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

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

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the templateScale of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
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 % 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] templateScale of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] the templateTitle[0] .

generated_template: This statistic presents the templateTitle[2] of users in the templateTitleSubject[0] accessing templateTitle[0] . As of the third quarter of templateTitleDate[0] , it was found that templateYValue[max] templateScale of templateTitle[4] templateTitle[0] users accessed the social platform templateXValue[0] a templateXValue[0] . templateTitle[0] is the most popular social media site in the templateTitle[4] .
generated: This statistic presents the EIFS of users in the EIFS accessing Most .  . As of the third quarter of 2018 , it was found that 34.6 % of brands Most users accessed the social platform Dryvit a .  . Most is the most popular social media site in the brands .

Example 3:
titleEntities: {'Subject': ['Pennsylvania'], 'Date': ['2000', '2018']}
title: Pennsylvania - real GDP 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Real', 'GDP', 'in', 'billion', 'U.S.', 'dollars']: ['712.1', '697.18', '685.46', '677.87', '664.79', '651.32', '641.32', '631.37', '622.47', '605.5', '623.82', '612.02', '592.03', '588.05', '578.03', '562.43', '550.25', '544.79', '537.22']

gold: This statistic shows the development of Pennsylvania 's real GDP from 2000 to 2018 . In 2018 , the real GDP of Pennsylvania was 712.1 billion U.S. dollars .
gold_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of templateTitleSubject[0] was about templateYValue[idxmax(X)] templateScale 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 2018 , Sony 's templateXValue[0] was ranked first with templateYValue[max] templateScale templateTitleSubject[0] templateTitle[7] . templateXValue[4] was ranked fifth with templateYValue[4] templateScale templateTitle[7] .

generated_template: This statistic gives information on the most templateTitle[3] templateTitle[4] templateTitle[5] on templateTitleSubject[0] , ranked by templateTitle[1] of templateTitle[2] on the social network . As of 2020 , personal care templateYLabel[0] templateXValue[0] Body templateXValue[0] was ranked first with close to templateYValue[max] templateScale templateTitleSubject[0] templateTitle[2] .
generated: This statistic gives information on the most brands Twitter 2018 on Twitter , ranked by video of gaming on the social network .  . As of 2020 , personal care Brand PlayStation Body was ranked first with close to 15.63 millions Twitter gaming .

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

gold: There were approximately 135,000 people directly employed by the defense industry in the United Kingdom in 2018 . This was a decrease of 31 percent since the beginning of the reporting period in 2010 . According to ADS - the UK trade organization representing the aerospace/space , defense and security sectors , the country is currently the second largest exporter of defense equipment and services in the world .
gold_template: There were approximately templateYValue[min] templateTitle[1] directly templateTitle[2] by the templateTitle[3] templateTitle[4] in the United Kingdom in templateXValue[idxmin(Y)] . This was a templateNegativeTrend of 31 templateScale 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 depicts the total templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] is a health care company headquartered in Minnetonka , Minnesota . The total templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] the templateTitleSubject[0] , as of 31 , templateXValue[max] , was about templateYValue[idxmax(X)] templateTitle[1] worldwide templateYValue[idxmax(X)]
generated: This statistic depicts the total Employees of people employed defense UK from 2010 to 2018 .  . The UK is a health care company headquartered in Minnetonka , Minnesota .  . The total Employees of thousands employed defense the UK , as of 31 , 2018 was about 135 people worldwide 135 . 

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] templatePositiveTrend 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 gives information on templateTitleSubject[0] 's templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] from the second templateXLabel[0] of templateTitleDate[min] to the second templateXLabel[0] of templateTitleDate[max] . As of the last reported templateXLabel[0] , the website 's templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic gives information on Dublin 's Cost rental prices Dublin 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 approximately 538 million meter euros .

Example 7:
titleEntities: {'Subject': ['Orioles'], 'Date': ['2001', '2018']}
title: Baltimore Orioles revenue 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['251', '252', '253', '239', '245', '198', '206', '179', '175', '171', '174', '166', '158', '156', '148', '129', '129', '133']

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

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] templateTitle[1] are owned by Ted Lerner who bought the franchise for 450 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[12] .
generated: The statistic depicts the Revenue of the Orioles from 2001 to 2018 .  . In 2018 , the Revenue of the Major League Baseball franchise amounted to 251 million U.S. dollars.The Orioles are owned by Ted Lerner who bought the franchise for 450 million U.S. dollars in 2006 .

Example 8:
titleEntities: {'Subject': ['Wisconsin'], 'Date': ['2000', '2018']}
title: Wisconsin - poverty rate from 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'population']: ['11', '11.3', '11.8', '12.1', '13.2', '13.5', '13.2', '13.1', '13.2', '12.4', '10.4', '10.8', '11', '10.2', '10.7', '10.5', '9.7', '9.8', '8.9']

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

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the poverty rate in Wisconsin from 2000 to 2018 .  . In 2018 , about 11 percentage of Wisconsin 's population lived below the poverty line .

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

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

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitle[2] between 1982 and 26 , 2020 , templateTitle[4] templateXLabel[0] and ethnicity of the shooter ( s ) . Between 1982 and 2020 , templateYValue[max] templateScale of 118 templateTitle[0] templateTitle[1] were initiated templateTitle[4] templateXValue[0] templateTitle[3] . The Las Vegas strip massacre in 2017 had the highest templateYLabel[0] of victims between 1982 and templateTitleDate[0] , with 58 people killed , and over 500 injured .
generated: This statistic shows the Number of Ultra high in the net between 1982 and 26 , 2020 individuals Region and ethnicity of the shooter ( s ) .  . Between 1982 and 2020 , 84054 million of 118 Ultra high were initiated individuals North America worth .  . The Las Vegas strip massacre in 2017 had the highest Number of victims between 1982 and 2019 , with 58 people killed , and over 500 injured .

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

Example 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] templateScale templateYLabel[3] ( or about 74.43 templateScale US dollars ) .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] amounted to about templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] is a United States-based multinational oil and gas corporation headquartered in Irving , Texas .
generated: The statistic shows the Total assets of Audi from 2002 to 2018 .  . In 2018 , Audi total assets amounted to about 65598 million euros .  . Audi is a United States-based multinational oil and gas corporation headquartered in Irving , Texas .

