U.S. dollars , which made it the leading movie of the month by revenue . The romantic comedy `` Perfect Man , '' where the main character was played by a popular Russian singer Egor Kreed , ranked second with the box office of over 6.6 million U.S. dollars .
gold_template: Over three weekends of templateTitle[6] templateTitle[7] , the Russian comedy templateTitle[9] `` templateXValue[0] , '' translated as `` Serf , '' had the largest aggregate gross templateTitle[1] templateTitle[2] in Armenia , Belarus , Kazakhstan , Moldova , and templateTitleSubject[0] , measuring at approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , which made it the leading movie of the templateXLabel[0] templateTitle[8] templateYLabel[0] . The romantic comedy `` templateXValue[1] , '' where the main character was played templateTitle[8] a popular Russian singer Egor Kreed , ranked second with the templateTitle[1] templateTitle[2] of over templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: Sophie Elise ranked first among templateTitle[2] templateTitle[3] in templateTitleSubject[0] , as of August templateTitleDate[0] . Her channel covered a variety of topics , but the 21-year-old described herself as blogger , influencer , DJ and author on her page . She reached over templateYValue[max] thousand templateYLabel[1] for the evaluated period .
generated: Sophie Elise ranked first among office revenue in Russia , as of August . Her channel covered a variety of topics , but the 21-year-old described herself as blogger , influencer , DJ and author on her page . She reached over 12530.82 thousand for the evaluated period .

Example 604:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: COPD prevalence in the U.S. 2017 , by state
X_Axis['State']: ['Alabama', 'Alaska', 'Arizona', 'Arkansas', 'California', 'Colorado', 'Connecticut', 'Delaware', 'District_of_Columbia', 'Florida', 'Georgia', 'Hawaii', 'Idaho', 'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', 'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota', 'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', 'New_Hampshire', 'New_Jersey', 'New_Mexico', 'New_York', 'North_Carolina', 'North_Dakota', 'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', 'Rhode_Island', 'South_Carolina', 'South_Dakota', 'Tennessee', 'Texas', 'Total', 'Utah', 'Vermont', 'Virginia', 'Washington', 'West_Virginia', 'Wisconsin', 'Wyoming']
Y_Axis['COPD', 'prevalence']: ['10.1', '6.3', '5.9', '9.3', '4.4', '4.2', '5.3', '7.3', '5.8', '7.1', '6.8', '3.4', '4.7', '6.4', '8', '5.9', '6.2', '11.3', '8.4', '6.5', '5.4', '5', '8', '4', '7.5', '7.9', '5.7', '5.3', '6.5', '6', '5.8', '5.6', '5', '7.3', '4.8', '7.6', '8.1', '4.9', '5.9', '7', '7.2', '4.4', '8.9', '4.8', '6.2', '4.1', '5.7', '6.6', '5.4', '13.8', '4.7', '6.1']

gold: This statistic shows the prevalence of Chronic Obstructive Pulmonary Disease ( COPD ) in the U.S. in 2017 , by state . As of that year , around 11.3 percent of adults in Kentucky suffered from COPD .
gold_template: This statistic shows the templateYLabel[1] of Chronic Obstructive Pulmonary Disease ( templateYLabel[0] ) in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . As of that year , around templateYValue[17] percent of adults in templateXValue[17] suffered from templateYLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of unique templateTitle[4] of templateTitle[0] templateTitle[1] in the templateTitle[2] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , about templateYValue[14] unique templateTitle[4] of templateTitle[0] templateTitle[1] were templateTitle[3] in templateXValue[14] .
generated: This statistic shows the COPD of unique by of COPD prevalence in the U.S. in 2017 , state . In 2017 , about 8 unique by of COPD prevalence were 2017 in Indiana .

Example 605:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2005', '2017']}
title: Mexico : number of households 2005 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2010', '2005']
Y_Axis['Number', 'of', 'households', 'in', 'millions']: ['34.07', '32.9', '31.95', '28.16', '24.8']

gold: The statistic presents a timeline with the number of households in Mexico between 2005 and 2017 . In 2017 , there were more than 34 million households in Mexico , up from nearly 33 million households a year earlier .
gold_template: The statistic presents a timeline with the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , there were more than templateYValue[max] templateYLabel[2] templateYLabel[1] in templateTitleSubject[0] , up from nearly templateYValue[1] templateYLabel[2] templateYLabel[1] a templateXLabel[0] earlier .

generated_template: This statistic represents the projected templateTitle[0] templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In the templateTitle[0] , there were approximately templateYValue[max] templateYLabel[1] in this templateTitle[3] in templateXValue[min] .
generated: This statistic represents the projected Mexico Number of households in the Mexico number households from 2005 to 2017 . In the Mexico , there were approximately 34.07 households in this 2005 in 2005 .

Example 606:
titleEntities: {'Subject': ['Amazon'], 'Date': ['2017', '2019']}
title: U.S. Amazon Prime Day conversion rate 2017 to 2019
X_Axis['Year']: ['2019', '2018', '2017']
Y_Axis['Conversion', 'rate']: ['13.5', '11.6', '11.2']

gold: During the Amazon Prime Day shopping event in July 2019 , the desktop conversion rate amounted to 13.5 percent , which represented a 16 percent growth from the previous year . Prime Day does not only drive conversion on Amazon but also on other retail platforms .
gold_template: During the templateTitleSubject[0] Day shopping event in July templateXValue[max] , the desktop templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] percent , which represented a 16 percent growth from the previous templateXLabel[0] . templateTitleSubject[0] does not only drive templateYLabel[0] on templateTitleSubject[0] but also on other retail platforms .

generated_template: In the templateYLabel[1] many templateTitle[0] pay a portion of templateTitle[2] templateTitle[3] templateYLabel[0] for employees . As of templateXValue[min] the total annual medical templateYLabel[0] for employees was 11.7 thousand templateYLabel[1] templateYLabel[2] . That cost is expected to increase to templateYValue[max] thousand templateYLabel[1] templateYLabel[2] by templateXValue[max] .
generated: In the rate many U.S. pay a portion of Prime Day Conversion for employees . As of 2017 the total annual medical Conversion for employees was 11.7 thousand rate . That cost is expected to increase to 13.5 thousand rate by 2019 .

Example 607:
titleEntities: {'Subject': ['Saudi Arabia'], 'Date': ['1984']}
title: Inflation in Saudi Arabia since 1984
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2.09', '2.09', '2.12', '2.09', '2.23', '-1.05', '2.48', '-0.85', '2.03', '1.27', '2.2', '3.52', '2.88', '3.76', '3.78', '4.18', '6.08', '5.06', '1.9', '0.55', '0.27', '0.55', '0.14', '-1.22', '-1.08', '-2.11', '-0.39', '-0.26', '0.26', '5.25', '1.26', '1.28', '-0.98', '3.79', '-1.01', '1.17', '-0.35', '-2.39', '-3.12', '-2.31', '-0.68']

gold: The statistic shows the inflation rate in Saudi Arabia from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate amounted to 2.48 percent compared to the previous year . Oil production in Saudi Arabia Saudi Arabia 's economy relies heavily on production and export of oil and petroleum .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] amounted to templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . Oil production in templateTitleSubject[0] Saudi templateTitleSubject[0] 's economy relies heavily on production and export of oil and petroleum .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Saudi Arabia from 1984 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Saudi Arabia amounted to about 2.48 percent compared to the previous Year .

Example 608:
titleEntities: {'Subject': ['Americans'], 'Date': ['2018']}
title: Americans ' moral stance towards pornography in 2018
X_Axis['Response']: ['Morally_acceptable', 'Morally_wrong', 'Depends', 'No_opinion']
Y_Axis['Share', 'of', 'respondents']: ['43', '55', '1', '1']

gold: This statistic shows the moral stance of Americans regarding pornography in 2018 . During the survey , 43 percent of respondents stated they think pornography is morally acceptable , while 1 percent stated it depends on the situation .
gold_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitleSubject[0] regarding templateTitle[5] in templateTitleDate[0] . During the survey , templateYValue[0] percent of templateYLabel[1] stated they think templateTitle[5] is templateXValue[0] , while templateYValue[min] percent stated it templateXValue[2] on the situation .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitleSubject[0] regarding templateTitle[5] or templateTitle[6] templateTitle[7] in templateTitleDate[0] . During this survey , templateYValue[max] percent of templateYLabel[1] stated they think templateTitle[5] or templateTitle[6] templateTitle[7] are templateXValue[0] templateXValue[0] , while templateYValue[min] percent said it templateXValue[2] on the situation .
generated: This statistic shows the moral stance of Americans regarding pornography or 2018 in 2018 . During this survey , 55 percent of respondents stated they think pornography or 2018 are Morally acceptable , while 1 percent said it Depends on the situation .

Example 609:
titleEntities: {'Subject': ['BSI'], 'Date': ['2019']}
title: Top 10 strongest nation brands by BSI score 2019
X_Axis['Country']: ['Singapore', 'Switzerland', 'Netherlands', 'Germany', 'Luxembourg', 'United_Arab_Emirates', 'Finland', 'Japan', 'United_States', 'Denmark']
Y_Axis['Brand', 'Strength', 'Index', 'Score']: ['90.5', '89.9', '89.6', '88.2', '86.9', '86.6', '86.4', '85.8', '85.7', '85.6']

gold: The statistic depicts the top ten strongest nation brands of 2019 as measured by the Brand Strength Index ( BSI ) . In 2019 , Singapore received the highest BSI score of any nation with a score of 90.5 .
gold_template: The statistic depicts the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleDate[0] as measured templateTitle[5] the templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitleSubject[0] ) . In templateTitleDate[0] , templateXValue[0] received the highest templateTitleSubject[0] templateYLabel[3] of any templateTitle[3] with a templateYLabel[3] of templateYValue[max] .

generated_template: This statistic shows the top ten templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] templateYLabel[2] as of January templateTitle[7] . As of the measured period , the templateXLabel[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateYLabel[3] was the templateXValue[0] , templateTitle[1] an templateTitle[3] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[max] templateYLabel[3] .
generated: This statistic shows the top ten BSI 10 the strongest nation Brand Strength Index as of January score . As of the measured period , the Country 10 the strongest nation Brand Strength Index in Score was the Singapore , 10 an nation Brand Strength Index of 90.5 Score .

Example 610:
titleEntities: {'Subject': ['Norway'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Norway 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['469.04', '455.73', '443.84', '432.97', '422.06', '417.63', '434.17', '398.39', '368.83', '385.8', '498.41', '523.5', '510.23', '498.83', '429.13', '386.62', '462.55', '401.09', '345.42', '308.72', '264.36', '228.75', '195.42', '174.0', '171.32', '162.29', '154.17', '161.35', '163.52', '152.03', '127.13', '120.58', '130.84', '121.87', '119.79', '102.63', '101.9', '94.23', '78.69', '65.42', '62.06']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateTitle[3] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .
generated: The statistic shows the Gross domestic product ( GDP ) in Norway from 1984 to 2018 , with projections up until 2024 . GDP refers to the total market value of all goods and services that are produced within a country per Year . It is an important indicator of the economic strength of a country .

Example 611:
titleEntities: {'Subject': ['Utah'], 'Date': ['2019']}
title: Number of active physicians in Utah 2019 , by specialty area
X_Axis['Specialty', 'area']: ['Psychiatry', 'Surgery', 'Anesthesiologists', 'Emergency_medicine', 'Radiology', 'Cardiology', 'Oncology_(cancer)', 'Endocrinology_diabetes_&_metabolism', 'All_other_specialities', 'Total_specialty']
Y_Axis['Number', 'of', 'physicians']: ['304', '316', '439', '426', '311', '174', '106', '33', '1587', '3696']

gold: This statistic depicts the number of active physicians in Utah as of March 2019 , ordered by specialty area . At that time , there were 439 anesthesiologists active in Utah . In total , there were almost 4,000 physicians in the state .
gold_template: This statistic depicts the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] as of March templateTitleDate[0] , ordered templateTitle[5] templateXValue[last] templateXLabel[1] . At that time , there were templateYValue[2] templateXValue[2] templateTitle[1] in templateTitleSubject[0] . In templateXValue[last] , there were almost 4,000 templateYLabel[1] in the state .

generated_template: This statistic depicts the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] as of March templateTitleDate[0] , ordered templateTitle[5] templateXValue[last] templateXLabel[1] . At that time , there were templateYValue[2] templateXValue[2] templateTitle[1] in templateTitleSubject[0] . There are approximately 21,400 templateXValue[last] templateYLabel[1] templateTitle[1] in the state .
generated: This statistic depicts the Number of active physicians in Utah as of March 2019 , ordered by Total specialty area . At that time , there were 439 Anesthesiologists active in Utah . There are approximately 21,400 Total specialty physicians active in the state .

Example 612:
titleEntities: {'Subject': ['Europe'], 'Date': ['2017']}
title: General practitioners practicing in Europe in 2017 , by country
X_Axis['Country']: ['France', 'Germany', 'United_Kingdom', 'Italy', 'Spain', 'Portugal', 'Netherlands', 'Belgium', 'Poland', 'Austria', 'Ireland', 'Greece', 'Slovenia', 'Estonia', 'Luxembourg']
Y_Axis['Number', 'of', 'employees']: ['60214', '58170', '49824', '43731', '35378', '24248', '14641', '12992', '8418', '6637', '3942', '3647', '1237', '937', '534']

gold: In 2017 , there were over 60 thousand general practitioners ( GP ) practicing in France , the highest number recorded in Europe , followed by Germany with approximately 58.1 thousand GPs and the United Kingdom with almost 49.8 thousand . These three countries having the highest number of GPs goes in direct correlation with their population sizes being the highest in Europe . Visiting the doctor In 2018 , a survey of French individuals revealed that 83 percent of respondents consult with a physician at least once a year .
gold_template: In templateTitleDate[0] , there were over templateYValue[max] thousand templateTitle[0] templateTitle[1] ( GP ) templateTitle[2] in templateXValue[0] , the highest templateYLabel[0] recorded in templateTitleSubject[0] , followed templateTitle[5] templateXValue[1] with approximately 58.1 thousand GPs and the templateXValue[2] with almost templateYValue[2] thousand . These three countries having the highest templateYLabel[0] of GPs goes in direct correlation with their population sizes being the highest in templateTitleSubject[0] . Visiting the doctor In 2018 , a survey of French individuals revealed that 83 percent of respondents consult with a physician at least once a year .

generated_template: In templateTitleDate[0] , templateXValue[0] was the leading templateTitleSubject[0] population outside of Italy with almost templateYValue[max] million Italians residing in the South American templateXLabel[0] . This templateTitleSubject[0] community represented a fifth of all Italians residing outside the templateXLabel[0] . Moreover , the second and third biggest groups lived in European countries : templateXValue[1] hosted templateYValue[1] thousand Italians , while templateXValue[2] templateYValue[2] thousand .
generated: In 2017 , France was the leading Europe population outside of Italy with almost 60214 million Italians residing in the South American Country . This Europe community represented a fifth of all Italians residing outside the Country . Moreover , the second and third biggest groups lived in European countries : Germany hosted 58170 thousand Italians , while United Kingdom 49824 thousand .

Example 613:
titleEntities: {'Subject': ['Norway'], 'Date': ['2019']}
title: Leading companies in Norway 2019 , by number of employees
X_Axis['Month']: ['Helse_Sør-Øst_RHF', 'Telenor_ASA', 'Aker_ASA', 'Equinor_ASA_/_Statoil', 'Posten_Norge_AS', 'Orkla_ASA', 'Yara_International_ASA', 'Aker_Solutions_ASA', 'Tallyman_AS', 'Norges_Statsbaner_AS', 'Norsk_Hydro_ASA', 'Marine_Harvest_ASA', 'Strawberry_Holding_AS', 'Nordic_Choice_Hospitality_Group_AS', 'Kongsberg_Automotive_ASA', 'DNB_ASA', 'Hfn_Group_AS', 'Evry_ASA', 'Hospitality_Invest_AS', 'Nokas_AS']
Y_Axis['Number', 'of', 'employees']: ['60368', '31000', '20753', '20245', '18327', '18154', '14736', '14300', '13760', '13006', '12911', '12717', '10412', '10320', '9791', '9561', '9172', '9100', '9001', '8273']

gold: This statistic shows the 20 biggest companies in Norway as of March 2019 , by number of employees . Helse Sør-Øst RHF was ranked first with over 60 thousand employees , while Telenor ASA was ranked second with 31 thousand employees .
gold_template: This statistic shows the 20 biggest templateTitle[1] in templateTitleSubject[0] as of March templateTitleDate[0] , templateTitle[4] templateYLabel[0] of templateYLabel[1] . templateXValue[0] RHF was ranked first with over templateYValue[max] thousand templateYLabel[1] , while templateXValue[1] was ranked second with templateYValue[1] thousand templateYLabel[1] .

generated_template: templateXValue[0] templateXValue[0] is the all-time templateTitle[1] leader of the National Football League ( templateTitleSubject[0] ) with templateYValue[max] templateTitle[0] templateTitle[1] templateYLabel[1] . The running back , who spent the majority of his 15-year templateTitle[0] with the Dallas Cowboys , became the all-time leader rusher in the templateTitleSubject[0] in the 2002 season . templateXValue[2] templateXValue[2] ( Buffalo Bills in templateTitleDate[0] ) and templateXValue[4] templateXValue[4] ( Washington Redskins in templateTitleDate[0] ) are the only active players in the top 10 of the ranking .
generated: Helse Sør-Øst RHF is the all-time companies leader of the National Football League ( Norway ) with 60368 Leading companies employees . The running back , who spent the majority of his 15-year Leading with the Dallas Cowboys , became the all-time leader rusher in the Norway in the 2002 season . Aker ASA ( Buffalo Bills in 2019 ) and Posten Norge AS ( Washington Redskins in 2019 ) are the only active players in the top 10 of the ranking .

Example 614:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2009']}
title: Golf industry in the U.S. : total economic output by state 2009
X_Axis['State']: ['California', 'Florida', 'Texas', 'New_York', 'North_Carolina', 'Georgia', 'Ohio', 'Illinois', 'Michigan', 'Arizona', 'Virginia', 'New_Jersey', 'Massachusetts', 'Oregon', 'Hawaii', 'Washington', 'Wisconsin', 'Minnesota', 'Pennsylvania', 'South_Carolina', 'Colorado', 'Indiana', 'Connecticut', 'New_Mexico', 'Louisiana', 'Iowa', 'Kentucky']
Y_Axis['Total', 'economic', 'output', '(in', 'billion', 'U.S.', 'dollars)']: ['15.1', '13.8', '7.4', '5.3', '5.3', '5.1', '4.8', '4.8', '4.2', '3.4', '3.1', '2.8', '2.8', '2.5', '2.5', '2.5', '2.4', '2.4', '2.3', '2.3', '1.7', '1.7', '1.1', '0.99', '0.81', '0.77', '0.71']

gold: This graph depicts the total economic output of the golf industry in the U.S. by state as of 2009 . In New Mexico , the total economic output was at 985 million U.S. dollars in 2006 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[0] templateTitle[1] in the templateYLabel[5] templateTitle[6] templateXLabel[0] as of templateTitleDate[0] . In templateXValue[3] templateXValue[23] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at 985 templateYLabel[4] templateYLabel[5] dollars in 2006 .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] templateYLabel[2] of households in the templateTitleSubject[0] templateTitleSubject[0] on templateTitle[2] and templateTitle[3] in templateTitle[6] , split templateTitle[7] templateXLabel[0] . In the templateXValue[0] templateXValue[0] , households spent a total of 76.29 templateYLabel[3] templateYLabel[4] on templateTitle[2] and templateTitle[3] in templateTitle[6] .
generated: This statistic displays the Total economic output of households in the U.S. on U.S. and total in by , split state . In the California , households spent a total of 76.29 (in billion on U.S. and total in by .

Example 615:
titleEntities: {'Subject': ['North America'], 'Date': ['2015']}
title: Forecast : printer cartridge revenue in North America 2007 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['23616', '23628', '23886', '23695', '23348', '23081', '22992', '23767', '23813']

gold: The statistic shows a forecast for revenue from printer cartridges in North America between 2007 and 2015 . In 2012 , revenue of about 23.7 billion U.S. dollars are expected .
gold_template: The statistic shows a templateTitle[0] for templateYLabel[0] from templateTitle[1] cartridges in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[3] , templateYLabel[0] of about templateYValue[3] templateYLabel[1] templateYLabel[2] templateYLabel[3] are expected .

generated_template: This statistic illustrates the templateYLabel[0] of chemical company templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] generated some templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateYLabel[0] . templateTitleSubject[0] , with full name E. I. du Pont de Nemours and Company , was a U.S.-based chemical company , and one of the largest companies in this industry worldwide .
generated: This statistic illustrates the Revenue of chemical company North America from 2007 to 2015 . In 2015 , North America generated some 23616 million U.S. dollars of Revenue . North America , with full name E. I. du Pont de Nemours and Company , was a U.S.-based chemical company , and one of the largest companies in this industry worldwide .

Example 616:
titleEntities: {'Subject': ['United Kingdom', 'HMRC'], 'Date': ['2019']}
title: Inheritance tax : United Kingdom HMRC tax receipts 2000 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05', '2003/04', '2002/03', '2001/02', '2000/01']
Y_Axis['Inheritance', 'tax', 'receipts', 'in', 'billion', 'GBP']: ['5.36', '5.2', '4.8', '4.7', '3.8', '3.4', '3.1', '2.9', '2.7', '2.4', '2.8', '3.8', '3.5', '3.3', '2.9', '2.5', '2.4', '2.4', '2.2']

gold: This statistic shows the total United Kingdom ( UK ) HMRC inheritance tax receipts from fiscal year 2000/01 to fiscal year 2018/19 . Despite a dip in 2008/09 and 2009/10 the overall trend was one of increase . The peak was in 2018/19 at 5.36 billion British pounds ( GBP ) .
gold_template: This statistic shows the total templateTitleSubject[0] ( UK ) templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] templateXValue[0] . Despite a dip in templateXValue[10] and templateXValue[9] the overall trend was one of increase . The peak was in templateXValue[0] at templateYValue[max] templateYLabel[3] British pounds ( templateYLabel[4] ) .

generated_template: This statistic shows the total of templateTitle[0] in templateTitleSubject[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , about templateYValue[0] templateYLabel[1] people were produced in templateTitleSubject[0] .
generated: This statistic shows the total of Inheritance in United Kingdom from 2019 to 2019 . In 2019 , about 5.36 tax people were produced in United Kingdom .

Example 617:
titleEntities: {'Subject': ['LVMH Group'], 'Date': ['2008', '2019']}
title: LVMH Group 's R & D expenditure worldwide 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['R&D', 'expenditure', 'in', 'million', 'euros']: ['140', '130', '130', '111', '97', '79', '71', '68', '63', '46', '45', '43']

gold: This statistic highlights the trend in research and development ( R & D ) expenditure of the LVMH Group worldwide from 2008 to 2019 . In 2019 , LVMH Group 's global R & D expenditure amounted to about 140 million euros .
gold_template: This statistic highlights the trend in research and development ( templateTitleSubject[0] templateTitle[4] templateTitle[5] ) templateYLabel[1] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's global templateTitleSubject[0] templateTitle[4] templateTitle[5] templateYLabel[1] amounted to about templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: templateTitleSubject[0] had a templateYLabel[0] of templateYValue[max] templateYLabel[2] in operation around the world as of templateXValue[max] , up from templateYValue[min] in templateXValue[min] . templateTitleSubject[0] , an amalgamation of Louis Vuitton , Moët , and Hennessy , is the top selling luxury personal goods companies in the world . The company operates globally selling a diverse range of products .
generated: LVMH Group had a R&D of 140 million in operation around the world as of 2019 , up from 43 in 2008 . LVMH Group , an amalgamation of Louis Vuitton , Moët , and Hennessy , is the top selling luxury personal goods companies in the world . The company operates globally selling a diverse range of products .

Example 618:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018', '2018']}
title: Murder in the U.S. : number of offenders by age 2018
X_Axis['Age', 'of', 'offender', 'in', 'years']: ['Infant_(<1)', '1_to_4', '5_to_8', '9_to_12', '13_to_16', '17_to_19', '20_to_24', '25_to_29', '30_to_34', '35_to_39', '40_to_44', '45_to_49', '50_to_54', '55_to_59', '60_to_64', '65_to_69', '70_to_74', '75+', 'Unknown']
Y_Axis['Number', 'of', 'offenders']: ['0', '1', '1', '8', '496', '1479', '2254', '1998', '1440', '1161', '651', '495', '439', '346', '186', '106', '82', '93', '5099']

gold: 2,254 murderers in the United States in 2018 were individuals between the ages of 20 and 24 . In the same year , the youngest murder offender was between the ages of one and four , and there were 93 murder offenders over the age of 75 . Murder rate in the United States Despite some feeling that violent crime in the United States is on the rise , perhaps due to sensationalized media coverage , the murder and nonnegligent manslaughter rate has declined steeply since 1990 .
gold_template: templateYValue[6] murderers in the templateTitle[1] in templateTitle[6] were individuals between the ages of templateXValue[6] and templateXValue[6] . In the same year , the youngest templateTitle[0] templateXLabel[1] was between the ages of templateXValue[1] and templateXValue[1] , and there were templateYValue[17] templateTitle[0] templateYLabel[1] over the templateXLabel[0] of 75 . templateTitle[0] rate in the templateTitle[1] Despite some feeling that violent crime in the templateTitle[1] is on the rise , perhaps due to sensationalized media coverage , the templateTitle[0] and nonnegligent manslaughter rate has declined steeply since 1990 .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[4] templateYLabel[1] who have every templateXLabel[0] in the templateTitle[0] in templateTitleDate[0] . In templateTitleDate[0] , there were a total of templateYValue[18] templateYLabel[2] templateTitle[4] templateTitle[5] in the templateTitle[6] .
generated: This statistic shows the Number of by offenders who have every Age in the Murder in 2018 . In 2018 , there were a total of 5099 offenders by age in the 2018 .

Example 619:
titleEntities: {'Subject': ['Panama'], 'Date': ['2024']}
title: Unemployment rate in Panama 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Unemployment', 'rate']: ['5.77', '5.77', '5.77', '5.8', '5.91', '6.11', '5.96', '6.13', '5.49', '5.05', '4.82']

gold: This statistic shows the unemployment rate in Panama from 2014 to 2018 , with projections up until 2024 . In 2018 , the unemployment rate in Panama was 5.96 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[6] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at around templateYValue[min] percent .
generated: The statistic shows the Unemployment rate in Panama from 2014 to 2018 , with projections up until 2024 . In 2018 , the Unemployment rate in Panama was at around 4.82 percent .

Example 620:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2008', '2018']}
title: Number of students in upper secondary education in Denmark 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'students']: ['148551', '150608', '149788', '148144', '147760', '148051', '144791', '140259', '132619', '122837', '118217']

gold: The statistic shows the number of students in upper secondary education in Denmark from 2008 to 2018 . The number increased from about 118 thousand upper secondary education students in 2008 to about 149 thousand students in 2018 .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] increased from about templateYValue[min] thousand templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] in templateXValue[min] to about templateYValue[0] thousand templateYLabel[1] in templateXValue[max] .

generated_template: In the fall semester templateXValue[max] , templateYValue[0] templateYLabel[1] were templateTitle[1] in universities and other higher education institutions in templateTitleSubject[0] . Since 2000 , the templateYLabel[0] of individuals in templateTitleSubject[0] with an upper secondary education increased , while the individuals without decreased . In templateXValue[max] , 21.7 percent of the female population had an upper secondary education degree of three years and 24.9 percent of the male population .
generated: In the fall semester 2018 , 148551 students were students in universities and other higher education institutions in Denmark . Since 2000 , the Number of individuals in Denmark with an upper secondary education increased , while the individuals without decreased . In 2018 , 21.7 percent of the female population had an upper secondary education degree of three years and 24.9 percent of the male population .

Example 621:
titleEntities: {'Subject': ['UK'], 'Date': ['14', '19']}
title: Monthly hours of sunlight in UK 2014 to 2019
X_Axis['Month']: ["Dec_'19", "Nov_'19", "Oct_'19", "Sep_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Apr_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17", "Nov_'17", "Oct_'17", "Sep_'17", "Aug_'17", "Jul_'17", "Jun_'17", "May_'17", "Apr_'17", "Mar_'17", "Feb_'17", "Jan_'17", "Dec_'16", "Nov_'16", "Oct_'16", "Sep_'16", "Aug_'16", "Jul_'16", "Jun_'16", "May_'16", "Apr_'16", "Mar_'16", "Feb_'16", "Jan_'16", "Dec_'15", "Nov_'15", "Oct_'15", "Sep_'15", "Aug_'15", "Jul_'15", "Jun_'15", "May_'15", "Apr_'15", "Mar_'15", "Feb_'15", "Jan_'15", "Dec_'14", "Nov_'14", "Oct_'14", "Sep_'14", "Aug_'14", "Jul_'14", "Jun_'14", "May_'14", "Apr_'14", "Mar_'14", "Feb_'14", "Jan_'14"]
Y_Axis['Number', 'of', 'hours']: ['46.2', '48.1', '87.9', '144.0', '173.6', '173.2', '160.8', '188.5', '168.9', '115.6', '100.6', '47.9', '37.6', '63.0', '113.2', '134.1', '147.4', '237.6', '239.9', '246.0', '132.6', '85.0', '95.6', '48.7', '45.3', '71.1', '72.7', '109.0', '155.5', '168.7', '155.7', '208.3', '158.0', '119.7', '55.0', '55.1', '40.7', '74.7', '105.3', '119.9', '181.7', '156.4', '136.5', '209.6', '160.8', '117.3', '84.9', '37.1', '29.2', '35.6', '91.2', '157.8', '148.8', '160.6', '189.7', '174.4', '212.9', '121.9', '76.0', '58.5', '57.1', '51.9', '82.8', '123.3', '171.0', '223.0', '178.4', '149.6', '144.9', '126.7', '75.0', '42.8']

gold: In the period of consideration , the total monthly hours of sunlight in the UK followed a similar pattern each year . The most notable change occurred in 2018 , when the hours of sunlight shot up in May , June and July to 246 , 240 and 238 hours respectively . Unsurprisingly it was the end of each year when sunlight hours were lowest .
gold_template: In the period of consideration , the total templateTitle[0] templateYLabel[1] of templateTitle[2] in the templateTitleSubject[0] followed a similar pattern each year . The most notable change occurred in 2018 , when the templateYLabel[1] of templateTitle[2] shot up in templateXValue[7] , June and July to templateYValue[max] , templateYValue[18] and templateYValue[17] templateYLabel[1] respectively . Unsurprisingly it was the end of each year when templateTitle[2] templateYLabel[1] were lowest .

generated_template: This statistic shows the templateTitle[0] templateTitleSubject[1] templateTitleSubject[1] ( UK ) templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] from July templateTitleDate[0] to December templateTitleDate[1] . The company 's templateYLabel[0] templateYLabel[1] had a range between templateYValue[min] and templateYValue[max] percent during this time period . In December templateTitleDate[1] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] was around templateYValue[0] percent .
generated: This statistic shows the Monthly UK ( UK ) Number hours for UK from July 14 to December 19 . The company 's Number hours had a range between 29.2 and 246.0 percent during this time period . In December 19 , UK 's Number hours was around 46.2 percent .

Example 622:
titleEntities: {'Subject': ['Countries'], 'Date': ['2017']}
title: Countries with the lowest access to electricity 2017
X_Axis['Country']: ['Burundi', 'Chad', 'Malawi', 'Dem._Republic_of_the_Congo', 'Niger', 'Liberia', 'Uganda', 'Sierra_Leone', 'Madagascar', 'South_Sudan', 'Burkina_Faso', 'Guinea-Bissau', 'Mozambique', 'Central_African_Republic', 'Tanzania', 'Somalia', 'Lesotho', 'Rwanda', 'Guinea', 'Zambia', 'Global_average']
Y_Axis['Access', 'rate']: ['9.3', '10.9', '12.7', '19.1', '20', '21.5', '22', '23.4', '24.1', '25.4', '25.5', '26', '27.4', '30', '32.8', '32.9', '33.7', '34.1', '35.4', '40.3', '88.8']

gold: This statistic shows the countries with the lowest access to electricity in 2017 based on access rate . As of that time , about 12.7 percent of the population in Malawi had access to electricity .
gold_template: This statistic shows the templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] to templateTitle[4] in templateTitleDate[0] based on templateYLabel[0] templateYLabel[1] . As of that time , about templateYValue[2] percent of the population in templateXValue[2] had templateYLabel[0] to templateTitle[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from templateTitleDate[0] to templateTitleDate[1] . The templateYLabel[0] templateYLabel[1] was around templateYValue[max] templateYLabel[2] people in templateXValue[1] . The global templateYLabel[1] As shown above , the total number of people living on templateTitleSubject[0] templateTitle[1] templateYLabel[0] in templateTitleDate[1] .
generated: This statistic shows the Access rate of the Countries from 2017 to 2017 . The Access rate was around 88.8 rate people in Chad . The global rate As shown above , the total number of people living on Countries lowest Access in 2017 .

Example 623:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2014', '2019']}
title: Number of cash machines in the United Kingdom ( UK ) Q1 2014 -Q3 2019
X_Axis['Quarter']: ["Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14"]
Y_Axis['Number', 'of', 'cash', 'machines']: ['60534', '61967', '62581', '63360', '64362', '65379', '67419', '69603', '70045', '70114', '70045', '70020', '70254', '70682', '70330', '70270', '70018', '69876', '70006', '69382', '69120', '68819', '68135']

gold: This statistic illustrates the number of cash machines in the United Kingdom ( UK ) from the first quarter of 2014 to the third quarter of 2019 . Automated transaction machines ( ATMs ) are banking outlets designed for maximum access to banking transactions and information , outside of bank working hours and without the assistance of a clerk . It can be seen that the total number of cash machines increased between the first quarter of 2014 and the second quarter of 2016 , reaching a total of more than 70.1 thousand as of the second quarter of 2016 .
gold_template: This statistic illustrates the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from the first templateXLabel[0] of templateTitle[7] to the third templateXLabel[0] of templateTitle[9] . Automated transaction templateYLabel[2] ( ATMs ) are banking outlets designed for maximum access to banking transactions and information , outside of bank working hours and without the assistance of a clerk . It can be seen that the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] increased between the first templateXLabel[0] of templateTitle[7] and the second templateXLabel[0] of 2016 , reaching a total of more than templateYValue[9] thousand as of the second templateXLabel[0] of 2016 .

generated_template: templateTitleSubject[0] 's templateYLabel[1] customer count has never been higher than it was in the second templateXLabel[0] of templateTitleDate[1] . The Berlin-based online fashion retailer had templateYValue[max] templateYLabel[3] templateYLabel[2] who have placed an order through templateTitleSubject[0] 's website in the last twelve months running up to June 30 , templateTitleDate[1] . Since the first templateXLabel[0] of the previous year , templateTitleSubject[0] increased its base by adding over five templateYLabel[3] templateYLabel[1] templateYLabel[2] .
generated: United Kingdom 's cash customer count has never been higher than it was in the second Quarter of 2019 . The Berlin-based online fashion retailer had 70682 machines who have placed an order through United Kingdom 's website in the last twelve months running up to June 30 , 2019 . Since the first Quarter of the previous year , United Kingdom increased its base by adding over five machines cash machines .

Example 624:
titleEntities: {'Subject': ['CPG'], 'Date': ['2016']}
title: Global operating margin of CPG companies 2016 , by company
X_Axis['Company']: ['Kraft_Heinz', 'Kimberly-Clark', 'General_Mills', 'PepsiCo', 'Nestlé']
Y_Axis['Operating', 'margin']: ['21.9', '18.2', '15.9', '15.6', '14.7']

gold: This statistic shows the operating margins of consumer packaged goods ( CPG ) companies worldwide in 2016 , sorted by company . In that year , Kraft Heinz had an operating margin of 21.9 percent , the highest among the referenced CPG companies .
gold_template: This statistic shows the templateYLabel[0] margins of consumer packaged goods ( templateTitleSubject[0] ) templateTitle[4] worldwide in templateTitleDate[0] , sorted templateTitle[6] templateXLabel[0] . In that year , templateXValue[0] had an templateYLabel[0] templateYLabel[1] of templateYValue[max] percent , the highest among the referenced templateTitleSubject[0] templateTitle[4] .

generated_template: This statistic depicts the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] , templateTitle[7] templateTitle[8] . According to the source , the templateXValue[0] templateXValue[0] of the templateTitleSubject[0] templateTitleSubject[0] in templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to approximately templateYValue[max] percent .
generated: This statistic depicts the Global operating margin CPG in the CPG ( CPG ) in 2016 , company . According to the source , the Kraft Heinz of the CPG in Operating margin amounted to approximately 21.9 percent .

Example 625:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Cities with the largest parkland percentage in the U.S. 2018
X_Axis['State']: ['Anchorage', 'Fremont', 'Irvine', 'Scottsdale', 'North_Las_Vegas', 'Chesapeake', 'New_Orleans', 'Albuquerque', 'New_York', 'Washington_D.C.', 'San_Francisco', 'Las_Vegas', 'El_Paso', 'San_Diego', 'Jersey_City']
Y_Axis['Share', 'of', 'parkland']: ['84.2', '49.4', '27.4', '26.9', '26.2', '26', '25.9', '23.2', '21.7', '21.1', '19.6', '19.4', '19.2', '19.1', '18.1']

gold: This statistic shows the cities with the largest parkland percentage of the city area in the United States in 2018 . In Anchorage , Alaska , 84.2 percent of the city 's area was comprised of parkland in 2018 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[1] templateTitle[4] of the templateXValue[last] area in the templateTitle[5] in templateTitleDate[0] . In templateXValue[0] , Alaska , templateYValue[max] percent of the templateXValue[last] 's area was comprised of templateYLabel[1] in templateTitleDate[0] .

generated_template: The statistic above presents the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] in the templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , templateXValue[2] templateTitle[0] templateTitle[1] templateYLabel[1] accounted for templateYValue[2] percent of all templateTitle[0] templateTitle[1] templateTitle[2] .
generated: The statistic above presents the Share parkland of Cities largest in the percentage in 2018 , 2018 State . In 2018 , Irvine Cities largest parkland accounted for 27.4 percent of all Cities largest parkland .

Example 626:
titleEntities: {'Subject': ['Summer Olympic Games'], 'Date': ['1896', '2016']}
title: Number of sports at the Summer Olympic Games 1896 to 2016
X_Axis['Year']: ['2016', '2012', '2008', '2004', '2000', '1996', '1992', '1988', '1984', '1980', '1976', '1972', '1968', '1964', '1960', '1956', '1952', '1948', '1936', '1932', '1928', '1924', '1920', '1912', '1908', '1906', '1904', '1900', '1896']
Y_Axis['Number', 'of', 'sports', 'played']: ['35', '32', '34', '34', '34', '31', '29', '27', '26', '23', '23', '23', '20', '21', '19', '18', '19', '20', '24', '18', '17', '20', '25', '17', '24', '13', '18', '20', '9']

gold: The statistic illustrates the number of sports at the Summer Olympic Games between 1896 and 2016 . In 1900 , 20 sporting events took place at the Summer Olympic Games .
gold_template: The statistic illustrates the templateYLabel[0] of templateYLabel[1] at the templateTitleSubject[0] Games between templateXValue[min] and templateXValue[max] . In templateXValue[27] , templateYValue[12] sporting events took place at the templateTitleSubject[0] Games .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] parent families with a female householder and no spouse present in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[28] percent of all templateTitleSubject[0] families with a templateTitle[3] mother in the templateTitle[5] lived below the templateYLabel[0] level . In templateXValue[max] , that figure was at templateYValue[min] percent .
generated: This statistic shows the Number sports of Summer Olympic Games Olympic parent families with a female householder and no spouse present in the Summer Olympic Games from 1896 to 2016 . In 1896 , 9 percent of all Summer Olympic Games families with a Olympic mother in the 1896 lived below the Number level . In 2016 , that figure was at 9 percent .

Example 627:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2018']}
title: Urbanization in Denmark 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['87.87', '87.76', '87.64', '87.53', '87.41', '87.29', '87.14', '86.96', '86.8', '86.65', '86.49']

gold: This statistic shows the degree of urbanization in Denmark from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 87.87 percent of Denmark '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[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the percentage of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[max] percent of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
generated: This statistic shows the degree of Urbanization in Denmark from 2008 to 2018 and details the percentage of the entire population , living in urban areas . In 2018 , 87.87 percent of the total population in Denmark lived in cities .

Example 628:
titleEntities: {'Subject': ['Canada'], 'Date': ['2000', '2019']}
title: Number of births in Canada 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']
Y_Axis['Number', 'of', 'births']: ['382533', '378848', '379675', '383579', '383315', '382281', '381607', '378840', '376951', '379373', '379290', '373695', '360916', '346082', '339270', '337762', '330523', '328155', '327107']

gold: In 2018 , there were an estimated 382,533 babies born in Canada . This is an increase from 327,107 births in the year 2001 . Births in Canada In 2018 , there were more male babies born than female babies , and overall births have been increasing since 2000 .
gold_template: In templateXValue[1] , there were an estimated templateYValue[0] babies born in templateTitleSubject[0] . This is an increase from templateYValue[min] templateYLabel[1] in the templateXLabel[0] templateXValue[min] . templateYLabel[1] in templateTitleSubject[0] In templateXValue[1] , there were more male babies born than female babies , and overall templateYLabel[1] have been increasing since templateTitleDate[0] .

generated_template: Over the last two decades , the templateTitle[0] templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] has remained relatively stable . In templateXValue[max] , the templateTitle[0] templateYLabel[0] templateYLabel[1] in the templateTitleSubject[1] was templateYValue[0] templateYLabel[2] . Speeds peaked during this period in templateXValue[4] at templateYValue[max] templateYLabel[2] , before falling to templateYValue[3] templateYLabel[2] the following templateXLabel[0] .
generated: Over the last two decades , the Number births in the Canada has remained relatively stable . In 2019 , the Number births in the Canada was 382533 births . Speeds peaked during this period in 2015 at 383579 births , before falling to 383579 births the following Year .

Example 629:
titleEntities: {'Subject': ['Indonesia'], 'Date': ['2006', '2019']}
title: Per capita poultry consumption in Indonesia 2006 to 2019
X_Axis['Year']: ['2025', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Consumption', 'per', 'capita', 'in', 'kilograms']: ['8.39', '7.57', '7.57', '7.68', '7.91', '7.05', '6.81', '6.54', '6.25', '6.07', '5.68', '5.27', '5.17', '5.08', '4.93']

gold: In 2019 , Indonesians consumed around 7.6 kilograms of poultry meat per capita . In 2025 , this was expected to increase to 8.4 kilograms per capita . Indonesia 's meat consumption had been increasing in the last few years , indicating improved economic prosperity for the population .
gold_template: In templateXValue[1] , Indonesians consumed around templateYValue[1] templateYLabel[3] of templateTitle[2] meat templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this was expected to increase to templateYValue[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] 's meat templateYLabel[0] had been increasing in the last few years , indicating improved economic prosperity for the population .

generated_template: In templateXValue[1] , the templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was approximately templateYValue[1] templateYLabel[3] . In templateXValue[max] , this was forecasted to reach around templateYValue[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] 's templateTitle[2] meat templateYLabel[0] in templateXValue[1] was above the OECD average of that templateXLabel[0] .
generated: In 2019 , the poultry Consumption per capita in Indonesia was approximately 7.57 kilograms . In 2025 , this was forecasted to reach around 8.39 kilograms per capita . Indonesia 's poultry meat Consumption in 2019 was above the OECD average of that Year .