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

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

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

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] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] The templateTitleSubject[0] are a professional basketball team of the National Basketball Association ( NBA ) , entering the league as an expansion team in 1968 .
generated: The statistic shows the Revenue of the Brooklyn Nets franchise from the 2001/02 season to the 2018/19 season .  . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 304 million U.S. dollars .  . Brooklyn Nets The Brooklyn Nets are a professional basketball team of the National Basketball Association ( NBA ) , entering the league as an expansion team in 1968 .

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

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

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] templateScale 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 have personally experienced hacking .  . According to the source , 51 % of female students in the consumers were experienced hacking as of 2013 .

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

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

generated_template: This statistic shows the largest templateTitle[3] templateTitle[4] of the templateXValue[6] States regarding trade goods in templateTitleDate[0] , by templateYLabel[0] templateYLabel[1] . In templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] of trade goods from templateXValue[2] amounted to templateYValue[2] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the largest trading partners of the Korea South States regarding trade goods in 2018 , by Export value .  . In 2018 , the Export value of trade goods from China amounted to 120.3 billion U.S. dollars .

Example 16:
titleEntities: {'Subject': ['Kansas'], 'Date': ['2000', '2018']}
title: Kansas - poverty rate 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'population']: ['12', '11.9', '12.1', '13', '13.6', '14', '14', '13.8', '13.6', '13.4', '11.3', '11.2', '12.4', '11.7', '10.5', '10.8', '12.1', '11.3', '9.5']

gold: This statistic shows the poverty rate in Kansas from 2000 to 2018 . In 2018 , about 12 percent of Kansas ' population lived below the poverty line .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] ' templateYLabel[1] lived below the templateTitle[1] line .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale 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 , 12 percentage 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 templateTitleDate[0] . The templateXLabel[0] in templateXValue[1] in templateXValue[1] caused an templateYLabel[0] templateYLabel[1] of almost templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic represents the templateTitle[0] templateTitleSubject[0] templateTitle[1] templateTitle[2] on templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . With some 842,000 templateYLabel[0] templateYLabel[1] , templateXValue[0] was ranked first that year . The total number of templateYLabel[0] templateYLabel[1] was more than 8.7 templateScale units in templateTitleDate[0] .
generated: This statistic represents the Economic loss due on Economic loss in 2016 .  . With some 842,000 Economic loss , United States June 2012 was ranked first that year .  . The total number of Economic loss was more than 8.7 billion units in 2016 .

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 templateScale 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 templateScale templateNegativeTrend by a total of 6.44 templateScale points .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , some templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[0] templateTitle[1] in the templateTitleSubject[0] , an templatePositiveTrend from the previous templateXLabel[0] .
generated: This statistic shows the Trade union density Percentage in the Germany from 2000 to 2013 .  . In 2001 , some 24.57 percentage employees of Trade union in the Germany , an increase from 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[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the templateScale of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
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 % 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[max] , templateTitleSubject[0] averaged templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] , a decline from its all-time high of templateYValue[max] MAU in the first templateXLabel[0] of 2018 . As of the first templateXLabel[0] of templateTitleDate[max] , the company switched its user reporting metric to monetizable daily templateYLabel[2] templateYLabel[3] ( mDAU ) . templateTitleSubject[0] is a social networking and microblogging service , enabling registered templateYLabel[3] to read and post short messages called tweets .

generated_template: This statistic shows a timeline with the amount of templateYLabel[1] templateYLabel[2] templateTitle[4] templateTitleSubject[0] templateYLabel[3] as of the first templateXLabel[0] of templateTitleDate[max] . Excluding the country , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[4] templateTitleSubject[0] templateYLabel[3] amounted to templateYValue[0] templateScale as of the most recent templateXLabel[0] . In total , templateTitleSubject[0] had 330 templateScale global templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows a timeline with the amount of monthly active users Twitter as of the first Quarter of 2019 .  . Excluding the country , the Number of monthly active users Twitter amounted to 330.0 millions as of the most recent Quarter .  . In total , Twitter had 330 millions global monthly active users .

Example 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] templateScale of those between the ages of 25 and 64 . templateXValue[last] had the least amount of people templateYLabel[1] a templateYLabel[2] templateYLabel[3] , at templateYValue[min] templateScale of people between the ages of 25 and 64 . templateYLabel[2] around the world Deciding which templateYLabel[2] to attend can be a difficult decision for some and in today 's world , people are not left wanting for choice .

generated_template: This statistic shows the 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 Percent of university in 2007 .  . In 2007 , at least 41 people were executed in New Zealand .  . The figures represent minimum values according to Amnesty International .

Example 22:
titleEntities: {'Subject': ['Projected'], 'Date': ['1950', '2100']}
title: Projected global median age 1950 to 2100
X_Axis['Year']: ['2100', '2095', '2090', '2085', '2080', '2075', '2070', '2065', '2060', '2055', '2050', '2045', '2040', '2035', '2030', '2025', '2020', '2015', '2010', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960', '1955', '1950']
Y_Axis['Median', 'age']: ['41.9', '41.3', '40.8', '40.3', '39.7', '39.2', '38.7', '38.2', '37.6', '36.9', '36.2', '35.4', '34.6', '33.9', '33.0', '32.0', '30.9', '29.6', '28.5', '27.4', '26.3', '25.1', '24.0', '23.3', '22.6', '21.9', '21.5', '22.0', '22.6', '23.1', '23.6']

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

generated_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around 8.32 templateScale people were employed in the templateTitle[3] .
generated: This statistic shows the total Median of age in the age from 1950 to 2100 .  . In 2100 , around 8.32 million people were employed in the age .

Example 23:
titleEntities: {'Subject': ['Automobile'], 'Date': ['2010']}
title: Automobile production - automatic transmission forecast 2010 to 2015
X_Axis['Year']: ['2010', '2011', '2012', '2013', '2014', '2015']
Y_Axis['Million', 'transmissions']: ['25.03', '25.36', '26.72', '27.82', '28.46', '28.65']

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

generated_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[2] as a templateYLabel[0] of templateYLabel[1] templateTitle[5] templateYLabel[2] from templateXValue[min] to templateXValue[max] . templateTitle[1] systems have vastly improved vehicle safety over the evolution of the templateTitle[5] , such as airbags and anti-lock braking systems . In templateXValue[max] , it is forecasted that on average , electronic systems will account for half of the templateYLabel[1] price of a new templateTitle[5] .
generated: This statistic shows Automobile production transmissions as a Million of transmissions 2010 from to 2015 .  . production systems have vastly improved vehicle safety over the evolution of the 2010 , such as airbags and anti-lock braking systems .  . In 2015 , it is forecasted that on average , electronic systems will account for half of the transmissions price of a new 2010 .