Example 630:
titleEntities: {'Subject': ['United States'], 'Date': ['2011']}
title: Geographic distance between grandparents and their grandchildren in the United States in 2011
X_Axis['Year']: ['10_miles_or_less', '11_-_50_miles', '51_-_100_miles', '101_-_150_miles', '151_-_200_miles', 'More_than_200_miles', 'Only_have_grandchildren_who_live_with_me', "Don't_know"]
Y_Axis['Percentage', 'of', 'respondents']: ['21', '17', '7', '4', '4', '43', '1', '2']

gold: This statistic shows the results of a survey among grandparents in the United States in 2011 on the geographic distance between themselves and their grandchildren . In 2011 , 43 percent of the respondents stated they live more than 200 miles away from their grandchildren , whereas 21 percent said they live 10 or less miles away from their grandchildren .
gold_template: This statistic shows the results of a survey among templateTitle[3] in the templateTitleSubject[0] in templateTitleDate[0] on the templateTitle[0] templateTitle[1] templateTitle[2] themselves and templateTitle[4] templateXValue[6] . In templateTitleDate[0] , templateYValue[max] percent of the templateYLabel[1] stated they templateXValue[6] templateXValue[5] 200 templateXValue[0] away from templateTitle[4] templateXValue[6] , whereas templateYValue[0] percent said they templateXValue[6] templateXValue[0] or templateXValue[0] away from templateTitle[4] templateXValue[6] .

generated_template: The statistic shows the share of a survey among Americans on how much ' _ ) in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . During the survey period , templateYValue[1] percent of templateYLabel[1] aged between 18 and 29 years used the social networking app .
generated: The statistic shows the share of a survey among Americans on how much ' _ ) in the United States from Don't know to 10 miles or less . During the survey period , 17 percent of respondents aged between 18 and 29 years used the social networking app .

Example 631:
titleEntities: {'Subject': ['Armour', 'United States'], 'Date': ['2014']}
title: Product quality rating of Under Armour footwear United States 2014
X_Axis['Response']: ['Extremely_positive', 'Somewhat_positive', 'Neutral', 'Somewhat_negative', 'Extremely_Negative']
Y_Axis['Share', 'of', 'respondents']: ['72', '22', '5', '-', '-']

gold: This statistic shows how consumers rate the product quality of Under Armour footwear . 72 % of respondents rated Under Armour 's quality as extremely positive .
gold_template: This statistic shows how consumers rate the templateTitle[0] templateTitle[1] of templateTitle[3] templateTitleSubject[0] templateTitle[5] . templateYValue[0] % of templateYLabel[1] rated templateTitle[3] templateTitleSubject[0] 's templateTitle[1] as templateXValue[0] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] survey templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] with sensitive information templateTitle[8] or templateTitle[7] , while traveling . templateYValue[max] percent of templateYLabel[1] templateTitle[4] templateTitle[8] or templateTitle[5] a templateXValue[0] templateTitle[7] whilst traveling .
generated: The statistic shows the Share of Armour survey respondents Under Armour footwear United with sensitive information 2014 or States , while traveling . 72 percent of respondents Armour 2014 or footwear a Extremely positive States whilst traveling .

Example 632:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2020']}
title: Public opinion on the most important problem facing the U.S. 2020
X_Axis['Response']: ['Dissatisfaction_with_government/Poor_leadership', 'Immigration', 'Healthcare', 'Ethics/moral/religious/family_decline', 'Unifying_the_country', 'Poverty/Hunger/Homelessness', 'Lack_of_respect_for_each_other', 'Environment/Pollution/Climate_change', 'Race_relations/Racism', 'Situation_in_Iraq/ISIS', 'Foreign_policy/Foreign_aid/Focus_overseas', 'Economy_in_general', 'Guns/Gun_control', 'Gap_between_rich_and_poor', 'Education', 'Wars/War_(nonspecific)/Fear_of_war']
Y_Axis['Share', 'of', 'respondents']: ['28', '6', '6', '5', '5', '5', '4', '4', '3', '2', '2', '2', '2', '2', '2', '2']

gold: This statistic represents American adults ' view of the most important problem facing the United States . In January 2020 , 28 percent of the participants stated that poor leadership and a general dissatisfaction with the government were the most important problems facing the U.S .
gold_template: This statistic represents American adults ' view of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] the templateTitle[6] . In January templateTitleDate[0] , templateYValue[max] percent of the participants stated that templateXValue[13] templateXValue[0] and a templateXValue[11] templateXValue[0] the government were the templateTitle[2] templateTitle[3] problems templateTitle[5] the templateTitle[6] .

generated_template: This statistic shows the results of a survey about the most templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] in the third quarter of templateTitleDate[0] . The most used templateTitle[3] templateTitle[4] templateXLabel[0] in templateTitleSubject[0] was templateXValue[0] at templateYValue[max] percent , closely followed by templateXValue[1] at templateYValue[1] percent . On the opposite side , templateXValue[last] is used by four percent of templateYLabel[1] .
generated: This statistic shows the results of a survey about the most important problem facing in U.S. in the third quarter of 2020 . The most used important problem Response in U.S. was Dissatisfaction with government/Poor leadership at 28 percent , closely followed by Immigration at 6 percent . On the opposite side , Wars/War (nonspecific)/Fear of war is used by four percent of respondents .

Example 633:
titleEntities: {'Subject': ['Yemen'], 'Date': ['2002', '2019']}
title: U.S. airstrikes in Yemen 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2002']
Y_Axis['Number', 'of', 'airstrikes']: ['8', '35', '125', '44', '23', '23', '26', '42', '10', '4', '2', '1']

gold: This statistic shows the number of U.S. airstrikes in Yemen from 2002 to 2019 . In 2018 , there were 35 United States airstrikes in Yemen .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , there were templateYValue[1] templateTitle[0] templateYLabel[1] in templateTitleSubject[0] .

generated_template: This statistic shows the templateTitle[0] of templateYLabel[0] templateYLabel[1] in templateTitle[3] templateTitleSubject[0] from templateXValue[10] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[0] templateYLabel[2] templateYLabel[1] in templateTitle[3] templateTitleSubject[0] had been templateYLabel[0] . Employment and labor force in templateTitleSubject[0] – additional information According to the graph at hand , absolute unemployment in templateTitleSubject[0] has gradually risen over the past decade .
generated: This statistic shows the U.S. of Number airstrikes in 2002 Yemen from 2009 to 2019 . In 2019 , approximately 8 airstrikes in 2002 Yemen had been Number . Employment and labor force in Yemen – additional information According to the graph at hand , absolute unemployment in Yemen has gradually risen over the past decade .

Example 634:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2024']}
title: Budget balance in Mexico in relation to gross domestic product ( GDP ) 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'balance', 'to', 'GDP', 'ratio']: ['-2.4', '-2.3', '-2.3', '-2.2', '-2.6', '-2.8', '-2.2', '-1.07', '-2.77', '-4', '-4.54']

gold: The statistic shows the budget balance in Mexico from 2014 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 . In 2018 , the state deficit of Mexico was at around 2.2 percent of the gross domestic product .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , the state deficit of templateTitleSubject[0] was at around 2.2 percent of the templateTitle[4] templateTitle[5] templateTitle[6] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to about templateYValue[min] percent of the templateTitle[4] templateTitle[5] templateTitle[6] . See templateYLabel[2] of templateTitleSubject[0] for additional information .
generated: The statistic shows the Budget balance in Mexico from 2014 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 . In 2018 , Mexico 's Budget balance amounted to about -4.54 percent of the gross domestic product . See GDP of Mexico for additional information .

Example 635:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2017']}
title: Gross margin on furniture in U.S. wholesale 2000 to 2017
X_Axis['Year']: ['17', '16', '15', '14', '13', '12', '11', '10', '9', '8', '7', '6', '5', '4', '3', '2', '1', '0']
Y_Axis['Gross', 'margin', 'in', 'billion', 'U.S.', 'dollars']: ['27.92', '26.54', '25.0', '23.93', '22.73', '21.11', '19.2', '18.73', '15.81', '20.32', '21.17', '21.19', '19.09', '18.25', '17.61', '16.94', '15.49', '15.97']

gold: This timeline depicts the U.S. merchant wholesalers ' gross margin on furniture and home furnishings from 2000 to 2017 . In 2017 , the gross margin on furniture and home furnishings in U.S. wholesale was about 27.92 billion U.S. dollars .
gold_template: This timeline depicts the templateYLabel[3] merchant wholesalers ' templateYLabel[0] templateYLabel[1] on templateTitle[2] and home furnishings from templateTitleDate[0] to templateTitle[6] . In templateTitle[6] , the templateYLabel[0] templateYLabel[1] on templateTitle[2] and home furnishings in templateYLabel[3] templateTitle[4] was about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] of the National Basketball Association from templateXValue[last] to templateXValue[0] . In templateXValue[0] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] are owned by the Lamar Hunt family , who bought the templateYLabel[0] in 1960 .
generated: This statistic shows the Gross margin of the U.S. of the National Basketball Association from 0 to 17 . In 17 , the Gross margin came to 27.92 billion U.S. dollars . The U.S. are owned by the Lamar Hunt family , who bought the Gross in 1960 .

Example 636:
titleEntities: {'Subject': ['Volcanic'], 'Date': ['2016']}
title: Volcanic eruptions - people affected worldwide up to 2016
X_Axis['Location', 'and', 'Date']: ['Volcanic_eruption_in_the_Philippines_(June_9_1991)', 'Volcano_eruption_in_Ecuador_(August_14_2015)', 'Volcanic_eruption_in_Nicaragua_(April_9_1992)', 'Volcano_eruption_in_Ecuador_(August_14_2006)', 'Volcano_eruption_in_Indonesia_(April_5_1982)', 'Volcano_eruption_in_Indonesia_(1969)', 'Volcanic_eruption_in_Comoros_(November_24_2005)', 'Volcanic_eruption_in_the_Philippines_(Feb._6_1993)', 'Volcanic_eruption_in_Papua_New_Guinea_(September_19_1994)', 'Volcanic_eruption_in_Indonesia_(October_24_2002)']
Y_Axis['Number', 'of', 'victims']: ['1036065', '800000', '300075', '300013', '300000', '250000', '245000', '165009', '152002', '137140']

gold: The statistic shows the number of people , who were affected by the world 's most significant volcanic eruptions from 1900 to 2016* . In 1991 , total 1,036,035 were affected due to volcanic eruption in Philippines .
gold_template: The statistic shows the templateYLabel[0] of templateTitle[2] , who were templateTitle[3] by the world 's most significant templateXValue[0] templateTitle[1] from 1900 to 2016* . In 1991 , total 1,036,035 were templateTitle[3] due to templateXValue[0] in templateXValue[0] .

generated_template: The statistic presents the templateYLabel[0] of templateTitleSubject[0] templateTitle[2] A templateYLabel[1] in the templateTitle[4] as of September templateTitleDate[0] , templateTitle[6] templateXLabel[0] templateXLabel[1] . In that year , there were templateYValue[0] templateTitleSubject[0] templateTitle[2] A transactions valued at templateXValue[0] templateXValue[0] one templateXLabel[2] templateXLabel[3] templateXLabel[4] in the templateTitle[4] . The overall templateYLabel[0] of templateTitleSubject[0] templateTitle[2] A templateYLabel[1] in the 12 months ending September 30 , templateTitleDate[0] amounted to templateYValue[max] .
generated: The statistic presents the Number of Volcanic people A victims in the worldwide as of September 2016 , 2016 Location Date . In that year , there were 1036065 Volcanic people A transactions valued at Volcanic eruption in the Philippines (June 9 1991) one Date in the worldwide . The overall Number of Volcanic people A victims in the 12 months ending September 30 , 2016 amounted to 1036065 .

Example 637:
titleEntities: {'Subject': ['Black Friday'], 'Date': ['2017']}
title: U.S. consumer sentiments towards Black Friday shopping 2017
X_Axis['Response']: ['It_is_a_great_opportunity_to_buy_gifts_for_the_holidays', "It's_a_tradition", 'I_like_it_even_more_now_that_I_can_shop_online', 'It_is_the_best_opportunity_to_buy_expensive_items_at_a_discount', 'It_is_when_you_find_promotions_that_are_not_available_at_any_other_time_of_year', 'It_is_a_good_way_to_spend_quality_time_with_friends/family', 'I_will_wait_until_Cyber_Monday_to_do_most_of_my_shopping', 'Promotions_are_never_on_products_I_am_interested_in', 'Retailers_just_discount_their_worst_brands', 'None_of_these']
Y_Axis['Share', 'of', 'respondents']: ['42', '39', '37', '33', '31', '19', '18', '16', '9', '5']

gold: This statistic shows the results of a 2017 survey in which U.S. consumers were asked about their attitude towards Black Friday shopping . According to the survey , 42 percent of respondents said that Black Friday is a great opportunity to buy gifts for the holidays .
gold_template: This statistic shows the results of a templateTitleDate[0] survey in which templateTitle[0] consumers were asked about templateXValue[8] attitude templateTitle[3] templateTitleSubject[0] shopping . According to the survey , templateYValue[max] percent of templateYLabel[1] said templateXValue[2] templateTitleSubject[0] is a templateXValue[0] to templateXValue[0] for the templateXValue[0] .

generated_template: templateXValue[0] breaks were the most popular type of vacation templateTitle[2] by holidaymakers in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) , with nearly half of survey templateYLabel[1] choosing to go on a templateXValue[0] templateXValue[0] templateXValue[1] in templateTitleDate[0] . templateXValue[1] templateTitle[1] ranked second at templateYValue[1] percent . Most popular destinations for Brits It 's clear from the results that Brits tend to go for templateXValue[0] breaks and templateXValue[1] templateTitle[1] over other templateTitle[0] of travel .
generated: It is a great opportunity to buy gifts for the holidays breaks were the most popular type of vacation sentiments by holidaymakers in the Black Friday ( Black Friday ) , with nearly half of survey respondents choosing to go on a It is a great opportunity to buy gifts for the holidays It's a tradition in 2017 . It's a tradition consumer ranked second at 39 percent . Most popular destinations for Brits It 's clear from the results that Brits tend to go for It is a great opportunity to buy gifts for the holidays breaks and It's a tradition consumer over other U.S. of travel .

Example 638:
titleEntities: {'Subject': ['YouTube'], 'Date': ['2020']}
title: All-time most viewed YouTube channel owners 2020
X_Axis['Month']: ["Ryan's_World", 'PewDiePie', 'Like_Nastya_Vlog', '✿_Kids_Diana_Show', 'DanTDM_(TheDiamondMinecart)', 'Fun_Toys_Collector_Disney', 'Vlad_and_Nikita', 'FGTeeV', 'Family_Fun_Pack', 'CookieSwirlC', 'Markiplier']
Y_Axis['All-time', 'channel', 'views', 'in', 'billions']: ['35.18', '24.44', '22.68', '17.01', '16.01', '14.86', '14.07', '13.11', '12.66', '12.42', '12.29']

gold: As of January 2020 , Ryan from Ryan 's World ( formerly known as Ryan ToysReview ) had reached almost 35.2 billion lifetime video views , making the elementary schooler the most viewed YouTube channel owner of all time . This achievement unseated former first-ranked video game commentator Felix Kjellberg , who produces videos under the name PewDiePie . Ryan has been uploading YouTube videos since March 2015 , and mainly features in videos where he is playing with and reviews toys `` for kids , by a kid '' .
gold_template: As of January templateTitle[6] , Ryan from Ryan 's templateXValue[0] ( formerly known as Ryan ToysReview ) had reached almost templateYValue[max] templateYLabel[3] lifetime video templateYLabel[2] , making the elementary schooler the templateTitle[1] templateTitle[2] templateTitleSubject[0] templateYLabel[1] owner of all time . This achievement unseated former first-ranked video game commentator Felix Kjellberg , who produces videos under the name templateXValue[1] . Ryan has been uploading templateTitleSubject[0] videos since March 2015 , and mainly features in videos where he is playing with and reviews templateXValue[5] `` for templateXValue[3] , by a kid '' .

generated_template: As of June templateTitleDate[0] , templateXValue[0] templateXValue[0] still holds the record for most templateYLabel[1] templateYLabel[2] in the templateTitle[2] templateTitleSubject[1] of templateTitleSubject[0] templateTitleSubject[1] templateTitleSubject[1] with a total of templateYValue[max] templateYLabel[1] throughout his career . templateXValue[0] also shares the record for most appearances in templateTitleSubject[1] templateTitleSubject[1] templateYLabel[3] with Paolo Maldini , both played in six templateYLabel[3] . More all-time records for templateXValue[0] templateXValue[0] is also the all-time templateTitle[1] goal scorer in the templateTitleSubject[1] templateTitleSubject[1] with 127 templateYLabel[1] .
generated: As of June 2020 , Ryan's World still holds the record for most channel views in the viewed YouTube of YouTube with a total of 35.18 channel throughout his career . Ryan's World also shares the record for most appearances in YouTube billions with Paolo Maldini , both played in six billions . More all-time records for Ryan's World is also the all-time most goal scorer in the YouTube with 127 channel .

Example 639:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2018']}
title: Brazil : most popular music genres 2018
X_Axis['Response']: ['Pop', 'Brazilian_pop', 'Sertanejo', 'Rock', 'Samba/pagode', 'Electronic/dance_music', 'Dance', 'Gospel', 'Hip_hop', 'Reggae', 'Forró', 'Funk/soul', 'Blues', 'Latin', 'Rap', 'Country', 'Metal', 'Techno/EDM', 'R&B/soul', 'Jazz', 'Heavy_metal', 'Classical/opera', 'Reggaeton', 'Easy_listening', 'Punk', 'Folk', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['55.5', '54', '50.3', '48.8', '38.1', '37.9', '35.6', '35.1', '31.8', '31', '30.2', '25.2', '24.7', '23.4', '23.2', '22.6', '17.6', '17.4', '17.3', '16.8', '14.5', '14.3', '13.2', '10.7', '10.4', '8.9', '4.8']

gold: This statistic shows the results of a Deezer survey on music listening habits among adults in Brazil as of 2018 . That year , 55.5 percent of Brazilian respondents claimed to listen to pop music , whereas 54 percent said they listened to Brazilian pop .
gold_template: This statistic shows the results of a Deezer survey on templateXValue[5] templateXValue[23] habits among adults in templateTitleSubject[0] as of templateTitleDate[0] . That year , templateYValue[max] percent of templateXValue[1] templateYLabel[1] claimed to listen to templateXValue[0] templateXValue[5] , whereas templateYValue[1] percent said they listened to templateXValue[1] templateXValue[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[6] working templateTitle[7] in templateTitleSubject[0] templateTitle[7] in selected templateTitle[3] in templateTitleDate[0] . During the survey period , templateYValue[max] percent of templateYLabel[1] stated that they templateYLabel[3] templateYLabel[4] walking templateXValue[1] .
generated: This statistic shows the Share of respondents 2018 working 2018 in Brazil 2018 in selected music in 2018 . During the survey period , 55.5 percent of respondents stated that they respondents walking Brazilian pop .

Example 640:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: Per capita consumption of butter in the U.S. 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['5.8', '5.7', '5.7', '5.6', '5.5', '5.5', '5.5', '5.4', '4.9', '5.0', '5.0', '4.7', '4.7', '4.5', '4.5', '4.5', '4.4', '4.3', '4.5']

gold: This statistic shows the per capita consumption of butter in the United States from 2000 to 2018 . The U.S. per capita consumption of butter amounted to 5.8 pounds in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] amounted to templateYValue[max] templateYLabel[3] in templateXValue[max] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of U.S. 2000 in the Per from 2000 to 2018 . According to the report , the U.S. Per capita consumption of U.S. 2000 amounted to approximately 5.8 pounds in 2018 .

Example 641:
titleEntities: {'Subject': ['Daily'], 'Date': ['2018']}
title: Daily online video usage in selected countries 2018
X_Axis['Country']: ['Saudi_Arabia', 'Turkey', 'Brazil', 'New_Zealand', 'Australia', 'Mexico', 'Spain', 'Canada', 'United_States', 'South_Korea', 'France', 'Philippines', 'India', 'Germany', 'Japan', 'China', 'Indonesia', 'South_Africa', 'Nigeria']
Y_Axis['Share', 'of', 'respondents']: ['64', '64', '62', '61', '60', '56', '53', '50', '50', '44', '42', '34', '33', '32', '32', '30', '21', '19', '16']

gold: This statistic gives information on the share of internet users in selected countries who watch online videos every day as of January 2018 . During the survey , it was found that 50 percent of U.S. internet users watched online video content on a daily basis . Additionally , more than half of the internet users in Mexico watched online videos every day .
gold_template: This statistic gives information on the templateYLabel[0] of internet users in templateTitle[4] templateTitle[5] who watch templateTitle[1] videos every day as of January templateTitleDate[0] . During the survey , it was found that templateYValue[7] percent of U.S. internet users watched templateTitle[1] templateTitle[2] content on a templateTitleSubject[0] basis . Additionally , more than half of the internet users in templateXValue[5] watched templateTitle[1] videos every day .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] in templateTitleDate[0] . In templateTitleDate[0] , at least templateYValue[1] people were executed in templateXValue[1] . The figures represent minimum values according to Amnesty International .
generated: This statistic shows the Share of respondents video in 2018 . In 2018 , at least 64 people were executed in Turkey . The figures represent minimum values according to Amnesty International .

Example 642:
titleEntities: {'Subject': ['Singapore'], 'Date': ['2005', '2018']}
title: Youth unemployment rate in Singapore 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Youth', 'unemployment', 'rate']: ['4.2', '4.6', '4.1', '3.8', '6.3', '6.3', '6.5', '6.7', '7.1', '9.9', '9.2', '8.8', '8.8', '10.7']

gold: This statistic presents the unemployment rate for individuals aged 15 to 24 years in Singapore from 2005 to 2018 . In 2018 , approximately 4.2 percent of the labor force aged 15 to 24 years in Singapore were unemployed .
gold_template: This statistic presents the templateYLabel[1] templateYLabel[2] for individuals aged 15 to 24 years in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[0] percent of the labor force aged 15 to 24 years in templateTitleSubject[0] were unemployed .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[0] percent .
generated: The statistic shows the Youth unemployment rate in the Singapore from 2005 to 2018 . In 2018 , the Youth unemployment rate was at 4.2 percent .

Example 643:
titleEntities: {'Subject': ['Worldwide'], 'Date': ['1990', '2017']}
title: Worldwide commercial space launches 1990 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995', '1990']
Y_Axis['Commercial', 'space', 'launches']: ['33', '21', '22', '23', '23', '20', '18', '23', '24', '28', '23', '21', '18', '35', '23', '15']

gold: This statistic represents worldwide commercial space launches from 1990 to 2017 . Globally , there were 33 commercial space launches in 2017 . The major nations conducting space launches include Russia , the United States and the member states of ESA .
gold_template: This statistic represents templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] from templateXValue[min] to templateXValue[max] . Globally , there were templateYValue[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[max] . The major nations conducting templateYLabel[1] templateYLabel[2] include Russia , the country and the member states of ESA .

generated_template: In templateXValue[max] , there were templateYValue[0] templateTitle[2] in the templateTitleSubject[0] templateTitleSubject[0] , a slight decrease from the templateXLabel[0] before . The templateYLabel[0] of templateTitle[0] visitors in templateTitleSubject[0] has been increasing over the last years , reaching an amount of templateYValue[max] templateYLabel[2] in templateXValue[10] .
generated: In 2017 , there were 33 space in the Worldwide , a slight decrease from the Year before . The Commercial of Worldwide visitors in Worldwide has been increasing over the last years , reaching an amount of 35 launches in 2007 .

Example 644:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2008', '2018']}
title: Number of divorces in Denmark 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'divorces']: ['14936', '15169', '17146', '16290', '19387', '18858', '15709', '14484', '14460', '14940', '14695']

gold: In 2017 and 2018 , most Danes were never married ; the number of never married inhabitants was around 2.8 million in the fourth quarter of 2018 . By contrast , among all Danes , the fewest were divorced . In general , the number of divorces between different sexes fluctuated in recent years , peaking in 2014 at about 19 thousand divorces .
gold_template: In templateXValue[1] and templateXValue[max] , most Danes were never married ; the templateYLabel[0] of never married inhabitants was around 2.8 million in the fourth quarter of templateXValue[max] . By contrast , among all Danes , the fewest were divorced . In general , the templateYLabel[0] of templateYLabel[1] between different sexes fluctuated in recent years , peaking in templateXValue[4] at about templateYValue[max] thousand templateYLabel[1] .

generated_template: The templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] peaked in templateXValue[5] , when almost templateYValue[max] thousand couples got divorced . Since then the divorce templateYLabel[0] decreased until templateXValue[max] , when it again increased and amounted to almost templateYValue[0] thousand templateYLabel[1] . A similar trend can be seen in the neighbor country Norway , where the templateYLabel[0] of templateYLabel[1] decreased for several years but increased again in templateXValue[1] .
generated: The Number of divorces in Denmark peaked in 2013 , when almost 19387 thousand couples got divorced . Since then the divorce Number decreased until 2018 , when it again increased and amounted to almost 14936 thousand divorces . A similar trend can be seen in the neighbor country Norway , where the Number of divorces decreased for several years but increased again in 2017 .

Example 645:
titleEntities: {'Subject': ['Germany'], 'Date': ['2018']}
title: GDP of Germany 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['GDP', 'in', 'billion', 'euros']: ['3386.0', '3277.34', '3159.75', '3048.86', '2938.59', '2826.24', '2758.26', '2703.12', '2580.06', '2460.28', '2561.74', '2513.23']

gold: In 2018 , Germany 's gross domestic product ( GDP ) amounted to 3,386 billion euros . Germany is thus among the leading five countries in the world GDP ranking . Ze Germans are living large Germany 's economy is not only one of the largest worldwide , it is also the largest in Europe and predicted to be among the countries with the largest GDP by the year 2030 .
gold_template: In templateXValue[max] , templateTitleSubject[0] 's gross domestic product ( templateYLabel[0] ) amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] is thus among the leading five countries in the world templateYLabel[0] ranking . Ze Germans are living large templateTitleSubject[0] 's economy is not only one of the largest worldwide , it is also the largest in Europe and predicted to be among the countries with the largest templateYLabel[0] by the templateXLabel[0] 2030 .

generated_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateYLabel[1] operating templateTitle[4] has risen from 3.751 to templateYValue[0] . The multinational-operating grocery and general merchandise retailer templateTitleSubject[0] PLC was founded in 1919 by Jack Cohen ( 1898 - 1979 ) . The product portfolio includes food and beverages , clothing , home appliances , and even financial services .
generated: Between 2007 and 2018 , the GDP of Germany billion operating 2018 has risen from 3.751 to 3386.0 . The multinational-operating grocery and general merchandise retailer Germany PLC was founded in 1919 by Jack Cohen ( 1898 - 1979 ) . The product portfolio includes food and beverages , clothing , home appliances , and even financial services .

Example 646:
titleEntities: {'Subject': ['Latin American'], 'Date': ['2018']}
title: 2018 FIFA World Cup : average age of Latin American soccer teams
X_Axis['Country']: ['Costa_Rica', 'Argentina', 'Mexico', 'Panama', 'Brazil', 'Colombia', 'Uruguay', 'Peru']
Y_Axis['Average', 'age', 'in', 'years']: ['29.8', '29.6', '29.3', '28.9', '28.6', '28.4', '28.2', '27.5']

gold: The statistic presents the average age of all Latin American soccer teams participating in the 2018 FIFA World Cup in Russia . Costa Rica was the Latin American soccer team with the oldest average age ( 29.8 years ) , followed by Argentina with team players averaging 29.6 years old .
gold_template: The statistic presents the templateYLabel[0] templateYLabel[1] of all templateTitleSubject[0] soccer templateTitle[9] participating in the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in Russia . templateXValue[0] was the templateTitleSubject[0] soccer team with the oldest templateYLabel[0] templateYLabel[1] ( templateYValue[max] templateYLabel[2] ) , followed by templateXValue[1] with team players averaging templateYValue[1] templateYLabel[2] old .

generated_template: In templateTitleSubject[0] templateTitle[7] , the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateXValue[0] templateXValue[last] stood at 0.807 templateYLabel[3] templateYLabel[4] templateYLabel[1] templateYLabel[2] . In the templateXValue[0] templateXValue[0] in the same month , the templateYLabel[0] stood at 1.704 templateYLabel[3] templateYLabel[4] templateYLabel[1] templateYLabel[2] . This data concerns the templateYLabel[0] of automotive templateTitle[0] ( for non-commercial use ) templateYLabel[1] templateYLabel[2] in templateTitle[4] templateTitle[5] .
generated: In Latin American American , the 2018 Average age years in the Costa Rica Peru stood at 0.807 years age years . In the Costa Rica in the same month , the Average stood at 1.704 years age years . This data concerns the Average of automotive 2018 ( for non-commercial use ) age years in average age .

Example 647:
titleEntities: {'Subject': ['PlayStation'], 'Date': ['2014']}
title: Suggested retail price of a PlayStation 4 in 2014 , by country
X_Axis['Country']: ['Brazil', 'Argentina', 'India', 'Indonesia', 'Thailand', 'South_Africa', 'United_Kingdom', 'Philippines', 'Germany', 'Malaysia', 'Russia', 'Singapore', 'South_Korea', 'Australia', 'United_Arab_Emirates', 'Canada', 'Hong_Kong', 'Taiwan', 'United_States', 'Japan']
Y_Axis['Price', 'in', 'U.S.', 'dollars']: ['1702.43', '1387.9', '653.54', '619.76', '614.77', '585.79', '580.94', '559.51', '557.07', '550.76', '523.85', '505.7', '466.82', '492.84', '462.56', '451.42', '435.23', '427.83', '399.99', '392.38']

gold: The ranking shows the suggested retail price of a PlayStation 4 in selected countries worldwide as of March 2014 . Brazil ranked first with a suggested retail price of more than 1,702 U.S. dollars , almost four times as much as the price in the United States ( 399.99 dollars ) . Global unit sales data from 2014 and 2015 shows that PlayStation 4 was the highest selling platform worldwide in those years .
gold_template: The ranking shows the templateTitle[0] templateTitle[1] templateYLabel[0] of a templateTitleSubject[0] templateTitleDate[0] in selected countries worldwide as of March templateTitleDate[0] . templateXValue[0] ranked first with a templateTitle[0] templateTitle[1] templateYLabel[0] of more than templateYValue[max] templateYLabel[1] templateYLabel[2] , almost templateTitleDate[0] times as much as the templateYLabel[0] in the templateXValue[6] templateXValue[18] ( templateYValue[18] templateYLabel[2] ) . Global unit sales data from templateTitleDate[0] and 2015 shows that templateTitleSubject[0] templateTitleDate[0] was the highest selling platform worldwide in those years .

generated_template: As of templateTitleDate[0] , there were 52 templateTitle[2] templateYLabel[1] templateTitle[4] templateTitle[5] worldwide , with the majority being built in templateXValue[0] . Comparatively , there were 186 templateTitle[2] templateYLabel[1] shut down permanently as of that year . The templateXValue[3] templateXValue[9] had permanently shut down 37 templateTitle[2] facilities as of February templateTitleDate[0] .
generated: As of 2014 , there were 52 price U.S. 4 2014 worldwide , with the majority being built in Brazil . Comparatively , there were 186 price U.S. shut down permanently as of that year . The Indonesia Malaysia had permanently shut down 37 price facilities as of February 2014 .

Example 648:
titleEntities: {'Subject': ['Manufacturing'], 'Date': ['2016']}
title: Manufacturing costs in pharmaceutical industry by country 2016
X_Axis['Country']: ['Mexico', 'Canada', 'Netherlands', 'Italy', 'United_Kingdom', 'Australia', 'France', 'Germany', 'Japan', 'United_States']
Y_Axis['Manufacturing', 'costs', 'index', '(U.S.', '=', '100)']: ['82.9', '88.8', '89.9', '90.3', '90.8', '91.3', '91.8', '93.4', '93.6', '100']

gold: This statistic compares the manufacturing costs of the pharmaceutical industry in selected countries with costs in the United States in 2016 , based on a cost index . Manufacturing costs in all selected countries were less than in the United States , with costs in Mexico being 17.1 percent less than in the United States .
gold_template: This statistic compares the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] in selected countries with templateYLabel[1] in the templateXValue[4] templateXValue[last] in templateTitleDate[0] , based on a cost templateYLabel[2] . templateYLabel[0] templateYLabel[1] in all selected countries were less than in the templateXValue[4] templateXValue[last] , with templateYLabel[1] in templateXValue[0] being 17.1 percent less than in the templateXValue[4] templateXValue[last] .

generated_template: During the year templateTitleDate[0] , the total templateTitle[3] exports templateTitle[4] templateTitleSubject[0] to the templateXValue[0] templateXValue[0] amounted to about 27 templateYLabel[2] of 75 cl . templateYLabel[1] . The UK is the first templateTitle[1] market for French templateTitle[3] .
generated: During the year 2016 , the total industry exports by Manufacturing to the Mexico amounted to about 27 index of 75 cl . costs . The UK is the first costs market for French industry .

Example 649:
titleEntities: {'Subject': ['European'], 'Date': ['2028']}
title: European Union-27 : poultry meat consumption volume forecast 2015 to 2028
X_Axis['Year']: ['2028', '2027', '2026', '2025', '2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Volume', 'in', 'thousand', 'metric', 'tons']: ['12182', '12111', '12041', '11976', '11921', '11869', '11817', '11751', '11690', '11664', '11861', '11606', '11667', '11102']

gold: Forecasts up until the year 2018 show that poultry meat consumption across the European Union is expected to increase to 11.86 million metric tons . In the following decade consumption will likely slow down , with the forecast up until 2028 remaining constant . By the end of the period in consideration , consumption will amount to an estimated 12.18 million metric tons .
gold_template: Forecasts up until the templateXLabel[0] templateXValue[10] show that templateTitle[2] templateTitle[3] templateTitle[4] across the templateTitleSubject[0] Union is expected to increase to templateYValue[10] templateYLabel[1] templateYLabel[2] templateYLabel[3] . In the following decade templateTitle[4] will likely slow down , with the templateTitle[6] up until templateXValue[max] remaining constant . By the end of the period in consideration , templateTitle[4] will amount to an estimated templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The timeline shows the templateYLabel[0] of templateTitle[0] templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[8] , it is estimated that around templateYValue[0] templateYLabel[1] templateYLabel[2] in the templateTitle[5] , an increase of 14.4 percent compared to the previous templateXLabel[0] . More statistics and facts on recreational boating
generated: The timeline shows the Volume of European poultry meat consumption from 2015 to 2028 . In 2020 , it is estimated that around 12182 thousand metric in the volume , an increase of 14.4 percent compared to the previous Year . More statistics and facts on recreational boating

Example 650:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2003', '2013']}
title: Great Britain : Households that use WiFi to access the Internet 2003 to 2013
X_Axis['Year']: ['2013', '2011', '2009', '2007', '2005', '2003']
Y_Axis['Share', 'of', 'respondents']: ['96', '80', '54', '30', '6', '1']

gold: This survey presents the percentage of British households that use WiFi at home to access the Internet from 2003 to 2013 . In 2009 , 54 percent of respondents reported accessing the internet via WiFi , whereas in 2013 the share of respondents increased to 96 percent .
gold_template: This survey presents the percentage of British templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] at home to templateTitle[6] the templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYValue[2] percent of templateYLabel[1] reported accessing the templateTitle[7] via templateTitle[5] , whereas in templateXValue[max] the templateYLabel[0] of templateYLabel[1] increased to templateYValue[max] percent .

generated_template: This statistic illustrates the templateTitle[7] distribution of templateTitle[1] at templateTitleSubject[0] in the templateTitleSubject[1] templateTitleSubject[1] as of templateTitleDate[0] . According to the annual report , templateYValue[max] percent of templateTitleSubject[0] templateTitle[1] are in the templateTitle[7] templateTitle[8] 45 - 54 . templateTitleSubject[0] is a British retailer selling clothing , furniture and other household items in a department store format .
generated: This statistic illustrates the Internet distribution of Britain at Great Britain in the Great Britain as of 2003 . According to the annual report , 96 percent of Great Britain Britain are in the Internet 2003 45 - 54 . Great Britain is a British retailer selling clothing , furniture and other household items in a department store format .

Example 651:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. binge drinking among adults by state 2018
X_Axis['State']: ['District_of_Columbia', 'North_Dakota', 'Wisconsin', 'Iowa', 'Nebraska', 'Illinois', 'Minnesota', 'Alaska', 'Montana', 'Hawaii', 'Colorado', 'Ohio', 'Missouri', 'Massachusetts', 'New_Hampshire', 'Pennsylvania', 'Louisiana', 'Rhode_Island', 'Michigan', 'Wyoming', 'Maine', 'Nevada', 'Texas', 'California', 'New_York', 'South_Dakota', 'United_States', 'Vermont', 'Kansas', 'New_Jersey', 'Indiana', 'Oregon', 'Virginia', 'Kentucky', 'Washington', 'South_Carolina', 'North_Carolina', 'Connecticut', 'Maryland', 'Arizona', 'Florida', 'Idaho', 'Arkansas', 'Delaware', 'New_Mexico', 'Oklahoma', 'Tennessee', 'Georgia', 'Mississippi', 'Alabama', 'Utah', 'West_Virginia']
Y_Axis['Percentage', 'of', 'binge', 'drinkers']: ['25.9', '23.3', '22.7', '21.1', '20.6', '20.3', '20', '19.6', '19.5', '19.5', '18.9', '18.9', '18.8', '18.8', '18.7', '18.2', '18.1', '18.1', '18.1', '18', '17.9', '17.9', '17.8', '17.6', '17.5', '17.4', '17.4', '17.4', '17.2', '16.7', '16.6', '16.1', '16', '15.8', '15.6', '15.5', '15.4', '15.4', '15.3', '15.2', '15.1', '15.1', '15.1', '14.8', '14.7', '13.4', '13.1', '12.9', '12.6', '12.4', '11.5', '11.5']

gold: This statistic represents the percentage of binge in the United States of America as of 2018 , in the last 30 days by state . As of that year , 17.8 percent of adults in Texas consumed more than 4 ( women ) or 5 ( men ) alcoholic beverages on a single occasion within the preceding 30 days .
gold_template: This statistic represents the templateYLabel[0] of templateYLabel[1] in the templateXValue[26] of America as of templateTitleDate[0] , in the last 30 days templateTitle[5] templateXLabel[0] . As of that year , templateYValue[22] percent of templateTitle[4] in templateXValue[22] consumed more than 4 ( women ) or 5 ( men ) alcoholic beverages on a single occasion within the preceding 30 days .

generated_template: As of templateTitleDate[0] , the states with the highest rates of templateTitle[0] included templateXValue[1] templateXValue[1] , templateXValue[1] templateXValue[2] , templateXValue[3] , and templateXValue[4] . As of that time , there were around templateYValue[1] templateYLabel[1] templateYLabel[2] 1,000 templateYLabel[4] in the templateXLabel[0] of templateXValue[1] templateXValue[1] . The overall templateTitle[1] of legal templateYLabel[1] in the templateTitle[2] has gradually decreased since the mid-1980s .
generated: As of 2018 , the states with the highest rates of U.S. included North Dakota , North Dakota Wisconsin , Iowa , and Nebraska . As of that time , there were around 23.3 binge drinkers 1,000 drinkers in the State of North Dakota . The overall binge of legal binge in the drinking has gradually decreased since the mid-1980s .

Example 652:
titleEntities: {'Subject': ['Austria'], 'Date': ['2018']}
title: Urbanization in Austria 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['58.3', '58.09', '57.91', '57.72', '57.53', '57.34', '57.15', '57.12', '57.4', '57.68', '57.97']

gold: This statistic shows the degree of urbanization in Austria from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 58.3 percent of Austria '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[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the percentage of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[max] percent of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
generated: This statistic shows the degree of Urbanization in Austria from 2008 to 2018 and details the percentage of the entire population , living in urban areas . In 2018 , 58.3 percent of the total population in Austria lived in cities .

Example 653:
titleEntities: {'Subject': ['Bundesliga', 'Germany'], 'Date': ['2020']}
title: Market value of first Bundesliga football clubs in Germany in 2020
X_Axis['Club', 'Name']: ['FC_Bayern_München', 'Borussia_Dortmund', 'RasenBallsport_Leipzig', 'Bayer_04_Leverkusen', 'Borussia_Mönchengladbach', 'FC_Schalke_04', 'TSG_1899_Hoffenheim', 'Hertha_BSC', 'VfL_Wolfsburg', 'Eintracht_Frankfurt', 'SV_Werder_Bremen', '1._FSV_Mainz_05', 'SC_Freiburg', 'FC_Augsburg', '1._FC_Köln', 'Fortuna_Düsseldorf', '1._FC_Union_Berlin', 'SC_Paderborn']
Y_Axis['Market', 'value', 'in', 'million', 'euros']: ['933.15', '637.4', '594.4', '445.75', '312.0', '242.75', '238.23', '233.2', '230.95', '215.8', '189.75', '147.4', '145.4', '131.15', '102.2', '93.15', '43.05', '31.25']

gold: This statistic shows the market value of the first Bundesliga football clubs in Germany as of February 11 , 2020 . The market value of FC Bayern Munich was highest at 933.15 million euros , followed by 637.4 million euros for Borussia Dortmund and 594.4 million euros for RB Leipzig .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] football templateTitle[5] in templateTitleSubject[1] as of February 11 , templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] of templateXValue[0] Munich was highest at templateYValue[max] templateYLabel[2] templateYLabel[3] , followed by templateYValue[1] templateYLabel[2] templateYLabel[3] for templateXValue[1] and templateYValue[2] templateYLabel[2] templateYLabel[3] for RB templateXValue[2] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[2] templateTitle[3] templateTitle[4] toiletries/grooming/aftershave templateTitle[6] in the templateTitle[6] in templateTitleDate[0] . In that year , templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateXLabel[1] were in operation in the templateTitle[6] .
generated: The statistic shows the Market of the first Bundesliga football toiletries/grooming/aftershave Germany in the Germany in 2020 . In that year , 933.15 value million euros Name were in operation in the Germany .

Example 654:
titleEntities: {'Subject': ['France'], 'Date': ['2017']}
title: Distribution of women practicing nudism at the beach in France 2017 , by age
X_Axis['Year']: ['18_to_24_years', '25_to_34_years', '35_to_49_years', '50_to_59_years', '60_years_and_older']
Y_Axis['Share', 'of', 'women', 'surveyed']: ['6', '5', '9', '11', '13']

gold: This statistic indicates the share of French women who have already practiced naturism on the beach or in a nudist camp in 2017 , by age group . We can see that more than 10 percent of women aged 50 to 59 had already practiced nudism at the beach or in a naturist camp . Discover also the level of interest of the French for naturism .
gold_template: This statistic indicates the templateYLabel[0] of French templateYLabel[1] who have already practiced naturism on the templateTitle[4] or in a nudist camp in templateTitleDate[0] , templateTitle[7] templateTitle[8] group . We can see that more than 10 percent of templateYLabel[1] aged templateXValue[3] to templateXValue[3] had already practiced templateTitle[3] at the templateTitle[4] or in a naturist camp . Discover also the level of interest of the French for naturism .

generated_template: This statistic displays the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per day in the templateTitleSubject[0] templateTitleSubject[0] between templateTitle[7] and templateTitle[8] , templateTitle[9] templateTitle[10] . In this four templateXLabel[0] period , individuals aged between 4 and 10 templateXValue[0] old templateTitle[3] templateYValue[max] templateYLabel[1] of templateTitle[1] templateTitle[2] per day .
generated: This statistic displays the Distribution of women practicing nudism per day in the France between by and age , age . In this four Year period , individuals aged between 4 and 10 18 to 24 years old nudism 13 women of women practicing per day .

Example 655:
titleEntities: {'Subject': ['Dell'], 'Date': ['1996', '2019']}
title: Dell : Number of employees 1996 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"]
Y_Axis['Number', 'of', 'employees', 'in', 'thousands']: ['157.0', '145.0', '138.0', '102.0', '98.3', '108.3', '111.3', '109.4', '103.3', '96.0', '78.9', '88.2', '90.5', '65.2', '55.2', '46.0', '39.1', '34.6', '40.0', '36.5', '24.4', '16.2', '10.35', '8.4']

gold: As of early 2019 , Dell 's employee count was 157 thousand . The majority , approximately 145 thousand , of these employees are full-time employees . 37 percent of Dell 's full-time employees are located in the company 's home market , the United States .
gold_template: As of early templateTitleDate[1] , templateTitleSubject[0] 's employee count was templateYValue[max] thousand . The majority , approximately templateYValue[1] thousand , of these templateYLabel[1] are full-time templateYLabel[1] . templateYValue[19] percent of templateTitleSubject[0] 's full-time templateYLabel[1] are located in the company 's home market , the country .

generated_template: This statistic illustrates the templateTitle[0] templateYLabel[0] of templateTitle[2] in the templateTitleSubject[0] templateTitleSubject[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , the templateTitle[0] templateYLabel[0] of templateTitle[2] was templateYValue[max] templateYLabel[1] .
generated: This statistic illustrates the Dell Number of employees in the Dell from 1996 to 2019 . In 2019 , the Dell Number of employees was 157.0 employees .

Example 656:
titleEntities: {'Subject': ['Bhutan'], 'Date': ['2019']}
title: Youth unemployment rate in Bhutan 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']: ['9.69', '9.58', '9.57', '9.88', '9.97', '9', '9.25', '6.99', '8.93', '8.6', '12.46', '10.81', '11.2', '9.57', '9.41', '7.74', '5.82', '6.06', '6.26', '5.8', '4.89']

gold: The statistic shows the youth unemployment rate in Bhutan from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated youth unemployment rate in Bhutan was at 9.69 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[0] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[0] percent .
generated: The statistic shows the Youth unemployment rate in Bhutan from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated Youth unemployment rate in Bhutan was at 9.69 percent .

Example 657:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2017']}
title: Facebook reactions on top shared content 2017
X_Axis['Response']: ['Love', 'Haha', 'Wow', 'Sad', 'Angry']
Y_Axis['Share', 'of', 'reactions']: ['41', '28', '15', '12', '5']

gold: This statistic presents the reaction usage in top shared posts on Facebook in September 2017 . During the measured period , Love was the most popular Facebook reaction on top shared posts on the social network .
gold_template: This statistic presents the reaction usage in templateTitle[2] templateTitle[3] posts on templateTitleSubject[0] in September templateTitleDate[0] . During the measured period , templateXValue[0] was the most popular templateTitleSubject[0] reaction on templateTitle[2] templateTitle[3] posts on the social network .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateXValue[0] templateXValue[0] the templateTitle[6] as of March templateTitleDate[0] . During the survey , templateYValue[max] percent of the templateYLabel[1] were templateTitle[0] aggravated by the templateXValue[0] templateXValue[0] templateXValue[0] templateTitle[3] it . The average daily rate of hotels in the templateTitle[6] was 123.97 templateTitleSubject[0] dollars in 2016 , a figure which has grown annually since 2009 .
generated: This statistic shows the Facebook reactions top of shared Love the 2017 as of March 2017 . During the survey , 41 percent of the reactions were Facebook aggravated by the Love shared it . The average daily rate of hotels in the 2017 was 123.97 Facebook dollars in 2016 , a figure which has grown annually since 2009 .

Example 658:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Household income of black families in the U.S. 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Median', 'income', 'in', '2018', 'CPI-U-RS', 'adjusted', 'U.S.', 'dollars']: ['41361', '40324', '40339', '38178', '36689', '37232', '35641', '35203', '36195', '37319', '39054', '40196', '38963', '38828', '39151', '39607', '39661', '40902', '42348', '41192', '38212', '38269', '36649', '35880', '34503', '32721', '32210', '33103', '34068']

gold: This statistic shows the household income of black families in the United States from 1990 to 2018 . The median income in 2018 was at 41,361 U.S. dollars for black households .
gold_template: This statistic shows the templateTitle[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] in templateXValue[max] was at templateYValue[0] templateYLabel[5] templateYLabel[6] for templateTitle[2] households .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] parent families with a female householder and no spouse present in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[28] percent of all templateTitleSubject[0] families with a templateTitle[3] mother in the templateTitle[5] lived below the templateYLabel[0] level . In templateXValue[max] , that figure was at templateYValue[min] percent .
generated: This statistic shows the Median income of U.S. families parent families with a female householder and no spouse present in the U.S. from 1990 to 2018 . In 1990 , 34068 percent of all U.S. families with a families mother in the 1990 lived below the Median level . In 2018 , that figure was at 32210 percent .

Example 659:
titleEntities: {'Subject': ['Sales'], 'Date': ['2013']}
title: Sales of the leading toy companies worldwide 2013
X_Axis['Company']: ['Mattel', 'Lego', 'Hasbro', 'MGA_Entertainment', 'Playmobil', 'Jakks_Pacific', 'LeapFrog', 'MEGA_Bloks', 'Melissa_&_Doug']
Y_Axis['Sales', 'in', 'million', 'U.S.', 'dollars']: ['6300', '4500', '4000', '2000', '790', '700', '580', '400', '325']

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

generated_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] children 's templateTitle[3] templateTitle[4] worldwide in templateTitleDate[0] . In that year , the templateTitleSubject[0] children 's templateTitle[3] retailer in the world was Carter 's with a templateTitle[1] templateYLabel[0] of approximately templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Sales of the Sales children 's companies worldwide in 2013 . In that year , the Sales children 's companies retailer in the world was Carter 's with a leading Sales of approximately 6300 million U.S. .

Example 660:
titleEntities: {'Subject': ['Denver Broncos', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Denver Broncos ( NFL ) 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['3000', '2650', '2600', '2400', '1940', '1450', '1161', '1132', '1046', '1049', '1081', '1061', '994', '975', '907', '815', '683', '604']

gold: This graph depicts the franchise value of the Denver Broncos from the National Football League from 2002 to 2019 . In 2019 , the franchise value came to three billion U.S. dollars . The Denver Broncos are owned by the Pat Bowlen Trust , who bought the franchise for 78 million U.S. dollars in 1984 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by the Pat Bowlen Trust , who bought the templateYLabel[0] for 78 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1984 .

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] 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] templateTitleSubject[0] templateTitleSubject[0] are owned by Rogers Communications , who bought the templateYLabel[0] for 137 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 2000 .
generated: This graph depicts the value of the Denver Broncos Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 3000 million U.S. dollars . The Denver Broncos are owned by Rogers Communications , who bought the Franchise for 137 million U.S. dollars in 2000 .

Example 661:
titleEntities: {'Subject': ['Aramark'], 'Date': ['2008', '2019']}
title: Facilities management industry - Aramark worldwide revenue 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['16227.3', '15789.6', '14604.4', '14415.8', '14329.1', '14832.9', '13945.7', '13505.4', '13082.4', '12419.1', '12138.1', '13252.1']

gold: This statistic shows the annual total worldwide revenue of Aramark from 2008 to 2019 . In 2019 , Aramark had total revenues of over 16.2 billion U.S. dollars . The Aramark Corporation is an American foodservice , facilities , and clothing provider headquartered in Philadelphia , Pennsylvania .
gold_template: This statistic shows the annual total templateTitle[4] templateYLabel[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] had total revenues of over templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . The templateTitleSubject[0] Corporation is an American foodservice , templateTitle[0] , and clothing provider headquartered in Philadelphia , Pennsylvania .

generated_template: templateTitleSubject[0] had a templateYLabel[0] of templateYValue[max] templateYLabel[2] in operation around the world as of templateXValue[max] , up from templateYValue[min] in templateXValue[min] . templateTitleSubject[0] , an amalgamation of Louis Vuitton , Moët , and Hennessy , is the top selling luxury personal goods companies in the world . The company operates globally selling a diverse range of products .
generated: Aramark had a Revenue of 16227.3 U.S. in operation around the world as of 2019 , up from 12138.1 in 2008 . Aramark , an amalgamation of Louis Vuitton , Moët , and Hennessy , is the top selling luxury personal goods companies in the world . The company operates globally selling a diverse range of products .

Example 662:
titleEntities: {'Subject': ['RIM/Blackberry'], 'Date': ['2019']}
title: Revenue of RIM/Blackberry worldwide 2004 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['904', '932', '1309', '2160', '3335', '6813', '11073', '18423', '19907', '14953', '11065', '6009', '3037', '2066', '1350', '595']

gold: In its 2019 fiscal year , Canadian company BlackBerry recorded revenues of less than one billion U.S. dollars for only the second time in the past 15 years . The company , previously known as Research in Motion ( RIM ) , has transitioned from developing BlackBerry brand smartphones to enterprise software and services involving the internet of things . Transitioning to the Enterprise of Things In the past decade , BlackBerry has seen a significant decline in their revenue figures and share of the global and U.S. smartphone market .
gold_template: In its templateXValue[max] fiscal templateXLabel[0] , Canadian company BlackBerry recorded revenues of less than templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] for only the second time in the past 15 years . The company , previously known as Research in Motion ( RIM ) , has transitioned from developing BlackBerry brand smartphones to enterprise software and services involving the internet of things . Transitioning to the Enterprise of Things In the past decade , BlackBerry has seen a significant decline in their templateYLabel[0] figures and share of the global and templateYLabel[2] smartphone market .

generated_template: The timeline presents templateTitleSubject[0] templateTitleSubject[0] 's templateTitle[1] 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 Germany-based automotive company generated templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The timeline presents RIM/Blackberry 's RIM/Blackberry Revenue from the fiscal Year of 2004 to the fiscal Year of 2019 . In the fiscal Year of 2019 , the Germany-based automotive company generated 904 million U.S. dollars .

Example 663:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Revenue share of various apparel decorating services in the U.S. 2014
X_Axis['Decorating', 'service']: ['Embroidery', 'Screen_printing', 'Heat_transfers', 'Vinyl_(cut)_letters/designs', 'Digitizing/artwork_services', 'Sublimation_printing', 'Emblems/patches', 'Direct-to-garment_printing', 'Rhinestones/crystals', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['45', '26', '7', '6', '5', '3', '3', '3', '1', '1']

gold: This statistic depicts the revenue share of various apparel decorating services in the United States in 2014 . The survey revealed that some 45 percent of the respondents felt that embroidery decorating services for apparel generated the most revenue .
gold_template: This statistic depicts the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] templateXLabel[0] templateXValue[4] in the templateTitle[6] in templateTitleDate[0] . The survey revealed that some templateYValue[max] percent of the templateYLabel[1] felt that templateXValue[0] templateXLabel[0] templateXValue[4] for templateTitle[3] generated the most templateTitle[0] .

generated_template: templateXValue[0] breaks were the most popular type of vacation templateTitle[2] by holidaymakers in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) , with nearly half of survey templateYLabel[1] choosing to go on a templateXValue[0] templateXValue[0] templateXValue[1] in templateTitleDate[0] . templateXValue[1] templateTitle[1] ranked second at templateYValue[1] percent . Most popular destinations for Brits It 's clear from the results that Brits tend to go for templateXValue[0] breaks and templateXValue[1] templateTitle[1] over other templateTitle[0] of travel .
generated: Embroidery breaks were the most popular type of vacation various by holidaymakers in the U.S. ( U.S. ) , with nearly half of survey respondents choosing to go on a Embroidery Screen printing in 2014 . Screen printing share ranked second at 26 percent . Most popular destinations for Brits It 's clear from the results that Brits tend to go for Embroidery breaks and Screen printing share over other Revenue of travel .

Example 664:
titleEntities: {'Subject': ['Bulgaria'], 'Date': ['2006', '2018']}
title: Number of arrivals in tourist accommodation Bulgaria 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'arrivals', 'in', 'millions']: ['7.8', '7.5', '7.2', '6.3', '5.9', '5.8', '5.5', '4.9', '4.3', '4.3', '5.0', '4.8', '4.3']

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Bulgaria from 2006 to 2018 . In 2018 , the number of arrivals in travel accommodation ( including both international and domestic tourists ) amounted to approximately 7.8 million .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] in travel templateTitle[3] ( including both international and domestic tourists ) amounted to approximately templateYValue[max] templateYLabel[2] .

generated_template: Approximately templateYValue[max] templateYLabel[2] templateYLabel[1] were recorded at travel templateTitle[3] in templateTitleSubject[0] in templateXValue[max] International tourism was the highest contributor , with around two thirds of templateYLabel[1] in visitor templateTitle[3] coming from a foreign country . templateTitleSubject[0] tourism growth Since templateXValue[9] international visitor numbers to templateTitleSubject[0] have steadily increased . Including both overnight and same-day visitors , figures reached over 30 templateYLabel[2] by templateXValue[1] .
generated: Approximately 7.8 millions arrivals were recorded at travel accommodation in Bulgaria in 2018 International tourism was the highest contributor , with around two thirds of arrivals in visitor accommodation coming from a foreign country . Bulgaria tourism growth Since 2009 international visitor numbers to Bulgaria have steadily increased . Including both overnight and same-day visitors , figures reached over 30 millions by 2017 .

Example 665:
titleEntities: {'Subject': ['Florida'], 'Date': ['2000', '2018']}
title: Per capita real GDP of Florida 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'real', 'GDP', 'in', 'chained', '2012', 'U.S.', 'dollars']: ['43423', '42719', '42013', '41491', '40547', '40080', '39806', '40001', '40656', '40652', '43353', '45507', '45926', '45193', '43471', '42074', '41062', '40267', '40049']

gold: This statistic shows the per capita real Gross Domestic Product of Florida from 2000 to 2018 . In 2018 , the per capita real GDP of Florida stood at 43,423 chained 2012 U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[0] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[max] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .
generated: This statistic shows the Per capita real Gross Domestic Product of Florida from 2000 to 2018 . In 2018 , the Per capita real GDP of Florida stood at 45926 chained 2012 U.S. dollars .

Example 666:
titleEntities: {'Subject': ['Scotland'], 'Date': ['2014', '2039']}
title: Scotland : forecasted median age of population 2014 to 2039
X_Axis['Year']: ['2039', '2034', '2029', '2024', '2019', '2014']
Y_Axis['Age']: ['45.2', '44.3', '43.5', '42.9', '42.4', '41.9']

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

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . The templateYLabel[0] of templateTitle[2] templateTitle[3] has continuously decreased during this period .
generated: This statistic shows the Scotland Age of median age in the Scotland from 2014 to 2039 . The Age of median age has continuously decreased during this period .

Example 667:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. company data loss prevention methods 2017
X_Axis['Response']: ['Training_and_awareness_programs', 'Expanded_use_of_encryption', 'Endpoint_security_solutions', 'Identity_and_access_management_solutions', 'Additional_manual_procedures_and_controls', 'Data_loss_prevention_(DLP)_solutions', 'Security_intelligence_solutions', 'Other_system_control_practices', 'Security_certification_or_audit', 'Strenghtening_of_perimeter_controls']
Y_Axis['Share', 'of', 'respondents']: ['60', '55', '49', '44', '39', '36', '35', '26', '19', '16']

gold: This statistic presents a ranking of common data loss prevention controls and activities of organizations in the United States in 2017 . During the survey period , it was found that 35 percent of U.S. companies had implemented security intelligence solutions .
gold_template: This statistic presents a ranking of common templateXValue[5] prevention templateXValue[4] and activities of organizations in the templateTitle[0] in templateTitleDate[0] . During the survey period , it was found that templateYValue[6] percent of templateTitleSubject[0] companies had implemented templateXValue[2] templateXValue[6] templateXValue[2] .

generated_template: This statistic shows the templateTitle[0] ten templateTitle[1] in templateTitle[2] templateTitle[3] templateXValue[1] on templateTitle[5] templateTitle[6] in the templateTitle[7] as forecasted by professional chefs for templateTitleDate[0] . During the survey , templateYValue[2] percent of the templateYLabel[1] stated that templateXValue[2] templateTitle[2] templateTitle[3] templateXValue[1] would be a `` hot trend '' for templateTitle[5] templateTitle[6] in templateTitleDate[0] .
generated: This statistic shows the U.S. ten company in data loss Expanded use of encryption on methods 2017 in the 2017 as forecasted by professional chefs for 2017 . During the survey , 49 percent of the respondents stated that Endpoint security solutions data loss Expanded use of encryption would be a `` hot trend '' for methods 2017 in 2017 .

Example 668:
titleEntities: {'Subject': ['European'], 'Date': ['2016']}
title: Selected European countries ranked by retail banking customer satisfaction 2016
X_Axis['Country']: ['Netherlands', 'Czech_Republic', 'Austria', 'Switzerland', 'Portugal', 'Germany', 'Poland', 'Sweden', 'Italy', 'United_Kingdom', 'Finland', 'Belgium', 'Denmark', 'Norway', 'France', 'Spain']
Y_Axis['Share', 'of', 'customers', 'with', 'positive', 'experience']: ['70.6', '67', '66.8', '64.8', '63', '62.3', '61.6', '60.7', '59.5', '58.4', '58.2', '56.7', '55.9', '53.9', '52.3', '35.7']

gold: This statistic illustrates the share of customers with a positive retail banking experience in the leading selected European banking systems ( countries ) as of 2016 . Approximately 70.6 percent of surveyed bank customers in the Netherlands indicated high levels of satisfaction , ranking the country highest among European banking locations in 2016 . This was followed by the Czech Republic , with 67 percent of bank customers with a positive experience throughout the year .
gold_template: This statistic illustrates the templateYLabel[0] of templateYLabel[1] templateYLabel[2] a templateYLabel[3] templateTitle[5] templateTitle[6] templateYLabel[4] in the leading templateTitle[0] templateTitleSubject[0] templateTitle[6] systems ( templateTitle[2] ) as of templateTitleDate[0] . Approximately templateYValue[max] percent of surveyed bank templateYLabel[1] in the templateXValue[0] indicated high levels of templateTitle[8] , ranking the templateXLabel[0] highest among templateTitleSubject[0] templateTitle[6] locations in templateTitleDate[0] . This was followed templateTitle[4] the templateXValue[1] , templateYLabel[2] templateYValue[1] percent of bank templateYLabel[1] templateYLabel[2] a templateYLabel[3] templateYLabel[4] throughout the year .

generated_template: This statistic illustrates the annual templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] . The templateTitleSubject[0] templateTitle[1] templateTitle[3] templateTitle[4] templateYLabel[0] templateYLabel[1] in templateTitleDate[0] was estimated at templateYValue[max] percent in templateTitleDate[0] .
generated: This statistic illustrates the annual European the countries ranked by retail banking in 2016 . The European ranked by Share customers in 2016 was estimated at 70.6 percent in 2016 .

Example 669:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Total value of international U.S. imports of goods and services 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['Trillion', 'U.S.', 'dollars']: ['3.12', '3.13', '2.9', '2.72', '2.76', '2.87', '2.76', '2.76', '2.68', '2.35', '1.97', '2.55', '2.36', '2.22', '2.0', '1.77', '1.51', '1.4', '1.37', '1.45']

gold: The timeline shows the total value of international U.S. imports of goods and services from 2000 to 2019 . In 2019 , the total value of international U.S. imports of goods and services amounted to 3.1 trillion U.S. dollars .
gold_template: The timeline shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateYLabel[1] templateTitle[4] of templateTitle[5] and templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitle[2] templateYLabel[1] templateTitle[4] of templateTitle[5] and templateTitle[6] amounted to templateYValue[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the number of people in thousands on a zero-hour contract in the United Kingdom from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[19] thousand people on zero-hour templateTitle[4] , templateTitle[1] this number increasing to approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the number of people in thousands on a zero-hour contract in the United Kingdom from 2000 to 2019 . In 2019 , there were 1.45 thousand people on zero-hour imports , value this number increasing to approximately 3.12 dollars .

Example 670:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Homicide - number of murders by U.S. state in 2018
X_Axis['State']: ['California', 'Texas', 'Florida', 'Illinois', 'Pennsylvania', 'Georgia', 'North_Carolina', 'Missouri', 'Ohio', 'New_York', 'Michigan', 'Louisiana', 'Tennessee', 'Maryland', 'Indiana', 'South_Carolina', 'Virginia', 'Alabama', 'Arizona', 'New_Jersey', 'Kentucky', 'Washington', 'Arkansas', 'Colorado', 'Oklahoma', 'Nevada', 'Wisconsin', 'Mississippi', 'New_Mexico', 'District_of_Columbia', 'Massachusetts', 'Kansas', 'Minnesota', 'Connecticut', 'Oregon', 'West_Virginia', 'Utah', 'Iowa', 'Delaware', 'Alaska', 'Nebraska', 'Hawaii', 'Idaho', 'Montana', 'Maine', 'New_Hampshire', 'North_Dakota', 'Rhode_Island', 'Wyoming', 'South_Dakota', 'Vermont']
Y_Axis['Number', 'of', 'murder', 'victims']: ['1739', '1322', '1107', '884', '784', '642', '628', '607', '564', '562', '551', '530', '498', '490', '438', '392', '391', '383', '369', '286', '244', '236', '216', '210', '206', '202', '176', '171', '167', '160', '136', '113', '106', '83', '82', '67', '60', '54', '48', '47', '44', '36', '35', '34', '24', '21', '18', '16', '13', '12', '10']

gold: This statistic displays the number of murders in the United States by state . Data includes murder and nonnegligent manslaughter . In 2018 , the number of murders in California amounted to 1,739 victims .
gold_template: This statistic displays the templateYLabel[0] of templateTitle[2] in the templateTitle[4] templateTitle[3] templateXLabel[0] . Data includes templateYLabel[1] and nonnegligent manslaughter . In templateTitleDate[0] , the templateYLabel[0] of templateTitle[2] in templateXValue[0] amounted to templateYValue[max] templateYLabel[2] .

generated_template: The statistic above provides data on the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[0] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , templateXValue[0] of templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[1] with templateYValue[max] templateYLabel[3] .
generated: The statistic above provides data on the Number murder victims in the Homicide in 2018 , state . In 2018 , California of California had the highest Number murder victims in Texas with 1739 victims .

Example 671:
titleEntities: {'Subject': ['National Basketball Association'], 'Date': ['1946', '2020']}
title: National Basketball Association all-time triple double leaders 1946 to 2020
X_Axis['Player']: ['Oscar_Robertson', 'Russell_Westbrook', 'Magic_Johnson', 'Jason_Kidd', 'LeBron_James', 'Wilt_Chamberlain', 'Larry_Bird', 'James_Harden', 'Fat_Lever', 'Nikola_Jokić', 'Bob_Cousy', 'Rajon_Rondo', 'John_Havlicek']
Y_Axis['Number', 'of', 'triple', 'doubles']: ['181', '146', '138', '107', '92', '78', '59', '45', '43', '39', '33', '32', '31']

gold: Which player has the most triple doubles ? Oscar Robertson - nicknamed ‘ The Big O ' _ , is the all-time leader in triple doubles in the National Basketball Assocation . He compiled 181 triple doubles during his playing career with the Cincinnati Royals and the Milwaukee Bucks ( 1960 to 1974 ) . The highest ranked active player is Russell Westbrook of the Oklahoma City Thunder with 144 triple doubles in second place .
gold_template: Which templateXLabel[0] has the most templateYLabel[1] templateYLabel[2] ? templateXValue[0] - nicknamed ‘ The Big O ' _ , is the templateTitle[3] leader in templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] Assocation . He compiled templateYValue[max] templateYLabel[1] templateYLabel[2] during his playing career with the Cincinnati Royals and the Milwaukee Bucks ( 1960 to 1974 ) . The highest ranked active templateXLabel[0] is templateXValue[1] of the Oklahoma City Thunder with 144 templateYLabel[1] templateYLabel[2] in second place .

generated_template: Taking full advantage of the perks of the fantasy genre , J.R.R . Tolkien not only invented a whole universe , he also did not adhere to known templateYLabel[0] structures when he wrote The templateTitleSubject[0] of The templateTitleSubject[0] . The ages of his templateTitle[2] differ wildly ; templateXValue[0] is almost 600 templateYLabel[1] old , while Elrond , the elf , is several thousand templateYLabel[1] old , and Gandalf the Grey – and later the White – is immortal ( for display reasons , both are thus not mentioned here ) .
generated: Taking full advantage of the perks of the fantasy genre , J.R.R . Tolkien not only invented a whole universe , he also did not adhere to known Number structures when he wrote The National Basketball Association of The National Basketball Association . The ages of his Association differ wildly ; Oscar Robertson is almost 600 triple old , while Elrond , the elf , is several thousand triple old , and Gandalf the Grey – and later the White – is immortal ( for display reasons , both are thus not mentioned here ) .

Example 672:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita United Kingdom 2024 ( in U.S. dollars )
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']: ['45934.7', '44311.93', '42842.47', '41504.89', '40391.84', '41030.23', '42579.82', '39976.78', '40657.86', '44494.86', '47003.88', '42981.25', '42023.1', '41649.66', '39122.19', '38601.32', '47469.38', '50315.56', '44403.81', '41842.7', '40111.75', '34302.42', '29912.99', '27510.33', '28043.87', '28435.06', '28077.34', '26647.95', '24256.46', '23026.71', '21344.25', '19925.66', '22305.36', '21671.88', '20808.23', '17617.85', '17364.25', '14294.99', '11551.07', '9491.99', '8943.27']

gold: The statistic shows GDP per capita in the United Kingdom from 1984 to 2018 , with projections up until 2024 . In 2018 , GDP per capita in the United Kingdom was at around 42,579.82 US dollars . The same year , the total UK population amounted to about 64.6 million people .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] was at around templateYValue[6] US templateYLabel[4] . The same templateXLabel[0] , the total UK population amounted to about 64.6 million people .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a change in it is a sign of economic growth .
generated: The statistic shows the Gross domestic product ( GDP ) per capita in United Kingdom from 1984 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a Year . It is considered to be a very important indicator of the economic strength of a country and a change in it is a sign of economic growth .

Example 673:
titleEntities: {'Subject': ['Global'], 'Date': ['2030']}
title: Global energy commodity price index 2013 to 2030
X_Axis['Year']: ['2030', '2025', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Price', 'index', 'in', 'real', '2010', 'U.S.', 'dollars']: ['87.2', '79.1', '74.7', '73.3', '72.0', '74.3', '87.0', '68.1', '55.1', '65.0', '111.7', '120.1']

gold: This statistic shows a commodity price index of energy from 2013 through 2018 , and gives projections for 2030 . In 2018 , the index stood at around 87 real US dollars . Real 2010 US dollars reflect buying power in 2010 .
gold_template: This statistic shows a templateTitle[2] templateYLabel[0] templateYLabel[1] of templateTitle[1] from templateXValue[min] through templateXValue[6] , and gives projections for templateXValue[max] . In templateXValue[6] , the templateYLabel[1] stood at around templateYValue[0] templateYLabel[2] US templateYLabel[5] . templateYLabel[2] templateYLabel[3] US templateYLabel[5] reflect buying power in templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[7] , the templateTitleSubject[0] templateYLabel[0] of templateTitle[2] templateTitle[3] amounted to templateYValue[7] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Global energy of commodity price from 2013 to 2030 . In 2017 , the Global Price of commodity price amounted to 68.1 index real 2010 .

Example 674:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Most used paint brands in the U.S. 2018
X_Axis['Brand']: ['Sherwin-Williams', 'Benjamin_Moore', 'Behr_Paint_Cooperation', 'Kelly_Moore', 'Valspar', 'PPG_Pittsburgh_Paints', 'Zar_(United_Gilsonite_Labs)', 'Devoe_&_Raynolds', 'Dutch_Boy', 'Olympic', 'None_of_these']
Y_Axis['Share', 'of', 'respondents']: ['49.5', '22.4', '12.1', '2.8', '1.9', '1.9', '1.9', '0.9', '0.9', '0.9', '4.7']

gold: This statistic depicts paints used the most by U.S. construction firms in 2018 . The survey revealed that 49.5 percent of the respondents used Sherwin-Williams brand paints the most .
gold_template: This statistic depicts templateXValue[5] 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] templateXLabel[0] templateXValue[5] the templateTitle[0] .

generated_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[2] templateTitle[3] in the templateTitle[4] as of templateTitleDate[0] . The survey revealed that templateYValue[max] percent of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] templateTitle[2] the templateTitle[0] . Residential involves the building and selling of both individual and multi-family dwellings .
generated: This statistic presents the Most used U.S. paint brands in the U.S. as of 2018 . The survey revealed that 49.5 percent of the respondents used Sherwin-Williams Brand paint the Most . Residential involves the building and selling of both individual and multi-family dwellings .

Example 675:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Sales growth of the top U.S. cosmetic brands 2014
X_Axis['Brand/Segment']: ['Neutrogena/_makeup_remover_implements', 'CoverGirl_Last_Blast/_mascara', 'Revlon_Super_Lustrous/_lipstick', "L'Oréal_Voluminous/_mascara", "Maybelline_Volum'Express_Falsies/_mascara", 'CoverGirl_Clean/_powder', 'Revlon_ColorStay/_foundation', 'Maybelline_Great_Lash/_mascara', 'CoverGirl_Outlast/_lipstick', 'CoverGirl_Clean/_foundation', 'Revlon_ColorStay/_eyeliner', "L'Oréal_True_Match/_foundation", "L'Oréal_Colour_Riche/_lipstick", 'CoverGirl_Perfect_Point_Plus/_eyeliner', "Maybelline_Volum'Express_Colossal/_mascara", 'Maybelline_Color_Sensational/_lipstick', "L'Oréal_True_Match/_powder", 'Maybelline_Expert_Wear/_eyeshadow', "Maybelline_Volum'Express_Rocket/_mascara", 'CoverGirl_Eye_Enhancers/_eyeshadow']
Y_Axis['Percent', 'sales', 'change']: ['23.7', '-14.1', '9.9', '2.9', '-17.2', '-2.4', '9.9', '-9.6', '2', '-3.3', '-6.5', '-0.6', '-2.8', '-0.4', '5.4', '14.9', '5.4', '-7.5', '144', '-8.7']

gold: The statistic shows the sales growth of the leading cosmetic brands in 2014 . Neutrogena 's makeup remover implements saw a 23.7 percent sales increase while Maybelline 's Volum'Express Rocket mascara experienced a 144 percent increase compared to last year .
gold_template: The statistic shows the templateYLabel[1] templateTitle[1] of the leading templateTitle[4] templateTitle[5] in templateTitleDate[0] . Neutrogena 's templateXValue[0] implements saw a templateYValue[0] templateYLabel[0] templateYLabel[1] increase while templateXValue[4] 's templateXValue[4] Rocket templateXValue[1] experienced a templateYValue[max] templateYLabel[0] increase compared to templateXValue[1] year .

generated_template: The statistic shows the templateYLabel[0] of the leading templateTitle[3] templateTitle[4] in templateTitleDate[0] . Neutrogena templateXValue[0] templateXValue[0] was the leading templateTitle[3] brand in the templateTitle[2] in templateTitleDate[0] with templateYLabel[0] that amounted to approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , a templateYLabel[0] growth of 23.7 percent compared to the previous year . In the templateYLabel[2] , the beauty and personal care market market is a booming industry .
generated: The statistic shows the Percent of the leading U.S. cosmetic in 2014 . Neutrogena Neutrogena/ makeup remover implements was the leading U.S. brand in the top in 2014 with Percent that amounted to approximately 144 sales change , a Percent growth of 23.7 percent compared to the previous year . In the change , the beauty and personal care market is a booming industry .

Example 676:
titleEntities: {'Subject': ['Global'], 'Date': ['2012', '2022']}
title: Global ulcerative colitis market 2012 and 2022
X_Axis['Year']: ['2012', '2022']
Y_Axis['Market', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['4.2', '6.6']

gold: This statistic displays the global ulcerative colitis market value in 2012 , and a forecast for 2022 . In 2012 , the ulcerative colitis market was valued at 4.2 billion U.S. dollars . Ulcerative colitis is a type of inflammatory bowel disease ( IBD ) which is characterized by the formation of ulcers in the colon .
gold_template: This statistic displays the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] in templateXValue[min] , and a forecast for templateXValue[max] . In templateXValue[min] , the templateTitle[1] templateTitle[2] templateYLabel[0] was valued at templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateTitle[1] templateTitle[2] is a type of inflammatory bowel disease ( IBD ) which is characterized by the formation of ulcers in the colon .

generated_template: The statistic shows a growth forecast for the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] in templateXValue[max] and shows the actual projected templateYLabel[0] value of the templateTitle[1] templateTitle[2] templateYLabel[0] in templateXValue[min] . For templateXValue[max] , the templateTitleSubject[0] smart templateTitle[1] templateTitle[2] templateYLabel[0] is forecasted to reach templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The predicted compound annual growth rate between templateXValue[min] and templateXValue[max] should be 26.3 percent .
generated: The statistic shows a growth forecast for the Global ulcerative colitis Market in 2022 and shows the actual projected Market value of the ulcerative colitis Market in 2012 . For 2022 , the Global smart ulcerative colitis Market is forecasted to reach 6.6 billion U.S. dollars . The predicted compound annual growth rate between 2012 and 2022 should be 26.3 percent .

Example 677:
titleEntities: {'Subject': ['Estonia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Estonia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.8', '2.8', '2.8', '2.8', '2.9', '3.2', '4.76', '5.75', '2.63', '1.85', '2.99']

gold: The statistic shows the growth in real GDP in Estonia from 2014 to 2018 , with projections up until 2024 . In 2018 , Estonia 's real gross domestic product grew by around 4.76 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[max] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Estonia from 2014 to 2018 , with projections up until 2024 . In 2018 , Estonia 's real Gross domestic product grew by around 5.75 percent compared to the previous Year .

Example 678:
titleEntities: {'Subject': ['Northern Ireland', 'UK'], 'Date': ['2013', '2019']}
title: Youth unemployment rate in Northern Ireland ( UK ) 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Unemployment', 'rate']: ['7.6', '8.4', '12.6', '13.9', '19.5', '19.4', '20.4']

gold: This statistic shows the unemployment rate of young people ( aged 18 to 24 ) in Northern Ireland from 2013 to 2019 . At the start of this period the youth unemployment rate stood at over 20 percent , but by 2019 this had decreased to 7.6 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of young people ( aged 18 to 24 ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . At the start of this period the templateTitle[0] templateYLabel[0] templateYLabel[1] stood at over templateYValue[4] percent , but by templateXValue[max] this had decreased to templateYValue[min] percent .

generated_template: The templateTitle[0] templateTitle[1] rate as templateTitle[2] of population in templateTitleSubject[0] was forecasted at templateYValue[max] percent for templateXValue[max] , five times higher than in templateXValue[min] . This prediction was based on the assumption that the continuing declining of mobile device prices will support the increase of smartphones . Telecommunications market As of templateXValue[max] , the cellular subscription growth rate in templateTitleSubject[0] was 3.8 percent , ranking it in the middle field of Asia Pacific countries for this development .
generated: The Youth unemployment rate as rate of population in Northern Ireland was forecasted at 20.4 percent for 2019 , five times higher than in 2013 . This prediction was based on the assumption that the continuing declining of mobile device prices will support the increase of smartphones . Telecommunications market As of 2019 , the cellular subscription growth rate in Northern Ireland was 3.8 percent , ranking it in the middle field of Asia Pacific countries for this development .

Example 679:
titleEntities: {'Subject': ['Viki'], 'Date': ['12', '15']}
title: Viki : number of monthly active users 2012 to 2015
X_Axis['Month']: ["Mar_'12", "Jun_'12", "Sep_'12", "Dec_'12", "Mar_'13", "Jun_'13", "Sep_'13", "Dec_'13", "Mar_'14", "Jun_'14", "Sep_'14", "Dec_'14", "Mar_'15", "Jun_'15"]
Y_Axis['Number', 'of', 'monthly', 'active', 'users', 'in', 'millions']: ['3.8', '4.9', '7.3', '14.9', '15.9', '19.9', '23.9', '28.2', '29.3', '31.9', '35.2', '39.4', '40.1', '39.5']

gold: This statistic presents the number of monthly active Viki video platform users as of June 2015 . As of that month , the video portal had 39.5 million monthly active users worldwide . The VoD service had been acquired by Japanese e-commerce company Rakuten in September 2013 .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitleSubject[0] video platform templateYLabel[3] as of June templateTitleDate[1] . As of that templateXLabel[0] , the video portal had templateYValue[13] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide . The VoD service had been acquired by Japanese e-commerce company Rakuten in September 2013 .

generated_template: As of December templateTitleDate[1] , templateXValue[0] templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[0] ) of approximately templateYValue[0] templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] templateTitleSubject[0] is also known as `` templateTitleSubject[0] light sweet '' , and is a grade of templateTitle[4] templateTitle[5] often used as a benchmark for templateTitle[5] pricing . templateYLabel[0] and its templateTitleSubject[1] volume from templateTitleSubject[0] templateTitleSubject[0] is also known as `` templateTitleSubject[0] and templateTitleSubject[0] light sweet '' and is a grade of templateTitle[4] templateTitle[5] .
generated: As of December 15 , Mar '12 had the highest Number monthly of Viki ( Viki ) of approximately 3.8 active users . Viki is also known as `` Viki light sweet '' , and is a grade of users 2012 often used as a benchmark for 2012 pricing . Number and its Viki volume from Viki is also known as `` Viki and Viki light sweet '' and is a grade of users 2012 .

Example 680:
titleEntities: {'Subject': ['Citigroup'], 'Date': ['2011', '2018']}
title: Total direct staff of Citigroup 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Number', 'of', 'direct', 'staff', 'in', 'thousands']: ['204', '209', '219', '231', '241', '251', '259', '266']

gold: This statistic shows the number of direct employees of Citigroup from 2011 to 2018 . In 2018 , the direct staff of Citigroup amounted to approximately 204,000 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] employees of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] amounted to approximately templateYValue[min] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] Motor Corporation templateYLabel[1] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[max] templateYLabel[2] templateTitleSubject[0] templateYLabel[1] templateTitle[4] templateTitle[5] , up from templateYValue[min] templateYLabel[2] in templateXValue[min] .
generated: The statistic shows the Number of Citigroup Motor Corporation direct 2011 from 2011 to 2018 . In 2018 , there were 266 staff Citigroup direct 2011 2018 , up from 204 staff in 2011 .

Example 681:
titleEntities: {'Subject': ['Mattel'], 'Date': ['2006', '2019']}
title: Gross profit of toy manufacturer Mattel 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Gross', 'profit', 'in', 'million', 'U.S.', 'dollars']: ['1980.78', '1798.68', '1824.57', '2546.69', '2806.36', '3001.02', '3478.88', '3409.2', '3145.83', '2954.97', '2714.7', '2684.41', '2777.3', '2611.79']

gold: This statistic shows the gross profit of the U.S. toy manufacturer Mattel worldwide from 2006 to 2019 . In 2019 , their gross profit came to around 1.98 billion U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateTitle[2] templateTitle[3] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , their templateYLabel[0] templateYLabel[1] came to around templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: In templateXValue[max] , templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] was measured around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . This figure has increased significantly over the course of the past decade , when the yearly templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] was well below 30 templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateTitle[1] templateTitle[2] is an iron alloy that is resistant to stains or rusts due to a high level of chromium .
generated: In 2019 , Mattel profit toy Gross was measured around 3478.88 million U.S. dollars . This figure has increased significantly over the course of the past decade , when the yearly Mattel profit toy Gross was well below 30 million U.S. dollars . profit toy is an iron alloy that is resistant to stains or rusts due to a high level of chromium .

Example 682:
titleEntities: {'Subject': ['March'], 'Date': ['2', '2020']}
title: COVID-19 cases worldwide as of March 2 , 2020 , by country
X_Axis['Country']: ['Total_(worldwide)', 'China', 'Republic_of_Korea', 'Italy', 'Iran_(Islamic_Republic_of)', 'Cases_on_an_international_conveyance_(Japan)', 'Japan', 'Germany', 'Singapore', 'France', 'United_States_of_America', 'Kuwait', 'Bahrain', 'Spain', 'Thailand', 'United_Kingdom', 'Australia', 'Malaysia', 'Switzerland', 'United_Arab_Emirates', 'Norway', 'Iraq', 'Canada', 'Viet_Nam', 'Sweden', 'Netherlands', 'Lebanon', 'Austria', 'Israel', 'Croatia', 'Greece', 'Oman', 'Finland', 'Mexico', 'Pakistan', 'Denmark', 'India', 'Czechia', 'Romania', 'Georgia', 'Philippines', 'Azerbaijan', 'Qatar', 'Indonesia', 'Iceland', 'Egypt', 'Brazil', 'Russian_Federation', 'Armenia', 'Ecuador', 'Dominican_Republic', 'Estonia', 'Ireland', 'Lithuania', 'Luxembourg', 'Monaco', 'Algeria', 'New_Zealand', 'Cambodia', 'North_Macedonia', 'San_Marino', 'Nepal', 'Sri_Lanka', 'Afghanistan', 'Nigeria', 'Belarus', 'Belgium']
Y_Axis['Number', 'of', 'cases']: ['88948', '80174', '4212', '1689', '978', '706', '254', '129', '106', '100', '62', '56', '47', '45', '42', '36', '27', '24', '24', '21', '19', '19', '19', '16', '14', '13', '10', '10', '7', '7', '7', '6', '6', '5', '4', '4', '3', '3', '3', '3', '3', '3', '3', '2', '2', '2', '2', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1']

gold: As of March 2 , 2020 , the outbreak of the coronavirus disease ( COVID-19 ) had been confirmed in 65 countries , with the overwhelming majority of cases reported in China . The virus had infected 88,948 people worldwide , and the number of deaths had totaled 3,043 . The most severely affected countries outside of China were the Republic of Korea and Italy .
gold_template: As of templateTitleSubject[0] templateYValue[43] , templateTitle[5] , the outbreak of the coronavirus disease ( templateTitle[0] ) had been confirmed in 65 countries , with the overwhelming majority of templateXValue[5] reported in templateXValue[1] . The virus had infected templateYValue[max] people templateTitle[2] , and the templateYLabel[0] of deaths had totaled 3,043 . The most severely affected countries outside of templateXValue[1] were the templateXValue[2] of templateXValue[2] and templateXValue[3] .

generated_template: This graph shows the templateYLabel[0] of templateYLabel[1] of templateTitle[0] templateTitleSubject[0] in templateTitle[5] templateTitle[6] in templateTitleDate[0] , distinguished templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , templateXValue[1] , templateXValue[3] had the highest templateYLabel[0] of roughly templateYValue[max] templateYLabel[1] . The highest templateYLabel[0] of templateTitle[2] was recorded in templateXValue[0] at templateYValue[max] templateYLabel[1] .
generated: This graph shows the Number of cases of COVID-19 March in 2020 by in 2 , distinguished 2020 Country . In 2 , China , Italy had the highest Number of roughly 88948 cases . The highest Number of worldwide was recorded in Total (worldwide) at 88948 cases .

Example 683:
titleEntities: {'Subject': ['Duty U.S. Navy'], 'Date': ['1995', '2018']}
title: Active Duty U.S. Navy personnel numbers from 1995 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995']
Y_Axis['Number', 'of', 'Navy', 'personnel']: ['325395', '319492', '320101', '323334', '321599', '319838', '314339', '320141', '323139', '324239', '326684', '332269', '345098', '357853', '367371', '429630']

gold: This graph shows the number of active duty U.S. Navy personnel from 1995 to 2018 . In 2018 , there were 325,395 active duty Navy members in the United States Department of Defense . In 2000 , there were 367,371 active duty members .
gold_template: This graph shows the templateYLabel[0] of templateTitleSubject[0] U.S. templateYLabel[1] templateYLabel[2] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] templateTitleSubject[0] Navy members in the templateTitle[2] Department of Defense . In templateXValue[14] , there were templateYValue[14] templateTitleSubject[0] members .

generated_template: In templateXValue[max] , approximately templateYValue[0] babies were born in templateTitleSubject[0] , a slight decrease from the templateXLabel[0] before . templateTitleSubject[0] is the country with the highest templateYLabel[0] of templateYLabel[2] in the European Union , with over 20 thousand more templateYLabel[2] more than France , the country with the next highest templateYLabel[0] of templateYLabel[1] templateYLabel[2] in Europe . Population growth in templateTitleSubject[0] has about 83 million inhabitants , making it the most populous country in Europe .
generated: In 2018 , approximately 325395 babies were born in Duty U.S. Navy , a slight decrease from the Year before . Duty U.S. Navy is the country with the highest Number of personnel in the European Union , with over 20 thousand more personnel more than France , the country with the next highest Number of Navy personnel in Europe . Population growth in Duty U.S. Navy has about 83 million inhabitants , making it the most populous country in Europe .