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

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

generated_template: This statistic presents the templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] on templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , there were templateYValue[idxmin(X)] templateScale templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] on the social platform . The volume of templateTitle[3] content on templateTitleSubject[0] is projected to amount to templateYValue[3] templateScale templateYLabel[1] in templateXValue[3] .
generated: This statistic presents the Motors of adjusted EBIT 2015 (adjusted; on General Motors from 2015 to 2019 .  . In 2015 , there were 11449 million adjusted EBIT 2015 (adjusted; on the social platform .  . The volume of EBIT content on General Motors is projected to amount 12848 million (adjusted; in 2016 .

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 templatePositiveTrend , reaching around templateYValue[2] templateScale templateYLabel[2] in templateXValue[2] .

generated_template: The templateTitle[0] and templateTitle[1] industry contributed to the French Gross Domestic Product ( templateTitle[5] ) in templateTitle[3] with approximately templateYValue[2] templateScale templateYLabel[2] in templateXValue[2] . The source predicted that this templateTitle[4] will templatePositiveTrend in the coming ten years , reaching an expected templateTitle[3] of around templateYValue[max] templateScale templateYLabel[2] in templateXValue[idxmax(Y)] . In templateXValue[2] , templateTitleSubject[0] welcomed almost 87 templateScale international tourists , making it the most visited country in the world in that templateXLabel[0] .
generated: The Travel and tourism industry contributed to the French Gross Domestic Product ( GDP ) in total with approximately 348.1 billion euros in 2017 .  . The source predicted that this contribution will increase in the coming ten years , reaching an expected total of around 411.0 billion euros in 2028 .  . In 2017 , Germany welcomed almost 87 billion international tourists , making it the most visited country in the world in that Year .

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

gold: This statistic gives information on the internet penetration in Argentina from 2000 to 2017 . In 2017 , about 74.3 percent of the Argentinean population accessed the internet , a considerable increase when compared to only seven percent in 2000 .
gold_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[max] templateScale of the Argentinean population accessed the templateYLabel[3] , a considerable templatePositiveTrend when compared to only templateYValue[min] templateScale in templateXValue[idxmin(Y)] .

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] templateScale of the Chilean population accessed the templateYLabel[3] , up from templateYValue[16] templateScale in templateXValue[16] . In templateXValue[1] , templateYValue[max] templateScale of the Chilean population acceded to templateYLabel[3] .
generated: This statistic gives information on the internet penetration in Argentina from 2000 to 2017 .  . In 2017 , 74.29 percentage of the Chilean population accessed the internet , up from 9.78 percentage in 2001 .  . In 2016 , 74.29 percentage of the Chilean population acceded to internet .

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

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

generated_template: This statistic shows the total volume of the templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . Over the last five years the number of pets in the templateTitleSubject[1] has declined . The templateTitle[0] templateYLabel[0] peaked in templateXValue[4] at templateYValue[max] templateScale , but as of templateXValue[max] is estimated to be around templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the total volume of the RPMs billions in the Southwest Airlines ( ) from 2010 to 2019 .  . Over the last five years the number of pets in the Southwest Airlines has declined .  . The Revenue RPMs peaked in 2015 at 133.32 billions , but as of 2019 is estimated to be around 131.35 billions .

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

gold: This statistic displays a timeline of online purchasing take up in Great Britain from 2008 to 2019 . In 2019 , 82 percent of polled Britons made purchases online . This is greater penetration than in 2008 , when 53 percent of individuals made purchases online .
gold_template: This statistic displays a timeline of templateTitle[0] templateTitle[1] take up in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of polled Britons made purchases templateTitle[0] . This is greater templateTitle[2] than in templateXValue[min] , when templateYValue[idxmin(X)] templateScale 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] 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: This statistic shows templateTitleSubject[0] templateYLabel[1] in templateYLabel[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , the templateYLabel[2] templateTitle[3] templateYLabel[3] templateNegativeTrend by 0.6 templateScale compared to the previous templateXLabel[0] . templateTitle[3] templateTitle[4] were forecasted to templatePositiveTrend by templateYValue[idxmax(X)] templateScale in templateXValue[max] .
generated: This statistic shows United Kingdom university in applicants Kingdom UK from 2010 to 2018 .  . In 2014 , the applicants Kingdom decreased by 0.6 % compared to the previous Year .  . Kingdom UK were forecasted to increase by 636960 million in 2018 .

Example 30:
titleEntities: {'Subject': ['ITV', 'UK'], 'Date': ['2019']}
title: ITV viewers reached quarterly in the UK Q1 2012-Q3 2019
X_Axis['Quarter']: ['Q1_2012', 'Q2_2012', 'Q3_2012', 'Q4_2012', 'Q1_2013', 'Q2_2013', 'Q3_2013', 'Q4_2013', 'Q1_2014', 'Q2_2014', 'Q3_2014', 'Q4_2014', 'Q1_2015', 'Q2_2015', 'Q3_2015', 'Q4_2015', 'Q1_2016', 'Q2_2016', 'Q3_2016', 'Q4_2016', 'Q1_2017', 'Q2_2017', 'Q3_2017', 'Q4_2017', 'Q1_2018', 'Q2_2018', 'Q3_2018', 'Q4_2018', 'Q1_2019', 'Q2_2019', 'Q3_2019']
Y_Axis['Viewers', 'in', 'thousands']: ['55196', '55302', '54443', '55327', '55277', '55014', '54013', '55559', '55443', '55628', '54636', '55195', '53997', '53716', '53457', '54227', '53928', '54201', '52843', '54150', '53038', '51936', '51546', '53303', '52906', '51628', '47762', '48375', '48236', '46376', '45308']

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

generated_template: This statistic shows the total templateTitle[2] templateYLabel[0] of the templateYLabel[2] templateTitle[1] from 2012 to templateTitleDate[0] , templateTitle[4] templateXLabel[0] . The total templateTitle[2] templateYLabel[0] of the templateTitle[1] added up to about templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in the third templateXLabel[0] of templateTitleDate[0] .
generated: This statistic shows the total reached Viewers of the thousands viewers from 2012 to 2019 , UK Quarter .  . The total reached Viewers of the viewers added up to about 55196 thousands in the third Quarter of 2019 .

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] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] Red templateTitle[6] club of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[0] received a salary of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Player expenses ( Payroll ) of the San Jose Red 2019 club of Major League Soccer by Player in 2019 .  . Valeri "Vako" Qazaishvili received a salary of 1604.04 thousand U.S. dollars .

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

gold: This timeline depicts PepsiCo 's net revenue worldwide from 2007 to 2018 . In 2018 , PepsiCo 's net revenue worldwide amounted to about 64.66 billion U.S. dollars . PepsiCoPepsiCo is an American food and beverage corporation , based in Purchase , NY .
gold_template: This timeline depicts templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[4] amounted to about templateYValue[idxmax(X)] templateScale 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[idxmax(X)] templateScale 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 templateYLabel[0] templateYLabel[1] of provinces in templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] of templateXValue[0] , templateTitleSubject[0] , was 0.322 , making it templateTitleSubject[0] 's most inequitable templateTitle[5] in terms of after-tax templateTitle[1] .
generated: This statistic shows the Number home of provinces in Oakland Athletics 2019 .  . In 2019 , the Number home of Mark McGwire , Oakland Athletics was 0.322 , making it Oakland Athletics 's most inequitable leaders in terms of after-tax Athletics .

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

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

generated_template: This statistic shows the templateYLabel[0] of adults users in the templateTitleSubject[1] who were using templateTitleSubject[0] as of 2019 , sorted templateTitle[6] templateXLabel[0] templateXLabel[1] . During that period of time , templateYValue[max] templateScale of templateYLabel[1] who had attained a templateXValue[1] degree used the photo sharing app .
generated: This statistic shows the Number of adults users in the BuzzFeed who were using BuzzFeed as of 2019 , sorted 2016 Month .  . During that period of time , 7000 millions of monthly who had attained a Oct '15 degree used the photo sharing app .

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

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

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . There were roughly templateYValue[idxmax(X)] templateYLabel[0] crimes reported per 100,000 residents in templateTitleSubject[0] in templateXValue[max] .
generated: This statistic shows the Number of robberies in Canada from 2000 to 2018 .  . There were roughly 22450 Number crimes reported per 100,000 residents in Canada 2018 .

Example 36:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. per capita consumption of fresh citrus fruit 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['23.95', '23.82', '24.07', '22.75', '23.28', '23.97', '23.54', '22.81', '21.59', '20.69', '20.62', '17.94', '21.64', '21.63', '22.7', '23.82', '23.37', '23.93', '23.54']

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

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[6] and templateTitle[7] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[6] and templateTitle[7] templateTitle[5] amounted to approximately templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of fresh fruit and 2000 citrus in the U.S. from 2000 to 2018 .  . According to the report , the U.S. Per capita consumption of fresh fruit and 2000 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] templatePositiveTrend to around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] , about 1.59 templateScale vehicles were purchased in templateXValue[max] by templateTitle[1] companies in the templateTitle[3] .

generated_template: The statistic shows global templateYLabel[0] from templateTitleSubject[0] and templateTitleSubject[0] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , earphones and headphones were estimated to have a market value of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] templateTitle[3] .
generated: The statistic shows global Revenue from U.S. and 2002 from to 2017 .  . In 2017 , earphones and headphones were estimated to have a market value of 28.63 billion U.S. dollars .

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

gold: This timeline shows Google 's quarterly net income from 2003 to 2015 . In the third quarter of 2015 , the California-based web company earned 3.98 billion US dollars , an increase from the 3.93 billion in the previous quarter . Due to restructuring under new parent company Alphabet , Google net income is not longer reported separately .
gold_template: This timeline shows templateTitleSubject[0] 's templateTitle[1] templateYLabel[0] templateYLabel[1] from templateTitleDate[min] to templateTitleDate[max] . In the third quarter of templateTitleDate[max] , the California-based web company earned templateYValue[0] templateScale US templateYLabel[4] , an templatePositiveTrend from the templateYValue[1] templateScale 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] 100,000 templateYLabel[6] in the templateXValue[26] in templateTitleDate[0] , distinguished templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , templateXValue[7] of templateXValue[7] was ranked first with templateYValue[max] cases templateYLabel[4] 100,000 templateYLabel[6] in the templateTitle[4] .
generated: This statistic shows the Net income million U.S. dollars 100,000 in the Q1 '09 in 2003 , distinguished 2015 Financial .  . In 2003 , Q4 '13 of was ranked first with 4675.0 cases dollars 100,000 in the 2003 .

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

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

generated_template: The statistic shows the templateYLabel[0] market/transfer templateYLabel[1] of the participating templateTitle[2] templateTitle[3] at the templateTitle[4] templateTitle[5] templateTitle[6] templateTitleDate[0] in templateXValue[2] . The Spanish squad has a combined market/transfer templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] . Transfer templateYLabel[1] of templateTitle[5] templateTitle[6] squads – additional information In international football , a team 's templateYLabel[0] market/transfer templateYLabel[1] refers to the sum of all of the transfer fees that each individual player could command in the current market at their club level .
generated: The statistic shows the Index market/transfer value of the participating Case Shiller at the National Home Price 2017 in Jun 19 .  . The Spanish squad has a combined market/transfer value of 212.06 billion value .  . Transfer value of Home Price squads – additional information In international football , a team 's Index market/transfer value refers to the sum of all the transfer fees that each individual player could command in the current market at their club level .

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

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

generated_template: The templateTitleSubject[0] group saw a two-year templatePositiveTrend in employment between templateXValue[3] and templateXValue[1] before templateNegativeTrend in templateXValue[max] . There were approximately templateYValue[last] thousand people employed by templateTitleSubject[0] group globally in templateXValue[min] . As of templateXValue[max] the total templateYLabel[0] of global templateYLabel[1] at the templateTitleSubject[0] group reached almost templateYValue[0] thousand templateYLabel[1] .
generated: The Alibaba group saw a two-year increase in employment between 2016 and 2018 before falling in 2019 .  . There were approximately 21930 thousand people employed by Alibaba group globally in 2012 .  . As of 2019 the total Number of global employees at the Alibaba group reached almost 101958 thousand employees .