Example 684:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2002', '2015']}
title: Commodity prices of wheat in the United Kingdom ( UK ) 2002 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Price', 'per', 'tonne', 'in', 'GBP']: ['127.15', '143.06', '175.95', '179.26', '169.17', '123.76', '107.05', '137.87', '120.97', '78.88', '67.43', '80.3', '79.32', '65.02']

gold: This statistic shows the average price per tonne of wheat in the United Kingdom ( UK ) between 2002 and 2015 by year , according to published agricultural and commodity price figures . In 2012 , the price of wheat was 179.26 British Pound Sterling ( GBP ) per tonne .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) between templateXValue[min] and templateXValue[max] by templateXLabel[0] , according to published agricultural and templateTitle[0] templateYLabel[0] figures . In templateXValue[3] , the templateYLabel[0] of templateTitle[2] was templateYValue[max] British Pound Sterling ( templateYLabel[3] ) templateYLabel[1] templateYLabel[2] .

generated_template: The statistic shows the templateYLabel[0] amount of templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[12] and gives forecasts for up to templateXValue[max] . In templateXValue[max] , templateTitle[4] templateTitle[0] templateTitle[1] cost approximately templateYValue[max] US templateYLabel[2] templateYLabel[3] templateYLabel[4] British thermal units .
generated: The statistic shows the Price amount of Commodity prices from 2002 to 2003 and gives forecasts for up to 2015 . In 2015 , Kingdom Commodity prices cost approximately 179.26 US tonne GBP British thermal units .

Example 685:
titleEntities: {'Subject': ['England'], 'Date': ['2019']}
title: Share of the population who gave to charity in England 2019 , by age
X_Axis['Year']: ['16_to_24', '25_to_34', '35_to_49', '50_to_64', '65_to_74', '75_and_over']
Y_Axis['Share', 'of', 'respondents']: ['59', '69', '76', '79', '82', '83']

gold: This statistic shows the share of the population who said they gave to charity in the last four weeks in 2018/19 , by age group . Proportionally , those aged 75 and more gave most to charity . At 59 percent , 16 to 24 year olds had the smallest proportion of charitable givers .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] said they templateTitle[3] to templateTitle[4] in the last four weeks in 2018/19 , templateTitle[7] templateTitle[8] group . Proportionally , those aged templateXValue[last] and more templateTitle[3] most to templateTitle[4] . At templateYValue[min] percent , templateXValue[0] to templateXValue[0] templateXLabel[0] olds had the smallest proportion of charitable givers .

generated_template: This statistic illustrates the templateTitle[7] distribution of templateTitle[1] at templateTitleSubject[0] in the templateTitleSubject[1] templateTitleSubject[1] as of templateTitleDate[0] . According to the annual report , templateYValue[max] percent of templateTitleSubject[0] templateTitle[1] are in the templateTitle[7] templateTitle[8] 45 - 54 . templateTitleSubject[0] is a British retailer selling clothing , furniture and other household items in a department store format .
generated: This statistic illustrates the by distribution of population at England in the England as of 2019 . According to the annual report , 83 percent of England population are in the by age 45 - 54 . England is a British retailer selling clothing , furniture and other household items in a department store format .

Example 686:
titleEntities: {'Subject': ['Romania'], 'Date': ['2006', '2018']}
title: Number of road deaths in Romania 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'fatalities']: ['1867', '1951', '1913', '1893', '1818', '1861', '2042', '2018', '2377', '2797', '3065', '2800', '2587']

gold: This statistic illustrates the annual number of road traffic fatalities in Romania between 2006 and 2018 . In the period of consideration , road fatalities presented a trend of decline in Romania despite some oscillation . The peak was recorded in 2008 , with 3,065 fatalities on Romanian roads .
gold_template: This statistic illustrates the annual templateYLabel[0] of templateTitle[1] traffic templateYLabel[1] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In the period of consideration , templateTitle[1] templateYLabel[1] presented a trend of decline in templateTitleSubject[0] despite some oscillation . The peak was recorded in templateXValue[10] , with templateYValue[max] templateYLabel[1] on Romanian roads .

generated_template: Between templateXValue[min] and templateXValue[max] , templateTitle[1] templateYLabel[1] in templateTitleSubject[0] had seen a net decline of 27 percent . The lowest figure of such templateYLabel[1] was recorded in templateXValue[1] at templateYValue[min] . These figures stand in contrast to an increase in the volume of passenger templateTitle[1] transport .
generated: Between 2006 and 2018 , road fatalities in Romania had seen a net decline of 27 percent . The lowest figure of such fatalities was recorded in 2017 at 1818 . These figures stand in contrast to an increase in the volume of passenger road transport .

Example 687:
titleEntities: {'Subject': ['USB', 'Germany'], 'Date': ['2018']}
title: Sales volume of USB flash drives in Germany 2004 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Sales', 'volume', 'in', 'millions']: ['12.88', '13.51', '16.17', '15.94', '15.46', '13.5', '15.85', '13.82', '11.78', '12.99', '13.0', '8.18', '5.0', '3.2', '2.03']

gold: USB flash drives experienced fluctuating sales numbers in recent years , with almost 12.9 million units sold in 2018 . Meanwhile , revenue generated amounted to 155 million euros in the same year , a decrease on the one before . Storage media USB flash drives revolutionized data storage for computer users everywhere when they emerged on the market , joining such well-known storage media as floppy discs , hard drives and optical storage units like CD-R and CD-RW discs .
gold_template: templateTitleSubject[0] templateTitle[3] templateTitle[4] experienced fluctuating templateYLabel[0] numbers in recent years , with almost templateYValue[0] templateYLabel[2] units sold in templateXValue[max] . Meanwhile , revenue generated amounted to 155 templateYLabel[2] euros in the same templateXLabel[0] , a decrease on the one before . Storage media templateTitleSubject[0] templateTitle[3] templateTitle[4] revolutionized data storage for computer users everywhere when they emerged on the market , joining such well-known storage media as floppy discs , hard templateTitle[4] and optical storage units like CD-R and CD-RW discs .

generated_template: The templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitle[3] reached a record high of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] and has risen by roughly a third since templateXValue[min] . templateTitle[0] templateTitle[1] templateYLabel[0] increased consistently from templateXValue[9] to templateXValue[2] , after over a decade of fluctuation from templateXValue[min] to templateXValue[max] . High demand for templateTitle[0] templateTitle[1] templateTitle[0] templateTitle[1] is a colorless , odorless fossil fuel that comes from underground rock formations .
generated: The Sales of Sales volume in the flash reached a record high of 16.17 volume millions in 2018 and has risen by roughly a third since 2004 . Sales volume Sales increased consistently from 2009 to 2016 , after over a decade of fluctuation from 2004 to 2018 . High demand for Sales volume Sales volume is a colorless , odorless fossil fuel that comes from underground rock formations .

Example 688:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2009', '2018']}
title: Working age population in Vietnam 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Working', 'age', 'population', 'in', 'millions']: ['72.59', '71.89', '70.94', '69.74', '69.34', '68.69', '68.19', '67.38', '65.71', '64.44']

gold: In 2018 , the working age population in Vietnam amounted to approximately 72.59 million people . In that year , the labor participation rate was at 76.7 percent while the employment rate was at 75.2 percent .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to approximately templateYValue[max] templateYLabel[3] people . In that templateXLabel[0] , the labor participation rate was at 76.7 percent while the employment rate was at 75.2 percent .

generated_template: The statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , a total of templateYValue[0] thousand templateYLabel[1] were recorded in templateTitleSubject[0] .
generated: The statistic presents the Working of age population in Vietnam from 2009 to 2018 . In 2018 , a total of 72.59 thousand age were recorded in Vietnam .

Example 689:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Most followed sports leagues in the U.S. 2019
X_Axis['Response']: ['NFL', 'MLB', 'NBA', 'NHL', 'MLS', "I_don't_follow_any_of_these_leagues"]
Y_Axis['Share', 'of', 'respondents']: ['33', '16', '10', '5', '3', '32']

gold: There are widely considered to be four major professional men 's sports leagues in the United States and Canada - NFL , NBA , MLB , and NHL . The professional soccer league ( MLS ) has also achieved some popularity in the United States in recent years . During a 2019 survey , 33 percent of respondents stated that the National Football League , NFL , was their favorite men 's U.S. professional sports league to follow .
gold_template: There are widely considered to be four major professional men 's templateTitle[2] templateXValue[last] in the templateTitle[4] and Canada - templateXValue[0] , templateXValue[2] , templateXValue[1] , and templateXValue[3] . The professional soccer league ( templateXValue[4] ) has also achieved some popularity in the templateTitle[4] in recent years . During a templateTitleDate[0] survey , templateYValue[max] percent of templateYLabel[1] stated that the National Football League , templateXValue[0] , was their favorite men 's templateTitleSubject[0] professional templateTitle[2] league to templateXValue[last] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] . As of October templateTitleDate[0] , templateXValue[0] programs were templateTitle[0] templateTitle[1] among survey templateYLabel[1] , at templateYValue[max] percent .
generated: This statistic shows the Most followed sports leagues in the U.S. ( U.S. ) in 2019 . As of October 2019 , NFL programs were Most followed among survey respondents , at 33 percent .

Example 690:
titleEntities: {'Subject': ['United States'], 'Date': ['2019']}
title: Pinterest usage reach in the United States 2019 , by household income
X_Axis['Annual', 'household', 'income']: ['Under_$30000', '$30000-$74999', '$75000+']
Y_Axis['Reach']: ['18', '27', '41']

gold: This statistic shows the share of adults in the United States who were using Pinterest as of February 2019 , sorted by income . During that period of time , 18 percent of respondents earning 30,000 U.S. dollars or less used the social networking site .
gold_template: This statistic shows the share of adults in the templateTitleSubject[0] who were using templateTitle[0] as of February templateTitleDate[0] , sorted templateTitle[6] templateXLabel[2] . During that period of time , templateYValue[min] percent of respondents earning 30,000 templateTitle[4] dollars or less used the social networking site .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] templateTitleSubject[0] who were using templateTitle[0] as of February templateTitleDate[0] , sorted templateTitle[6] templateXLabel[0] templateXLabel[1] . During that period of time , templateYValue[2] percent of respondents between 18 and 29 years used the social networking site .
generated: This statistic shows the Reach of adults in the United States who were using Pinterest as of February 2019 , sorted by Annual household . During that period of time , 41 percent of respondents between 18 and 29 years used the social networking site .

Example 691:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2024']}
title: Total population of South Korea 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['52.91', '52.69', '52.48', '52.27', '52.06', '51.85', '51.64', '51.43', '51.22', '51.02', '50.75']

gold: The statistic shows the total population of South Korea from 2014 to 2018 , with projections up until 2024 . In 2018 , the total population of South Korea was about 51.64 million people . Population of South Korea South Korea , also called Republic of Korea , has one of the highest population densities worldwide , i.e .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was about templateYValue[6] templateYLabel[1] people . templateTitle[1] of templateTitleSubject[0] South templateTitleSubject[0] , also called Republic of templateTitleSubject[0] , has one of the highest templateTitle[1] densities worldwide , i.e .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] is the second largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .
generated: The statistic shows the Total population of South Korea from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of South Korea amounted to around 51.64 millions Inhabitants . population of South Korea is the second largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .

Example 692:
titleEntities: {'Subject': ['Market'], 'Date': ['100', '2019']}
title: Market capitalization of leading 100 banks worldwide 2016 to 2019
X_Axis['Quarter']: ["Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16"]
Y_Axis['Market', 'capitalization', 'in', 'trillion', 'Euros']: ['5.3', '5.2', '5.2', '4.8', '5.3', '5.2', '5.4', '5.6', '5.4', '5.3', '5.4', '5.2', '4.4', '4.1', '4.2']

gold: This statistic presents the market capitalization of leading 100 banks worldwide from the first quarter 2016 to the third quarter 2019 . The market cap of top 100 global banks amounted to 5.3 trillion Euros in the third quarter of 2019 .
gold_template: This statistic presents the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from the first templateXLabel[0] templateTitleDate[0] to the third templateXLabel[0] templateTitleDate[1] . The templateYLabel[0] cap of top templateTitle[3] global templateTitle[4] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] in the third templateXLabel[0] of templateTitleDate[1] .

generated_template: This statistic illustrates templateTitle[0] 's templateTitle[1] average revenue per user ( templateYLabel[0] ) . As of the fourth templateXLabel[0] of templateTitleDate[1] , the photo app company 's templateYLabel[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] per templateXLabel[0] of the previous year .
generated: This statistic illustrates Market 's capitalization average revenue per user ( Market ) . As of the fourth Quarter of 2019 , the photo app company 's Market amounted to 5.6 capitalization trillion per Quarter of the previous year .

Example 693:
titleEntities: {'Subject': ['American'], 'Date': ['2013']}
title: Frequency of American families having dinner together at home 2013
X_Axis['Response']: ['0_to_3_nights', '4_to_5_nights', '6_to_7_nights']
Y_Axis['Share', 'of', 'respondents']: ['21', '28', '53']

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

generated_template: templateXValue[1] case of templateTitle[3] ( COVID-19 ) was confirmed in templateTitleSubject[0] as of February 27 , templateTitleDate[0] . Nevertheless , more than 60 percent of the society believes that templateTitleSubject[0] is not prepared templateTitle[2] a possible virus templateTitle[4] .
generated: 4 to 5 nights case of having ( COVID-19 ) was confirmed in American as of February 27 , 2013 . Nevertheless , more than 60 percent of the society believes that American is not prepared families a possible virus dinner .

Example 694:
titleEntities: {'Subject': ['Iran'], 'Date': ['2024']}
title: Iran 's national debt in relation to gross domestic product 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Share', 'in', 'GDP']: ['30.26', '29.18', '28.24', '28.06', '28.79', '30.67', '32.18', '39.53', '47.47', '38.42', '11.82']

gold: This statistic shows the national debt of Iran in relation to gross domestic product ( GDP ) from 2014 to 2018 , with projections up until 2024 . In 2018 , Iran 's national debt amounted to 32.18 percent of gross domestic product .
gold_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitleSubject[0] in templateTitle[4] to templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[1] ) from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] amounted to templateYValue[6] percent of templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: The statistic shows the templateTitleSubject[0] templateTitleSubject[0] templateTitle[2] templateYLabel[0] of the templateYLabel[1] templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[2] ) adjusted for Purchasing Power Parity ( PPP ) from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitleSubject[0] templateTitleSubject[0] templateTitle[2] templateYLabel[0] of templateYLabel[1] templateYLabel[2] ( in relation to PPP dollars ) amounted to about templateYValue[0] percent .
generated: The statistic shows the Iran national Share of the GDP gross domestic product ( GDP ) adjusted for Purchasing Power Parity ( PPP ) from 2014 to 2024 . In 2018 , the Iran national Share of GDP ( in relation to PPP dollars ) amounted to about 30.26 percent .

Example 695:
titleEntities: {'Subject': ['Dubai', 'UAE'], 'Date': ['2006', '2026']}
title: Direct tourism contribution of Dubai to GDP of the UAE 2006 to 2026
X_Axis['Year']: ['2026', '2016', '2006']
Y_Axis['GDP', 'contribution', 'in', 'billion', 'U.S.', 'dollars']: ['20.9', '11.4', '4.0']

gold: This statistic described the direct tourism contribution of Dubai to the gross domestic product of the United Arab Emirates from 2006 to 2016 and a forecast for 2026 . The forecast of the direct tourism contribution of Dubai to the GDP of the United Arab Emirates for 2026 was approximately 20.9 billion U.S. dollars .
gold_template: This statistic described the templateTitle[0] templateTitle[1] templateYLabel[1] of templateTitleSubject[0] to the gross domestic product of the United Arab Emirates from templateXValue[min] to templateXValue[1] and a forecast for templateXValue[max] . The forecast of the templateTitle[0] templateTitle[1] templateYLabel[1] of templateTitleSubject[0] to the templateYLabel[0] of the United Arab Emirates for templateXValue[max] was approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the percentage of adults in the templateTitleSubject[0] templateTitleSubject[0] who were using templateTitle[0] as of February templateTitleDate[0] , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateYValue[max] percent of respondents between 18 and 29 years used the social networking site .
generated: This statistic shows the percentage of adults in the Dubai who were using Direct as of February 2006 , sorted 2006 2026 . During that period of time , 20.9 percent of respondents between 18 and 29 years used the social networking site .

Example 696:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2018']}
title: Participants in hunting 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']: ['15.69', '15.63', '15.47', '15.53', '14.85', '13.53', '14.71', '14.89', '14.01', '15.27', '13.98', '14.14', '15.1']

gold: This statistic shows the number of participants in hunting in the United States from 2006 to 2018 . In 2018 , the number of participants ( aged six years and older ) in hunting amounted to approximately 15.69 million .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] in the templateTitle[2] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] amounted to approximately templateYValue[max] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] templateTitle[2] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] templateTitle[2] amounted to approximately templateYValue[0] templateYLabel[2] .
generated: This statistic shows the Number of participants in hunting U.S. in the from 2006 to 2018 . In 2018 , the Number of participants ( aged six years and older ) in hunting U.S. amounted to approximately 15.69 millions .

Example 697:
titleEntities: {'Subject': ['England'], 'Date': ['2017']}
title: Total household waste in England 2010 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Kilograms', 'per', 'person', 'per', 'year']: ['403', '412', '406', '413', '402', '412', '421', '425']

gold: Household waste volumes per person in England remained at a similar level between 2010 and 2017 . Although there was an overall decrease during this period , the household volumes were still over 400 kilograms per person in 2017 . The region which generated the largest volume of residual waste per household was the North East of England , where an average of 601 kilograms of waste was generated per person in 2017/2018 .
gold_template: templateTitle[1] templateTitle[2] volumes templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] remained at a similar level between templateXValue[min] and templateXValue[max] . Although there was an overall decrease during this period , the templateTitle[1] volumes were still over 400 templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[max] . The region which generated the largest volume of residual templateTitle[2] templateYLabel[1] templateTitle[1] was the North East of templateTitleSubject[0] , where an average of 601 templateYLabel[0] of templateTitle[2] was generated templateYLabel[1] templateYLabel[2] in 2017/2018 .

generated_template: In templateXValue[max] , the templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] Group reported a templateYLabel[0] of almost templateYValue[min] templateYLabel[1] templateYLabel[2] . Despite the impressive figure , the templateYLabel[0] of templateXValue[max] represented a decrease for templateTitle[4] compared to the previous years . Indeed , in templateXValue[min] , the first templateXLabel[0] considered in this graph , the templateYLabel[0] of the templateTitle[3] amounted to approximately templateYValue[max] templateYLabel[3] .
generated: In 2017 , the England waste England 2010 Group reported a Kilograms of almost 402 per person . Despite the impressive figure , the Kilograms of 2017 represented a decrease for 2010 compared to the previous years . Indeed , in 2010 , the first Year considered in this graph , the Kilograms of the England amounted to approximately 425 per .

Example 698:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Preferred modes of transportation when taking a family vacation in the U.S. 2015
X_Axis['Preferred', 'mode', 'of', 'travel']: ['Car', 'Plane', 'RV', 'Train', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['63', '31', '3', '2', '1']

gold: The statistic shows the preferred ways to travel when taking a family vacation in the United States in 2015 . The survey revealed that 63 percent of respondents prefer to travel by car .
gold_template: The statistic shows the templateXLabel[0] ways to templateXLabel[2] templateTitle[3] templateTitle[4] a templateTitle[5] templateTitle[6] in the templateTitle[7] in templateTitleDate[0] . The survey revealed that templateYValue[max] percent of templateYLabel[1] prefer to templateXLabel[2] by templateXValue[0] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] survey templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] with sensitive information templateTitle[8] or templateTitle[7] , while traveling . templateYValue[max] percent of templateYLabel[1] templateTitle[4] templateTitle[8] or templateTitle[5] a templateTitle[7] templateTitle[7] whilst traveling .
generated: The statistic shows the Share of U.S. survey respondents when taking family vacation with sensitive information 2015 or U.S. , while traveling . 63 percent of respondents taking 2015 or family a U.S. whilst traveling .

Example 699:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2024']}
title: Inflation rate in Vietnam 2024*
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['4', '4', '3.9', '3.8', '3.75', '3.6', '3.54', '3.52', '2.67', '0.63', '4.09', '6.6', '9.1', '18.67', '9.21', '6.72', '23.12', '8.35', '7.5', '8.39', '7.89', '3.3', '4.08', '-0.31', '-1.77', '4.11', '8.11', '3.1', '5.59', '16.93', '9.49', '8.38', '37.71', '81.82', '36.03', '95.77', '374.35', '360.36', '453.54', '91.6', '64.9']

gold: In 2018 , the average inflation rate in Vietnam amounted to 3.54 percent compared to the previous year . After a severe drop below one percent in 2015 , Vietnam 's inflation seems to have stabilized again and is expected to level off at around four percent in the next few years . Vietnam 's economic struggles Around 2012 , Vietnam suffered the consequences of the global economic crisis and domestic economic mismanagement , which saw enterprises going bankrupt , inflation peaking at over nine percent , and gross domestic product slumping to a dramatic low .
gold_template: In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . After a severe drop below templateYValue[9] percent in templateXValue[9] , templateTitleSubject[0] 's templateYLabel[0] seems to have stabilized again and is expected to level off at around templateYValue[0] percent in the next few years . templateTitleSubject[0] 's economic struggles Around templateXValue[12] , templateTitleSubject[0] suffered the consequences of the global economic crisis and domestic economic mismanagement , which saw enterprises going bankrupt , templateYLabel[0] peaking at over templateYValue[12] percent , and gross domestic product slumping to a dramatic low .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Vietnam from 1984 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Vietnam amounted to about 3.54 percent compared to the previous Year .

Example 700:
titleEntities: {'Subject': ['Global'], 'Date': ['2016', '2022']}
title: Global smart augmented reality glasses revenue 2016 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017', '2016']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['19718.88', '15686.56', '10936.67', '7275.43', '4421.74', '409.67', '138.61']

gold: The statistic shows smart AR glasses revenue worldwide from 2016 to 2022 . Smart augmented reality glasses revenue reached 138.6 million U.S. dollars in 2016 and is forecast to amount to around 19.7 billion U.S. dollars by 2022 .
gold_template: The statistic shows templateTitle[1] AR templateTitle[4] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] reached templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[min] and is forecast to amount to around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] by templateXValue[max] .

generated_template: This statistic represents templateYLabel[2] templateYLabel[0] generated with online apparel and templateTitle[2] retail sales between templateXValue[min] and templateXValue[max] . In templateXValue[max] , retail e-commerce revenues from apparel and templateTitle[2] sales amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] and are projected to increase to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . E-commerce templateYLabel[0] from apparel in the templateYLabel[2] – additional information With retail e-commerce sales worldwide expected to reach just over four templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[3] , the global e-commerce industry appears to show no signs of slowing down .
generated: This statistic represents U.S. Revenue generated with online apparel and augmented retail sales between 2016 and 2022 . In 2022 , retail e-commerce revenues from apparel and augmented sales amounted to 19718.88 million U.S. dollars and are projected to increase to 19718.88 million U.S. dollars in 2022 . E-commerce Revenue from apparel in the U.S. – additional information With retail e-commerce sales worldwide expected to reach just over four million U.S. dollars in 2019 , the global e-commerce industry appears to show no signs of slowing down .

Example 701:
titleEntities: {'Subject': ['West Virginia'], 'Date': ['1990', '2018']}
title: West Virginia - 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']: ['50573', '45392', '44354', '42824', '39552', '40241', '43553', '41821', '42777', '40490', '37994', '42091', '38419', '36445', '33373', '32763', '29359', '29673', '29411', '29297', '26704', '27488', '25247', '24880', '23564', '22421', '20271', '23147', '22137']

gold: This statistic shows the median household income in West Virginia from 1990 to 2018 . In 2018 , the median household income in West Virginia amounted to 50,573 U.S. dollars .
gold_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] parent families with a female householder and no spouse present in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[28] percent of all templateTitleSubject[0] families with a templateTitle[3] mother in the templateTitle[5] lived below the templateYLabel[0] level . In templateXValue[max] , that figure was at templateYValue[min] percent .
generated: This statistic shows the Household income of West Virginia household parent families with a female householder and no spouse present in the West Virginia from 1990 to 2018 . In 1990 , 22137 percent of all West Virginia families with a household mother in the 1990 lived below the Household level . In 2018 , that figure was at 20271 percent .

Example 702:
titleEntities: {'Subject': ['Electronic Arts'], 'Date': ['2010', '2020']}
title: Quarterly revenue of Electronic Arts from Q3 2010 to Q2 2020
X_Axis['Quarter']: ["Q2_'20", "Q1_'20", "Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11", "Q3_'11", "Q2_'11", "Q1_'11", "Q4_'10", "Q3_'10"]
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['1348', '1209', '1238', '1289', '1286', '1137', '1582', '1160', '959', '1449', '1527', '1149', '898', '1271', '1308', '1070', '815', '1203', '1185', '1126', '990', '1214', '1123', '808', '695', '949', '1209', '922', '711', '955', '1368', '1061', '715', '999', '1090', '1053', '631', '815', '979', '1243']

gold: This time series depicts the quarterly revenue of Electronic Arts from the third quarter of the fiscal year 2010 to the second quarter of the fiscal year 2020 . In the second fiscal quarter of 2020 , which ended on September 30 , 2019 , Electronic Arts generated a net revenue of 1.35 billion U.S. dollars . Here you can find information about EA 's quarterly net income .
gold_template: This time series depicts the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] from the third templateXLabel[0] of the fiscal year templateTitle[6] to the second templateXLabel[0] of the fiscal year templateTitle[8] . In the second fiscal templateXLabel[0] of templateTitle[8] , which ended on September 30 , 2019 , templateTitleSubject[0] generated a net templateYLabel[0] of templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Here you can find information about EA 's templateTitle[0] net income .

generated_template: In the fourth templateXLabel[0] of templateXValue[36] , The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] generated a total templateYLabel[0] of templateYValue[39] templateYLabel[1] templateYLabel[2] templateYLabel[3] , marking a small increase from the corresponding templateXLabel[0] of templateXValue[32] . The fourth templateXLabel[0] of the year is generally the one in which the templateTitleSubject[0] makes the most templateYLabel[0] , and at the end of templateXValue[32] the figure surpassed 500 templateYLabel[1] for the first time in years .
generated: In the fourth Quarter of Q2 '11 , The Electronic Arts generated a total Revenue of 1243 million U.S. dollars , marking a small increase from the corresponding Quarter of Q2 '12 . The fourth Quarter of the year is generally the one in which the Electronic Arts makes the most Revenue , and at the end of Q2 '12 the figure surpassed 500 million for the first time in years .

Example 703:
titleEntities: {'Subject': ['Leading'], 'Date': ['2014']}
title: Leading global travel booking sites by number of page visits 2014
X_Axis['Month']: ['Booking.com', 'TripAdvisor_Family', 'Expedia_Family', 'Hotels.com', 'Priceline.com', 'Agoda.com', 'Hotelurbano', 'Kayak.com', 'Travel.yahoo.com', 'Cheapoair.com', 'Makemytrip.com', 'Orbitz.com', 'Travelocity', 'Hotwire.com', 'Airbnb.com', 'Travelzoo.com', 'Decolar.com', 'Slyscanner.com', 'Ctrip.com', 'HomeAway.com']
Y_Axis['Number', 'of', 'site', 'visits', 'in', 'millions']: ['166.0', '159.9', '59.3', '34.5', '31.3', '30.7', '25.5', '24.4', '24.1', '20.2', '17.5', '17.2', '15.0', '13.2', '12.4', '12.2', '11.3', '9.6', '8.6', '7.4']

gold: This statistic shows the number of visits to travel booking sites worldwide in January 2014 . Booking.com had the most visits in January 2014 , with an estimated number of visits of 166 million .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[2] to templateTitle[2] templateTitle[3] templateTitle[4] worldwide in January templateTitleDate[0] . templateXValue[0] had the most templateYLabel[2] in January templateTitleDate[0] , with an estimated templateYLabel[0] of templateYLabel[2] of templateYValue[max] templateYLabel[3] .

generated_template: As of 23rd January templateTitleDate[0] , templateXValue[0] templateXValue[0] is the templateTitle[8] templateTitle[9] leader in templateYLabel[0] templateYLabel[1] for the international templateTitle[2] team of templateTitleSubject[0] templateTitle[4] a total of templateYValue[max] templateYLabel[0] templateYLabel[1] , followed by templateXValue[1] templateXValue[1] templateTitle[4] templateYValue[1] templateYLabel[0] . templateTitleSubject[0] 3rd at World Cup 2018 Even though there was some political controversial about the 2018 World Cup being held in Russia , when asked , the majority of Belgians agreed that Russia would be a successful venue host for the games . After templateTitleSubject[0] was defeated by France at the semi-finals templateTitle[4] 1:0 , France went on to win the tournament and become World Champions .
generated: As of 23rd January 2014 , Booking.com is the visits 2014 leader in Number site for the international travel team of Leading sites a total of 166.0 Number site , followed by TripAdvisor Family sites 159.9 Number . Leading 3rd at World Cup 2018 Even though there was some political controversial about the 2018 World Cup being held in Russia , when asked , the majority of Belgians agreed that Russia would be a successful venue host for the games . After Leading was defeated by France at the semi-finals sites 1:0 , France went on to win the tournament and become World Champions .

Example 704:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2009', '2019']}
title: Unemployment rate in the Netherlands 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Share', 'of', 'individuals']: ['4.3', '4.8', '5.9', '7.3', '8.6', '9', '8.9', '7.1', '6.1', '6.1', '5.5']

gold: In 2019 , the unemployment rate in the Netherlands was just over four percent . Unemployment peaked in 2013 and 2014 . At the height of the financial crisis , the annual unemployment rate in the country reached 8.9 and 9 percent respectively .
gold_template: In templateXValue[max] , the templateTitle[0] templateTitle[1] in the templateTitleSubject[0] was just over templateYValue[min] percent . templateTitle[0] peaked in templateXValue[6] and templateXValue[5] . At the height of the financial crisis , the annual templateTitle[0] templateTitle[1] in the country reached templateYValue[6] and templateYValue[4] percent respectively .

generated_template: The statistic shows the templateTitle[0] of templateTitle[1] to templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[1] worth approximately templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] were imported to templateTitleSubject[0] . templateYLabel[0] in templateTitleSubject[0] – additional information In templateXValue[4] , templateTitleSubject[0] had surpassed the country as the world 's largest templateTitle[1] trader .
generated: The statistic shows the Unemployment of rate to Netherlands from 2009 to 2019 . In 2019 , rate worth approximately 4.3 individuals were imported to Netherlands . Share in Netherlands – additional information In 2015 , Netherlands had surpassed the country as the world 's largest rate trader .

Example 705:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2019']}
title: Mexico : most important issues facing women and girls in 2019
X_Axis['Response']: ['Sexual_harassment', 'Sexual_violence', 'Physical_violence', 'Domestic_abuse', 'Equal_pay', 'Workplace_discrimination', 'Gender_stereotyping', 'Sexualization_of_women_and_girls_in_the_media', 'Access_to_employment', 'Balancing_work_and_caring_responsibilities', 'Lack_of_women_in_leadership_roles_in_business_and_public_life', 'Abuse_on_social_media', 'Support_for_pregnant_women_and_new_mothers', 'The_amount_of_unpaid_work_that_women_do_(e.g._cooking_cleaning_childcare)', 'Lack_of_financial/economic_independence']
Y_Axis['Share', 'of', 'respondents']: ['40', '37', '32', '19', '19', '18', '16', '15', '9', '8', '7', '7', '6', '6', '5']

gold: The statistic presents the results of a survey conducted in December 2018 and January 2019 to find out about the situation of women and gender ( in ) equality across 27 countries . When asked which were the main issues that women and girls were facing in Mexico , 40 percent of respondents answered sexual harassment .
gold_template: The statistic presents the results of a survey conducted in December 2018 and January templateTitleDate[0] to find out about the situation of templateXValue[7] and templateXValue[6] ( in ) equality across 27 countries . When asked which were the main templateTitle[3] templateXValue[13] templateXValue[7] and templateXValue[7] were templateTitle[4] in templateTitleSubject[0] , templateYValue[max] percent of templateYLabel[1] answered templateXValue[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of the average templateTitleSubject[0] templateTitle[5] templateTitle[6] user as of templateTitleSubject[0] templateTitle[9] . During the survey , templateYValue[1] percent of templateYLabel[1] stated they would choose templateXValue[1] templateTitle[1] if they could only listen to templateYValue[11] genre of templateTitle[1] for the rest of their lives . The most popular answer was templateXValue[0] , which templateYValue[max] percent of templateYLabel[1] selected as the templateYValue[11] genre of templateTitle[1] they would listen to for the rest of their lives .
generated: This statistic shows the Mexico most important of the average Mexico women girls user as of Mexico 2019 . During the survey , 37 percent of respondents stated they would choose Sexual violence most if they could only listen to 7 genre of most for the rest of their lives . The most popular answer was Sexual harassment , which 40 percent of respondents selected as the 7 genre of most they would listen to for the rest of their lives .

Example 706:
titleEntities: {'Subject': ['Nicaragua'], 'Date': ['2024']}
title: Inflation rate in Nicaragua 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['5.04', '4.94', '4.79', '4.67', '4.19', '5.63', '4.97', '3.85', '3.52', '4', '6.04', '7.14', '7.19', '8.08', '5.46', '3.69', '19.83', '11.13', '9.14', '9.6', '8.47', '5.3', '3.75', '7.36', '11.55', '11.21', '13.05', '9.19', '11.65', '11.12', '3.7', '13.5', '21.9', '116.6', '3004.1', '7428.7', '4775.2', '13109.5', '885.2', '571.4', '141.3']

gold: This statistic shows the average inflation rate in Nicaragua from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Nicaragua amounted to about 4.97 percent compared to the previous year . Nicaragua 's economy Nicaragua 's inflation rate has been on the decline since 2011 , but it is expected to rise again in 2016 .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . templateTitleSubject[0] 's economy templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] has been on the decline since templateXValue[13] , but it is expected to rise again in templateXValue[8] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Nicaragua from 1984 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Nicaragua amounted to about 4.97 percent compared to the previous Year .

Example 707:
titleEntities: {'Subject': ['Chile'], 'Date': ['2014', '2018']}
title: Chile : gender gap index 2014 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014']
Y_Axis['Index', 'score']: ['0.72', '0.7', '0.7', '0.7', '0.7']

gold: The graph presents the gender gap index in Chile from 2014 to 2018 . In 2018 , Chile scored 0.72 points , which shows a gender gap of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . In 2018 , the gender gap in the area of political empowerment in Chile amounted to 69 percent .
gold_template: The graph presents the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] scored templateYValue[max] points , which shows a templateTitle[1] templateTitle[2] of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . In templateXValue[max] , the templateTitle[1] templateTitle[2] in the area of political empowerment in templateTitleSubject[0] amounted to 69 percent .

generated_template: The graph presents the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] scored templateYValue[max] , which shows a templateTitle[1] templateTitle[2] of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . That same templateXLabel[0] , the templateTitle[1] templateTitle[2] in the area of political empowerment in templateTitleSubject[0] amounted to 72 percent .
generated: The graph presents the gender gap Index in Chile from 2014 to 2018 . In 2018 , Chile scored 0.72 , which shows a gender gap of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . That same Year , the gender gap in the area of political empowerment in Chile amounted to 72 percent .

Example 708:
titleEntities: {'Subject': ['United States'], 'Date': ['2019']}
title: Instagram usage reach in the United States 2019 , by age group
X_Axis['Age', 'group']: ['18-29', '30-49', '50-64', '65+']
Y_Axis['Share', 'of', 'respondents']: ['67', '47', '23', '8']

gold: As of February 2019 , 67 percent of U.S. adults aged between 18 and 29 years used the photo sharing app Instagram . Furthermore , it was found that 43 percent of female adults in the United States used Instagram compared to only 31 percent of adult men . Instagram usage in the United StatesInstagram is one of the most popular social networks in the United States with a 37 percent usage reach among the adult population .
gold_template: As of February templateTitleDate[0] , templateYValue[max] percent of templateTitle[4] adults aged between 18 and 29 years used the photo sharing app templateTitleSubject[0] . Furthermore , it was found that 43 percent of female adults in the templateTitleSubject[1] used templateTitleSubject[0] compared to only 31 percent of adult men . templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] StatesInstagram is one of the most popular social networks in the templateTitleSubject[1] with a 37 percent templateTitle[1] templateTitle[2] among the adult population .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] templateTitleSubject[0] who were using templateTitle[0] as of February templateTitleDate[0] , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateYValue[max] percent of templateYLabel[1] aged between 18 and 29 years stated that they used the visual blogging site .
generated: This statistic shows the Share of adults in the United States who were using Instagram as of February 2019 , sorted by age group . During that period of time , 67 percent of respondents aged between 18 and 29 years stated that they used the visual blogging site .

Example 709:
titleEntities: {'Subject': ['Colombia'], 'Date': ['1990', '2018']}
title: District of Colombia - 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']: ['85750', '83382', '70982', '70071', '68277', '60675', '65246', '55251', '56928', '53141', '55590', '50783', '48477', '44993', '43451', '45044', '39070', '41169', '41222', '38670', '33433', '31860', '31966', '30748', '30116', '27304', '30247', '29885', '27392']

gold: This statistic shows the median household income in the District of Colombia from 1990 to 2018 . In 2018 , the median household income in the District of Colombia amounted to 85,750 U.S. dollars .
gold_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in the templateTitle[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in the templateTitle[0] of templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] parent families with a female householder and no spouse present in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[28] percent of all templateTitleSubject[0] families with a templateTitle[3] mother in the templateTitle[5] lived below the templateYLabel[0] level . In templateXValue[max] , that figure was at templateYValue[min] percent .
generated: This statistic shows the Household income of Colombia household parent families with a female householder and no spouse present in the Colombia from 1990 to 2018 . In 1990 , 27392 percent of all Colombia families with a household mother in the 1990 lived below the Household level . In 2018 , that figure was at 27304 percent .

Example 710:
titleEntities: {'Subject': ['Iraq'], 'Date': ['2024']}
title: Total population of Iraq 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['44.47', '43.35', '42.25', '41.18', '40.13', '39.12', '38.12', '37.14', '36.17', '35.21', '35.0']

gold: This statistic shows the total population of Iraq from 2014 to 2024 . In 2018 , the estimated total population of Iraq amounted to approximately 38.12 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the estimated templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateYLabel[1] templateYLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] is the second largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .
generated: The statistic shows the Total population of Iraq from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Iraq amounted to around 38.12 millions Inhabitants . population of Iraq is the second largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .

Example 711:
titleEntities: {'Subject': ['European'], 'Date': []}
title: European football clubs average attendance 2013/14
X_Axis['Club', 'Name']: ['Boussia_Dortmund', 'Manchester_United', 'Barcelona', 'Real_Madrid', 'Bayern_Munich', 'Schalke_04', 'Arsenal', 'Borussia_Mönchengladbach', 'Hertha_BSC', 'Hamburger_SV', 'Ajax_Amsterdam', 'VfB_Stuttgart', 'Newcastle_United', 'Manchester_City', 'Eintracht_Frankfurt', 'Celtic_FC', 'FC_Internazionale', 'Atletico_Madrid', 'FC_Köln', 'Feyenoord', 'Hannover_96', 'Paris_Saint_Germain', 'Liverpool', 'SL_Benfica', 'Rangers_FC']
Y_Axis['Average', 'attendance']: ['80295', '75205', '72115', '71565', '71000', '61750', '60015', '52240', '51890', '51825', '50905', '50500', '50395', '47075', '47055', '46810', '46245', '46245', '46235', '45755', '45635', '45420', '44670', '43615', '42935']

gold: The statistic shows the European football clubs with the highest average per game attendance in the 2013/14 season . Germany 's Borussia Dortmund had the highest average attendance throughout Europe , with an average of over 80,000 fans attending each of their home games .
gold_template: The statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] with the highest templateYLabel[0] per game templateYLabel[1] in the templateTitle[5] season . Germany 's templateXValue[7] templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] throughout Europe , with an templateYLabel[0] of over 80,000 fans attending each of their home games .

generated_template: As of 2019 , templateXValue[0] templateXValue[0] , won the templateTitle[0] templateTitleSubject[0] at the templateTitle[2] templateTitleSubject[0] templateTitleSubject[0] in Russia , having a total of templateYValue[max] templateYLabel[1] . templateYValue[6] of the Tottenham Hotspur striker 's templateYLabel[1] came in England 's templateYValue[max] - 1 defeat of Panama in the group stages . His final goal of the tournament came in England 's round of 16 game against Columbia where he opened the scoring from the penalty spot .
generated: As of 2019 , Boussia Dortmund , won the European at the clubs European in Russia , having a total of 80295 attendance . 60015 of the Tottenham Hotspur striker 's attendance came in England 's 80295 - 1 defeat of Panama in the group stages . His final goal of the tournament came in England 's round of 16 game against Columbia where he opened the scoring from the penalty spot .

Example 712:
titleEntities: {'Subject': ['Northern Ireland', 'UK'], 'Date': ['2000', '2019']}
title: Unemployment rate in Northern Ireland ( UK ) 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Unemployment', 'rate']: ['2.7', '3.6', '4.6', '5.7', '6.1', '6.4', '7.5', '7.4', '7.2', '7.1', '6.4', '4.4', '3.9', '4.4', '4.6', '5', '5.6', '5.9', '6', '6.2']

gold: This statistic shows the unemployment rate in Northern Ireland from 2000 to 2019 . Unemployment in Northern Ireland peaked in 2013 when there were 7.5 percent of the population unemployed , compared with just 2.7 percent in the most recent reporting year of 2019 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateYLabel[0] in templateTitleSubject[0] peaked in templateXValue[6] when there were templateYValue[max] percent of the population unemployed , compared with just templateYValue[min] percent in the most recent reporting templateXLabel[0] of templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[min] percent of the labor force in templateTitleSubject[0] was unemployed .
generated: This statistic shows the Unemployment rate in Northern Ireland from 2000 to 2019 . In 2019 , 2.7 percent of the labor force in Northern Ireland was unemployed .

Example 713:
titleEntities: {'Subject': ['Video'], 'Date': ['2022']}
title: Video analytics market revenues worldwide 2015 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Market', 'in', 'million', 'U.S.', 'dollars']: ['2997.8', '2692.7', '2347.1', '1998.4', '1665.5', '1405.1', '1137.7', '858.0']

gold: The statistic shows the size of the video analytics market worldwide , from 2015 to 2022 . In 2015 , revenues from video analytics reached 858 million U.S. dollars .
gold_template: The statistic shows the size of the templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateTitle[4] , from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateTitle[3] from templateTitleSubject[0] templateTitle[1] reached templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic illustrates the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , it is estimated that there will be around templateYValue[max] templateYLabel[2] templateTitle[2] templateTitle[3] templateYLabel[1] in the country , up from templateYValue[5] templateYLabel[2] in templateXValue[min] .
generated: This statistic illustrates the Video Market of market revenues in Video from 2015 to 2022 . In 2018 , it is estimated that there will be around 2997.8 U.S. market revenues million in the country , up from 1405.1 U.S. in 2015 .