Example 41:
titleEntities: {'Subject': ['Australia'], 'Date': ['2015', '2022']}
title: Number of social network users Australia 2015 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['19.27', '18.94', '18.58', '18.17', '17.7', '17.19', '16.62', '15.99']

gold: This statistic shows the number of social network users in Australia from 2015 to 2022 . In 2022 , it is estimated that there will be around 19.27 million social network users in Australia , up from around 17.19 million in 2017 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that there will be around templateYValue[idxmax(X)] templateScale templateTitle[1] templateTitle[2] templateYLabel[1] in templateTitleSubject[0] , up from around templateYValue[5] templateScale in templateXValue[5] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] is expected to reach templateYValue[idxmax(X)] templateScale , up from templateYValue[5] templateScale in templateXValue[5] . templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] – additional information templateTitleSubject[1] , headquartered in Menlo Park , California , is by far the leading social network in the world .
generated: This statistic shows the Number of Australia users in Australia from 2015 to 2022 .  . In 2022 , the Number of Australia users in Australia is expected to reach 19.27 millions , up from 17.19 millions in 2017 .  . Australia users in Australia – additional information Australia , headquartered in Menlo Park , California is by far the leading social network in the world .

Example 42:
titleEntities: {'Subject': ['Nicaragua'], 'Date': ['2019']}
title: Unemployment rate in Nicaragua 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['4.87', '4.48', '4.21', '4.34', '4.4', '4.52', '5.28', '5.21', '6.38', '7.83', '8.16', '6.2', '4.89', '5.31', '5.37', '6.41', '7.6', '7.61', '7.44', '7.47', '7.35']

gold: This statistic shows the unemployment rate in Nicaragua from 1999 to 2019 . In 2019 , the unemployment rate in Nicaragua was at approximately 4.87 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale . templateYLabel[0] in templateTitleSubject[0] 's templateYLabel[0] is relatively low and stable at around templateYValue[0] templateScale which means the population is experiencing close to full employment .
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 % .  . Unemployment in Nicaragua 's Unemployment is relatively low and stable at around 4.87 % which means the population is experiencing close to full employment .

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

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

generated_template: This statistic represents templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] through templateXValue[max] . In the most recently reported period , the internet company 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateScale US dollars , up from 12.92 templateScale US dollars in templateXValue[12] . That templateXLabel[0] , templateTitleSubject[0] 's revenue came to 66 templateScale US dollars .
generated: This statistic represents France 's Number internet from 2002 through 2016 .  . In the most recently reported period , the internet company 's Number internet amounted to 55.86 millions US dollars , up from 12.92 millions US dollars in 2004 .  . That Year , France 's revenue came to 66 millions US dollars .

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: This statistic shows the templateTitle[0] templateTitle[1] the largest number of templateTitle[3] templateYLabel[0] templateYLabel[1] templateYLabel[2] 10,000 templateYLabel[4] in the templateTitle[9] in templateTitleDate[0] . In templateXValue[1] , California , there were templateYValue[1] templateTitle[3] templateYLabel[0] templateYLabel[1] templateYLabel[2] 10,000 templateYLabel[4] in templateTitleDate[0] .
generated: This statistic shows the Cities highest the largest number of parks Spending per resident 10,000 dollars in the 2018 in .  . In Seattle , California there were 268.42 parks Spending per resident 10,000 dollars in 2018 .

Example 45:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2019']}
title: Employment rate of men in the U.S. 1990 to 2019
X_Axis['Year']: ["'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92", "'91", "'90"]
Y_Axis['Employment', 'rate']: ['66.6', '66.3', '66', '65.8', '65.3', '64.9', '64.4', '64.4', '63.9', '63.7', '64.5', '68.5', '69.8', '70.1', '69.6', '69.2', '68.9', '69.7', '70.9', '71.9', '71.6', '71.6', '71.3', '70.9', '70.8', '70.4', '70', '69.8', '70.4', '72']

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

generated_template: Since templateTitleDate[min] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] in the templateTitle[3] has stayed more or less steady . In templateTitleDate[min] , the female templateYLabel[0] templateYLabel[1] was templateYValue[last] templateScale , and in 2018 , the templateYLabel[0] templateYLabel[1] was at templateYValue[0] templateScale . However , it reached a peak in 2000 at templateYValue[max] templateScale .
generated: 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 % , and in 2018 , the Employment rate was at 66.6 % .  . However , it reached a peak in 2000 at 72 % .

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

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

generated_template: This statistic shows the ten templateTitle[0] templateTitle[1] in templateTitleSubject[0] as of templateTitleDate[0] . In templateTitleDate[0] , around templateYValue[max] templateScale people lived in templateXValue[0] , making it the templateTitle[0] city in templateTitleSubject[0] .
generated: This statistic shows the ten Fatality numbers in U.S.from as of 1900 .  . In 1900 , around 1260 thousand people lived in Heat wave 1980 Kansas City Missouri St Loius , making it the Fatality city in U.S.from .

Example 47:
titleEntities: {'Subject': ['UK'], 'Date': ['2017', '2019']}
title: Amounts outstanding of notes and coin in circulation in the UK 2017 to 2019
X_Axis['Month']: ["Dec_'19", "Nov_'19", "Oct_'19", "Sep_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Apr_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17", "Nov_'17", "Oct_'17", "Sep_'17", "Aug_'17", "Jul_'17", "Jun_'17", "May_'17", "Apr_'17", "Mar_'17", "Feb_'17", "Jan_'17"]
Y_Axis['Amounts', 'outstanding', 'in', 'million', 'GBP']: ['82648', '82820', '82980', '82933', '82920', '82806', '82764', '82649', '82546', '82536', '82471', '82318', '82117', '81945', '81817', '81812', '81704', '81698', '81637', '81561', '81503', '81345', '81610', '81804', '81938', '82069', '82139', '82114', '82088', '82132', '82156', '82170', '82037', '81627', '81526', '81210']

gold: As of December 2019 , the value of outstanding notes and coins in circulation in the United Kingdom reached approximately 82.65 billion British pounds . This was an increase of over 1.4 billion British pounds as compared to January 2017 . When broken down by denomination , the twenty-pound note accounted for the highest share of notes in circulation .
gold_template: As of 2019 , the value of templateYLabel[1] templateTitle[2] and coins in templateTitle[4] in the United Kingdom reached approximately templateYValue[0] templateScale British pounds . This was an templatePositiveTrend of over 1.4 templateScale British pounds as compared to 2017 . When broken down by denomination , the twenty-pound note accounted for the highest share of templateTitle[2] in templateTitle[4] .

generated_template: The first case of the new templateTitle[1] ( templateTitle[2] ) in templateTitleSubject[0] was recorded in the state of Bavaria on templateTitle[6] templateXValue[5] , templateTitleDate[0] . The virus originated in Wuhan , a Chinese city populated by templateScale and located in the province of Hubei . More statistics and facts about the virus are available here .
generated: The first case of the new outstanding ( notes ) in UK was recorded in the state of Bavaria on 2017 Jul '19 , 2017 .  . The virus originated in Wuhan , a Chinese city populated by million and located in the province of Hubei .  . More statistics and facts about the virus are available here .

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

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

generated_template: This statistic presents the templateTitle[2] of templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] as of 2018 . During an online survey conducted by We Are Flint , it was found that templateYValue[0] templateScale of internet users in the templateTitleSubject[1] accessed the messenger app templateXValue[1] templateXValue[2] days .
generated: This statistic presents the payment of U.S. mobile in the U.S. as of 2018 .  . During an online survey conducted by We Are Flint , it was found that 18 % of internet users in the U.S. accessed the messenger app Retailer mobile app Apple Pay days .

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] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] The 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] templateScale 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] templateScale templateYLabel[0] .

generated_template: This statistic shows the total templateTitle[2] templateYLabel[0] of the templateYLabel[2] templateTitle[1] from 2012 to templateTitleDate[0] , templateTitle[4] templateXLabel[0] . The total templateTitle[2] templateYLabel[0] of the templateTitle[1] added up to about templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in the third templateXLabel[0] of templateTitleDate[0] .
generated: This statistic shows the total reached Viewers of the thousands viewers from 2012 to 2019 , United Quarter .  . The total reached Viewers of the viewers added up to about 10610 thousands in the third Quarter of 2019 .

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] templateScale of templateTitleSubject[0] templateXValue[4] attend templateTitle[0] or synagogue , compared to templateYValue[0] templateScale 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 templateScale 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: This statistic shows the results of a templateTitleDate[0] survey among Americans on the templateTitle[0] of templateTitle[1] templateTitle[2] . The survey shows that templateYValue[min] templateScale of Americans consider themselves experts at templateTitle[1] templateTitle[2] . templateYValue[max] templateScale of Americans stated that they have templateXValue[3] templateXValue[last] to use templateTitle[2] .
generated: This statistic shows the results of a 2019 survey among Americans on the Church of attendance Americans .  . The survey shows that 3 % of Americans consider themselves experts at attendance Americans .  . 29 % of Americans stated that they have Seldom No opinion to use Americans .

Example 52:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2000', '2018']}
title: Hospitals in Belgium 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Number', 'of', 'hospitals']: ['174', '175', '177', '178', '187', '191', '192', '195', '198', '203', '209', '210', '215', '216', '214', '218', '219', '225', '228']

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

generated_template: This statistic charts the templateYLabel[0] of templateYLabel[1] who have obtained a legal divorce and have not remarried in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateYLabel[1] templateTitle[1] templateYLabel[1] were living in templateTitleSubject[0] .
generated: This statistic charts the Number of hospitals who have obtained a legal divorce and have not remarried in Belgium from 2000 to 2018 .  . In 2018 , about 174 hospitals Belgium were living in Belgium .

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

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

generated_template: In the fourth templateXLabel[0] of templateTitleDate[max] , the templateTitle[3] of templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to more than templateYValue[max] templateScale . The most recent figure constitutes an templatePositiveTrend of about 8.6 templateScale compared to fourth templateXLabel[0] of the previous year . The Japanese e-commerce company is headquartered in Tokyo , where it was founded in 1997 .
generated: In the fourth Quarter of 2016 , the active of Number mobile-only users amounted to more than 1149 millions .  . The most recent figure constitutes an increase of about 8.6 millions compared to fourth Quarter of the previous year .  . The Japanese e-commerce company is headquartered in Tokyo , where it was founded in 1997 .

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

gold: This statistic shows the most popular games with casino visitors in the United States as of May 2014 . During the survey , six percent of respondents said that poker was their favorite game to play at casinos .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of 2014 . During the survey , templateYValue[2] templateScale of templateYLabel[1] said that templateXValue[2] was their templateXValue[last] game to play at casinos .

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] '' – was not as high up on the list as it used to be , but at least templateYValue[7] templateScale decided to finally give up templateXValue[7] once and templateTitle[5] all . templateTitle[3] makers , templateTitle[3] keepers ? While some might say that they do not need templateXValue[5] templateTitleSubject[0] templateTitle[2] Eve to finally turn their life around , making resolutions on 31 is a common , well-liked tradition , especially in the Western world .
generated: Poker than half of U.S. wanted to start 2014 by saving Slot machines and by getting in Black Jack .  . The most popular Craps U.S. games casino ever – `` Baccarat '' – was not as high up on the list as it used to be , but at least 1 % decided to finally give up Baccarat once and U.S. all .  . casino makers , casino keepers ? While some might say that they do not need Craps U.S. games Eve to finally turn their life around , making resolutions on 31 is a common , well-liked tradition , especially in the Western world .

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[idxmax(Y)] to around templateYValue[min] thousand in templateXValue[idxmin(Y)] .

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] templateNegativeTrend until templateXValue[max] , when it again templatePositiveTrend and amounted to almost templateYValue[0] thousand templateYLabel[1] . A similar trend can be seen in the neighbor country Norway , where the templateYLabel[0] of templateYLabel[1] templateNegativeTrend for several years but templatePositiveTrend again in templateXValue[1] .
generated: The Number of live in Norway peaked in 2013 , when almost 61807 thousand couples got divorced .  . Since then the divorce Number decreased until 2018 , when it again increased and amounted to almost 55120 thousand live .  . A similar trend can be seen in the neighbor country Norway , where the Number of live decreased for several years but increased again in 2017 .

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

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

generated_template: templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] sank was about templateYValue[0] templateScale to the templateXLabel[0] templateXLabel[1] of templateXValue[0] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the country had templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] . templateTitleSubject[0] Companies is headquartered in Midtown Manhattan , New York City .
generated: HPE research development 's Expenditure million U.S. dollars sank was about 1842 million to the Fiscal year of 2019 .  . The Expenditure million U.S. dollars in the country had 2338 million dollars in 2015 .  . HPE research development Companies is headquartered in Midtown Manhattan , New York City .