Example 714:
titleEntities: {'Subject': ['American'], 'Date': ['2007', '2019']}
title: American Customer Satisfaction Index : full-service restaurants in the U.S. 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['ACSI', 'score']: ['81', '81', '78', '81', '82', '82', '81', '80', '82', '81', '84', '80', '81']

gold: This statistic shows the American Customer Satisfaction Index scores for full-service restaurants in the United States from 2007 to 2019 . In 2019 , the ACSI score for full-service restaurants in the U.S. was 81 .
gold_template: This statistic shows the templateTitleSubject[0] Satisfaction templateTitle[3] scores for templateTitle[4] templateTitle[5] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] for templateTitle[4] templateTitle[5] in the templateTitle[6] was templateYValue[0] .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] scores for templateTitleSubject[1] templateTitleSubject[1] templateTitle[6] in the templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] for templateTitleSubject[1] templateTitleSubject[1] templateTitle[6] in the templateTitle[7] was templateYValue[min] , down from templateYValue[1] the previous templateXLabel[0] .
generated: This statistic shows the American Customer Satisfaction Index scores for American U.S. in the 2007 from 2007 to 2019 . In 2019 , the ACSI for American U.S. in the 2007 was 78 , down from 81 the previous Year .

Example 715:
titleEntities: {'Subject': ['Eastman Chemical'], 'Date': ['2008', '2018']}
title: Eastman Chemical 's revenue 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['10151', '9549', '9008', '9648', '9527', '9350', '8102', '7178', '5842', '4396', '5936']

gold: This statistic shows the revenues of Eastman Chemical from 2007 to 2018 . United States-based Eastman Chemical Company is engaged in the manufacture and sale of chemicals , fibers , and plastics . In 2018 , the company generated approximately 10.15 billion U.S. dollars of sales revenues .
gold_template: This statistic shows the revenues of templateTitleSubject[0] from 2007 to templateXValue[max] . United States-based templateTitleSubject[0] Company is engaged in the manufacture and sale of chemicals , fibers , and plastics . In templateXValue[max] , the company generated approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] of sales revenues .

generated_template: In templateXValue[max] , templateTitleSubject[0] generated templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] . Officially Robert templateTitleSubject[0] GmbH , the multinational engineering and electronics company is one of Germany 's top brands and among the largest engineering and electronics companies in Germany . Takeovers boost templateYLabel[0] In templateXValue[3] , the templateYLabel[0] of templateTitleSubject[0] jumped by over 20 templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: In 2018 , Eastman Chemical generated 10151 million U.S. dollars in Revenue . Officially Robert Eastman Chemical GmbH , the multinational engineering and electronics company is one of Germany 's top brands and among the largest engineering and electronics companies in Germany . Takeovers boost Revenue In 2015 , the Revenue of Eastman Chemical jumped by over 20 million U.S. dollars .

Example 716:
titleEntities: {'Subject': ['Sears Holdings'], 'Date': ['2009', '2017']}
title: Number of stores of Sears Holdings worldwide 2009 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'stores']: ['1002', '1430', '1672', '1725', '2429', '2548', '4010', '3949', '3862']

gold: This statistic depicts the total number of stores of Sears Holdings from 2009 to 2017 . In 2017 , Sears Holdings had a total of 1,002 stores worldwide . The company was founded in 2005 in Hoffman Estates , Illinois and is the tenth largest retailer by annual revenue in the United States .
gold_template: This statistic depicts the total templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] had a total of templateYValue[min] templateYLabel[1] templateTitle[4] . The company was founded in 2005 in Hoffman Estates , Illinois and is the tenth largest retailer by annual revenue in the country .

generated_template: How many templateYLabel[1] are there in the US ? According to a pet owners survey , there were approximately templateYValue[max] templateYLabel[2] templateYLabel[1] owned in the templateTitle[2] in templateXValue[max] . This is an increase of over 20 templateYLabel[2] since the beginning of the survey period in templateXValue[min] , when around templateYValue[8] templateYLabel[2] templateYLabel[1] were owned in the templateTitle[2] . Why has this figure increased ? The resident population of the templateTitle[2] has also increased significantly within this time period .
generated: How many stores are there in the US ? According to a pet owners survey , there were approximately 4010 stores owned in the Sears in 2017 . This is an increase of over 20 stores since the beginning of the survey period in 2009 , when around 3862 stores were owned in the Sears . Why has this figure increased ? The resident population of the Sears has also increased significantly within this time period .

Example 717:
titleEntities: {'Subject': ['Colombia'], 'Date': ['2005', '2017']}
title: Colombia : poverty headcount ratio at 3.20 U.S. dollars a day 2005 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2005']
Y_Axis['Percentage', 'of', 'population']: ['10.8', '11.8', '11.9', '13.1', '14.2', '15.4', '16.3', '18.3', '20.5', '22.4', '23.4']

gold: In Colombia , the poverty rate has been decreasing throughout recent years . In 2017 , approximately 10.8 percent of Colombians were living on less than 3.20 U.S. dollars per day , down from 23.4 percent of the country 's population in 2005.Moreover , it was recently found that the incidence rate of poverty in Colombia is higher in families whose heads of household were women .
gold_template: In templateTitleSubject[0] , the templateTitle[1] rate has been decreasing throughout recent years . In templateXValue[max] , approximately templateYValue[min] percent of Colombians were living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] percent of the country 's templateYLabel[1] in 2005.Moreover , it was recently found that the incidence rate of templateTitle[1] in templateTitleSubject[0] is higher in families whose heads of household were women .

generated_template: The statistic shows the templateTitleSubject[0] of templateTitle[0] in templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] templateYLabel[1] of templateTitle[0] amounted to templateYValue[min] percent .
generated: The statistic shows the Colombia of Colombia in Colombia from 2005 to 2017 . In 2017 , the Colombia population of Colombia amounted to 10.8 percent .

Example 718:
titleEntities: {'Subject': ['Iran'], 'Date': ['2011', '2011']}
title: Iran 's oil exports 2011
X_Axis['Country']: ['China', 'European_Union_(total)', 'Japan', 'India', 'South_Korea', 'Italy', 'Turkey', 'Spain', 'France', 'Netherlands', 'Germany', 'United_Kingdom']
Y_Axis['Oil', 'imports', 'in', 'thousand', 'barrels', 'per', 'day']: ['543', '450', '341', '328', '244', '183', '182', '137', '49', '33', '17', '11']

gold: This statistic depicts the volume of crude oil imported from Iran by its leading destination countries between January and June 2011 . The European Union imported a total of around 450,000 barrels of oil per day from Iran during that period . Iran has stopped oil exports to France , where crude oil is the second most important energy source and Britain , where crude oil production has been declining since 2002 .
gold_template: This statistic depicts the volume of crude templateYLabel[0] imported from templateTitleSubject[0] by its leading destination countries between January and June templateTitleDate[0] . The templateXValue[1] imported a total of around templateYValue[1] templateYLabel[3] of templateYLabel[0] templateYLabel[4] templateYLabel[5] from templateTitleSubject[0] during that period . templateTitleSubject[0] has stopped templateYLabel[0] templateTitle[3] to templateXValue[8] , where crude templateYLabel[0] is the second most important energy source and Britain , where crude templateYLabel[0] production has been declining since 2002 .

generated_template: This statistic shows the distribution of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . That year , the templateXValue[0] templateXValue[0] accounted for more than one third of the total templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide .
generated: This statistic shows the distribution of Iran imports thousand barrels in 2011 , 2011 Country . That year , the China accounted for more than one third of the total imports thousand barrels worldwide .

Example 719:
titleEntities: {'Subject': ['Samsung Electronics'], 'Date': ['2009', '2019']}
title: Samsung Electronics ' operating profit 2009 - 2019 , by quarter
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11", "Q3_'11", "Q2_'11", "Q1_'11", "Q4_'10", "Q3_'10", "Q2_'10", "Q1_'10", "Q4_'09", "Q3_'09", "Q2_'09", "Q1_'09"]
Y_Axis['Operating', 'profit', 'in', 'trillion', 'South', 'Korean', 'won']: ['7.16', '7.78', '6.6', '6.23', '10.8', '17.57', '14.87', '15.64', '15.15', '14.53', '14.07', '9.9', '9.22', '5.2', '8.14', '6.68', '6.14', '7.39', '6.9', '5.98', '5.29', '4.06', '7.2', '8.5', '8.3', '10.2', '9.5', '8.8', '8.8', '8.1', '6.5', '5.7', '4.7', '4.3', '3.8', '2.8', '3.0', '4.9', '5.0', '4.4', '3.4', '4.2', '2.7', '0.6']

gold: In the fourth quarter of 2019 , Korean consumer electronics company Samsung Electronics reported an operating profit of nearly 7.16 trillion Korean Won or around 6.5 billion U.S. dollars . Many of the company 's segments experienced increases in their overall sales numbers in the third quarter of 2019 , but growing competition throughout the consumer electronics industry meant that profitability fell . Samsung Samsung ranks among the largest companies in the world in terms of market value and is perhaps best known for its various lines of successful consumer electronics products .
gold_template: In the fourth templateXLabel[0] of templateTitle[6] , templateYLabel[4] consumer templateTitleSubject[0] company templateTitleSubject[0] reported an templateYLabel[0] templateYLabel[1] of nearly templateYValue[0] templateYLabel[2] templateYLabel[4] templateYLabel[5] or around templateYValue[30] templateYLabel[2] U.S. dollars . Many of the company 's segments experienced increases in their overall sales numbers in the third templateXLabel[0] of templateTitle[6] , but growing competition throughout the consumer templateTitleSubject[0] industry meant that profitability fell . templateTitleSubject[0] ranks among the largest companies in the world in terms of market value and is perhaps best known for its various lines of successful consumer templateTitleSubject[0] products .

generated_template: During the fourth templateXLabel[0] of templateTitleDate[1] , adjusted templateYLabel[3] templateTitle[3] templateYLabel[0] templateYLabel[1] amounted to over templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateTitle[3] templateYLabel[0] templateYLabel[1] in the templateTitle[1] are estimated from samples used for the Monthly templateTitle[3] Trade Survey and exclude online travel services , ticket templateYLabel[1] agencies and financial brokers . Online templateTitle[3] templateYLabel[1] currently account for one tenth of total templateTitle[3] and approximately 5 percent of annual templateYLabel[0] revenue in the templateTitle[1] .
generated: During the fourth Quarter of 2019 , adjusted South operating profit amounted to over 17.57 trillion South Korean . operating profit in the Electronics are estimated from samples used for the Monthly operating Trade Survey and exclude online travel services , ticket profit agencies and financial brokers . Online operating profit currently account for one tenth of total operating and approximately 5 percent of annual Operating revenue in the Electronics .

Example 720:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013', '2018']}
title: Retail sales of the vision care market in the U.S. 2013 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Retail', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['35725.0', '34782.0', '40357.8', '39767.0', '37595.7', '36236.2']

gold: This statistic depicts retail sales of the vision care market in the United States from 2013 to 2018 . In 2016 , the U.S. vision care market generated approximately 40.36 billion U.S. dollars , up from 39.77 billion U.S. dollars the previous year .
gold_template: This statistic depicts templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateYLabel[3] templateTitle[2] templateTitle[3] templateTitle[4] generated approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] , up from templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] the previous templateXLabel[0] .

generated_template: As of templateXValue[max] there were approximately templateYValue[0] templateYLabel[1] and contractors globally working for templateTitleSubject[0] . This is a significant decrease from templateXValue[min] when there were about 204,000 templateYLabel[1] working for the multinational commodity trading and mining company . Despite decreasing employment numbers , templateTitleSubject[0] remains the second largest mining company worldwide based on employment .
generated: As of 2018 there were approximately 35725.0 sales and contractors globally working for U.S. . This is a significant decrease from 2013 when there were about 204,000 sales working for the multinational commodity trading and mining company . Despite decreasing employment numbers , U.S. remains the second largest mining company worldwide based on employment .

Example 721:
titleEntities: {'Subject': ['UFC'], 'Date': ['2012', '2018']}
title: UFC : number of events 2012 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'UFC', 'events']: ['39', '39', '41', '41', '46', '33', '31']

gold: In 2018 , a total of 39 Ultimate Fighting Championship ( UFC ) events were hosted around the world featuring 474 fights . The highest live attendance in 2018 was at UFC Fight Night 136 – Hunt vs Oleinik with 22,603 ringside spectators and the highest attendance to date was at UFC 193 – Rousey vs Holm in 2015 with 56,214 attendees . Pay-Per-View In 2017 , the UFC was among the 10 most valuable sports business brands worldwide which is in part thanks to its ' Pay-Per-View ( PPV ) business model .
gold_template: In templateXValue[max] , a total of templateYValue[0] Ultimate Fighting Championship ( templateYLabel[1] ) templateYLabel[2] were hosted around the world featuring 474 fights . The highest live attendance in templateXValue[max] was at templateYLabel[1] Fight Night 136 – Hunt vs Oleinik with 22,603 ringside spectators and the highest attendance to date was at templateYLabel[1] 193 – Rousey vs Holm in templateXValue[3] with 56,214 attendees . Pay-Per-View In templateXValue[1] , the templateYLabel[1] was among the 10 most valuable sports business brands worldwide which is in part thanks to its ' Pay-Per-View ( PPV ) business model .

generated_template: The templateTitle[0] templateTitle[1] spending in templateTitleSubject[0] was forecasted at templateYValue[max] percent in templateXValue[max] . This was a decrease compared to the previous templateXLabel[0] . According to OECD , consuming meat was equated with living standards , diet , livestock production and consumer prices .
generated: The UFC number spending in UFC was forecasted at 46 percent in 2018 . This was a decrease compared to the previous Year . According to OECD , consuming meat was equated with living standards , diet , livestock production and consumer prices .

Example 722:
titleEntities: {'Subject': ['United States'], 'Date': []}
title: Ratio of government expenditure to gross domestic product ( GDP ) in the United States
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Ratio', 'of', 'government', 'expenditure', 'to', 'GDP']: ['36.77', '36.75', '36.73', '36.51', '36.41', '36.19', '35.14', '35.25', '35.46', '35.15', '35.47']

gold: The statistic shows the ratio of government expenditure to gross domestic product ( GDP ) in the United States from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure amounted to 35.14 percent of the gross domestic product . See the US GDP for further information .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] amounted to templateYValue[min] percent of the templateTitle[3] templateTitle[4] templateTitle[5] . See the US templateYLabel[3] for further information .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to about templateYValue[6] percent of the country 's templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The statistic shows the Ratio of government expenditure to gross domestic product ( GDP ) in United States from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure in United States amounted to about 35.14 percent of the country 's gross domestic product .

Example 723:
titleEntities: {'Subject': ['Croatia'], 'Date': ['2006', '2018']}
title: Croatia : Number of road deaths 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'fatalities']: ['317', '331', '307', '348', '308', '368', '393', '418', '426', '548', '664', '619', '614']

gold: This statistic illustrates the number of road traffic fatalities per year in Croatia between 2006 and 2018 . In the period of consideration , road fatalities presented an overall trend of decline . The year with the lowest amount of fatalities was 2016 , with a total of 207 road traffic fatalities in Croatia .
gold_template: This statistic illustrates the templateYLabel[0] of templateTitle[2] traffic templateYLabel[1] per templateXLabel[0] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In the period of consideration , templateTitle[2] templateYLabel[1] presented an overall trend of decline . The templateXLabel[0] with the lowest amount of templateYLabel[1] was templateXValue[2] , with a total of 207 templateTitle[2] traffic templateYLabel[1] in templateTitleSubject[0] .

generated_template: There were templateYValue[0] reported templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateXValue[max] . This was a slight increase compared to the previous templateXLabel[0] , but an increase of 14 incidents compared to the low reported in templateXValue[4] . The Northern European island state is known for enforcing a strict templateTitle[1] safety policy in order to ensure the security of its residents and tourists in the country .
generated: There were 317 reported Number road in Croatia in 2018 . This was a slight increase compared to the previous Year , but an increase of 14 incidents compared to the low reported in 2014 . The Northern European island state is known for enforcing a strict Number safety policy in order to ensure the security of its residents and tourists in the country .

Example 724:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2017']}
title: Fatality rate per 100,000 drivers licensed in the U.S. 1990 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995', '1990']
Y_Axis['Fatalities', 'per', '100,000', 'licensed', 'drivers']: ['16.48', '17.05', '16.27', '15.29', '15.5', '15.95', '15.33', '15.71', '16.16', '17.96', '20.05', '21.06', '21.7', '22.0', '23.68', '26.7']

gold: The timeline shows the fatality rate per 100,000 drivers licensed to operate a motor vehicle in the United States from 1990 to 2017 . The fatality rate stood at 16.5 deaths per 100,000 licensed drivers in 2017 .
gold_template: The timeline shows the templateTitle[0] templateTitle[1] templateYLabel[1] 100,000 templateYLabel[4] templateYLabel[3] to operate a motor vehicle in the templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] stood at templateYValue[0] deaths templateYLabel[1] 100,000 templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: In templateXValue[max] , there were templateYValue[0] templateTitle[1] templateTitle[2] templateYLabel[1] across the templateTitle[4] . This was a decrease from templateXValue[2] , when there were templateYValue[2] templateTitle[1] templateTitle[2] templateYLabel[1] across the nation . templateTitle[1] templateTitle[2] in the templateTitleSubject[0] templateTitle[1] templateTitle[2] in the templateTitle[4] refers to colleges and universities in the country .
generated: In 2017 , there were 16.48 rate per across the drivers . This was a decrease from 2015 , when there were 16.27 rate per across the nation . rate per in the U.S. rate per in the drivers refers to colleges and universities in the country .

Example 725:
titleEntities: {'Subject': ['Chicago White Sox'], 'Date': ['2019']}
title: Franchise value of the Chicago White Sox 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1600', '1500', '1350', '1050', '975', '695', '692', '600', '526', '466', '450', '443', '381', '315', '262', '248', '233', '223']

gold: This graph depicts the value of the Chicago White Sox franchise of Major League Baseball from 2002 to 2019 . In 2019 , the franchise had an estimated value of 1.6 billion U.S. dollars . The Chicago White Sox are owned by Jerry Reinsdorf , who bought the franchise for 20 million U.S. dollars in 1981 .
gold_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] Sox templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Sox are owned by Jerry Reinsdorf , who bought the templateYLabel[0] for 20 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1981 .

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] 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] templateTitleSubject[0] templateTitleSubject[0] are owned by Stuart Sternberg , who bought the templateYLabel[0] for 200 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 2000 .
generated: This graph depicts the value of the Chicago White Sox Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 1600 million U.S. dollars . The Chicago White Sox are owned by Stuart Sternberg , who bought the Franchise for 200 million U.S. dollars in 2000 .

Example 726:
titleEntities: {'Subject': ['Bitcoins'], 'Date': ['2012', '2019']}
title: Number of Bitcoins in circulation 2012 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12"]
Y_Axis['Number', 'of', 'Bitcoins', 'in', 'millions']: ['18.13', '17.97', '17.79', '17.62', '17.45', '17.3', '17.12', '16.95', '16.78', '16.6', '16.42', '16.25', '16.08', '15.9', '15.72', '15.38', '15.03', '14.67', '14.33', '14.0', '13.67', '13.33', '12.97', '12.59', '12.2', '11.77', '11.35', '10.97', '10.61']

gold: In the fourth quarter of 2019 , there were 18.13 million Bitcoins in the world . Bitcoin is a virtual currency . It can be bought and sold through online exchanges or ATMs .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[1] , there were templateYValue[max] templateYLabel[2] templateYLabel[1] in the world . Bitcoin is a virtual currency . It can be bought and sold through online exchanges or ATMs .

generated_template: During the third templateXLabel[0] of templateTitleDate[1] , the templateTitleSubject[0] templateTitleSubject[0] ( IT ) sector of the Technical Consumer Goods industry generated a templateYLabel[0] of approximately 2.302 templateYLabel[2] in the fourth templateXLabel[0] of the fourth templateXLabel[0] of templateTitle[7] . In the fourth templateXLabel[0] of templateTitleDate[1] , templateTitle[0] reported daily templateYLabel[2] templateTitle[0] templateTitle[1] globally .
generated: During the third Quarter of 2019 , the Bitcoins ( IT ) sector of the Technical Consumer Goods industry generated a Number of approximately 2.302 millions in the fourth Quarter of the fourth Quarter of 2019 . In the fourth Quarter of 2019 , Number reported daily millions Number Bitcoins globally .

Example 727:
titleEntities: {'Subject': ['WarnerMedia'], 'Date': ['2018']}
title: WarnerMedia television network revenue 2018
X_Axis['Year']: ['2018']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['10.58']

gold: This statistic contains data on the revenue that WarnerMedia generated with its TV network business in 2018 . In 2018 , the media giant generated 10.58 billion U.S. dollars with , among others , HBO , CNN and Cartoon Network . Due to the acquisition of Time Warner by AT & T and its subsequent renaming ( now WarnerMedia ) , results for previous years are not considered meaningful and as such were not reported by AT & T in 2018 .
gold_template: This statistic contains data on the templateYLabel[0] that templateTitleSubject[0] generated with its TV templateTitle[2] business in templateXValue[max] . In templateXValue[max] , the media giant generated templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] with , among others , HBO , CNN and Cartoon templateTitle[2] . Due to the acquisition of Time Warner by AT & T and its subsequent renaming ( now templateTitleSubject[0] ) , results for previous years are not considered meaningful and as such were not reported by AT & T in templateXValue[max] .

generated_template: The templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] was projected to grow in templateYLabel[1] from templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] between templateXValue[min] and templateXValue[max] . As such , templateTitle[1] templateTitle[2] seems to have increased in popularity in recent years . templateTitle[1] templateTitle[2] Candy A bar of templateTitle[1] templateTitle[2] may seem indistinguishable from a bar of regular templateTitle[2] ; the biggest difference between the two is that templateTitle[1] templateTitle[2] is made from cocoa beans grown without the use of synthetic pesticides or herbicides , as well as with templateTitle[1] sugar and milk .
generated: The WarnerMedia television network Revenue was projected to grow in billion from 10.58 U.S. dollars to 10.58 U.S. dollars between 2018 and 2018 . As such , television network seems to have increased in popularity in recent years . television network Candy A bar of television network may seem indistinguishable from a bar of regular network ; the biggest difference between the two is that television network is made from cocoa beans grown without the use of synthetic pesticides or herbicides , as well as with television sugar and milk .

Example 728:
titleEntities: {'Subject': ['Average'], 'Date': ['2009', '2015']}
title: Average global hotel rates from 2009 to 2015
X_Axis['Year']: ['2009', '2010', '2011', '2012', '2013', '2014', '2015']
Y_Axis['Average', 'hotel', 'rate', 'in', 'U.S.', 'dollars']: ['155', '162', '174', '171', '171', '174', '179']

gold: This statistic shows average global hotel rates from 2009 to 2015 . In 2013 , the average global hotel rate was 171 U.S. dollars . This figure was forecasted to increase to 174 U.S. dollars in 2014 and again to 179 dollars in 2015 .
gold_template: This statistic shows templateYLabel[0] templateTitle[1] templateYLabel[1] templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[4] , the templateYLabel[0] templateTitle[1] templateYLabel[1] templateYLabel[2] was templateYValue[3] templateYLabel[3] templateYLabel[4] . This figure was forecasted to increase to templateYValue[2] templateYLabel[3] templateYLabel[4] in templateXValue[5] and again to templateYValue[max] templateYLabel[4] in templateXValue[max] .

generated_template: This statistic illustrates the proportion of templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . As of January templateXValue[max] , there were approximately templateYValue[max] templateYLabel[2] templateYLabel[1] in templateTitleSubject[0] . In templateXValue[max] , this figure is projected to reach templateYValue[max] templateYLabel[2] in templateTitleSubject[0] .
generated: This statistic illustrates the proportion of Average hotel in Average from 2009 to 2015 . As of January 2015 , there were approximately 179 rate hotel in Average . In 2015 , this figure is projected to reach 179 rate in Average .

Example 729:
titleEntities: {'Subject': ['Algeria'], 'Date': ['2019']}
title: Unemployment rate in Algeria 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['12.35', '12.15', '12', '10.2', '11.21', '10.21', '9.82', '10.97', '9.96', '9.96', '10.16', '11.33', '13.79', '12.27', '15.27', '17.65', '23.72', '25.9', '27.3', '29.77', '28.45']

gold: This statistic shows the unemployment rate in Algeria from 1998 to 2019 . In 2019 , the unemployment rate in Algeria was 12.35 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from 1998 to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Algeria from 1999 to 2019 . In 2019 , the Unemployment rate in Algeria was at approximately 12.35 percent .

Example 730:
titleEntities: {'Subject': ['Kenya'], 'Date': ['2024']}
title: Total population of Kenya 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['56.43', '54.96', '53.52', '52.11', '50.72', '49.36', '48.03', '46.73', '45.45', '44.2', '43.0']

gold: This statistic shows the total population of Kenya from 2014 to 2024 . In 2018 , the total population of Kenya was estimated at approximately 48.03 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was estimated at approximately templateYValue[6] templateYLabel[1] templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] is the second largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .
generated: This statistic shows the Total population of Kenya from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Kenya amounted to approximately 48.03 millions Inhabitants . population of Kenya is the second largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .

Example 731:
titleEntities: {'Subject': ['Qatar'], 'Date': ['2018']}
title: Urbanization in Qatar 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['99.14', '99.08', '99.02', '98.95', '98.87', '98.79', '98.7', '98.6', '98.5', '98.34', '98.14']

gold: This statistic shows the degree of urbanization in Qatar from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 99.14 percent of Qatar '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[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the percentage of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[max] percent of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
generated: This statistic shows the degree of Urbanization in Qatar from 2008 to 2018 and details the percentage of the entire population , living in urban areas . In 2018 , 99.14 percent of the total population in Qatar lived in cities .

Example 732:
titleEntities: {'Subject': ['Michigan'], 'Date': ['2018']}
title: Michigan - 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']: ['468.39', '456.0', '448.71', '440.31', '430.5', '424.32', '418.86', '411.47', '400.94', '380.09', '416.7', '441.15', '443.31', '450.75', '444.2', '443.79', '435.25', '423.62', '438.28']

gold: This statistic shows the development of Michigan 's real GDP from 2000 to 2018 . In 2018 , the real GDP of Michigan was 468.39 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[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of templateTitleSubject[0] was about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the development of Michigan 's Real GDP from 2000 to 2018 . In 2018 , the GDP of Michigan was about 468.39 billion U.S. dollars .

Example 733:
titleEntities: {'Subject': ['Kuwait'], 'Date': ['2018']}
title: Urbanization in Kuwait 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['100', '100', '100', '100', '100', '100', '100', '100', '100', '100', '100']

gold: This statistic shows the degree of urbanization in Kuwait from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 100 percent of Kuwait '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[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the percentage of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[max] percent of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
generated: This statistic shows the degree of Urbanization in Kuwait from 2008 to 2018 and details the percentage of the entire population , living in urban areas . In 2018 , 100 percent of the total population in Kuwait lived in cities .

Example 734:
titleEntities: {'Subject': ['NCAA'], 'Date': ['2019']}
title: NCAA division I men 's basketball attendance leaders 2019
X_Axis['State']: ['Syracuse', 'Kentucky', 'North_Carolina', 'Tennessee', 'Wisconsin', 'Louisville', 'Kansas', 'Creighton', 'Marquette', 'Nebraska', 'Arkansas', 'Indiana', 'Michigan_St.', 'Perdue', 'Iowa_St.', 'Virginia', 'Memphis', 'Maryland', 'Ohio_St.', 'NC_State', 'Arizona', 'Dayton', 'Iowa', 'Michigan', 'Illinois', 'Texas_Tech', 'BYU', 'South_Carolina', 'Cincinnati', 'New_Mexico']
Y_Axis['Average', 'attendance']: ['21992', '21695', '19715', '19034', '17170', '16601', '16236', '15980', '15611', '15341', '15278', '15206', '14797', '14467', '14099', '14087', '14065', '14009', '13922', '13897', '13744', '12957', '12869', '12505', '12456', '12098', '11958', '11472', '11256', '11107']

gold: While the players on the court might still be college students , the National Collegiate Athletic Association men 's basketball top division still draws in big crowds . The Syracuse Orange men 's basketball , which represents Syracuse University in New York , attracted the highest average attendance during the 2019 season . The team , traditionally known as the Syracuse Orangemen , had an average home audience of almost 22 thousand in 2019 .
gold_template: While the players on the court might still be college students , the National Collegiate Athletic Association templateTitle[3] templateTitle[4] templateTitle[5] top templateTitle[1] still draws in big crowds . The templateXValue[0] Orange templateTitle[3] templateTitle[4] templateTitle[5] , which represents templateXValue[0] University in templateXValue[last] York , attracted the highest templateYLabel[0] templateYLabel[1] during the templateTitleDate[0] season . The team , traditionally known as the templateXValue[0] Orangemen , had an templateYLabel[0] home audience of almost templateYValue[max] thousand in templateTitleDate[0] .

generated_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in the country of the templateTitle[0] in templateTitleDate[0] . templateYLabel[0] templateYLabel[1] include social contributions , the cost of professional education and training , the templateYLabel[1] on templateYLabel[0] or number of employees , and other special expenses . In templateTitleDate[0] , templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateYLabel[3] templateYLabel[4] earning amounted to templateYValue[12] templateYLabel[3] in the templateTitle[7] .
generated: The statistic depicts the Average attendance of attendance in the country of the NCAA in 2019 . Average attendance include social contributions , the cost of professional education and training , the attendance on Average or number of employees , and other special expenses . In 2019 , Average attendance of attendance earning amounted to 14797 attendance in the leaders .

Example 735:
titleEntities: {'Subject': ['New Jersey'], 'Date': ['2000', '2018']}
title: New Jersey - 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']: ['9.5', '10', '10.4', '10.8', '11.1', '11.4', '10.8', '10.4', '10.3', '9.4', '8.7', '8.6', '8.7', '8.7', '8.5', '8.4', '7.5', '7.9', '7.9']

gold: This statistic shows the poverty rate in New Jersey from 2000 to 2018 . For instance , 9.5 percent of New Jersey 's population lived below the poverty line in 2018
gold_template: This statistic shows the templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . For instance , templateYValue[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[2] line in templateXValue[max]

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the Jersey poverty in New Jersey from 2000 to 2018 . In 2018 , about 9.5 percent of New Jersey 's population lived below the Jersey line .

Example 736:
titleEntities: {'Subject': ['Croatian'], 'Date': []}
title: Leading Croatian national team players at FIFA World Cup 2018 , by market value
X_Axis['Month']: ['Ivan_Rakitic', 'Ivan_Perisic', 'Mateo_Kovacic', 'Andrej_Kramaric', 'Marcelo_Brozovic', 'Luka_Modric', 'Sime_Vrsaljko', 'Dejan_Lovren', 'Mario_Mandzukic', 'Milan_Badelj', 'Marko_Pjaca', 'Nikola_Kalinic', 'Ante_Rebic', 'Duje_Caleta–Car', 'Domagoj_Vida', 'Lovre_Kalinic', 'Tin_Jedvaj', 'Danijel_Subasic', 'Vedran_Corluka', 'Ivan_Strinic', 'Filip_Bradaric', 'Josip_Pivaric', 'Dominik_Livakovic']
Y_Axis['Market', 'value', 'in', 'million', 'euros']: ['50.0', '40.0', '30.0', '27.0', '27.0', '25.0', '25.0', '20.0', '18.0', '15.0', '15.0', '14.0', '10.0', '10.0', '7.0', '6.5', '5.0', '4.5', '4.0', '4.0', '3.5', '2.0', '1.5']

gold: The statistic displays the leading players of the national football team of Croatia at FIFA World Cup as of June 2018 , by market value . The most valuable player was Ivan Rakitic , with a market value of 50 million euros .
gold_template: The statistic displays the templateTitle[0] templateTitle[4] of the templateTitle[2] football templateTitle[3] of Croatia at templateTitle[5] templateTitle[6] templateTitle[7] as of June templateTitle[8] , templateTitle[9] templateYLabel[0] templateYLabel[1] . The most valuable player was templateXValue[0] , with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the top-25 templateTitle[0] at the templateTitle[6] templateTitle[8] templateTitle[9] templateTitle[7] in templateTitleSubject[0] according to their current templateYLabel[0] / templateTitle[4] templateYLabel[1] . templateXValue[0] templateXValue[0] of Argentina is the templateTitle[2] valued player templateTitle[1] a templateTitle[4] templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] . templateYLabel[0] values of templateTitle[0] at the templateTitle[8] templateTitle[9] templateTitle[7] - additional information The market/transfer templateYLabel[1] of a football player is determined by a number of statistics , including the player 's age , salary , playing stats and recent performances .
generated: The statistic shows the top-25 Leading at the World 2018 by Cup in Croatian according to their current Market / players value . Ivan Rakitic of Argentina is the national valued player Croatian a players Market value of 50.0 million euros . Market values of Leading at the 2018 by Cup - additional information The market/transfer value of a football player is determined by a number of statistics , including the player 's age , salary , playing stats and recent performances .

Example 737:
titleEntities: {'Subject': ['Distribution'], 'Date': ['2018']}
title: Distribution of consumer transactions worldwide 2018 , by payment channel
X_Axis['Response']: ['In-store', 'Other_online', 'Buy_buttons', 'Other_mobile_transfers', 'P2P_transfer', 'Mobile_messenger_apps', 'QR_codes', 'Other_in-app_payments', 'Smart_home_device', 'Wearables_/_contactless', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['41', '14', '9', '8', '7', '7', '5', '4', '2', '2', '2']

gold: This statistic presents the most popular payment methods for everyday transactions according to internet users worldwide as of June 2018 . When asked to think about they payment methods for their ten most recent transactions , it was found that seven percent were made via P2P transfer . In-store still accounted for the single largest share of everyday transactions with 41 percent .
gold_template: This statistic presents the most popular templateTitle[6] methods for everyday templateTitle[2] according to internet users templateTitle[3] as of June templateTitleDate[0] . When asked to think about they templateTitle[6] methods for their ten most recent templateTitle[2] , it was found that templateYValue[4] percent were made via templateXValue[4] . templateXValue[0] still accounted for the single largest templateYLabel[0] of everyday templateTitle[2] with templateYValue[max] percent .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateXValue[last] templateYLabel[1] in the templateTitle[4] as of September templateTitleDate[0] . During the survey , templateYValue[max] percent of templateYLabel[1] stated they used templateXValue[0] as their templateTitle[0] preferred activity during templateTitle[2] templateXValue[2] .
generated: The statistic shows the Distribution consumer of transactions Other respondents in the 2018 as of September 2018 . During the survey , 41 percent of respondents stated they used In-store as their Distribution preferred activity during transactions Buy buttons .

Example 738:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: U.S. preterm birth rate 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1990']
Y_Axis['Percentage', 'of', 'all', 'births']: ['10.02', '9.93', '9.85', '9.63', '9.57', '9.62', '9.76', '9.81', '9.98', '10.07', '10.36', '10.44', '12.8', '12.73', '11.64', '10.62']

gold: This statistic depicts the percentage of births that were preterm births in the United States from 1990 to 2018 . In 1990 , some 10.6 percent of all births in the United States were preterm births . A preterm birth means that a child was delivered after less than 37 weeks of gestation .
gold_template: This statistic depicts the templateYLabel[0] of templateYLabel[2] that were templateTitle[1] templateYLabel[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , some templateYValue[15] percent of templateYLabel[1] templateYLabel[2] in the templateTitle[0] were templateTitle[1] templateYLabel[2] . A templateTitle[1] templateTitle[2] means that a child was delivered after less than 37 weeks of gestation .

generated_template: This statistic represents the templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] through templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[2] templateTitle[3] in templateTitleSubject[0] templateTitleSubject[0] amounted to templateYValue[0] percent .
generated: This statistic represents the birth rate all in U.S. from 1990 through 2018 . In 2018 , U.S. birth rate in U.S. amounted to 10.02 percent .

Example 739:
titleEntities: {'Subject': ['PV'], 'Date': ['2018']}
title: Solar PV capacity - new installations worldwide by country 2018
X_Axis['Country']: ['China', 'India', 'US', 'Japan', 'Australia', 'Germany', 'Mexico', 'Republic_of_Korea', 'Turkey', 'Netherlands']
Y_Axis['Percentage', 'of', 'newly', 'installed', 'capacity']: ['45', '11', '11', '7', '4', '3', '3', '2', '2', '1']

gold: This statistic shows the share of new installed solar PV capacity worldwide in 2018 , by country . In 2018 , new solar PV capacity installations in China accounted for around 45 percent of the world 's total new installed grid-connected PV capacity .
gold_template: This statistic shows the share of templateTitle[3] templateYLabel[2] templateTitle[0] templateTitleSubject[0] templateYLabel[3] templateTitle[5] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , templateTitle[3] templateTitle[0] templateTitleSubject[0] templateYLabel[3] templateTitle[4] in templateXValue[0] accounted for around templateYValue[max] percent of the world 's total templateTitle[3] templateYLabel[2] grid-connected templateTitleSubject[0] templateYLabel[3] .

generated_template: The statistic shows the ten templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In the measured period , templateXValue[0] was the templateTitleSubject[0] templateTitle[1] 's total templateTitle[2] templateTitle[4] , using a total of almost templateYValue[max] percent of templateYLabel[1] templateYLabel[2] .
generated: The statistic shows the ten PV the capacity new installations in 2018 . In the measured period , China was the PV 's total capacity installations , using a total of almost 45 percent of newly installed .

Example 740:
titleEntities: {'Subject': ['Groupon'], 'Date': ['2009', '2019']}
title: Groupon : annual net income 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['-11.7', '1.99', '26.63', '-183.32', '33.68', '-63.92', '-88.95', '-51.03', '-297.76', '-413.39', '-1.34']

gold: The statistic above shows the annual net income of Groupon from 2008 to 2019 . In 2019 , the coupon site accumulated a net loss of more than 11.6 million dollars , an decline from the previous year 's net income of two million US dollars .
gold_template: The statistic above shows the templateTitle[1] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from 2008 to templateXValue[max] . In templateXValue[max] , the coupon site accumulated a templateYLabel[0] loss of more than 11.6 templateYLabel[2] templateYLabel[4] , an decline from the previous templateXLabel[0] 's templateYLabel[0] templateYLabel[1] of templateYValue[1] templateYLabel[2] US templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitleSubject[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] templateYLabel[1] amounted to around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in the fiscal templateXLabel[0] of templateXValue[max] .
generated: This statistic shows the Net income of Groupon from the fiscal Year of 2009 to the fiscal Year of 2019 . Groupon Net income amounted to around 33.68 million U.S. dollars in the fiscal Year of 2019 .

Example 741:
titleEntities: {'Subject': ['China'], 'Date': ['2013', '2018']}
title: China smartphone unit shipments 2013 to 2018
X_Axis['Quarter']: ['Q1_2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Shipments', 'in', 'million', 'units']: ['109.6', '454.4', '448.5', '385.3', '392.8', '359.0']

gold: The statistic shows the smartphone unit shipments in China from 2013 to Q1 2018 . In Q1 2018 , 109.6 million smartphones were shipped in China .
gold_template: The statistic shows the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , templateYValue[min] templateYLabel[1] smartphones were shipped in templateTitleSubject[0] .

generated_template: During the second templateXLabel[0] of templateXValue[0] , templateYValue[min] percent of templateTitleSubject[0] e-commerce website visits were converted into purchases , down from templateYValue[1] percent during preceding templateXLabel[0] . In the country , templateTitle[1] templateTitle[2] templateYLabel[0] rates amounted to 2.57 percent . Despite mobile commerce catching up in terms of page views and revenue , traditional templateTitle[1] shopping visits via PC still hold the top spot for conversions .
generated: During the second Quarter of Q1 2018 , 109.6 percent of China e-commerce website visits were converted into purchases , down from 454.4 percent during preceding Quarter . In the country , smartphone unit Shipments rates amounted to 2.57 percent . Despite mobile commerce catching up in terms of page views and revenue , traditional smartphone shopping visits via PC still hold the top spot for conversions .

Example 742:
titleEntities: {'Subject': ['Kazakhstan'], 'Date': ['2018']}
title: Urbanization in Kazakhstan 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['57.43', '57.34', '57.26', '57.19', '57.12', '57.05', '56.97', '56.9', '56.83', '56.76', '56.68']

gold: This statistic shows the degree of urbanization in Kazakhstan from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 57.43 percent of Kazakhstan 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Kazakhstan from 2008 to 2018 . Urbanization means the Share of urban population in the total population of a country . In 2018 , 57.43 percent of Kazakhstan 's total population lived in urban areas and cities .

Example 743:
titleEntities: {'Subject': ['eBay'], 'Date': ['2014', '2019']}
title: eBay : quarterly classifieds revenue 2014 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14"]
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['269', '265', '271', '256', '263', '254', '259', '246', '244', '235', '219', '199', '201', '197', '207', '186', '183', '178', '180', '162', '180']

gold: eBay 's classifieds revenue in the fourth quarter of 2019 amounted to 269 million U.S. dollars . This represents a three percent year-on-year change . The classifieds revenue is counted towards the company 's marketing services and other revenues segment .
gold_template: templateTitleSubject[0] 's templateTitle[2] templateYLabel[0] in the fourth templateXLabel[0] of templateTitleDate[1] amounted to templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . This represents a three percent year-on-year change . The templateTitle[2] templateYLabel[0] is counted towards the company 's marketing services and other revenues segment .

generated_template: The statistic shows the templateYLabel[0] of the fourth templateXLabel[0] of templateTitleSubject[0] from the second templateXLabel[0] of templateTitleDate[0] to the fourth templateXLabel[0] of templateTitleDate[1] . In the last reported templateXLabel[0] , the estimated templateXLabel[0] , templateTitleSubject[0] 's templateYLabel[0] of templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the fourth Quarter of eBay from the second Quarter of 2014 to the fourth Quarter of 2019 . In the last reported Quarter , the estimated Quarter , eBay 's Revenue of 269 million U.S. dollars .

Example 744:
titleEntities: {'Subject': ['Countries'], 'Date': ['2017']}
title: Countries with the highest population decline rate 2017
X_Axis['Country']: ['Cook_Islands', 'Puerto_Rico', 'American_Samoa', 'Lebanon', 'Saint_Pierre_and_Miquelon', 'Latvia', 'Lithuania', 'Moldova', 'Bulgaria', 'Estonia', 'Federated_States_of_Micronesia', 'Northern_Mariana_Islands', 'Croatia', 'Serbia', 'Ukraine', 'Romania', 'Slovenia', 'Cuba', 'Montenegro', 'Virgin_Islands']
Y_Axis['Population', 'decline', 'compared', 'to', 'the', 'previous', 'year']: ['2.79', '1.74', '1.3', '1.1', '1.08', '1.08', '1.08', '1.05', '0.61', '0.57', '0.52', '0.51', '0.5', '0.46', '0.41', '0.33', '0.31', '0.29', '0.28', '0.25']

gold: This statistic shows the 20 countries with the highest population decline rate in 2017 . In the Cook Islands , the population decreased by about 2.8 percent compared to the previous year , making it the country with the highest population decline rate in 2017 . The population decline of Syria Due to the conflicts and instability in Syria in 2014 , thousands of people have fled have fled the country to cross into surrounding countries such as Turkey .
gold_template: This statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitleDate[0] . In the templateXValue[0] , the templateYLabel[0] decreased by about templateYValue[max] percent templateYLabel[2] to the templateYLabel[3] templateYLabel[4] , making it the templateXLabel[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] of Syria Due to the conflicts and instability in Syria in 2014 , thousands of people have fled have fled the templateXLabel[0] to cross into surrounding templateTitleSubject[0] such as Turkey .

generated_template: This statistic displays the templateTitleSubject[0] templateTitle[1] online templateTitle[3] based on relative templateYLabel[0] user templateYLabel[1] as of January templateTitleDate[0] . During the measured period , it was found that online audiences in the Republic of the templateXValue[0] grew templateYValue[max] percent in total .
generated: This statistic displays the Countries highest online decline based on relative Population user decline as of January 2017 . During the measured period , it was found that online audiences in the Republic of the Cook Islands grew 2.79 percent in total .