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

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] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Morocco from 2014 to 2018 , with projections up until 2024 .  . In 2018 , Morocco 's real Gross domestic product increased by around 2.96 % compared to the previous Year .

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

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

generated_template: This statistic represents the templateTitle[3] templateYLabel[1] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[5] templateTitle[6] between templateXValue[min] and templateXValue[max] . In templateXValue[1] , templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[5] templateTitle[6] is estimated to templatePositiveTrend by templateYValue[1] templateScale on the templateXLabel[0] before . By templateXValue[max] , templateTitle[2] is projected to grow with another templateYValue[idxmax(X)] templateScale .
generated: This statistic represents the worldwide profit in Net profit airlines 2020 between 2006 and 2020 .  . In 2019 , Net profit airlines 2020 is estimated to increase by 25.9 billion on the Year before .  . By 2020 , airlines is projected to grow with another 29.3 billion .

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

gold: This statistic represents solar photovoltaic capacity installations in the residential sector in the United States between 2005 and 2018 . In 2018 , residential sector PV installations reached a capacity of 2.4 gigawatts .
gold_template: This statistic represents templateTitle[4] photovoltaic templateYLabel[0] templateTitle[7] in the templateTitle[1] templateTitle[2] in the templateTitle[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitle[1] templateTitle[2] templateTitleSubject[0] templateTitle[7] reached a templateYLabel[0] of 2.4 gigawatts .

generated_template: In templateXValue[max] , the templateTitle[3] of templateTitle[0] and calves in the templateTitleSubject[0] was approximately templateYValue[min] templateScale , a slight templateNegativeTrend from the previous templateXLabel[0] . This was the lowest templateYLabel[0] for the entire period shown in this graph . Despite a small rebound in templateXValue[4] and templateXValue[3] this constitutes a slow long-term decline of herd sizes .
generated: In 2018 , the annual of U.S. and calves in the PV was approximately 27 % , a slight decrease from the previous Year .  . This was the lowest Capacity for the entire period shown in this graph .  . Despite a small rebound in 2014 and 2015 this constitutes a slow long-term decline of herd sizes .

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

generated_template: This statistic shows the total templateTitleSubject[0] ( UK ) templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] from templateXValue[min] to templateXValue[max] . In the most recent measured period , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to templateYValue[idxmax(X)] templateYLabel[4] .
generated: This statistic shows the total American ( UK ) 2019 Imports million bushels from 2001 to 2019 .  . In the most recent measured period , the American Imports million bushels amounted to 28 bushels .

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] templateScale of templateYLabel[1] in the country stated they templateTitle[3] templateTitle[4] a templateTitle[5] before .

generated_template: This statistic provides information on the templateYLabel[0] of templateTitle[1] templateTitle[2] networkers templateTitle[4] are afraid of missing something such as an event , news or important status updates if they do n't keep an eye on their templateTitle[2] templateTitle[3] . During a survey in templateTitle[8] templateTitleDate[0] , it was found that templateYValue[max] templateScale of templateTitle[1] templateTitle[2] network users suffered templateXValue[0] the fear of missing out , also known as templateXValue[0] .
generated: This statistic provides information on the Share of Americans who networkers had are afraid of missing something such as an event , news or important status updates if they do n't keep an eye on their who have .  . During a survey in 2012 , it was found that 58.1 % of Americans who network users suffered No the fear of missing out , also known as No .

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

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

generated_template: As of early templateTitleDate[0] , templateXValue[0] and templateXValue[1] were the templateTitleSubject[0] commonly used templateTitle[3] templateTitle[4] templateTitle[5] software templateTitle[6] around the world , with nearly templateYValue[max] templateScale of templateYLabel[1] stating that they used templateXValue[0] and templateYValue[1] templateScale using templateXValue[1] . templateXValue[2] . templateXValue[3] , and templateXValue[4] rounded out the top templateYValue[18] templateTitleSubject[0] templateTitle[1] used templateTitle[3] templateTitle[4] around the world .
generated: As of early 2016 , groupon.com and coupons.com were the U.S. commonly used awareness according U.S. software users around the world , with nearly 75 % of respondents stating that they used groupon.com and 64 % using coupons.com .  . retailmenot.com .  . livingsocial.com , and redplum.com rounded out the top 5 U.S. coupon used awareness according around the world .

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 templateScale templateTitle[1] templateTitle[2] templateYLabel[1] in the country , up from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that there will be around templateYValue[idxmax(X)] templateScale templateTitle[2] templateTitle[3] templateYLabel[1] in the country , up from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] .
generated: This statistic shows the Number of network users in Thailand from 2017 to 2023 .  . In 2023 , it is estimated that there will be around 37.2 millions network users in the country , 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[idxmax(X)] templateScale .

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

Example 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] templateScale templateTitle[2] were sold templateTitleSubject[0] .

generated_template: The statistic shows the total templateTitle[0] templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateTitle[0] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] is expected to reach templateYValue[1] templateScale units .
generated: The statistic shows the total Worldwide Unit shipments in the Worldwide from 2013 to 2019 .  . In 2018 , the Worldwide Unit shipments of the Worldwide is expected to reach 380.7 millions units .

Example 68:
titleEntities: {'Subject': ['Change'], 'Date': ['2018', '2019']}
title: Change of domestic heating oil price in selected countries 2018 to 2019
X_Axis['Country']: ['Canada', 'Italy', 'Spain', 'France', 'United_Kingdom', 'Japan', 'Germany', 'United_States']
Y_Axis['Change', 'in', 'heating', 'oil', 'price']: ['7', '5.9', '5.9', '4', '0.8', '-0.1', '-4.4', '-']

gold: This statistic shows the change in the price of domestic heating oil per liter in selected countries for the period between December 2018 and December 2019 . In December 2019 , the domestic heating oil price in Germany was some -4.4 percent lower than in December 2018 . Domestic heating oil The price of domestic heating oil per liter has decreased in many countries .
gold_template: This statistic shows the templateYLabel[0] in the templateYLabel[3] of templateTitle[1] templateYLabel[1] templateYLabel[2] per liter in templateTitle[5] templateTitle[6] for the period between 2018 and 2019 . In 2019 , the templateTitle[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[6] was some templateYValue[6] templateScale lower than in 2018 . templateTitle[1] templateYLabel[1] templateYLabel[2] The templateYLabel[3] of templateTitle[1] templateYLabel[1] templateYLabel[2] per liter has templateNegativeTrend in many templateTitle[6] .