Example 745:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010']}
title: Percentage of U.S. companies using self-insured health plans for employees 2010
X_Axis['Number', 'of', 'employees']: ['3_to_49', '50_to_199', '200_to_999', '1000_and_more']
Y_Axis['Share', 'of', 'companies']: ['8', '20', '48', '80']

gold: This statistic shows the percentage of U.S. companies using self-insured health plans for employees in 2010 , by the number of employees . 80 percent of companies with 1,000 and more employees used self-insured health plans in 2010 .
gold_template: This statistic shows the templateTitle[0] of templateTitleSubject[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateXLabel[1] in templateTitleDate[0] , by the templateXLabel[0] of templateXLabel[1] . templateYValue[max] percent of templateYLabel[1] with templateXValue[last] and templateXValue[last] templateXLabel[1] used templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] .

generated_template: This statistic shows the templateTitle[1] of templateYLabel[1] at templateTitleSubject[0] agricultural company in templateTitleDate[0] , templateTitle[3] templateXLabel[0] . In templateXValue[2] templateXValue[1] , the company employed approximately templateYValue[2] percent of the total people working for templateTitleSubject[0] .
generated: This statistic shows the U.S. of companies at U.S. agricultural company in 2010 , using Number . In 200 to 999 50 to 199 , the company employed approximately 48 percent of the total people working for U.S. .

Example 746:
titleEntities: {'Subject': ['BP'], 'Date': ['2010', '2018']}
title: BP 's revenue - Upstream segment 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['56399', '45440', '33188', '43235', '65424', '70374', '72225', '75754', '66266']

gold: This statistic shows the revenue of the BP Upstream segment from 2010 to 2018 . In 2018 , BP Upstream reported some 56.4 billion U.S. dollars of revenue . BP is a globally operating oil and gas company , and the eighth largest company generally worldwide as of 2017 , measured by revenue .
gold_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[3] reported some templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateYLabel[0] . templateTitleSubject[0] is a globally operating oil and gas company , and the eighth largest company generally worldwide as of templateXValue[1] , measured by templateYLabel[0] .

generated_template: templateTitle[2] spending on the National Football League ( templateTitleSubject[0] ) and its teams has increased annually since templateXValue[min] , reaching templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide during the templateXValue[max] season . Nike is one of the league 's main sponsors , providing uniform and apparel to all 32 templateTitleSubject[0] teams since templateXValue[6] – the two parties agreed a long-term extension to their rights partnership in templateXValue[max] . Why are templateTitle[2] deals worth so much ? Sponsors are prepared to spend large amounts of money to get their brand displayed on big screens and pitch-side hoardings in stadiums across the templateTitleSubject[0] .
generated: revenue spending on the National Football League ( BP ) and its teams has increased annually since 2010 , reaching 75754 million U.S. dollars worldwide during the 2018 season . Nike is one of the league 's main sponsors , providing uniform and apparel to all 32 BP teams since 2012 – the two parties agreed a long-term extension to their rights partnership in 2018 . Why are revenue deals worth so much ? Sponsors are prepared to spend large amounts of money to get their brand displayed on big screens and pitch-side hoardings in stadiums across the BP .

Example 747:
titleEntities: {'Subject': ['U.S. Instagram'], 'Date': ['2015', '2015']}
title: Share of U.S. teenagers who use Instagram 2015 , by gender and age
X_Axis['Response']: ['Boys_13-14', 'Boys_15-17', 'Girls_13-14', 'Girls_15-17']
Y_Axis['Share', 'of', 'respondents']: ['33', '51', '56', '64']

gold: This statistic shows the share of teenagers in the United States who were Instagram users as of March 2015 , sorted by gender and age group . During that period of time , 64 percent of female U.S. teens aged 15 to 17 years used the social networking app .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] in the templateTitle[1] templateTitle[3] were templateTitleSubject[0] users as of March templateTitle[6] , sorted templateTitle[7] templateTitle[8] and templateTitle[9] group . During that period of time , templateYValue[max] percent of female templateTitleSubject[0] teens aged 15 to 17 years used the social networking app .

generated_template: This statistic shows the results of a survey among female templateTitleSubject[0] high school students templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . According to the source , templateYValue[max] percent of female students in the templateTitle[1] were templateTitle[5] templateTitle[6] as of 2013 .
generated: This statistic shows the results of a survey among female U.S. Instagram high school students who use Instagram 2015 . According to the source , 64 percent of female students in the U.S. were Instagram 2015 as of 2013 .

Example 748:
titleEntities: {'Subject': ['EU-28'], 'Date': ['2017']}
title: Proportion of individuals who have tried waterpipe , shisha or hooka in EU-28 2017
X_Axis['Response']: ['Yes', 'Never', 'Spontaneous']
Y_Axis['Share', 'of', 'respondents']: ['13', '87', '0']

gold: This statistic displays the proportion of individuals who have tried water pipe , shisha or hookah in EU-28 countries in 2017 . A majority of 87 percent of respondents said they have never tried water pipe , shisha or hookah products . Additionally , the proportion of individuals who have tried oral , nasal or chewing tobacco can be found at the following .
gold_template: This statistic displays the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] water pipe , templateTitle[6] or hookah in templateTitleSubject[0] countries in templateTitleDate[0] . A majority of templateYValue[max] percent of templateYLabel[1] said they templateTitle[3] templateXValue[1] templateTitle[4] water pipe , templateTitle[6] or hookah products . Additionally , the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] oral , nasal or chewing tobacco can be found at the following .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] templateTitleSubject[0] who were using templateTitle[0] as of February templateTitleDate[0] , sorted templateTitle[6] educational background . During that period of time , templateYValue[2] percent of templateYLabel[1] who had attained a templateXValue[1] degree used the social networking site .
generated: This statistic shows the Share of adults in the EU-28 who were using Proportion as of February 2017 , sorted shisha educational background . During that period of time , 0 percent of respondents who had attained a Never degree used the social networking site .

Example 749:
titleEntities: {'Subject': ['The'], 'Date': ['2018']}
title: The 20 worst terrorist attacks by number of fatalities 2018
X_Axis['City,', 'country', '(date),', 'organisation']: ['Ghazni_Afghanistan_(Taliban)_(8/10/2018)', 'Farah_Afghanistan_(Taliban)_(5/15/2018)', 'Darengarh_Pakistan_(Khorasan_Chapter_of_the_Islamic_State)_(7/13/2018)', 'Kabul_Afghanistan_(Taliban)_(1/27/2018)', 'Dila_District_Afghanistan_(Taliban)_(10/12/2018)', 'Kabul_Afghanistan_(Khorasan_Chapter_of_the_Islamic_State)_(4/22/2018)', 'Muhmand_Dara_District_Afghanistan_(Unknown)_(9/11/2018)', 'Day_Mirdad_District_Afghanistan_(Taliban)_(9/9/2018)', 'Maywand_District_Afghanistan_(Taliban)_(9/11/2018)', 'Farah_Afghanistan_(Taliban)_(5/12/2018)', 'Gwaska_Nigeria_(Attributed_to_"Fulani_Extremists")_(5/5/2018)', 'Kabul_Afghanistan_(Khorasan_Chapter_of_the_Islamic_State)_(11/20/2018)', 'Sari_Pul_Afghanistan_(Taliban)_(9/10/2018)', 'Chora_District_Afghanistan_(Taliban)_(8/3/2018)', 'Pur_Chaman_District_Afghanistan_(Taliban)_(6/12/2018)', 'Albu_Kamal_Syria_(ISIL)_(6/8/2018)', 'Azra_District_Afghanistan_(Taliban)_(8/6/2018)', 'Kabul_Afghanistan_(Taliban)_(12/24/2018)', 'Oshan_Afghanistan_(Taliban)_(5/11/2018)', 'Tagbara_Central_African_Republic_(Anti-Balaka_Militia)_(4/3/2018)']
Y_Axis['Number', 'of', 'fatalities']: ['466', '330', '150', '104', '77', '70', '69', '62', '61', '61', '58', '56', '56', '51', '51', '51', '50', '47', '46', '44']

gold: The statistic shows the 20 worst terrorist attacks of 2018 , by number of fatalities . The worst terrorist attack in 2018 occurred on August 10 , 2018 , was carried out by the Taliban in Ghazni , Afghanistan , and caused 466 fatalities .
gold_template: The statistic shows the templateTitleDate[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleDate[0] , templateTitle[4] templateYLabel[0] of templateYLabel[1] . The templateTitle[1] templateTitle[2] attack in templateTitleDate[0] occurred on August 10 , templateTitleDate[0] , was carried out templateTitle[4] the Taliban in templateXValue[0] , templateXValue[0] , and caused templateYValue[max] templateYLabel[1] .

generated_template: There were templateYValue[min] templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] in templateTitleDate[0] , a decline of almost 24 percent of templateYLabel[1] templateYLabel[2] . In the templateTitleSubject[0] templateTitleSubject[0] is the second most populous city worldwide . The templateTitle[0] templateTitle[1] industry was the highest number of templateYLabel[1] in the templateTitleSubject[0] , with a templateYLabel[0] amounting to templateYValue[0] percent of templateYLabel[1] templateYLabel[2] in templateTitleDate[0] .
generated: There were 44 20 worst fatalities in the in 2018 , a decline of almost 24 percent of fatalities . In the is the second most populous city worldwide . The 20 worst industry was the highest number of fatalities in the , with a Number amounting to 466 percent of fatalities in 2018 .

Example 750:
titleEntities: {'Subject': ['Finland'], 'Date': ['2007', '2017']}
title: Number of hospitals in Finland 2007 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'hospitals']: ['247', '262', '268', '258', '259', '263', '275', '280', '298', '320', '325']

gold: The number of hospitals in Finland was down at the lowest point of the observed period in 2017 , when there were 247 hospitals . At the beginning of the observed period , in 2007 , the number of hospitals amounted to 325 .
gold_template: The templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] was down at the lowest point of the observed period in templateXValue[max] , when there were templateYValue[min] templateYLabel[1] . At the beginning of the observed period , in templateXValue[min] , the templateYLabel[0] of templateYLabel[1] amounted to templateYValue[max] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] amounted to approximately templateYValue[0] thousand .
generated: The statistic shows the Number of hospitals in Finland from 2007 to 2017 . In 2017 , the Number of hospitals in Finland amounted to approximately 247 thousand .

Example 751:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Leading men 's hair coloring brands in the U.S. 2019
X_Axis['Brand']: ['Just_For_Men', 'Just_For_Men_Autostop', 'Just_For_Men_Control_GX', 'Just_For_Men_Touch_of_Gray', 'Softsheen-Carson_Dark_&_Natural', 'Private_label', 'Grecian_Formula_16', 'Just_For_Men_Original_Formula', 'Creme_of_Nature', 'Grecian_5']
Y_Axis['Sales', 'in', 'million', 'U.S.', 'dollars']: ['138.0', '27.2', '18.2', '9.7', '5.7', '2.8', '2.3', '0.5', '0.3', '0.1']

gold: In 2019 , Just For Men was the leading men 's hair coloring brand in the United States with sales of approximately 138 million U.S. dollars . Ranked second , the Just For Men Autostop brand generated sales of around 27.2 million U.S. dollars that year .
gold_template: In templateTitleDate[0] , templateXValue[0] Men was the templateTitle[0] templateXValue[0] templateTitle[2] templateTitle[3] templateTitle[4] templateXLabel[0] in the templateTitle[6] with templateYLabel[0] of approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Ranked second , the templateXValue[0] Men templateXValue[1] templateXLabel[0] generated templateYLabel[0] of around templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] that year .

generated_template: The templateTitle[0] name templateXLabel[0] of templateTitle[1] templateTitle[2] and templateTitle[1] templateTitle[3] in the templateTitle[5] is templateXValue[1] templateXValue[1] templateXValue[1] , which generated some templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] in templateTitleDate[0] . However , templateXValue[0] templateXValue[0] templateTitle[1] lotions had higher dollar templateYLabel[0] than any name templateXLabel[0] in that year , at around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Sunscreen and skin protection Dermatologists highly recommend sunscreen to everyone , regardless of age or complexion .
generated: The Leading name Brand of men 's and men hair in the brands is Just For Men Autostop , which generated some 27.2 million U.S. dollars in Sales in 2019 . However , Just For Men men lotions had higher dollar Sales than any name Brand in that year , at around 138.0 million U.S. dollars . Sunscreen and skin protection Dermatologists highly recommend sunscreen to everyone , regardless of age or complexion .

Example 752:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018']}
title: Birth rate in Italy 2018 , by region
X_Axis['Month']: ['Trentino-South_Tyrol', 'Campania', 'Sicily', 'Calabria', 'Lombardy', 'Emilia-Romagna', 'Lazio', 'Apulia', 'Aosta_Valley', 'Veneto', 'Abruzzo', 'Piedmont', 'Tuscany', 'Marche', 'Umbria', 'Basilicata', 'Friuli-Venezia_Giulia', 'Molise', 'Liguria', 'Sardinia']
Y_Axis['Birth', 'rate', 'per', 'thousand', 'inhabitants']: ['9.0', '8.3', '8.1', '7.8', '7.5', '7.3', '7.2', '7.2', '7.2', '7.2', '6.8', '6.7', '6.7', '6.7', '6.6', '6.6', '6.4', '6.2', '5.8', '5.7']

gold: In 2018 , Trentino-South Tyrol was the region in Italy with the highest birth rate nationwide , with nine births per every 1,000 inhabitants . The following three positions of the ranking were occupied by Southern regions : Campania , Sicily , and Calabria . Indeed , South-Italy was the macro-region with the largest birth-rate in Italy .
gold_template: In templateTitleDate[0] , templateXValue[0] was the templateTitle[5] in templateTitleSubject[0] with the highest templateYLabel[0] templateYLabel[1] nationwide , with templateYValue[max] births templateYLabel[2] every 1,000 templateYLabel[4] . The following three positions of the ranking were occupied templateTitle[4] Southern regions : templateXValue[1] , templateXValue[2] , and templateXValue[3] . Indeed , South-Italy was the macro-region with the largest birth-rate in templateTitleSubject[0] .

generated_template: In templateTitleDate[0] , the Italian templateTitle[5] which registered the highest templateTitle[0] templateTitle[1] in the country was templateXValue[0] templateXValue[0] . In the year considered , the templateYLabel[0] templateYLabel[1] of templateYLabel[2] born templateYLabel[3] templateYLabel[4] in this area reached templateYValue[max] infants . Overall over the last years , the birth templateTitle[1] in templateTitleSubject[0] has constantly decreased . The island of volcanoes The symbol of templateXValue[0] , Mount Etna , is one of the most active volcanos on the planet .
generated: In 2018 , the Italian region which registered the highest Birth rate in the country was Trentino-South Tyrol . In the year considered , the Birth rate of per born thousand inhabitants in this area reached 9.0 infants . Overall over the last years , the birth rate in Italy has constantly decreased . The island of volcanoes The symbol of Trentino-South Tyrol , Mount Etna , is one of the most active volcanos on the planet .

Example 753:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2018']}
title: Ad blocker usage in the United Kingdom ( UK ) 2018
X_Axis['Response']: ['Use_ad_blocker', "Don't_use_ad_blocker", "Don't_know"]
Y_Axis['Share', 'of', 'respondents']: ['41', '53', '6']

gold: This statistic shows the survey on ad blocker usage in the United Kingdom in 2018 . According to the survey , 41 percent of the respondents used an ad blocker , while 53 percent did not . Six percent of respondents said they did n't know if they used ad blocking software .
gold_template: This statistic shows the survey on templateXValue[0] usage in the templateTitleSubject[0] in templateTitleDate[0] . According to the survey , templateYValue[0] percent of the templateYLabel[1] used an templateXValue[0] , while templateYValue[max] percent did not . templateYValue[min] percent of templateYLabel[1] said they did n't templateXValue[last] if they used templateXValue[0] blocking software .

generated_template: This statistic shows the results of a survey conducted templateTitle[3] the Hearth , Patio & Barbecue Association ( HPBA ) in July and August templateTitleDate[0] . During the survey , templateTitleSubject[0] consumers were asked about which types of grills they owned . Approximately templateYValue[max] percent of the templateYLabel[1] indicated templateTitle[2] of a templateXValue[0] templateXValue[0] .
generated: This statistic shows the results of a survey conducted United the Hearth , Patio & Barbecue Association ( HPBA ) in July and August 2018 . During the survey , United Kingdom consumers were asked about which types of grills they owned . Approximately 53 percent of the respondents indicated usage of a Use ad blocker .

Example 754:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2018']}
title: Number of pubs in the United Kingdom ( UK ) 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '48', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Number', 'of', 'pubs', 'in', 'thousands']: ['47.6', '48.35', '50.3', '50.8', '51.9', '52.5', '53.8', '54.7', '55.4', '52.5', '54.8', '56.8', '58.2', '58.6', '59.0', '59.4', '60.1', '60.7', '60.8']

gold: How many pubs are there in the UK ? There were approximately 47,600 pubs operating in the United Kingdom in 2018 . This represented a decrease of approximately 7,200 pubs in the last ten years , and over 13,200 pubs since 2000 . Pubs in decline Several factors have been suggested for the decline in pubs in the UK .
gold_template: How many templateYLabel[1] are there in the templateTitleSubject[1] ? There were approximately templateYValue[min] templateYLabel[1] operating in the templateTitleSubject[0] in templateXValue[max] . This represented a decrease of approximately 7,200 templateYLabel[1] in the last ten years , and over 13,200 templateYLabel[1] since templateXValue[18] . templateYLabel[1] in decline Several factors have been suggested for the decline in templateYLabel[1] in the templateTitleSubject[1] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in Slovakia from templateXValue[min] to templateXValue[max] . Over this period , the templateYLabel[1] templateYLabel[0] rose by over 5 thousand templateYLabel[3] , peaking at 13.8 thousand templateYLabel[3] in templateXValue[max] .
generated: This statistic shows the Number pubs thousands in Slovakia from 48 to 2018 . Over this period , the pubs Number rose by over 5 thousand thousands , peaking at 13.8 thousand thousands in 2018 .

Example 755:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Household income of white families in the U.S. 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Median', 'income', 'in', '2018', 'CPI-U-RS', 'adjusted', 'U.S.', 'dollars']: ['70642', '69851', '66440', '65133', '62453', '63588', '60979', '60526', '61361', '62374', '63378', '65089', '63892', '63900', '63627', '63832', '64084', '62773', '63609', '63654', '62480', '60548', '59128', '58184', '56297', '55914', '55842', '55568', '56917']

gold: This statistic shows the household income of white families in the U.S. from 1990 to 2018 . The median income in 2018 was at 70,642 U.S. dollars for white , non-Hispanic families . The median household income of the United States can be accessed here .
gold_template: This statistic shows the templateTitle[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the templateYLabel[5] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] in templateXValue[max] was at templateYValue[max] templateYLabel[5] templateYLabel[6] for templateTitle[2] , non-Hispanic templateTitle[3] . The templateYLabel[0] templateTitle[0] templateYLabel[1] of the templateTitle[4] can be accessed here .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] parent families with a female householder and no spouse present in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[28] percent of all templateTitleSubject[0] families with a templateTitle[3] mother in the templateTitle[5] lived below the templateYLabel[0] level . In templateXValue[max] , that figure was at templateYValue[min] percent .
generated: This statistic shows the Median income of U.S. families parent families with a female householder and no spouse present in the U.S. from 1990 to 2018 . In 1990 , 56917 percent of all U.S. families with a families mother in the 1990 lived below the Median level . In 2018 , that figure was at 55568 percent .

Example 756:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010', '2018']}
title: Average annual food away-from-home expenditures of U.S. households 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Expenditure', 'in', 'U.S.', 'dollars']: ['3459', '3365', '3154', '3008', '2787', '2625', '2678', '2620', '2505']

gold: This timeline depicts the average annual food away-from-home expenditure of United States households from 2010 to 2018 . In 2018 , average food away-from-home expenditure of U.S. households amounted to about 3,459 U.S. dollars .
gold_template: This timeline depicts the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] of templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[2] templateTitle[3] templateYLabel[0] of templateYLabel[1] templateTitle[6] amounted to about templateYValue[max] templateYLabel[1] templateYLabel[2] .

generated_template: The statistic presents the annual templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the developer of a popular mobile game - Angry Birds - templateTitle[1] templateYValue[0] templateYLabel[1] templateYLabel[2] in templateYLabel[0] .
generated: The statistic presents the annual Expenditure of U.S. food U.S. worldwide from 2010 to 2018 . In 2018 , the developer of a popular mobile game - Angry Birds - annual 3459 U.S. dollars in Expenditure .

Example 757:
titleEntities: {'Subject': ['Turkey'], 'Date': []}
title: Ratio of government expenditure in relation to gross domestic product ( GDP ) in Turkey
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budgetary', 'balance', 'in', 'relation', 'to', 'the', 'gross', 'domestic', 'product']: ['35.66', '35.68', '35.64', '35.61', '35.17', '34.81', '34.61', '33.62', '35.08', '33.37', '33.23']

gold: This statistic shows the ratio of government expenditure to gross domestic product ( GDP ) in Turkey from 2014 to 2018 , with projections up until 2024 . In 2018 , the ratio in relation to the GDP in Turkey was at approximately 34.61 percent .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] to templateYLabel[3] templateYLabel[4] templateYLabel[5] ( templateTitle[7] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] in templateYLabel[2] to the templateTitle[7] in templateTitleSubject[0] was at approximately templateYValue[6] percent .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateYLabel[1] templateYLabel[0] . See figures for Switzerland 's templateTitle[1] and the templateTitle[1] of Italy for comparison .
generated: The statistic shows the Ratio government of Turkey from 2014 to 2018 , with projections up until 2024 . In 2018 , the Ratio government of Turkey amounted to around 34.61 balance Budgetary . See figures for Switzerland 's government and the government of Italy for comparison .

Example 758:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Travelers in the U.S. who find family vacation planning stressful in 2014 , by gender
X_Axis['Response']: ['Women', 'Men']
Y_Axis['Share', 'of', 'respondents']: ['74', '67']

gold: This statistic shows the share of travelers who find family vacation planning stressful in the United States as of May 2014 , by gender . During the survey , 74 percent of women said that they found family vacation planning stressful .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitle[1] as of May templateTitleDate[0] , templateTitle[9] templateTitle[10] . During the survey , templateYValue[max] percent of templateXValue[0] said that they found templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] templateTitleSubject[0] who were using templateTitle[0] as of February templateTitleDate[0] , sorted templateTitle[6] templateTitle[7] . During that period of time , templateYValue[max] percent of female templateYLabel[1] stated that they used the social networking site .
generated: This statistic shows the Share of adults in the U.S. who were using Travelers as of February 2014 , sorted planning stressful . During that period of time , 74 percent of female respondents stated that they used the social networking site .

Example 759:
titleEntities: {'Subject': ['El Salvador'], 'Date': ['2024']}
title: Inflation rate in El Salvador 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['1', '1', '1.1', '1.1', '1.06', '0.89', '1.09', '1.01', '0.6', '-0.73', '1.14', '0.76', '1.73', '5.13', '1.18', '0.54', '7.26', '4.58', '4.04', '4.69', '4.45', '2.12', '1.87', '3.75', '2.27', '0.51', '2.55', '4.49', '9.79', '10.03', '10.58', '18.51', '11.22', '14.41', '28.29', '17.65', '19.77', '24.85', '31.95', '22.32', '11.71']

gold: This statistic shows the average inflation rate in El Salvador from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in El Salvador amounted to about 1.09 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

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

Example 760:
titleEntities: {'Subject': ['Global'], 'Date': ['2019']}
title: Global exports of broiler meat 2019 , by country
X_Axis['Country']: ['Brazil', 'United_States', 'EU', 'Thailand', 'China', 'Turkey', 'Ukraine', 'Belarus', 'Russia', 'Argentina', 'Canada', 'Others']
Y_Axis['Export', 'volume', 'in', 'thousand', 'metric', 'tons']: ['3775', '3248', '1500', '900', '475', '400', '350', '185', '180', '145', '130', '331']

gold: This statistic depicts the export volume of broiler meat worldwide in 2019 , by leading country , in thousand metric tons . The broiler meat exports of the United States amounted to approximately 3.25 million metric tons in that year .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] worldwide in templateTitleDate[0] , templateTitle[5] leading templateXLabel[0] , in thousand templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] templateTitle[1] of the templateXValue[1] amounted to approximately templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in that year .

generated_template: templateXValue[0] had the world 's largest templateYLabel[0] of templateTitleSubject[0] templateYLabel[0] as of templateTitleDate[0] , amounting to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] that year . templateTitleSubject[0] and its uses templateTitleSubject[0] is a naturally occurring element that is soft and malleable . It is a post-transition metal that is very dense and is easily tarnished to a dull grey color .
generated: Brazil had the world 's largest Export of Global Export as of 2019 , amounting to 3775 volume thousand metric that year . Global and its uses Global is a naturally occurring element that is soft and malleable . It is a post-transition metal that is very dense and is easily tarnished to a dull grey color .

Example 761:
titleEntities: {'Subject': ['France'], 'Date': ['2013']}
title: Distribution of young people according to the age of their first kiss in France 2013
X_Axis['Year']: ['Under_11_years_old', '11_to_12_years_old', '13_to_14_years_old', '15_years_old', '16_years_old', '17_years_old_and_over', 'Is_not_pronounced']
Y_Axis['Share', 'of', 'respondents']: ['16', '12', '31', '16', '10', '13', '2']

gold: In 2013 , it appears that the majority of French teenagers were in middle school when they had their first kiss . Love appears to be an important area of life at a young age , with more than 50 percent of young French people stating that love relationships were important for them . First love experiences Even though new technologies and smartphones may have changed the way teenagers live their love life , it seems that the age for first love and sex experiences has not really changed over the years .
gold_template: In templateTitle[9] , it appears that the majority of French teenagers were in middle school when they had templateTitle[5] templateTitle[6] templateTitle[7] . Love appears to be an important area of life at a templateTitle[1] templateTitle[4] , with more than 50 percent of templateTitle[1] French templateTitle[2] stating that love relationships were important for them . templateTitle[6] love experiences Even though new technologies and smartphones may have changed the way teenagers live templateTitle[5] love life , it seems that the templateTitle[4] for templateTitle[6] love and sex experiences has templateXValue[last] really changed templateXValue[5] the templateXValue[0] .

generated_template: This statistic illustrates the proportion of templateTitle[3] templateTitle[4] who read magazines outside of class in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The percentage of school children aged 8 to 18 years who read magazines declined since templateXValue[min] , and was templateYValue[min] percent in templateXValue[max] . Magazines ranked highly among media read templateTitle[2] templateTitle[3] templateTitle[4] .
generated: This statistic illustrates the proportion of according age who read magazines outside of class in the France from Under 11 years old to Under 11 years old . The percentage of school children aged 8 to 18 years who read magazines declined since Under 11 years old , and was 2 percent in Under 11 years old . Magazines ranked highly among media read people according age .

Example 762:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. rice exports - top destination country 2017
X_Axis['Country']: ['Mexico', 'Haiti', 'Japan', 'Venezuela', 'Canada', 'Costa_Rica', 'Korea_South', 'Jordan', 'Honduras', 'Saudi_Arabia']
Y_Axis['Exports', 'in', 'metric', 'tons']: ['894043', '508527', '302752', '240063', '221833', '164114', '152098', '146558', '137420', '124913']

gold: This statistic shows the major nations to which the U.S. exported rice ( milled basis ) in 2017 . Some 894,043 metric tons were exported to Mexico that year . Thus , Mexico was ranked first among the most important destinations for U.S. rice exports in 2017 .
gold_template: This statistic shows the major nations to which the templateTitleSubject[0] exported templateTitle[1] ( milled basis ) in templateTitleDate[0] . Some templateYValue[max] templateYLabel[1] templateYLabel[2] were exported to templateXValue[0] that year . Thus , templateXValue[0] was ranked first among the most important destinations for templateTitleSubject[0] templateTitle[1] templateYLabel[0] in templateTitleDate[0] .

generated_template: templateTitle[4] templateYLabel[1] are popular among people that want to enhance select features of their body through surgical and nonsurgical methods . As of templateTitleDate[0] , the templateXValue[0] templateXValue[0] had the templateTitle[2] templateYLabel[0] of templateTitle[4] templateYLabel[1] during that year templateTitle[1] over 4.3 million templateYLabel[1] . templateXValue[1] had the second templateTitle[2] templateYLabel[0] of templateTitle[4] templateYLabel[1] templateTitle[1] almost templateYValue[1] million templateYLabel[1] templateYLabel[2] in that year .
generated: destination metric are popular among people that want to enhance select features of their body through surgical and nonsurgical methods . As of 2017 , the Mexico had the exports of destination metric during that year rice over 4.3 million metric . Haiti had the second exports of destination metric rice almost 508527 million metric tons in that year .

Example 763:
titleEntities: {'Subject': ['National Football League', 'Dallas Cowboys'], 'Date': ['2001', '2018']}
title: National Football League : operating income of the Dallas Cowboys 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['Operating', 'income', 'in', 'million', 'U.S.', 'dollars']: ['420.0', '365.0', '350.0', '300.0', '270.0', '245.7', '250.5', '226.7', '119.0', '143.3', '9.2', '30.6', '4.3', '37.1', '54.3', '37.5', '52.3', '75.0']

gold: The statistic depicts the operating income of the Dallas Cowboys , a franchise of the National Football League , from 2001 to 2018 . In the 2018 season , the operating income of the Dallas Cowboys was at 420 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[5] templateTitle[6] , a franchise of the templateTitleSubject[0] League , from templateXValue[min] to templateXValue[max] . In the templateXValue[max] season , the templateYLabel[0] templateYLabel[1] of the templateTitle[5] templateTitle[6] was at templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the franchise had a team payroll , including benefits and bonuses , of templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The timeline shows the Operating income of the National Football League from 2001 to 2018 . In 2018 , the franchise had a team payroll , including benefits and bonuses , of 420.0 million U.S. dollars .

Example 764:
titleEntities: {'Subject': ['Tesla'], 'Date': ['2019', '2019']}
title: Tesla 's vehicle deliveries by quarter 2019
X_Axis['Quarter']: ['Q4_2019', 'Q3_2019', 'Q2_2019', 'Q1_2019', 'Q4_2018', 'Q3_2018', 'Q2_2018', 'Q1_2018', 'Q4_2017', 'Q3_2017', 'Q2_2017', 'Q1_2017', 'Q4_2016', 'Q3_2016', 'Q2_2016', 'Q1_2016', 'Q4_2015']
Y_Axis['Number', 'of', 'deliveries', 'in', 'units']: ['112000', '97000', '95200', '63000', '90700', '83500', '40740', '29980', '29870', '26150', '22000', '25000', '22200', '24500', '14370', '14820', '17400']

gold: How many Tesla vehicles were delivered in 2019 ? Annual deliveries rose by almost 50 percent between 2018 and 2019 . Year-to-date deliveries increased to between 367,000 and 368,000 units in 2019 , and Tesla delivered around 112,000 vehicles during the fourth quarter of 2019 alone . The quarterly figure represents a new record following the electric carmaker 's previous quarter which set the record at 97,000 deliveries worldwide .
gold_template: How many templateTitleSubject[0] vehicles were delivered in templateXValue[0] ? Annual templateYLabel[1] rose templateTitle[4] almost 50 percent between templateXValue[4] and templateXValue[0] . Year-to-date templateYLabel[1] increased to between 367,000 and 368,000 templateYLabel[2] in templateXValue[0] , and templateTitleSubject[0] delivered around templateYValue[max] vehicles during the fourth templateXLabel[0] of templateXValue[0] alone . The quarterly figure represents a new record following the electric carmaker templateTitle[1] previous templateXLabel[0] which set the record at templateYValue[1] templateYLabel[1] worldwide .

generated_template: During the second templateXLabel[0] of templateXValue[0] , templateTitleSubject[0] 's templateYLabel[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the preceding templateXLabel[0] . templateTitleSubject[0] 's main templateYLabel[0] source is advertising to 2.57 templateYLabel[1] templateYLabel[2] templateYLabel[3] in the preceding templateXLabel[0] .
generated: During the second Quarter of Q4 2019 , Tesla 's Number amounted to 112000 deliveries units , up from 97000 deliveries units in the preceding Quarter . Tesla 's main Number source is advertising to 2.57 deliveries units in the preceding Quarter .

Example 765:
titleEntities: {'Subject': ['Nigeria'], 'Date': ['2024']}
title: Inflation rate 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']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['11', '11.14', '11.4', '11.31', '11.73', '11.32', '12.09', '16.5', '15.7', '9.01', '8.05', '8.5', '12.23', '10.83', '13.74', '12.54', '11.58', '5.4', '8.22', '17.86', '15']

gold: Nigeria 's inflation has been higher than the average for African and Sub-Saharan countries for years now , and even exceeded 16 percent in 2017 – and a real , significant decrease is nowhere in sight . The bigger problem is its unsteadiness , however : An inflation rate that is bouncing all over the place , like this one , is usually a sign of a struggling economy , causing prices to fluctuate , and unemployment and poverty to increase . Nigeria 's economy - a so-called `` mixed economy '' , which means the market economy is at least in part regulated by the state – is not entirely in bad shape , though .
gold_template: templateTitleSubject[0] 's templateYLabel[0] has been higher than the average for African and Sub-Saharan countries for years now , and even exceeded templateYValue[8] percent in templateXValue[7] – and a real , significant decrease is nowhere in sight . The bigger problem is its unsteadiness , however : An templateYLabel[0] templateYLabel[1] that is bouncing all over the place , like this one , is usually a sign of a struggling economy , causing prices to fluctuate , and unemployment and poverty to increase . templateTitleSubject[0] 's economy - a so-called `` mixed economy '' , which means the market economy is at least in part regulated by the state – is not entirely in bad shape , though .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Nigeria from 2004 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Nigeria amounted to about 12.09 percent compared to the previous Year .

Example 766:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2019']}
title: Medicine : Online purchasing in Great Britain 2019 , by demographic
X_Axis['Year']: ['Men', 'Women', '16-24', '25-34', '35-44', '45-54', '55-64', '65+']
Y_Axis['Share', 'of', 'respondents']: ['15', '18', '14', '16', '26', '16', '15', '12']

gold: This statistic displays the share of individuals in Great Britain who purchased medicine online in 2019 , by age and gender . Purchasing online was most common among individuals within the 35 to 44 age demographic , at 26 percent of respondents .
gold_template: This statistic displays the templateYLabel[0] of individuals in templateTitleSubject[0] who purchased templateTitle[0] templateTitleSubject[0] in templateTitle[5] , templateTitle[6] age and gender . templateTitle[2] templateTitleSubject[0] was most common among individuals within the 35 to 44 age templateTitle[7] , at templateYValue[max] percent of templateYLabel[1] .

generated_template: In templateTitleDate[0] , templateYValue[1] percent of templateXValue[1] and around a quarter of templateXValue[0] had purchased templateTitle[0] or templateTitle[1] templateTitle[2] in the previous 12 months in templateTitleSubject[0] templateTitleSubject[0] . templateTitle[3] templateTitle[2] was most common among the 25 to 34-year-old templateTitle[8] , with templateYValue[max] percent of individuals within this group having made purchases in templateTitleDate[0] . European templateTitle[2] templateTitle[3] compared Overall , 29 percent of British adults bought templateTitle[1] templateTitle[2] in templateTitleDate[0] .
generated: In 2019 , 18 percent of Women and around a quarter of Men had purchased Medicine or Online purchasing in the previous 12 months in Great Britain . Great purchasing was most common among the 25 to 34-year-old demographic , with 26 percent of individuals within this group having made purchases in 2019 . European purchasing Great compared Overall , 29 percent of British adults bought Online purchasing in 2019 .

Example 767:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2006', '2018']}
title: Number of road deaths in the Netherlands 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'fatalities']: ['678', '613', '629', '621', '570', '570', '650', '661', '640', '720', '750', '791', '811']

gold: In 2018 , 678 people were killed on roads in the Netherlands . Between 2006 and 2018 , road traffic fatalities had seen a net decline of 16 percent , with the peak recorded at the beginning of the reporting period in 2006 . The Dutch Government is dedicated to making its roads even safer in the future and aims to bring the number of road deaths down to below 500 by 2020 .
gold_template: In templateXValue[max] , templateYValue[0] people were killed on roads in the templateTitleSubject[0] . Between templateXValue[min] and templateXValue[max] , templateTitle[1] traffic templateYLabel[1] had seen a net decline of 16 percent , with the peak recorded at the beginning of the reporting period in templateXValue[min] . The Dutch Government is dedicated to making its roads even safer in the future and aims to bring the templateYLabel[0] of templateTitle[1] templateTitle[2] down to below 500 by 2020 .

generated_template: There were templateYValue[0] reported templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateXValue[max] . This was a slight increase compared to the previous templateXLabel[0] , but an increase of 14 incidents compared to the low reported in templateXValue[4] . The Northern European island state is known for enforcing a strict templateTitle[1] safety policy in order to ensure the security of its residents and tourists in the country .
generated: There were 678 reported road deaths in Netherlands in 2018 . This was a slight increase compared to the previous Year , but an increase of 14 incidents compared to the low reported in 2014 . The Northern European island state is known for enforcing a strict road safety policy in order to ensure the security of its residents and tourists in the country .

Example 768:
titleEntities: {'Subject': ['Balfour Beatty Group'], 'Date': ['2011', '2018']}
title: Balfour Beatty Group 's average number of employees 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Average', 'number', 'of', 'employees']: ['26000', '28000', '22450', '23316', '39751', '41221', '50304', '50301']

gold: Balfour Beatty was employer to some 26,000 people in 2018 . The United Kingdom based heavy construction company let go 2,000 employees between 2017 and 2018 , whilst the overall decline in the period of consideration was even more prominent . Overall , employment figures have fallen by nearly 50 percent since a high of 50,304 was reported in 2012 .
gold_template: templateTitleSubject[0] was employer to some templateYValue[0] people in templateXValue[max] . The United Kingdom based heavy construction company let go 2,000 templateYLabel[2] between templateXValue[1] and templateXValue[max] , whilst the overall decline in the period of consideration was even more prominent . Overall , employment figures have fallen by nearly 50 percent since a high of templateYValue[max] was reported in templateXValue[6] .

generated_template: The templateYLabel[0] of the templateTitleSubject[0] luxury brand templateTitle[4] templateTitle[5] has increased twofold over the period surveyed , growing from roughly templateYValue[min] templateYLabel[1] templateYLabel[2] in templateXValue[min] to templateYValue[max] templateYLabel[1] templateYLabel[2] in the templateXLabel[0] templateXValue[max] . Despite the steady increase in templateYLabel[0] during the period considered , the templateTitle[3] reported a net loss of approximately 25 templateYLabel[1] templateYLabel[2] in templateXValue[max] . Worldwide recognition Founded in 1978 in Milan , templateTitle[4] templateTitle[5] is one of the leading international templateTitle[2] design houses .
generated: The Average of the Balfour Beatty Group luxury brand average number has increased twofold over the period surveyed , growing from roughly 22450 number employees in 2011 to 50304 number employees in the Year 2018 . Despite the steady increase in Average during the period considered , the 's reported a net loss of approximately 25 number employees in 2018 . Worldwide recognition Founded in 1978 in Milan , average number is one of the leading international Group design houses .

Example 769:
titleEntities: {'Subject': ['Cintas'], 'Date': ['2012', '2019']}
title: Cintas - annual revenue 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['6.89', '6.48', '5.32', '4.8', '4.37', '4.09', '3.88', '3.76']

gold: This statistic depicts the annual revenue of Cintas Corporation between the fiscal year of 2012 and the fiscal year of 2019 . For the fiscal year of 2019 , the Cincinnati-based specialized facility services company reported an annual revenue of just under 6.9 billion U.S. dollars .
gold_template: This statistic depicts the templateTitle[1] templateYLabel[0] of templateTitleSubject[0] Corporation between the fiscal templateXLabel[0] of templateXValue[min] and the fiscal templateXLabel[0] of templateXValue[max] . For the fiscal templateXLabel[0] of templateXValue[max] , the Cincinnati-based specialized facility services company reported an templateTitle[1] templateYLabel[0] of just under templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: In templateXValue[max] , templateTitleSubject[0] , an e-commerce website company in craft and vintage items , generated templateYLabel[0] worth templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from more than templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] . Most products on templateTitleSubject[0] are sold by independent sellers . templateTitleSubject[0] company details templateTitleSubject[0] launched in 2005 and went public in templateXValue[4] after a decade of operations , and is now one of the leading online marketplaces worldwide .
generated: In 2019 , Cintas , an e-commerce website company in craft and vintage items , generated Revenue worth 6.89 billion U.S. dollars , up from more than 6.48 billion U.S. dollars in the previous Year . Most products on Cintas are sold by independent sellers . Cintas company details Cintas launched in 2005 and went public in 2015 after a decade of operations , and is now one of the leading online marketplaces worldwide .

Example 770:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2002', '2017']}
title: U.S. wholesale sales of beer and wine 2002 to 2017
X_Axis['Year']: ['17', '16', '15', '14', '13', '12', '11', '10', '09', '08', '07', '06', '05', '04', '03', '02']
Y_Axis['Sales', 'in', 'billion', 'U.S.', 'dollars']: ['166.31', '161.16', '152.57', '147.34', '145.86', '136.77', '129.43', '122.9', '120.76', '121.58', '115.57', '108.56', '103.91', '96.25', '91.23', '87.56']

gold: The timeline shows the beer , wine , and distilled alcoholic beverages sales of merchant wholesalers in the United States from 2002 to 2017 . In 2017 , the beer , wine , and distilled alcoholic beverages sales of U.S. merchant wholesalers amounted to about 166.31 billion U.S. dollars . Alcohol in the United States During the recession , alcohol-related industries were some of the few seeing continued growth , proving alcohol to be a frequent purchase for many Americans .
gold_template: The timeline shows the templateTitle[3] , templateTitle[4] , and distilled alcoholic beverages templateYLabel[0] of merchant wholesalers in the templateTitle[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , the templateTitle[3] , templateTitle[4] , and distilled alcoholic beverages templateYLabel[0] of templateYLabel[2] merchant wholesalers amounted to about templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Alcohol in the templateTitle[0] During the recession , alcohol-related industries were some of the few seeing continued growth , proving alcohol to be a frequent purchase for many Americans .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] in the templateTitle[1] from templateXValue[11] to templateXValue[max] . In templateXValue[8] , templateTitle[0] templateTitle[2] of templateTitle[0] to templateYValue[2] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the U.S. Sales of sales in the wholesale from 06 to 17 . In 09 , U.S. sales of U.S. to 152.57 billion U.S. dollars .

Example 771:
titleEntities: {'Subject': ['Christmas U.S'], 'Date': []}
title: Average spending on Christmas gifts in the U.S .
X_Axis['Year']: ['2006', '2007', '2008', '2009', '2010', '2011']
Y_Axis['Average', 'estimated', 'amount', 'in', 'U.S.', 'dollars']: ['907', '909', '801', '740', '715', '712']

gold: The statistic depicts the results of a survey about the average Christmas spending of U.S. consumers from 2006 to 2011 . October of 2007 was the most generous regarding spending on gifts among Americans , estimating a likelihood of spending around 909 U.S. dollars ( on average ) . Since then , the amount reserved for Christmas presents has steadily declined .
gold_template: The statistic depicts the results of a survey about the templateYLabel[0] templateTitleSubject[0] templateTitle[1] of templateYLabel[3] consumers from templateXValue[min] to templateXValue[max] . October of templateXValue[1] was the most generous regarding templateTitle[1] on templateTitle[3] among Americans , estimating a likelihood of templateTitle[1] around templateYValue[max] templateYLabel[3] templateYLabel[4] ( on templateYLabel[0] ) . Since then , the templateYLabel[2] reserved for templateTitleSubject[0] presents has steadily declined .

generated_template: This statistic illustrates the average templateTitle[0] templateYLabel[0] templateYLabel[1] of e-Readers in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . Over the six templateXLabel[0] period the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] is expected to reach templateYValue[max] templateYLabel[2] in templateXValue[max] .
generated: This statistic illustrates the average estimated of e-Readers in the Christmas U.S ( Christmas U.S ) from 2006 to 2011 . Over the six Year period the Average estimated of Christmas gifts U.S is expected to reach 909 amount in 2011 .

Example 772:
titleEntities: {'Subject': ['Advance Publications'], 'Date': ['2006', '2014']}
title: Advance Publications ' revenue 2006 to 2014
X_Axis['Year']: ['2006', '2007', '2008', '2009', '2011', '2012', '2013', '2014']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['7.14', '7.97', '7.36', '7.16', '6.55', '6.78', '6.56', '8.0']

gold: The timeline shows estimated data on the revenue of the American media corporation Advance Publications , Inc. from 2006 to 2014 . Advance Publications is a privately-owned , unlisted company and owns such brands as Reddit - a social news and entertainment website and Conde Nast - a magazine publisher . Its 2006 revenue is estimated to have amounted to 7.14 billion US dollars .
gold_template: The timeline shows estimated data on the templateYLabel[0] of the American media corporation templateTitleSubject[0] , Inc. from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] is a privately-owned , unlisted company and owns such brands as Reddit - a social news and entertainment website and Conde Nast - a magazine publisher . Its templateXValue[min] templateYLabel[0] is estimated to have amounted to templateYValue[0] templateYLabel[1] US templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] of templateTitle[2] templateTitle[3] reached approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from templateYValue[6] templateYLabel[1] the previous templateXLabel[0] . In templateXValue[max] , the templateTitle[1] with the highest lottery templateYLabel[0] was New York , with around 9.7 templateYLabel[1] templateYLabel[2] in templateYLabel[0] .
generated: This statistic shows the Advance Revenue of ' revenue in the Publications from 2006 to 2014 . In 2014 , Revenue of ' revenue reached approximately 8.0 billion U.S. dollars , up from 6.56 billion the previous Year . In 2014 , the Publications with the highest lottery Revenue was New York , with around 9.7 billion U.S. in Revenue .

Example 773:
titleEntities: {'Subject': ['Nike'], 'Date': ['2016', '2020']}
title: Global brand value of Nike from 2016 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016']
Y_Axis['Brand', 'value', 'in', 'million', 'U.S.', 'dollars']: ['34792', '32421', '28030', '31762', '28041']

gold: In 2020 , the Nike brand was valued at approximately 34.8 billion U.S. dollars , which was an increase of over two billion U.S. dollars from 2019 . Nike 's popularity Nike 's footwear segment was the source of the most revenue for the company in 2019 , netting over 24 billion U.S. dollars that year . Among U.S. consumers , Nike was the most popular sports shoe , ahead of its main competitors Adidas .
gold_template: In templateXValue[max] , the templateTitleSubject[0] templateYLabel[0] was valued at approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] , which was an increase of over two templateYLabel[2] templateYLabel[3] templateYLabel[4] templateTitle[4] templateXValue[1] . templateTitleSubject[0] 's popularity templateTitleSubject[0] 's footwear segment was the source of the most revenue for the company in templateXValue[1] , netting over 24 templateYLabel[2] templateYLabel[3] templateYLabel[4] that templateXLabel[0] . Among templateYLabel[3] consumers , templateTitleSubject[0] was the most popular sports shoe , ahead of its main competitors Adidas .

generated_template: General Motors incurred around templateYValue[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] in R & D costs in templateXValue[max] . This figure puts the car manufacturer templateTitle[1] R & D intensity at five percent . That said , the company declared interest to enter into cost sharing arrangements with third parties .
generated: General Motors incurred around 28041 value million U.S. in R & D costs in 2020 . This figure puts the car manufacturer brand R & D intensity at five percent . That said , the company declared interest to enter into cost sharing arrangements with third parties .

Example 774:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2008']}
title: Stationery retail sales turnover in the United Kingdom ( UK ) 2008 to 207
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Turnover', 'in', 'million', 'GBP']: ['4784', '4892', '4372', '4304', '4620', '4397', '4432', '4025', '4423', '4446']

gold: This statistic shows the total annual turnover of the retail sales of stationery and drawing materials and miscellaneous printed matter in the United Kingdom , from 2008 to 2017 . In 2017 , turnover from stationery and drawing material retail sales reached 4.78 billion British pounds which was the highest point of turnover over the nine year period .
gold_template: This statistic shows the total annual templateYLabel[0] of the templateTitle[1] templateTitle[2] of templateTitle[0] and drawing materials and miscellaneous printed matter in the templateTitleSubject[0] , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] from templateTitle[0] and drawing material templateTitle[1] templateTitle[2] reached templateYValue[0] templateYLabel[1] British pounds which was the highest point of templateYLabel[0] over the nine templateXLabel[0] period .

generated_template: In templateXValue[max] , approximately templateYValue[0] templateYLabel[1] templateYLabel[0] in the templateTitle[0] had no templateTitle[3] templateTitle[4] . The share of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] was steadily decreasing until templateXValue[3] , but has been increasing since templateXValue[1] . The Tax Cuts and Jobs Act reached an increase of templateYValue[max] thousand templateYLabel[2] templateYLabel[3] .
generated: In 2017 , approximately 4784 million Turnover in the Stationery had no turnover United . The share of United Kingdom sales turnover United was steadily decreasing until 2014 , but has been increasing since 2016 . The Tax Cuts and Jobs Act reached an increase of 4892 thousand GBP .

Example 775:
titleEntities: {'Subject': ['Norway'], 'Date': ['2018', '2024']}
title: Forecast of smartphone user numbers in Norway 2018 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018']
Y_Axis['Smartphone', 'users', 'in', 'millions']: ['5.19', '5.15', '5.11', '5.0', '4.89', '4.77', '4.64']

gold: This statistic displays the development in smartphone user numbers in Norway in 2018 with a forecast from 2019 to 2024 . In 2018 , the number of smartphone users amounted to 4.64 million . In the same year , smartphone penetration rate was at 86.95 percent .
gold_template: This statistic displays the development in templateYLabel[0] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateXValue[min] with a templateTitle[0] from templateXValue[5] to templateXValue[max] . In templateXValue[min] , the number of templateYLabel[0] templateYLabel[1] amounted to templateYValue[min] templateYLabel[2] . In the same templateXLabel[0] , templateYLabel[0] penetration rate was at 86.95 percent .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateYValue[min] templateYLabel[5] people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[max] templateYLabel[5] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Smartphone of users millions in Norway from 2018 to 2024 . In 2018 , 4.64 millions people accessed the millions through their users millions . In 2024 , this figure is projected to amount to 5.19 millions users millions .

Example 776:
titleEntities: {'Subject': ['Haiti'], 'Date': ['2018']}
title: Infant mortality rate in Haiti 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Deaths', 'per', '1,000', 'live', 'births']: ['49.5', '50.8', '52.1', '53.3', '54.5', '55.7', '56.8', '57.9', '85.6', '60.2', '61.4']

gold: The statistic shows the infant mortality rate in Haiti from 2008 to 2018 . In 2018 , the infant mortality rate in Haiti was at about 49.5 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] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[min] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Infant mortality rate in Haiti from 2008 to 2018 . In 2018 , the Infant mortality rate in Haiti was at about 49.5 Deaths per 1,000 live births .

Example 777:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Important features of music streaming services in the U.S. 2018
X_Axis['Response']: ['The_variety_of_music_available', 'Low_price_point', 'The_ability_to_listen_on_multiple_divices', 'Clean_user_interface', 'Good_algorithms_to_find_new_music', 'The_ability_to_combine_your_music_library_with_your_streaming_service_library', 'The_ability_to_stream_on_smart_home_devices', 'Curated_playlists', 'Artist_exclusives']
Y_Axis['Share', 'of', 'respondents']: ['81', '80', '68', '66', '58', '64', '57', '52', '46']

gold: This statistic presents data on the most important features of music streaming services among adults in the United States as of March 2018 . During a survey , 81 percent of respondents stated that the variety of music available was the most important feature of music streaming services .
gold_template: This statistic presents data on the most templateTitle[0] templateTitle[1] of templateXValue[0] templateXValue[5] templateTitle[4] among adults in the templateTitle[5] as of March templateTitleDate[0] . During a survey , templateYValue[max] percent of templateYLabel[1] stated that the templateXValue[0] of templateXValue[0] was the most templateTitle[0] feature of templateXValue[0] templateXValue[5] templateTitle[4] .

generated_template: templateXValue[2] than half of templateTitleSubject[1] wanted to start templateTitleDate[0] by saving templateXValue[0] and by getting in templateXValue[1] . The most popular templateXValue[5] templateTitleSubject[0] templateTitle[2] templateTitle[3] ever – `` templateXValue[7] templateXValue[7] '' – was not as high up on the list as it used to be , but at least templateYValue[7] percent decided to finally give up templateXValue[7] once and templateTitle[5] all . templateTitle[3] makers , templateTitle[3] keepers ? While some might say that they do not need templateXValue[5] templateTitleSubject[0] templateTitle[2] Eve to finally turn their life around , making resolutions on December 31 is a common , well-liked tradition , especially in the Western world .
generated: The ability to listen on multiple divices than half of U.S. wanted to start 2018 by saving The variety of music available and by getting in Low price point . The most popular The ability to combine your music library with your streaming service library U.S. music streaming ever – `` Curated playlists '' – was not as high up on the list as it used to be , but at least 52 percent decided to finally give up Curated playlists once and U.S. all . streaming makers , streaming keepers ? While some might say that they do not need The ability to combine your music library with your streaming service library U.S. music Eve to finally turn their life around , making resolutions on December 31 is a common , well-liked tradition , especially in the Western world .

Example 778:
titleEntities: {'Subject': ['Gannett'], 'Date': ['2013', '2018']}
title: Gannett 's revenue 2013 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['2.92', '3.15', '3.05', '2.89', '3.17', '3.32']

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

generated_template: This statistic presents the templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , templateTitleSubject[0] 's templateTitle[1] templateTitle[2] amounted to about 182.76 templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic presents the 's revenue 2013 Revenue from 2013 to 2018 . In 2017 , Gannett 's revenue amounted to about 182.76 billion U.S. dollars .

Example 779:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2024']}
title: Forecast on U.S. petroleum refinery end-use market output 2020 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020']
Y_Axis['Change', 'from', 'year', 'to', 'year']: ['2', '0.5', '0.5', '0.8', '2.2']

gold: This statistic displays a forecast of the petroleum and refinery end-use market output in the United States from 2020 to 2024 . Through 2020 , the petroleum and refinery end-use market output is expected to increase by 2.2 percent . U.S. petroleum refinery market It is projected that the growth of output from the U.S. petroleum refinery end-use market will slow , from a rate of 2.2 percent in 2020 to 0.5 percent in 2023 , and grow again to 2.2 percent in 2024 .
gold_template: This statistic displays a templateTitle[0] of the templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[1] templateYLabel[1] templateXValue[min] to templateXValue[max] . Through templateXValue[min] , the templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] is expected to increase by templateYValue[max] percent . templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[5] It is projected that the growth of templateTitle[6] templateYLabel[1] the templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] will slow , templateYLabel[1] a rate of templateYValue[max] percent in templateXValue[min] to templateYValue[min] percent in templateXValue[1] , and grow again to templateYValue[max] percent in templateXValue[max] .

generated_template: This statistic displays a templateTitle[0] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . Through templateXValue[max] , the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] is expected to increase by templateYValue[max] percent . templateTitleSubject[0] templateTitle[2] templateTitle[4] It is expected that the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] will experience a decreasing rate of 1.3 percent from 2019 to templateXValue[min] .
generated: This statistic displays a Forecast of the petroleum refinery end-use market in the U.S. from 2020 to 2024 . Through 2024 , the petroleum refinery end-use market is expected to increase by 2.2 percent . U.S. petroleum end-use It is expected that the petroleum refinery end-use market will experience a decreasing rate of 1.3 percent from 2019 to 2020 .

Example 780:
titleEntities: {'Subject': ['Spanish'], 'Date': ['2008', '2018']}
title: Chocolate and cocoa products consumption in Spanish households 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Total', 'consumption', 'in', 'million', 'kilograms']: ['164.9', '162.4', '164.1', '165.5', '163.6', '165.3', '158.7', '151.5', '150.54', '147.44', '143.6']

gold: Chocolate has inspired uncountable desserts in many countries across the globe and throughout history . This delicacy originating in Mesoamerica is the favorite sweet of many places including Spain , which imported this delight to Europe during the country 's explorations in the Americas . Spain 's sweet tooth has been on the rise in recent years , with a peak in chocolate and cocoa product consumption amounting to 165.5 million kilograms in 2013 .
gold_template: templateTitle[0] has inspired uncountable desserts in many countries across the globe and throughout history . This delicacy originating in Mesoamerica is the favorite sweet of many places including Spain , which imported this delight to Europe during the country 's explorations in the Americas . Spain 's sweet tooth has been on the rise in recent years , with a peak in templateTitle[0] and templateTitle[1] product templateYLabel[1] amounting to templateYValue[max] templateYLabel[2] templateYLabel[3] in templateXValue[5] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] amounted to approximately templateYValue[0] thousand , as compared to the previous templateXLabel[0] .
generated: The statistic shows the Total of consumption in Spanish from 2008 to 2018 . In 2018 , the Total of consumption in Spanish amounted to approximately 164.9 thousand , as compared to the previous Year .

Example 781:
titleEntities: {'Subject': ['Burundi'], 'Date': ['2024']}
title: Inflation rate in Burundi 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['8.97', '8.97', '8.97', '8.97', '8.97', '7.32', '1.24', '16.64', '5.53', '5.55', '4.42', '7.94', '18.18', '9.58', '6.5', '10.56', '24.41', '8.41', '2.74', '13.25', '8.18', '10.57', '-1.26', '7.87', '25.52', '3.52', '12.47', '31.06', '26.42', '19.36', '14.71', '9.71', '5.33', '9.01', '6.99', '11.67', '4.49', '7.11', '1.67', '3.82', '14.3']

gold: This statistic shows the average inflation rate in Burundi from 1984 to 2017 , with projections up until 2024 . In 2017 , the average inflation rate in Burundi amounted to about 16.64 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[7] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

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

Example 782:
titleEntities: {'Subject': ['U.S. April'], 'Date': ['2014', '2014']}
title: Methods of ordering food for takeout or delivery in the U.S. as of April 2014
X_Axis['Response']: ['By_phone', 'In_person', 'Online', 'Via_mobile_app', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['50.5', '43.5', '32.1', '3.9', '0.2']

gold: This statistic shows the methods which consumers used to order food for takeout or delivery in the United States as of April 2014 . During the survey , 32.1 percent of respondents said they ordered food for takeout or delivery online .
gold_template: This statistic shows the templateTitle[0] which consumers used to order templateTitle[2] templateTitle[3] templateTitle[4] or templateTitle[5] in the templateTitle[6] as of templateTitleSubject[0] templateTitle[8] . During the survey , templateYValue[2] percent of templateYLabel[1] said they ordered templateTitle[2] templateTitle[3] templateTitle[4] or templateTitle[5] templateXValue[2] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] in the templateTitle[4] as of templateTitle[5] templateTitle[6] . During the survey , templateYValue[max] percent of Millennial templateYLabel[1] said they used the microblogging site .
generated: The statistic shows the Methods ordering food for U.S. April in the takeout as of delivery U.S. . During the survey , 50.5 percent of Millennial respondents said they used the microblogging site .

Example 783:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2019']}
title: Public sector expenditure as a share of GDP in the United Kingdom ( UK ) 2000 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02', '00/01']
Y_Axis['Expenditure', 'as', 'share', 'of', 'GDP']: ['34.1', '34.4', '34.8', '35.7', '36.4', '37.3', '38.3', '39.1', '39.7', '39.4', '36.9', '34.9', '34.5', '34.7', '34.9', '34', '32.8', '32.1', '31.8']

gold: This statistic shows total public sector current expenditure as a share of GDP in the United Kingdom ( UK ) from 2000/01 to 2018/19 . During this period public sector spending fluctuated , peaking in 2010/11 at 39.7 percent of GDP .
gold_template: This statistic shows total templateTitle[0] templateTitle[1] current templateYLabel[0] as a templateYLabel[1] of templateYLabel[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from 2000/01 to 2018/19 . During this period templateTitle[0] templateTitle[1] spending fluctuated , peaking in 2010/11 at templateYValue[max] percent of templateYLabel[2] .

generated_template: This statistic shows the total templateTitleSubject[0] templateTitleSubject[0] ( UK ) templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] 2018/19 . The overall trend was one of increase , seeing a peak in the templateTitle[6] .
generated: This statistic shows the total United Kingdom ( UK ) GDP Expenditure share GDP from fiscal Year 00/01 to fiscal Year 2018/19 . The overall trend was one of increase , seeing a peak in the Kingdom .

Example 784:
titleEntities: {'Subject': ['Luxembourg'], 'Date': ['2006', '2018']}
title: Number of arrivals in tourist accommodation Luxembourg 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012_', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'arrivals', 'in', 'thousands']: ['1139.0', '1156.0', '1161.8', '1196.1', '1142.9', '1044.3', '1021.7', '935.0', '854.72', '907.53', '936.65', '979.21', '967.88']

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Luxembourg from 2006 to 2018 . There were around 1.1 million arrivals at accommodation establishments in Luxembourg in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . There were around templateYValue[0] templateYLabel[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] in templateXValue[0] .

generated_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] of official templateYLabel[1] templateTitle[2] increased from approximately templateYValue[min] thousand to templateYValue[0] thousand , after which it experienced a slight decline . As of templateXValue[1] , England is the leader with 1.87 thousand official templateYLabel[1] templateTitle[2] followed by Germany and France with respectively 731 and 602 registered templateYLabel[1] templateTitle[2] . The rise in the templateYLabel[0] of templateYLabel[1] templateTitle[2] in templateTitleSubject[0] was accompanied with a rise in templateYLabel[1] popularity .
generated: Between 2018 and 2018 , the Number of official arrivals tourist increased from approximately 854.72 thousand to 1139.0 thousand , after which it experienced a slight decline . As of 2017 , England is the leader with 1.87 thousand official arrivals tourist followed by Germany and France with respectively 731 and 602 registered arrivals tourist . The rise in the Number of arrivals tourist in Luxembourg was accompanied with a rise in arrivals popularity .

Example 785:
titleEntities: {'Subject': ['Iberdrola'], 'Date': ['2009', '2018']}
title: Iberdrola - revenue 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'in', 'million', 'euros']: ['35075.9', '31263.26', '28759.15', '31418.69', '30032.27', '31077.11', '34201.0', '31648.0', '30431.0', '25891.9']

gold: This statistic represents Iberdrola 's global revenue between the fiscal year of 2009 and the fiscal year of 2018 . The Spain-based multinational electric utility company with headquarters in Bilbao generated around 35 billion euros in revenue in the fiscal year of 2018 .
gold_template: This statistic represents templateTitleSubject[0] 's global templateYLabel[0] between the fiscal templateXLabel[0] of templateXValue[min] and the fiscal templateXLabel[0] of templateXValue[max] . The Spain-based multinational electric utility company with headquarters in Bilbao generated around templateYValue[max] templateYLabel[1] templateYLabel[2] in templateYLabel[0] in the fiscal templateXLabel[0] of templateXValue[max] .

generated_template: templateTitleSubject[0] is an international pharmaceutical company based out of Germany . As of templateXValue[max] , the company had a total of templateYValue[0] templateYLabel[1] globally . This is a significant increase from the previous years .
generated: Iberdrola is an international pharmaceutical company based out of Germany . As of 2018 , the company had a total of 35075.9 million globally . This is a significant increase from the previous years .

Example 786:
titleEntities: {'Subject': ['United States'], 'Date': ['2012']}
title: Reasons for opposing same-sex marriage in the United States in 2012
X_Axis['Month']: ['Religion/Bible_says_it_is_wrong', 'Marriage_should_be_between_a_man_and_a_woman', 'Morally_wrong/Have_traditional_beliefs', 'Civil_unions_are_sufficient', 'Unnatural/Against_laws_of_nature', 'Undermines_traditional_family_structure/Mother_and_father', 'Other', 'No_opinion']
Y_Axis['Share', 'of', 'respondents']: ['47', '20', '16', '6', '5', '5', '7', '4']

gold: This statistic shows the results of a 2012 survey among American adults opposing legal same-sex marriage . They were asked to give reasons for this decision . 47 percent of respondents stated that they oppose same-sex marriage because their religion and/or the Bible says it 's wrong .
gold_template: This statistic shows the results of a templateTitleDate[0] survey among American adults templateTitle[2] legal templateTitle[3] templateXValue[1] . They were asked to give templateTitle[0] templateTitle[1] this decision . templateYValue[max] percent of templateYLabel[1] stated that they oppose templateTitle[3] templateXValue[1] because their religion and/or the Bible templateXValue[0] it 's templateXValue[0] .

generated_template: As of the third quarter of templateTitleDate[0] , it was found that templateYValue[max] percent of templateTitleSubject[0] users in the templateTitleSubject[1] templateTitleSubject[1] accessed the photo sharing app templateXValue[0] templateXValue[0] a templateXValue[0] . A further templateYValue[1] percent of templateYLabel[1] claimed that they used templateTitleSubject[0] on a templateXValue[1] basis .
generated: As of the third quarter of 2012 , it was found that 47 percent of United States users in the United States accessed the photo sharing app Religion/Bible says it is wrong a Religion/Bible says it is wrong . A further 20 percent of respondents claimed that they used United States on a Marriage should be between a man and a woman basis .

Example 787:
titleEntities: {'Subject': ['Nissan', 'Europe'], 'Date': ['2018']}
title: Nissan car sales in Europe 2003 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Number', 'of', 'units', 'sold']: ['487017', '560415', '547343', '554046', '477703', '421134', '436169', '470004', '411084', '372096', '338169', '313437', '332150', '387325', '409717', '409511']

gold: This statistic shows the number of cars sold by Nissan in Europe between 2003 and 2018 . European sales of the Nissan cars rose from 400 thousand units sold in 2003 to over 560 thousand units sold by 2017 . In 2018 , there were 487 thousand units of Nissan cars sold in Europe .
gold_template: This statistic shows the templateYLabel[0] of cars templateYLabel[2] by templateTitleSubject[0] in templateTitleSubject[1] between templateXValue[min] and templateXValue[max] . European templateTitle[2] of the templateTitleSubject[0] cars rose from 400 thousand templateYLabel[1] templateYLabel[2] in templateXValue[min] to over templateYValue[max] thousand templateYLabel[1] templateYLabel[2] by templateXValue[1] . In templateXValue[max] , there were templateYValue[0] thousand templateYLabel[1] of templateTitleSubject[0] cars templateYLabel[2] in templateTitleSubject[1] .

generated_template: In templateXValue[max] , over templateYValue[0] babies were born in templateTitleSubject[0] , a slight decrease from the templateXLabel[0] before . templateTitleSubject[0] is the country with the highest templateYLabel[0] of templateYLabel[2] in the European Union , with over 20 thousand more templateYLabel[2] more than France , the country with the next highest templateYLabel[0] of templateYLabel[1] templateYLabel[2] in Europe . Population growth in templateTitleSubject[0] has about 83 million inhabitants , making it the most populous country in Europe .
generated: In 2018 , over 487017 babies were born in Nissan , a slight decrease from the Year before . Nissan is the country with the highest Number of sold in the European Union , with over 20 thousand more sold more than France , the country with the next highest Number of units sold in Europe . Population growth in Nissan has about 83 million inhabitants , making it the most populous country in Europe .

Example 788:
titleEntities: {'Subject': ['Annual'], 'Date': ['2010', '2018']}
title: Annual growth in average global hotel rates 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Year-over-year', 'growth', 'in', 'average', 'hotel', 'rates']: ['3.7', '2.5', '2.5', '2.6', '1.8', '0', '-1.5', '7.4', '4.7']

gold: This statistic shows annual growth in average global hotel rates from 2010 to 2018 . Global hotel rates were forecasted to increase by 3.7 percent in 2018 . The average daily rate of the hotel industry in the Americas reached around 123.37 U.S. dollars in 2016 .
gold_template: This statistic shows templateTitleSubject[0] templateYLabel[1] in templateYLabel[2] templateTitle[3] templateYLabel[3] templateYLabel[4] from templateXValue[min] to templateXValue[max] . templateTitle[3] templateYLabel[3] templateYLabel[4] were forecasted to increase by templateYValue[0] percent in templateXValue[max] . The templateYLabel[2] daily rate of the templateYLabel[3] industry in the Americas reached around 123.37 U.S. dollars in templateXValue[2] .

generated_template: The statistic presents the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] of American templateTitle[2] templateTitle[3] was estimated to reach templateYValue[max] templateYLabel[2] .
generated: The statistic presents the Year-over-year of Annual growth average growth in the Annual from 2010 to 2018 . In 2018 , the Year-over-year of growth of American average global was estimated to reach 7.4 average .

Example 789:
titleEntities: {'Subject': ['Muslims', 'Spain'], 'Date': ['2018']}
title: Muslims in Spain 2018 , by nationality
X_Axis['Country']: ['Spain', 'Morocco', 'Pakistan', 'Senegal', 'Algeria', 'Nigeria', 'Mali', 'Gambia', 'Bangladesh', 'Guinea', 'Others']
Y_Axis['Number', 'of', 'Muslims']: ['847801', '769050', '82738', '66046', '60820', '39374', '23685', '19381', '15979', '10186', '58615']

gold: This statistic presents the number of Muslims in Spain in 2018 , broken down by nationality . That year , there were a total of approximately two million Muslims in Spain . Almost 848 thousand had Spanish nationality , followed by Muslims with a Moroccan nationality with figures that almost reached 770 thousand individuals .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] in templateXValue[0] in templateTitleDate[0] , broken down templateTitle[3] templateTitle[4] . That year , there were a total of approximately two million templateYLabel[1] in templateXValue[0] . Almost templateYValue[max] thousand had Spanish templateTitle[4] , followed templateTitle[3] templateYLabel[1] with a Moroccan templateTitle[4] with figures that almost reached 770 thousand individuals .

generated_template: This statistic shows the distribution of templateTitle[1] templateTitle[2] templateYLabel[1] templateTitle[5] region in templateTitleDate[0] . During this year , the templateXValue[0] templateXValue[2] consumed approximately templateYValue[2] percent of the world 's templateTitle[2] . This drug can be used as an analgesic and is about 100 times more potent than morphine .
generated: This statistic shows the distribution of Spain 2018 Muslims nationality region in 2018 . During this year , the Spain Pakistan consumed approximately 82738 percent of the world 's 2018 . This drug can be used as an analgesic and is about 100 times more potent than morphine .

Example 790:
titleEntities: {'Subject': ['Tampa Bay Buccaneers', 'NFL'], 'Date': ['2001', '2018']}
title: Revenue of the Tampa Bay Buccaneers ( 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']: ['400', '383', '367', '341', '313', '275', '267', '258', '245', '246', '241', '224', '205', '203', '195', '175', '168', '151']

gold: The statistic depicts the revenue of the Tampa Bay Buccaneers , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Tampa Bay Buccaneers was 400 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] Buccaneers , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] Buccaneers was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Tampa Bay Buccaneers , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the Revenue of the Tampa Bay Buccaneers was 400 million U.S. dollars .

Example 791:
titleEntities: {'Subject': ['Afghanistan'], 'Date': ['2018']}
title: Population growth in Afghanistan 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Population', 'growth', 'compared', 'to', 'previous', 'year']: ['2.38', '2.55', '2.78', '3.08', '3.36', '3.49', '3.41', '3.14', '2.75', '2.4', '2.27']

gold: This timeline shows the population growth in Afghanistan from 2008 to 2018 . In 2018 , Afghanistan 's population grew by an estimated 2.38 percent compared to the previous year . See Afghanistan 's population figures for comparison .
gold_template: This timeline shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] grew by an estimated templateYValue[0] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . See templateTitleSubject[0] 's templateYLabel[0] figures for comparison .

generated_template: In templateXValue[max] , the templateTitleSubject[0] templateTitleSubject[0] produced approximately templateYValue[max] templateYLabel[2] templateYLabel[3] of templateYLabel[0] worldwide , an increase of 53 templateYLabel[2] templateYLabel[3] on the previous templateXLabel[0] . The company templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] has increased by about 75 percent since templateXValue[6] . Asia at the center of templateTitle[3] operations network The templateTitleSubject[0] templateTitleSubject[0] templateTitle[2] templateYLabel[0] templateYLabel[1] has risen year-on-year since templateXValue[6] .
generated: In 2018 , the Afghanistan produced approximately 3.49 compared previous of Population worldwide , an increase of 53 compared previous on the previous Year . The company Afghanistan 2018 Population growth has increased by about 75 percent since 2012 . Asia at the center of 2018 operations network The Afghanistan Population growth has risen year-on-year since 2012 .

Example 792:
titleEntities: {'Subject': ['Philippines'], 'Date': ['2017', '2023']}
title: Philippines social media user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['55', '54', '54', '53', '52', '51', '49']

gold: The social media penetration in the Philippines was at 49 percent in 2017 , amounting to about 54 million people using a social network in the Philippines as of 2018 . Considering that the number of internet users in the Philippines was at just under 70 million in that year , the social media penetration was projected to increase to 55 percent of the population by 2023 . Social media in the Philippines The Philippines are an archipelagic country , which poses logistical problems for social interaction and communication between residents from the various islands .
gold_template: The templateTitle[1] templateTitle[2] templateTitle[4] in the templateTitleSubject[0] was at templateYValue[min] percent in templateXValue[min] , amounting to about templateYValue[1] million people using a templateTitle[1] network in the templateTitleSubject[0] as of templateXValue[5] . Considering that the number of internet users in the templateTitleSubject[0] was at just under 70 million in that templateXLabel[0] , the templateTitle[1] templateTitle[2] templateTitle[4] was projected to increase to templateYValue[max] percent of the templateYLabel[1] by templateXValue[max] . templateTitle[1] templateTitle[2] in the templateTitleSubject[0] The templateTitleSubject[0] are an archipelagic country , which poses logistical problems for templateTitle[1] interaction and communication between residents from the various islands .

generated_template: This statistic provides information on the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , it was estimated that templateYValue[max] percent of the Indian templateYLabel[1] will be accessing templateTitle[1] networks , up from templateYValue[min] percent in templateXValue[min] .
generated: This statistic provides information on the social media user in Philippines from 2017 to 2023 . In 2019 , it was estimated that 55 percent of the Indian population will be accessing social networks , up from 49 percent in 2017 .

Example 793:
titleEntities: {'Subject': ['USA'], 'Date': ['1990', '2018']}
title: USA - reported burglary rate 1990 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92", "'91", "'90"]
Y_Axis['Rate', 'per', '100,000', 'population']: ['376.0', '429.7', '468.9', '494.7', '537.2', '610.5', '672.2', '701.3', '701.0', '717.7', '733.0', '726.1', '733.1', '726.9', '730.3', '741.0', '747.0', '740.8', '728.8', '770.4', '863.0', '919.6', '944.8', '987.1', '1042.0', '1099.2', '1168.2', '1252.0', '1235.9']

gold: This graph shows the reported burglary rate in the United States from 1990 to 2018 . In 2018 , the nationwide rate was 376 cases per 100,000 of the population .
gold_template: This graph shows the templateTitle[1] templateTitle[2] templateYLabel[0] in the country from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , the nationwide templateYLabel[0] was templateYValue[min] cases templateYLabel[1] 100,000 of the templateYLabel[3] .

generated_template: In templateTitleDate[1] , the templateYLabel[0] of templateTitle[2] rapes in the country stood at templateYValue[0] templateYLabel[1] 100,000 inhabitants . While this figure is about the same as it was in 2007 , when the templateYLabel[0] was templateYValue[11] , it has decreased from templateTitleDate[0] , when there were templateYValue[28] templateTitle[2] rapes templateYLabel[1] 100,000 inhabitants . What is templateTitle[2] templateTitle[3] ? According to the FBI , templateTitle[2] templateTitle[3] is defined as `` sexual penetration , no matter how slight , with a body part or object without the consent of the victim .
generated: In 2018 , the Rate of burglary rapes in the country stood at 376.0 per 100,000 inhabitants . While this figure is about the same as it was in 2007 , when the Rate was 726.1 , it has decreased from 1990 , when there were 1235.9 burglary rapes per 100,000 inhabitants . What is burglary rate ? According to the FBI , burglary rate is defined as `` sexual penetration , no matter how slight , with a body part or object without the consent of the victim .

Example 794:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Cotton price received by U.S. farmers 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1995', '1990']
Y_Axis['Price', 'per', 'pound', 'in', 'U.S.', 'cents']: ['86.85', '84.48', '64.7', '61.49', '74.9', '77.23', '79.5', '88.02', '70.95', '49.15', '60.79', '49.96', '47.53', '42.69', '54.3', '51.65', '33.63', '38.86', '49.81', '77.21', '64.83']

gold: This statistic shows the average cotton price per pound as received by U.S. farmers from 1990 to 2018 . In the 1990 calendar year , a U.S. cotton farmer received an average price of 64.83 cents per one pound of upland cotton .
gold_template: This statistic shows the average templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] as templateTitle[2] templateTitle[3] templateYLabel[3] templateTitle[5] from templateXValue[min] to templateXValue[max] . In the templateXValue[min] calendar templateXLabel[0] , a templateYLabel[3] templateTitle[0] farmer templateTitle[2] an average templateYLabel[0] of templateYValue[20] templateYLabel[4] templateYLabel[1] one templateYLabel[2] of upland templateTitle[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of this templateTitle[5] came to around templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateYLabel[3] templateTitle[4] templateTitle[5] The templateTitle[4] templateTitle[5] in the templateTitle[3] is a huge sector – especially when oil and gas extraction is included .
generated: The statistic shows the Price per of the by U.S. farmers from 1990 to 2018 . In 2018 , the Price per of this farmers came to around 86.85 pound U.S. cents . U.S. farmers The U.S. farmers in the by is a huge sector – especially when oil and gas extraction is included .

Example 795:
titleEntities: {'Subject': ['Spain'], 'Date': ['2006', '2018']}
title: Number of deaths in Spain 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'deaths']: ['474523', '424523', '410611', '422568', '395830', '390419', '402950', '387911', '382047', '384933', '386324', '385361', '371478']

gold: According to data provided by the Spanish Statistics Institute , more people died in Spain than were being born in 2018 , with figures reaching over 427 thousand deaths versus 370 thousand newborns . The number of deaths experienced an upward trend over the 11-year period , presumably due to Spain 's aging population . Circulatory system diseases and cancer ranked as the most common causes of death in Spain The cause of death can vary significantly across the globe and depends highly on economic development , presence of a competent healthcare system and one 's choices in lifestyle .
gold_template: According to data provided by the Spanish Statistics Institute , more people died in templateTitleSubject[0] than were being born in templateXValue[max] , with figures reaching over 427 thousand templateYLabel[1] versus 370 thousand newborns . The templateYLabel[0] of templateYLabel[1] experienced an upward trend over the 11-year period , presumably due to templateTitleSubject[0] 's aging population . Circulatory system diseases and cancer ranked as the most common causes of death in templateTitleSubject[0] The cause of death can vary significantly across the globe and depends highly on economic development , presence of a competent healthcare system and one 's choices in lifestyle .

generated_template: Between templateXValue[min] and templateXValue[max] , templateTitle[1] templateYLabel[1] in templateTitleSubject[0] had seen a net decline of 27 percent . The lowest figure of such templateYLabel[1] was recorded in templateXValue[1] at templateYValue[min] . These figures stand in contrast to an increase in the volume of passenger templateTitle[1] transport .
generated: Between 2006 and 2018 , deaths in Spain had seen a net decline of 27 percent . The lowest figure of such deaths was recorded in 2017 at 371478 . These figures stand in contrast to an increase in the volume of passenger deaths transport .

Example 796:
titleEntities: {'Subject': ['Winter Olympic Games'], 'Date': ['2014']}
title: Number of participants Winter Olympic Games 2014
X_Axis['Year']: ['2014_Sochi', '2010_Vancouver', '2006_Torino', '2002_Salt_Lake_City', '1998_Nagano', '1994_Lillehammer', '1992_Albertville', '1988_Calgary', '1984_Sarajevo', '1980_Lake_Placid', '1976_Innsbruck', '1972_Sapporo', '1968_Grenoble', '1964_Innsbruck', '1960_Squaw_Valley', "1956_Cortina_d'Ampezzo", '1952_Oslo', '1948_St._Moritz', '1936_Garmisch-Partenkirchen', '1932_Lake_Placid', '1928_St._Moritz', '1924_Chamonix']
Y_Axis['Number', 'of', 'participants']: ['2800', '2536', '2494', '2402', '2180', '1738', '1801', '1424', '1273', '1072', '1129', '1008', '1160', '1094', '665', '821', '694', '668', '668', '252', '461', '292']

gold: The statistic shows the number of participants in the Winter Olympic Games from 1924 to 2014 . At the first Olympic Winter Games in Chamonix in 1924 , 292 athletes participated . This figure grew to 2,536 participating athletes from 82 nations during the 2010 Vancouver Winter Olympics .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] Games from templateXValue[last] to templateXValue[0] . At the first templateTitleSubject[0] Games in templateXValue[last] in templateXValue[last] , templateYValue[21] athletes participated . This figure grew to templateYValue[1] participating athletes from 82 nations during the templateXValue[1] Winter Olympics .

generated_template: In templateXValue[max] , there were approximately templateYValue[max] templateYLabel[2] people templateYLabel[1] in the templateTitle[2] sector in the templateTitle[4] . Employment in the templateTitle[2] templateTitle[3] – additional information Employment within the templateTitle[2] templateTitle[3] has shown significant growth since templateXValue[min] . Each decade , between templateXValue[min] and templateXValue[18] , there was an increase in the templateYLabel[0] of templateYLabel[1] which were employed in the templateTitle[3] – increasing by around 300,000 templateYLabel[1] each decade .
generated: In 2014 Sochi , there were approximately 2800 participants people participants in the Winter sector in the Games . Employment in the Winter Olympic – additional information Employment within the Winter Olympic has shown significant growth since 2014 Sochi . Each decade , between 2014 Sochi and 1936 Garmisch-Partenkirchen , there was an increase in the Number of participants which were employed in the Olympic – increasing by around 300,000 participants each decade .

Example 797:
titleEntities: {'Subject': ['California'], 'Date': ['2018']}
title: California - 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']: ['2677.94', '2587.57', '2498.84', '2426.14', '2309.93', '2220.87', '2144.5', '2091.59', '2058.14', '2026.49', '2111.14', '2103.62', '2072.18', '1990.14', '1902.32', '1825.42', '1743.65', '1702.78', '1709.94']

gold: This statistic shows the development of California 's real GDP from 2000 to 2018 . In 2018 , the real GDP of California was 2.67 trillion 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 2.67 templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of templateTitleSubject[0] was about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the development of California 's Real GDP from 2000 to 2018 . In 2018 , the GDP of California was about 2677.94 billion U.S. dollars .

Example 798:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in South Korea 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['1988.11', '1884.06', '1789.96', '1702.14', '1626.55', '1629.53', '1720.49', '1623.9', '1500.48', '1465.77', '1484.32', '1305.61', '1222.81', '1202.46', '1094.5', '901.94', '1002.22', '1122.68', '1011.8', '898.14', '764.88', '680.52', '609.02', '533.05', '561.63', '485.25', '374.24', '557.5', '598.1', '556.13', '455.61', '386.3', '350.05', '325.73', '279.35', '243.53', '196.97', '146.13', '115.54', '100.27', '96.6']

gold: The statistic shows gross domestic product ( GDP ) of South Korea from 1984 to 2018 , with projections up until 2024 . GDP or gross domestic product is the sum of all goods and services produced in a country in a year ; it is a strong indicator of economic strength . In 2018 , South Korea 's GDP was around 1.72 trillion U.S. dollars .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateTitle[3] or templateYLabel[0] templateYLabel[1] templateYLabel[2] is the sum of all goods and services produced in a country in a templateXLabel[0] ; it is a strong indicator of economic strength . In templateXValue[6] , templateTitleSubject[0] 's templateTitle[3] was around templateYValue[6] templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateTitle[3] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .
generated: The statistic shows the Gross domestic product ( GDP ) in South Korea from 1984 to 2018 , with projections up until 2024 . GDP refers to the total market value of all goods and services that are produced within a country per Year . It is an important indicator of the economic strength of a country .

Example 799:
titleEntities: {'Subject': ['Iraq'], 'Date': ['2000', '2018']}
title: U.S. petroleum imports from Iraq 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['Imports', 'in', 'thousand', 'barrels', 'per', 'day']: ['521', '604', '424', '229', '369', '341', '476', '459', '415', '450', '627', '484', '553', '531', '656', '481', '459', '795', '620']

gold: This statistic represents U.S. petroleum imports from Iraq between 2000 and 2018 . In 2018 , the United States imported an average of approximately 521,000 barrels of petroleum per day from the Middle Eastern country .
gold_template: This statistic represents templateTitle[0] templateTitle[1] templateYLabel[0] templateTitle[3] templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateTitle[0] imported an average of approximately templateYValue[0] templateYLabel[2] of templateTitle[1] templateYLabel[3] templateYLabel[4] templateTitle[3] the Middle Eastern country .

generated_template: This statistic shows the development of people employed in the templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . Once a major powerhouse of the templateTitleSubject[1] 's economy , the templateTitle[1] templateTitle[3] was the lifeblood of several regions , providing templateTitle[0] for more than templateYValue[min] templateYLabel[1] workers . Since that time , shifting attitudes towards templateTitle[1] and the emergence of alternative energy sources such as wind and solar have seen templateTitle[1] 's role in the templateTitleSubject[1] 's energy mix diminish in recent years .
generated: This statistic shows the development of people employed in the petroleum imports from in the Iraq ( Iraq ) from 2000 to 2018 . Once a major powerhouse of the Iraq 's economy , the petroleum from was the lifeblood of several regions , providing U.S. for more than 229 thousand workers . Since that time , shifting attitudes towards petroleum and the emergence of alternative energy sources such as wind and solar have seen petroleum 's role in the Iraq 's energy mix diminish in recent years .

Example 800:
titleEntities: {'Subject': ['Cree'], 'Date': ['2015', '2019']}
title: Cree 's revenue 2015 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015']
Y_Axis['Net', 'revenue', 'in', 'million', 'U.S.', 'dollars']: ['1080.0', '924.9', '771.5', '727.5', '726.0']

gold: This statistic represents Cree 's revenue from the fiscal year of 2015 to the fiscal year of 2019 . In the fiscal year of 2019 , the LED technology company reported revenue of about 1.08 billion U.S. dollars .
gold_template: This statistic represents templateTitleSubject[0] templateTitle[1] 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] , the LED technology company reported templateYLabel[1] of about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: As per recent data exploring the number of people employed by Hewlett Packard Enterprise ( previously a part of Hewlett-Packard Company ) worldwide between templateXValue[min] and templateXValue[max] , in templateXValue[max] the company had templateYValue[0] thousand templateYLabel[0] .
generated: As per recent data exploring the number of people employed by Hewlett Packard Enterprise ( previously a part of Hewlett-Packard Company ) worldwide between 2015 and 2019 , in 2019 the company had 1080.0 thousand Net .

Example 801:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2018']}
title: Population growth in Malaysia 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Population', 'growth', 'compared', 'to', 'previous', 'year']: ['1.35', '1.36', '1.36', '1.34', '1.34', '1.37', '1.45', '1.56', '1.69', '1.82', '1.91']

gold: This statistic shows the population growth in Malaysia from 2008 to 2018 . In 2018 , Malaysia 's population increased by approximately 1.35 percent compared to the previous year .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] increased by approximately templateYValue[0] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: In templateXValue[max] , the total templateYLabel[0] of companies producing templateTitle[0] and templateTitle[2] was approximately templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Between templateXValue[7] and templateXValue[max] the templateYLabel[0] fluctuated . However , prior to templateXValue[7] templateYLabel[0] was much higher , reaching over templateYValue[8] percent .
generated: In 2018 , the total Population of companies producing Population and Malaysia was approximately 1.35 growth compared previous . Between 2011 and 2018 the Population fluctuated . However , prior to 2011 Population was much higher , reaching over 1.69 percent .

Example 802:
titleEntities: {'Subject': ['Nepal'], 'Date': ['2024']}
title: Total population of Nepal 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['30.36', '29.97', '29.59', '29.2', '28.83', '28.46', '28.09', '27.63', '27.26', '27.02', '26.91']

gold: This statistic represents the total population of Nepal from 2014 to 2015 , with projections up until 2024 . In 2018 , the estimated total population of Nepal amounted to around 28.09 million people .
gold_template: This statistic represents the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[9] , with projections up until templateXValue[max] . In templateXValue[6] , the estimated templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateYLabel[1] people .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] Although templateTitle[0] templateTitle[1] increased on a yearly basis from 2004 to templateXValue[min] , templateTitle[1] growth has slowly decreased annually as of 2011 , despite remaining positive .
generated: The statistic shows the Total population of Nepal from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Nepal amounted to around 28.09 millions Inhabitants . population of Nepal Although Total population increased on a yearly basis from 2004 to 2014 , population growth has slowly decreased annually as of 2011 , despite remaining positive .

Example 803:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Leading U.S. consumer e-mail providers 2016
X_Axis['Response']: ['Google_(Gmail)', 'Yahoo', 'Outlook_(Hotmail)', 'AOL', 'Other', 'iCloud', 'Comcast']
Y_Axis['Share', 'of', 'respondents']: ['53', '18', '14', '8', '4', '2', '1']

gold: This statistic shows the most popular e-mail providers according to consumers in the United States as of 2016 . During the consumer survey , 53 percent of respondents stated that they used Gmail as their primary e-mail provider . Yahoo was ranked second with 18 percent .
gold_template: This statistic shows the most popular templateTitle[3] templateTitle[4] according to consumers in the templateTitle[1] as of templateTitleDate[0] . During the templateTitle[2] survey , templateYValue[max] percent of templateYLabel[1] stated that they used Gmail as their primary templateTitle[3] provider . templateXValue[1] was ranked second with templateYValue[1] percent .

generated_template: As of the third quarter of templateTitleDate[0] , it was found that templateYValue[max] percent of templateTitleSubject[0] users in the templateTitleSubject[1] templateTitleSubject[1] accessed the photo sharing app templateXValue[0] templateXValue[0] a templateXValue[0] . A further templateYValue[1] percent of templateYLabel[1] claimed that they used templateTitleSubject[0] on a templateXValue[1] basis .
generated: As of the third quarter of 2016 , it was found that 53 percent of U.S. users in the U.S. accessed the photo sharing app Google (Gmail) a Google (Gmail) . A further 18 percent of respondents claimed that they used U.S. on a Yahoo basis .

Example 804:
titleEntities: {'Subject': ['Global'], 'Date': ['2019']}
title: Global sulfur production by country 2019
X_Axis['Country']: ['China', 'United_States', 'Russia', 'Saudi_Arabia', 'Canada', 'Other', 'Kazakhstan', 'United_Arab_Emirates', 'India', 'Japan', 'South_Korea', 'Iran', 'Qatar', 'Chile', 'Poland', 'Finland', 'Kuwait', 'Australia', 'Germany', 'Venezuela', 'Italy', 'Netherlands', 'Brazil']
Y_Axis['Price', 'in', 'U.S.', 'dollars', 'per', 'ton']: ['17400', '8800', '7100', '6600', '5300', '3900', '3600', '3400', '3400', '3400', '3100', '2200', '2100', '1500', '1230', '940', '900', '900', '870', '700', '550', '520', '500']

gold: In 2019 , China produced around 17.4 megatons of sulfur , which makes China the world 's leading sulfur producer . China 's sulfur production includes byproduct elemental sulfur recovered from natural gas and petroleum , the estimated sulfur content of byproduct sulfuric acid from metallurgy , and the sulfur content of sulfuric acid from pyrite .
gold_template: In templateTitleDate[0] , templateXValue[0] produced around 17.4 megatons of templateTitle[1] , which makes templateXValue[0] the world 's leading templateTitle[1] producer . templateXValue[0] 's templateTitle[1] templateTitle[2] includes byproduct elemental templateTitle[1] recovered from natural gas and petroleum , the estimated templateTitle[1] content of byproduct sulfuric acid from metallurgy , and the templateTitle[1] content of sulfuric acid from pyrite .

generated_template: As of June templateTitleDate[0] , templateXValue[0] was the European templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateYLabel[1] in the world with a total of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateXValue[1] had a templateYLabel[0] templateYLabel[1] of templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . in templateXValue[last] , templateXValue[0] had the templateYLabel[0] templateYLabel[1] , with a total of templateYValue[13] percent .
generated: As of June 2019 , China was the European sulfur the production Price U.S. of Global U.S. in the world with a total of 17400 dollars per ton . United States had a Price U.S. of 8800 dollars per ton . in Brazil , China had the Price U.S. , with a total of 1500 percent .

Example 805:
titleEntities: {'Subject': ['Minnesota Wilds revenue'], 'Date': ['2019']}
title: Minnesota Wilds ' revenue 2005 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['149', '142', '139', '136', '125', '111', '81', '99', '97', '92', '95', '94', '78', '71']

gold: This graph depicts the annual National Hockey League revenue of the Minnesota Wild from the 2005/06 season to the 2018/19 season . The revenue of the Minnesota Wild amounted to 149 million U.S. dollars in the 2018/19 season .
gold_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] Wild from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] Wild amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .

generated_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .
generated: This graph depicts the annual National Hockey League Revenue of the Minnesota Wilds revenue from the 2005/06 season to the 2018/19 season . The Revenue of the Minnesota Wilds revenue amounted to 149 million U.S. dollars in the 2018/19 season .

Example 806:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: New issue volume of U.S. asset-backed securities 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2010', '2005', '2000']
Y_Axis['Volume', 'in', 'billion', 'U.S.', 'dollars']: ['517', '550', '325', '333', '393', '126', '474', '240']

gold: This statistic presents the new issue volume of the asset-backed securities of the United States from 2000 to 2018 . In 2018 , the new issue volume of the asset-backed securities of the United States was 517 billion U.S. dollars .
gold_template: This statistic presents the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitle[4] templateTitle[5] of the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitle[4] templateTitle[5] of the templateTitle[3] was templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: In templateXValue[max] , the templateTitle[0] total templateYLabel[0] of templateTitle[5] templateTitle[6] traded per day was over 547 templateYLabel[1] templateYLabel[2] templateYLabel[3] . This means that every day the market was open , the templateTitle[0] amount of templateYLabel[2] government templateTitle[6] bought and sold amounted to half a templateYLabel[1] templateYLabel[2] templateYLabel[3] in that templateXLabel[0] . What are templateTitle[5] templateTitle[6] ? templateTitle[5] templateTitle[6] are templateYLabel[2] government debt , bonds sold to finance the templateTitle[4] government .
generated: In 2018 , the New total Volume of securities 2000 traded per day was over 547 billion U.S. dollars . This means that every day the market was open , the New amount of U.S. government 2000 bought and sold amounted to half a billion U.S. dollars in that Year . What are securities 2000 ? securities 2000 are U.S. government debt , bonds sold to finance the asset-backed government .

Example 807:
titleEntities: {'Subject': ['Luxottica'], 'Date': ['2018']}
title: Share of global net sales of Luxottica by geographical area 2018
X_Axis['Geographical', 'area']: ['North_America', 'Europe', 'Asia-Pacific', 'Latin_America', 'Rest_of_the_world']
Y_Axis['Share', 'of', 'net', 'sales']: ['58', '21', '13', '6', '2']

gold: This statistic depicts the share of net sales of Luxottica worldwide in 2018 , by geographical area . In that year , 58 percent of Luxottica 's global net sales came from North America . Founded in 1961 in Agordo , Italy , the Luxottica Group S.p.A. is the world 's largest eyewear company .
gold_template: This statistic depicts the templateYLabel[0] of templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] worldwide in templateTitleDate[0] , templateTitle[5] templateXLabel[0] templateXLabel[1] . In that year , templateYValue[max] percent of templateTitleSubject[0] 's templateTitle[1] templateYLabel[1] templateYLabel[2] came from templateXValue[0] . Founded in 1961 in Agordo , Italy , the templateTitleSubject[0] Group S.p.A. is the templateXValue[last] 's largest eyewear company .

generated_template: This statistic depicts templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] templateTitle[5] templateTitle[6] average templateYLabel[0] templateYLabel[1] in templateTitle[9] . YouGov 's templateXLabel[0] templateYLabel[0] templateTitle[9] templateYLabel[0] results show that templateXValue[0] templateTitle[5] highest with a score of templateYValue[max] , followed templateTitle[6] templateXValue[1] at templateYValue[1] .
generated: This statistic depicts Share global net sales in the Luxottica by geographical average Share net in 2018 . YouGov 's Geographical Share 2018 Share results show that North America by highest with a score of 58 , followed geographical Europe at 21 .

Example 808:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2011', '2017']}
title: Retail sales of the frame market for eyewear in the U.S. 2011 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Retail', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['9732.2', '9710.0', '9627.0', '9153.0', '8871.0', '8475.0', '8213.0']

gold: This statistic depicts the retail sales of the frame market for eyewear in the United States from 2011 to 2017 . In 2017 , the U.S. frame market for eyewear generated about 9.73 billion U.S. dollars in retail sales .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[3] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] generated about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateYLabel[0] templateYLabel[1] .

generated_template: In templateXValue[max] , the proportion of monthly active templateTitle[2] templateYLabel[1] in templateTitleSubject[0] was at templateYValue[max] percent . The templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] had increased since templateXValue[min] , despite measures taken by the templateTitleSubject[0] government to reduce the templateYLabel[2] templateYLabel[3] . These include increasing taxes on cigarettes and restricting the areas in which templateYLabel[2] was allowed .
generated: In 2017 , the proportion of monthly active frame sales in U.S. was at 9732.2 percent . The Retail of sales million in U.S. had increased since 2011 , despite measures taken by the U.S. government to reduce the million U.S. . These include increasing taxes on cigarettes and restricting the areas in which million was allowed .

Example 809:
titleEntities: {'Subject': ['American'], 'Date': []}
title: American teenagers ' belief in existence of a God
X_Axis['Response']: ['Absolutely_certain_that_there_is_a_God', 'Somewhat_certain_that_there_is_a_God', 'Somewhat_certain_that_there_is_no_God', 'Absolutely_certain_that_there_is_no_God', 'Not_sure_whether_or_not_there_is_a_God']
Y_Axis['Share', 'of', 'respondents']: ['54', '15', '7', '9', '16']

gold: This survey , conducted by Harris Poll across the United States in February 2014 , shows the share of American teenagers who are certain or uncertain about the existence of a God . 54 percent of American teenagers are absolutely certain that there is a God .
gold_template: This survey , conducted by Harris Poll across the templateTitle[0] in February 2014 , shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] who are templateXValue[0] or uncertain about the templateTitle[4] of a templateXValue[0] . templateYValue[max] percent of templateTitleSubject[0] templateTitle[1] are templateXValue[0] that templateXValue[0] is a templateXValue[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateXValue[0] templateXValue[0] the templateTitle[6] as of March templateTitleDate[0] . During the survey , templateYValue[max] percent of the templateYLabel[1] were templateTitle[0] aggravated by the templateXValue[0] templateXValue[0] templateXValue[0] templateTitle[3] templateXValue[0] . The average daily rate of hotels in the templateTitle[6] was 123.97 templateTitleSubject[0] dollars in 2016 , a figure which has grown annually since 2009 .
generated: This statistic shows the American teenagers ' of belief Absolutely certain that there is a God the God as of March . During the survey , 54 percent of the respondents were American aggravated by the Absolutely certain that there is a God belief Absolutely certain that there is a God . The average daily rate of hotels in the God was 123.97 American dollars in 2016 , a figure which has grown annually since 2009 .

Example 810:
titleEntities: {'Subject': ['Nepal'], 'Date': ['2018']}
title: Population density in Nepal 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Inhabitants', 'per', 'square', 'kilometer']: ['195.94', '192.73', '190.17', '188.46', '187.7', '187.78', '188.28', '188.64', '188.44', '187.54', '186.02']

gold: The statistic shows the population density in Nepal from 2008 to 2018 . In 2018 , the population density in Nepal amounted to about 195.94 inhabitants per square kilometer .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: In templateXValue[max] , templateTitleSubject[0] had a templateTitle[0] templateTitle[1] of about templateYValue[max] people templateYLabel[1] templateYLabel[2] templateYLabel[3] . The country has one of the lowest templateTitle[0] densities in the world , as the total templateTitle[0] is very small in relation to the dimensions of the land . templateTitleSubject[0] has a relatively stable templateTitle[0] size , consistently with a growth of around one percent compared to the previous templateXLabel[0] .
generated: In 2018 , Nepal had a Population density of about 195.94 people per square kilometer . The country has one of the lowest Population densities in the world , as the total Population is very small in relation to the dimensions of the land . Nepal has a relatively stable Population size , consistently with a growth of around one percent compared to the previous Year .

Example 811:
titleEntities: {'Subject': ['Vending'], 'Date': ['2010']}
title: Vending machines : sales volume of vended products 2010
X_Axis['Year']: ['1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010']
Y_Axis['Vended', 'volume', '(in', 'billion', 'U.S.', 'dollars)']: ['36.6', '38.7', '41.0', '41.1', '42.2', '44.2', '46.0', '46.8', '47.5', '45.6', '42.9', '42.2']

gold: This graph depicts the total sales volume of products sold through vending machines in the U.S. from 1999 to 2010 . In 1999 , the sales volume was 36.6 billion U.S. dollars .
gold_template: This graph depicts the total templateTitle[2] templateYLabel[1] of templateTitle[5] sold through templateTitleSubject[0] templateTitle[1] in the templateYLabel[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[2] templateYLabel[1] was templateYValue[min] templateYLabel[3] templateYLabel[4] dollars .

generated_template: The statistic shows the templateTitle[1] of templateTitle[2] templateTitleSubject[0] as templateYLabel[0] of total e-mail templateYLabel[2] from templateXValue[last] to templateXValue[0] . In the most recently reported period , templateTitleSubject[0] messages accounted for templateYValue[11] percent of e-mail templateYLabel[2] worldwide , down from templateYValue[9] percent in templateXValue[9] .
generated: The statistic shows the machines of sales Vending as Vended of total e-mail (in from 2010 to 1999 . In the most recently reported period , Vending messages accounted for 42.2 percent of e-mail (in worldwide , down from 45.6 percent in 2008 .

Example 812:
titleEntities: {'Subject': ['Ohio'], 'Date': ['1990', '2018']}
title: Ohio - 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']: ['61633', '59768', '53985', '53301', '49644', '46398', '44375', '44648', '45886', '45879', '46934', '49099', '45900', '44203', '43055', '43520', '42684', '41785', '42962', '39489', '38925', '36134', '34070', '34941', '31855', '31285', '31404', '29790', '30013']

gold: This statistic shows the median household income in Ohio from 1990 to 2018 . In 2018 , the median household income in Ohio amounted to 61,633 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[max] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] parent families with a female householder and no spouse present in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[28] percent of all templateTitleSubject[0] families with a templateTitle[3] mother in the templateTitle[5] lived below the templateYLabel[0] level . In templateXValue[max] , that figure was at templateYValue[min] percent .
generated: This statistic shows the Household income of Ohio income parent families with a female householder and no spouse present in the Ohio from 1990 to 2018 . In 1990 , 30013 percent of all Ohio families with a income mother in the 2018 lived below the Household level . In 2018 , that figure was at 29790 percent .

Example 813:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Refugees arriving by age U.S. 2018
X_Axis['Age', 'in', 'years']: ['Under_1_year', '1_to_4_years', '5_to_9_years', '10_to_14_years', '15_to_19_years', '20_to_24_years', '25_to_29_years', '30_to_34_years', '35_to_39_years', '40_to_44_years', '45_to_49_years', '50_to_54_years', '55_to_59_years', '60_to_64_years', '65_to_69_years', '70_to_74_years', '75_years_and_over']
Y_Axis['Number', 'of', 'persons']: ['50', '2442', '2914', '2706', '2692', '2383', '1952', '1910', '1418', '1073', '872', '621', '447', '334', '269', '159', '163']

gold: This statistic shows the number of refugees arriving in the United States in 2018 , by age . In 2018 , about 163 refugees arrived in the United States aged 75 years or over . The total number of refugee arrivals amounted to 22,405 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitle[4] in templateTitleDate[0] , templateTitle[2] templateXLabel[0] . In templateTitleDate[0] , about templateYValue[16] templateTitle[0] arrived in the templateTitle[4] aged templateXValue[last] templateXValue[1] or templateXValue[last] . The total templateYLabel[0] of refugee arrivals amounted to 22,405 .

generated_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . There were templateYValue[max] templateYLabel[1] in the templateXValue[0] templateXValue[0] .
generated: This statistic shows the Number of the U.S. arriving by age U.S. in 2018 . There were 2914 persons in the Under 1 year .

Example 814:
titleEntities: {'Subject': ['Cyber'], 'Date': ['2019']}
title: Cyber bullying : common types of bullying 2019
X_Axis['Response']: ['I_have_been_cyber_bullied', 'Mean_or_hurtful_comments_online', 'Rumors_online', 'Threatened_to_hurt_me_through_a_cell_phone_text', 'Posted_mean_names_or_comments_online_about_me_with_a_sexual_meaning', 'Threatened_to_hurt_me_online', 'Posted_a_mean_or_hurtful_picture_online_of_me', 'Pretended_to_be_me_online', 'Posted_mean_names_or_comments_about_my_race_or_color', 'Posted_a_mean_or_hurtful_video_online_of_me', 'Posten_mean_names_or_comments_online_about_my_religion', 'Created_a_mean_or_hurtful_web_page_about_me', 'One_or_more_of_above_two_or_more_times']
Y_Axis['Share', 'of', 'respondents']: ['17.4', '24.9', '22.2', '12.2', '12', '11.7', '10.8', '10.1', '9.5', '7.1', '6.7', '6.4', '30.1']

gold: This statistic presents the percentage of middle and high school students in the United States who were cyber bullied , divided by the type of cyber bullying endured . During the April 2019 survey , 10.1 percent of cyber bullying victims had been impersonated online during the last 30 days . Cyber bullying includes unwanted contact by threatening or insulting the respondent via digital communications as well as sharing private information online .
gold_template: This statistic presents the percentage of middle and high school students in the country who were templateXValue[0] , divided by the type of templateXValue[0] templateTitle[1] endured . During the April templateTitleDate[0] survey , templateYValue[7] percent of templateXValue[0] templateTitle[1] victims had templateXValue[0] impersonated templateXValue[1] during the last templateYValue[max] days . templateXValue[0] templateTitle[1] includes unwanted contact by threatening or insulting the respondent via digital communications as well as sharing private information templateXValue[1] .

generated_template: This statistic shows the results of a survey that asked British adults which types of templateXValue[last] , if any , they ever templateXValue[last] in templateTitleSubject[0] templateTitleSubject[0] ( GB ) in templateTitleDate[0] . templateXValue[0] templateXValue[0] templateXValue[last] ranked highest with a templateYValue[max] percent templateYLabel[0] of templateYLabel[1] , followed by templateXValue[1] templateXValue[last] ( templateYValue[1] percent ) and templateXValue[2] templateXValue[2] ( templateYValue[2] percent ) . A majority of British consumers templateXValue[last] templateXValue[last] on a daily basis , with earlier times in the day the peak period for templateXValue[last] drinking .
generated: This statistic shows the results of a survey that asked British adults which types of One or more of above two or more times , if any , they ever One or more of above two or more times in Cyber ( GB ) in 2019 . I have been cyber bullied One or more of above two or more times ranked highest with a 30.1 percent Share of respondents , followed by Mean or hurtful comments online One or more of above two or more times ( 24.9 percent ) and Rumors online ( 22.2 percent ) . A majority of British consumers One or more of above two or more times on a daily basis , with earlier times in the day the peak period for One or more of above two or more times drinking .

Example 815:
titleEntities: {'Subject': ['LEGO Group'], 'Date': ['2009', '2018']}
title: LEGO Group operating profit 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Operating', 'profit', 'in', 'million', 'Euros']: ['1440.6', '1391.21', '1674.3', '1645.35', '1302.45', '1117.43', '1019.49', '762.19', '667.1', '389.5']

gold: This statistic shows the operating profit of the LEGO Group from 2009 to 2018 . In 2015 , the LEGO Group 's operating profit amounted to approximately 1.65 billion euros .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[3] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] amounted to templateYValue[0] thousand , up from templateYValue[min] thousand in the previous templateXLabel[0] .
generated: The statistic shows the Operating of profit at LEGO Group from 2009 to 2018 . In 2018 , the Operating of LEGO Group profit amounted to 1440.6 thousand , up from 389.5 thousand in the previous Year .

Example 816:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013', '2019']}
title: Mobile share of U.S. organic search engine visits 2013 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13"]
Y_Axis['Share', 'of', 'organic', 'search', 'visits']: ['58', '60', '59', '59', '57', '56', '55', '53', '53', '53', '51', '53', '51', '48', '46', '45', '43', '45', '45', '45', '39', '38', '34', '34', '33', '27']

gold: This statistic highlights the mobile share of organic search engine visits in the United States . As of the fourth quarter of 2019 , it was found that mobile devices accounted for 58 percent of organic search engine visits .
gold_template: This statistic highlights the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[5] templateYLabel[3] in the templateTitle[2] . As of the fourth templateXLabel[0] of templateTitleDate[1] , it was found that templateTitle[0] devices accounted for templateYValue[0] percent of templateYLabel[1] templateYLabel[2] templateTitle[5] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] from July 2018 to September templateTitleDate[1] . As of the third templateXLabel[0] of templateTitleDate[0] , templateTitleSubject[0] 's templateTitle[1] templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Mobile share of U.S. organic in the U.S. from July 2018 to September 2019 . As of the third Quarter of 2013 , U.S. 's share organic amounted to 58 search visits .

Example 817:
titleEntities: {'Subject': ['China'], 'Date': ['2000', '2018']}
title: Direct investment position of the U.S. in China 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Direct', 'investments', 'in', 'billion', 'U.S.', 'dollars']: ['116.52', '107.56', '97.29', '92.15', '82.24', '60.45', '54.51', '53.66', '59.0', '54.07', '53.93', '29.71', '26.46', '19.02', '17.62', '11.26', '10.57', '12.08', '11.14']

gold: This statistic shows the direct investment position of the United States in China from 2000 to 2018 , on a historical-cost basis . In 2018 , the U.S. investments made in China were valued at approximately 117 billion U.S. dollars . Direct investment position of the United States - additional information Foreign direct investment ( FDI ) , simply put , is an investment of one company into another company located in a different country .
gold_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitleSubject[0] were valued at approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] - additional information Foreign templateYLabel[0] templateTitle[1] ( FDI ) , simply put , is an templateTitle[1] of one company into another company located in a different country .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitleSubject[0] were valued at approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateYLabel[3] templateYLabel[0] templateTitle[1] abroad is defined as ownership by a templateYLabel[3] investor of at least templateYValue[12] percent of a foreign business .
generated: This statistic shows the Direct investment position of the U.S. in China from 2000 to 2018 , on a historical-cost basis . In 2018 , the U.S. investments made in China were valued at approximately 116.52 billion U.S. dollars . U.S. Direct investment abroad is defined as ownership by a U.S. investor of at least 26.46 percent of a foreign business .

Example 818:
titleEntities: {'Subject': ['R.R Martin'], 'Date': ['2011', '2016']}
title: George R.R . Martin - earnings 2011 to 2016
X_Axis['Month']: ['June_2015_to_June_2016', 'June_2014_to_June_2015', 'June_2013_to_June_2014', 'June_2012_to_June_2013', 'May_2011_to_May_2012']
Y_Axis['Earnings', 'in', 'million', 'U.S.', 'dollars']: ['9.5', '12.0', '12.0', '12.0', '15.0']

gold: The statistic presents data on the annual earnings of George R.R . Martin from May 2011 to June 2016 . The author earned 12 million U.S. dollars in the period June 2014 to June 2015 .
gold_template: The statistic presents data on the annual templateYLabel[0] of templateTitle[0] templateTitleSubject[0] . templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . The author earned templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the period templateXValue[0] templateXValue[1] to templateXValue[0] .

generated_template: templateTitleSubject[0] 's templateYLabel[0] in the templateTitleDate[0] fiscal year varied significantly across templateTitleSubject[0] 's sales regions . templateXValue[0] generated the largest amount of templateYLabel[0] for the templateTitleSubject[0] Motor Corporation in templateTitleDate[0] with templateYValue[max] templateYLabel[1] templateYLabel[2] per year .
generated: R.R Martin 's Earnings in the 2011 fiscal year varied significantly across R.R Martin 's sales regions . June 2015 to June 2016 generated the largest amount of Earnings for the R.R Martin Motor Corporation in 2011 with 15.0 million U.S. per year .

Example 819:
titleEntities: {'Subject': ['Lexus', 'United Kingdom', 'UK'], 'Date': ['2019']}
title: Lexus car sales in the United Kingdom ( UK ) 2016 to 2019
X_Axis['Month']: ['Dec_19', 'Nov_19', 'Oct_19', 'Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16']
Y_Axis['Units', 'sold']: ['745', '1044', '1098', '3852', '424', '1118', '1199', '943', '884', '3137', '262', '1007', '420', '663', '674', '2686', '270', '784', '1306', '851', '678', '3006', '180', '887', '772', '775', '725', '2908', '205', '843', '1161', '715', '672', '2888', '206', '800', '750', '931', '812', '2998', '234', '774']

gold: This statistic shows the monthly amount of cars sold by Lexus in the United Kingdom ( UK ) between July 2016 and December 2019 . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In September 2019 , 3,852 new Lexus cars were sold in the UK
gold_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between July templateTitleDate[0] and December templateTitleDate[1] . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In September templateTitleDate[1] , templateYValue[max] new templateTitleSubject[0] cars were templateYLabel[1] in the templateTitleSubject[2]

generated_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] in the templateTitleSubject[1] templateTitleSubject[1] ( templateTitleSubject[2] ) between July templateTitleDate[0] and December templateTitleDate[1] . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December templateTitleDate[1] , templateYValue[0] new templateTitleSubject[0] cars had been templateYLabel[1] .
generated: This statistic shows the monthly amount of cars sold by Lexus in the United Kingdom ( UK ) between July 2019 and December 2019 . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December 2019 , 745 new Lexus cars had been sold .

Example 820:
titleEntities: {'Subject': ['Banco Santander'], 'Date': ['2012', '2019']}
title: Banco Santander : customer numbers globally 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'customers', 'in', 'millions']: ['145.0', '144.0', '133.0', '125.0', '121.0', '117.0', '106.6', '102.1']

gold: Between 2018 and 2019 , the Banco Santander Group increased by one million customers worldwide . In 2019 , the Spanish giant had approximately196 thousand employees , across nearly12 thousand branches looking after the needs of its customers globally . As of 2019 , Banco Santander 's largest contributor to the company 's profit was Europe .
gold_template: Between templateXValue[1] and templateXValue[max] , the templateTitleSubject[0] Group increased by one templateYLabel[2] templateYLabel[1] worldwide . In templateXValue[max] , the Spanish giant had approximately196 thousand employees , across nearly12 thousand branches looking after the needs of its templateYLabel[1] templateTitle[4] . As of templateXValue[max] , templateTitleSubject[0] 's largest contributor to the company 's profit was Europe .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the report , there were 1.58 million templateYLabel[1] who had personal wealth of over 10 million yuan in templateTitleSubject[0] in templateXValue[1] , decreased from 1.61 million in the previous templateXLabel[0] .
generated: This statistic shows the Number of customers at Banco Santander from 2012 to 2019 . According to the report , there were 1.58 million customers who had personal wealth of over 10 million yuan in Banco Santander in 2018 , decreased from 1.61 million in the previous Year .

Example 821:
titleEntities: {'Subject': ['Guatemala'], 'Date': ['2019']}
title: Unemployment rate in Guatemala 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']: ['2.75', '2.73', '2.68', '2.83', '2.51', '2.72', '3.02', '2.77', '4.13', '3.5', '3.31', '2.84', '2.8', '2.89', '2.99', '2.97', '2.81', '2.85', '2.78', '2.9', '2.92']

gold: This statistic shows the unemployment rate in Guatemala from 1999 to 2019 . In 2019 , the unemployment rate in Guatemala was 2.75 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Guatemala from 1999 to 2019 . In 2019 , the Unemployment rate in Guatemala was at approximately 2.75 percent .

Example 822:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2001', '2018']}
title: Unemployment in U.S. motion picture and recording industries 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['Unemployment', 'rate']: ['5.5', '6.2', '7.3', '5.6', '7.1', '9.3', '12.9', '10.7', '13', '13.8', '9', '6.9', '5.9', '8.5', '8.7', '11.2', '10.3', '9.2']

gold: The statistic above presents the yearly unemployment rate for the U.S. motion picture and sound recording industry from 2001 to 2018 . In this industry , 5.5 percent of all private wage and salary workers were unemployed in 2018 .
gold_template: The statistic above presents the yearly templateYLabel[0] templateYLabel[1] for the templateTitleSubject[0] templateTitle[2] templateTitle[3] and sound templateTitle[4] industry from templateXValue[min] to templateXValue[max] . In this industry , templateYValue[min] percent of all private wage and salary workers were unemployed in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateYLabel[0] line .
generated: This statistic shows the Unemployment rate in U.S. from 2001 to 2018 . In 2018 , 5.5 percent of U.S. 's rate lived below the Unemployment line .

Example 823:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2012']}
title: Survey on amount of money spent on boating in the U.S. 2012
X_Axis['Yearly', 'expenses']: ['under_500$', '$500_to_$999', '$1000_to_$1999', '$2000_to_$4999', 'over_$5000']
Y_Axis['Percentage', 'of', 'boat', 'owners']: ['30.5', '15.5', '18.3', '17.4', '18.3']

gold: The statistic depicts the amount of money boat owners in the U.S. spent on boating in 2012 . 18.3 percent of the respondents stated that they spent between $ 1,000 and $ 1,999 on boating in 2012 .
gold_template: The statistic depicts the templateTitle[1] of templateTitle[2] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] templateTitle[3] on templateTitle[4] in templateTitleDate[0] . templateYValue[2] percent of the respondents stated that they templateTitle[3] between $ 1,000 and $ 1,999 on templateTitle[4] in templateTitleDate[0] .

generated_template: The graph shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] in templateTitleDate[0] , broken down templateTitle[5] templateXLabel[0] . In that year , there were templateYValue[0] percent of templateTitleSubject[0] templateTitleSubject[0] templateYLabel[1] were aged between 25 and 34 years .
generated: The graph shows the Percentage of Survey amount money boat in the U.S. in 2012 , broken down U.S. Yearly . In that year , there were 30.5 percent of U.S. boat were aged between 25 and 34 years .

Example 824:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013']}
title: Most popular leisure activities among men in the U.S. 2013
X_Axis['Response']: ['Watch_TV', 'Reading', 'Computer/internet', 'Playing_video_games_and_computer/internet_games', 'Spending_time_with_families_and_friends', 'Watching/going_to_the_movies', 'Exercise/working_out', 'Concerts/listening_to/playing_music', 'Walking/running/jogging', 'Golf']
Y_Axis['Share', 'of', 'respondents']: ['43', '24', '20', '13', '13', '11', '10', '10', '7', '7']

gold: This statistic shows the most popular leisure activities among men in the United States as of September 2013 . During the survey , 43 percent of the male respondents named watching TV as their most preferred activity during leisure time .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of September templateTitleDate[0] . During the survey , templateYValue[max] percent of the male templateYLabel[1] named watching templateXValue[0] as their templateTitle[0] preferred activity during templateTitle[2] templateXValue[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitleSubject[0] templateTitleSubject[0] in the templateTitle[7] as of September templateTitleDate[0] . During the survey , templateYValue[2] percent of templateYLabel[1] from templateTitleSubject[0] templateTitleSubject[0] stated templateXValue[3] templateXValue[3] templateXValue[3] templateXValue[3] and templateXValue[3] as a favorite templateTitle[2] activity .
generated: This statistic shows the Most popular leisure activities among U.S. in the 2013 as of September 2013 . During the survey , 20 percent of respondents from U.S. stated Playing video games and computer/internet games and Playing video games and computer/internet games as a favorite leisure activity .

Example 825:
titleEntities: {'Subject': ['Canadian Tire Corporation', 'Canada'], 'Date': ['2018']}
title: Number of stores operated by Canadian Tire Corporation in Canada by brand 2018
X_Axis['Month']: ['Canadian_Tire', 'SportChek', "Mark's", 'Canadian_Tire_gas_bar_locations', 'Other']
Y_Axis['Number', 'of', 'stores']: ['503', '409', '386', '297', '105']

gold: This statistic shows the number of stores of the retail company Canadian Tire Corporation in Canada in 2018 , by brand . There were SportChek stores operated by Canadian Tire Corporation in Canada in that year .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of the retail company templateXValue[0] Corporation in templateTitleSubject[1] in templateTitleDate[0] , templateTitle[3] templateTitle[9] . There were templateXValue[1] templateYLabel[1] templateTitle[2] templateTitle[3] templateXValue[0] Corporation in templateTitleSubject[1] in that year .

generated_template: templateTitleSubject[0] Inc. is a Canadian owned retail chain headquartered in Montreal . There were 1,225 templateTitleSubject[0] templateYLabel[1] in templateTitleSubject[1] as of February templateTitleDate[0] . templateXValue[0] had the highest templateYLabel[0] of templateYLabel[1] per templateTitle[5] , templateYValue[max] , and templateXValue[last] templateXValue[last] templateXValue[last] had just templateYValue[min] .
generated: Canadian Tire Corporation Inc. is a Canadian owned retail chain headquartered in Montreal . There were 1,225 Canadian Tire Corporation stores in Canada as of February 2018 . Canadian Tire had the highest Number of stores per Tire , 503 , and Other had just 105 .

Example 826:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2018']}
title: Deaths from unintentional carbon monoxide poisoning in the United Kingdom 1995 to 2018
X_Axis['Fossil', 'Fuel']: ['Gas_mains', 'Solid', 'Gas_portable', 'Petrol/diesel', 'Unknown', 'Oil', 'Parafin']
Y_Axis['Share', 'of', 'deaths']: ['35', '31', '16', '15', '2', '1', '0.4']

gold: This statistic shows the distribution of deaths from unintentional carbon monoxide poisoning in the United Kingdom ( UK ) from 1995 to 2018 , by fuel type . In this period , 35 percent of unintentional carbon monoxide poisoning were caused by gas mains during this period .
gold_template: This statistic shows the distribution of templateYLabel[1] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitleSubject[0] ( UK ) templateTitle[1] templateTitleDate[0] to templateTitleDate[1] , by templateXLabel[1] type . In this period , templateYValue[max] percent of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] were caused by templateXValue[0] during this period .

generated_template: As of templateXValue[0] templateXValue[0] , it was estimated that templateTitleSubject[0] had just templateYValue[0] thousand templateYLabel[1] in the templateTitle[2] , a significant drop from the templateYValue[max] templateYLabel[2] recorded in templateXValue[1] templateXValue[1] . templateTitleSubject[0] is no stranger to media attention , but after the recent claims from a source that they had obtained internal data about templateTitleSubject[0] 's floundering subscriber base , the company is once again in the spotlight . What is templateTitleSubject[0] ? templateTitleSubject[0] is a subscription-based movie ticketing service , designed to allow templateYLabel[1] to buy a select amount of movie tickets each templateXLabel[0] for a single fee .
generated: As of Gas mains , it was estimated that United Kingdom had just 35 thousand deaths in the unintentional , a significant drop from the 35 deaths recorded in Solid . United Kingdom is no stranger to media attention , but after the recent claims from a source that they had obtained internal data about United Kingdom 's floundering subscriber base , the company is once again in the spotlight . What is United Kingdom ? United Kingdom is a subscription-based movie ticketing service , designed to allow deaths to buy a select amount of movie tickets each Fossil for a single fee .

Example 827:
titleEntities: {'Subject': ['El Salvador'], 'Date': ['2019']}
title: Unemployment rate in El Salvador 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']: ['4.35', '4.39', '4.39', '4.42', '4', '4.16', '3.69', '3.85', '4.3', '4.89', '7.33', '5.88', '6.41', '6.57', '7.22', '6.05', '6.26', '5.73', '6.96', '6.96', '6.68']

gold: This statistic shows the unemployment rate in El Salvador from 1999 to 2019 . In 2019 , the unemployment rate in El Salvador amounted to approximately 4.35 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] amounted to approximately templateYValue[0] percent .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent of the total labor force . The templateYLabel[0] templateYLabel[1] is the percentage of a country 's labor force that are without jobs but are available to work and actively seeking employment . templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] is above the worldwide templateYLabel[0] templateYLabel[1] , and compared to other Sub-Saharan African countries and other regions , templateTitleSubject[0] has a relatively average templateYLabel[1] of templateYLabel[0] .
generated: In 2019 , the Unemployment rate in El Salvador was at approximately 4.35 percent of the total labor force . The Unemployment rate is the percentage of a country 's labor force that are without jobs but are available to work and actively seeking employment . El Salvador 's Unemployment rate is above the worldwide Unemployment rate , and compared to other Sub-Saharan African countries and other regions , El Salvador has a relatively average rate of Unemployment .

Example 828:
titleEntities: {'Subject': ['Canada'], 'Date': ['2024']}
title: Total population in Canada 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['39.22', '38.87', '38.52', '38.17', '37.81', '37.46', '36.99', '36.49', '36.05', '35.68', '35.39']

gold: The statistic shows the total population in Canada from 2014 to 2018 , with projections up until 2024 . In 2018 , the total population in Canada amounted to about 36.99 million inhabitants . Population of Canada Canada ranks second among the largest countries in the world in terms of area size , right behind Russia , despite having a relatively low total population .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] ranks second among the largest countries in the world in terms of area size , right behind Russia , despite having a relatively low templateTitle[0] templateTitle[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] is the second largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .
generated: This statistic shows the Total population of Canada from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Canada amounted to approximately 36.99 millions Inhabitants . population of Canada is the second largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .

Example 829:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['16', '19']}
title: Monthly watch and jewelry retail sales value index in Great Britain 2016 to 2019
X_Axis['Month']: ['Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16', 'Jun_16', 'May_16', 'Apr_16', 'Mar_16', 'Feb_16', 'Jan_16']
Y_Axis['Index', 'number', 'of', 'sales', 'per', 'week']: ['140.0', '136.8', '138.8', '139.3', '140.3', '132.8', '134.7', '126.7', '124.8', '129.6', '132.5', '131.2', '133.0', '135.8', '137.5', '112.8', '124.1', '118.7', '111.5', '116.8', '120.2', '114.3', '128.1', '115.7', '118.8', '118.6', '117.0', '114.4', '113.3', '116.5', '115.2', '118.3', '114.8', '107.9', '104.0', '107.3', '101.2', '100.3', '99.6', '104.1', '97.2', '97.7', '92.5', '95.3', '91.6']

gold: This statistic shows the monthly trend in the amount spent on watches and jewelry ( sales value ) in Great Britain from January 2016 to September 2019 , as an index of sales per week . During this period of time , retail sales increased significantly , measuring at 140 index points in September 2019 . The figures are seasonally adjusted estimates , measured using the Retail Sales Index ( RSI ) and published in index form with a reference year of 2016 equal to 100 .
gold_template: This statistic shows the templateTitle[0] trend in the amount spent on watches and templateTitle[2] ( templateYLabel[2] templateTitle[5] ) in templateTitleSubject[0] from January templateTitleDate[0] to September templateTitleDate[1] , as an templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateYLabel[4] . During this period of time , templateTitle[3] templateYLabel[2] increased significantly , measuring at templateYValue[0] templateYLabel[0] points in September templateTitleDate[1] . The figures are seasonally adjusted estimates , measured using the templateTitle[3] templateYLabel[2] templateYLabel[0] ( RSI ) and published in templateYLabel[0] form with a reference year of templateTitleDate[0] equal to templateYValue[37] .

generated_template: Cosmetics and templateTitle[1] are bought in larger quantities each passing templateXLabel[0] in the UK . Most recently , in September templateTitleDate[1] , the templateTitle[2] templateYLabel[2] templateTitle[4] of articles in these two categories reached an templateYLabel[0] templateYLabel[1] of templateYValue[0] as presented in the graph . This was lower than the templateTitle[4] recorded for the previous months of the year , but still higher than the figures recorded in templateTitleDate[0] , 2017 and the first half of 2018 .
generated: Cosmetics and watch are bought in larger quantities each passing Month in the UK . Most recently , in September 19 , the jewelry sales of articles in these two categories reached an Index number of 140.0 as presented in the graph . This was lower than the sales recorded for the previous months of the year , but still higher than the figures recorded in 16 , 2017 and the first half of 2018 .

Example 830:
titleEntities: {'Subject': ['Norway'], 'Date': ['2009', '2019']}
title: Population in Norway 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Inhabitants', 'in', 'millions']: ['5.33', '5.3', '5.26', '5.21', '5.17', '5.12', '5.05', '4.99', '4.92', '4.86', '4.8']

gold: This statistic shows the population in Norway over the years from 2009 to 2019 . In 2009 , the population of Norway was around 4.8 million people . In 2019 , the number of inhabitants increased to around 5.33 million .
gold_template: This statistic shows the templateTitle[0] in templateTitleSubject[0] over the years from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[0] of templateTitleSubject[0] was around templateYValue[min] templateYLabel[1] people . In templateXValue[max] , the number of templateYLabel[0] increased to around templateYValue[max] templateYLabel[1] .

generated_template: In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was around templateYValue[0] people templateYLabel[1] . This was an increase of 0.65 percent compared to the previous templateXLabel[0] . The templateTitle[0] of templateTitleSubject[0] The templateTitle[0] of templateTitleSubject[0] – additional information According to the graph at hand , templateTitleSubject[0] 's templateYLabel[0] have been growing steadily over the past decade , except in templateXValue[8] when financial crisis and global economic downturn slowed down global trade .
generated: In 2019 , the Population Norway of Norway was around 5.33 people millions . This was an increase of 0.65 percent compared to the previous Year . The Population of Norway The Population of Norway – additional information According to the graph at hand , Norway 's Inhabitants have been growing steadily over the past decade , except in 2011 when financial crisis and global economic downturn slowed down global trade .

Example 831:
titleEntities: {'Subject': ['Americans'], 'Date': ['2019']}
title: Share of Americans who believe in true love in 2019
X_Axis['Response']: ['Yes', 'No']
Y_Axis['Share', 'of', 'respondents']: ['94', '6']

gold: This statistic shows the results of a survey conducted in the United States in 2017 on whether the respondents believe in love , or not . During the survey , some 94 percent of respondents stated they believe in true love .
gold_template: This statistic shows the results of a survey conducted in the country in 2017 on whether the templateYLabel[1] templateTitle[3] in templateTitle[5] , or not . During the survey , some templateYValue[max] percent of templateYLabel[1] stated they templateTitle[3] in templateTitle[4] templateTitle[5] .

generated_template: The statistic shows the results of a templateTitle[0] among adult Americans who have one or more templateTitle[2] . The templateTitle[0] was conducted in templateTitleDate[0] , asking the templateYLabel[1] whether they ever regret getting any of their templateTitle[2] , or not . templateYValue[max] percent of templateYLabel[1] stated they do not regret getting any of their templateTitle[2] .
generated: The statistic shows the results of a Share among adult Americans who have one or more who . The Share was conducted in 2019 , asking the respondents whether they ever regret getting any of their who , or not . 94 percent of respondents stated they do not regret getting any of their who .