generated_template: This statistic illustrates the templateYLabel[0] in the templateYLabel[2] of automotive templateYLabel[1] per liter in templateTitle[3] templateTitle[4] for the period between 2018 and 2019 . The templateYLabel[2] changes exclude value added taxes from prices for automotive templateYLabel[1] . In 2019 , the templateYLabel[1] templateYLabel[2] per liter in the templateXValue[5] templateXValue[6] was 2.2 seven templateScale lower than it was in 2019 .
generated: This statistic illustrates the Change in the oil of automotive heating per liter in oil price for the period between 2018 and 2019 .  . The oil changes exclude value added taxes from prices for automotive heating .  . In 2019 , the heating oil per liter in the Japan Germany was 2.2 seven percent lower than it was in 2019 .

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 templateTitleDate[0] . templateXValue[0] ranked highest with a dog population of approximately templateYValue[max] templateScale in templateTitleDate[0] , followed templateTitle[5] the templateXValue[1] ( UK ) with templateYValue[max] templateScale . The templateYLabel[0] of templateYLabel[1] in Europe has seen a notable templatePositiveTrend since 2010 , with the templateYLabel[0] of templateYLabel[1] significantly templatePositiveTrend templateTitle[5] more than eleven templateScale from 2010 to templateTitleDate[0] .

generated_template: In templateTitleDate[0] , templateXValue[0] was the templateTitleSubject[0] American templateXLabel[0] with the highest templateTitle[4] templateTitle[5] templateTitle[6] 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[4] templateTitle[5] templateTitle[6] of 54 templateScale ( on average , women 's income in templateXValue[last] was estimated to be 54 templateScale lower than men 's ) .
generated: In 2018 , Germany was the European Union American Country with the highest 2018 by country Number , with 9400 points .  . Estonia , on the other hand , had the worst dogs in the region with 210 points , which shows a 2018 by country of 54 thousands ( on average , women 's income in Estonia was estimated to be 54 thousands lower than men 's ) .

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 templateScale of templateYLabel[1] in the templateTitle[3] diagnosed templateTitle[1] templateTitle[2] A , sorted templateTitle[5] templateXLabel[0] templateXLabel[1] , as of templateTitleDate[0] . In that year , templateYValue[min] templateScale of all Americans diagnosed templateTitle[1] templateTitle[2] A were between 0 and 4 templateXValue[0] of templateXLabel[0] .

generated_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] in templateTitleDate[0] . In templateTitleDate[0] , templateYValue[3] children were born templateYLabel[2] 1,000 templateYLabel[4] who held a Bachelor 's templateXValue[2] and templateYValue[max] children were born templateYLabel[2] 1,000 templateYLabel[4] with a templateXValue[0] or templateXValue[last] templateXValue[2] .
generated: This statistic shows the People hemophilia U.S. the Age group of the child 's age in the group in 2018 .  . In 2018 , 34 children were born people 1,000 who held a Bachelor 's 14-18 years and 34 children were born people 1,000 with a 0-4 years or Above 45 years 14-18 years .

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

gold: This statistic shows the electricity prices for household end users in Estonia semi-annually from 2010 to 2017 . In the first half of 2017 , the average electricity price for households was 12.07 euro cents per kWh .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitleSubject[0] templateTitle[7] from templateXValue[0] to templateXValue[last] . In the first half of templateXValue[last] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[last] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .

generated_template: 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 templatePositiveTrend 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 templateNegativeTrend over the period and was down at the lowest point in templateXValue[1] and templateXValue[max] , when templateYValue[idxmax(X)] deaths occurred templateYLabel[2] thousand templateYLabel[4] templateYLabel[5] . Just over 92 thousand individuals in templateTitleSubject[0] died during templateXValue[idxmin(Y)] , a number that templatePositiveTrend 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: This statistic shows the templateTitleSubject[0] 's templateYLabel[1] templateYLabel[2] in templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] templateTitle[1] in templateTitleSubject[1] was templateYValue[idxmax(X)] points , around 0.7 templateScale points lower than in templateXValue[9] . In the countries having relative equality in their distributions of income , the value of the templateTitleSubject[0] templateYLabel[2] usually ranges between the scores of 20 and templateYValue[max] .
generated: This statistic shows the Sweden 's rate per in Sweden from 2008 to 2018 .  . In 2018 , the Sweden death in Sweden was 9.1 points , around 0.7 thousand points lower than in 2009 .  . In the countries having relative equality in their distributions of income , the value of the Sweden per usually ranges between the scores of 20 and 9.9 .

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

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

generated_template: This statistic shows the number of templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[2] , with projections up until templateXValue[max] . In templateXValue[2] , the level of templateYLabel[0] in templateTitleSubject[0] was around templateYValue[2] templateScale people . Economy of templateTitleSubject[0] Before the economic crisis started in 2007 , templateTitleSubject[0] 's economy was one of the most thriving in the European Union , but since it was severely hit and officially entered recession in 2009 , gross domestic product / GDP growth in templateTitleSubject[0] has been struggling to recover .
generated: This statistic shows the number of Sales in Tesco from 2010 to 2018 , with projections up until 2020 .  . In 2018 , the level of Sales in Tesco was around 49342.45 million people .  . Economy of Tesco Before the economic crisis started in 2007 , Tesco 's economy was one of the most thriving in the European Union , but since it was severely hit and officially entered recession in 2009 , gross domestic product / GDP growth in Tesco has been struggling to recover .

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

gold: This statistic shows the number of visitors to the Tate Modern in London from 2007 to 2018 . Approximately 5.83 million people visited the Tate Modern art museum in London in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] to the templateTitleSubject[0] in templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . Approximately templateYValue[idxmax(X)] templateScale 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] National templateTitleSubject[0] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] to the templateTitleSubject[0] National templateTitleSubject[0] amounted to approximately templateYValue[0] templateYValue[idxmax(X)] in templateXValue[max] .
generated: This statistic shows the Number of recreational visitors to the Tate Modern National in the 2018 from 2007 to 2018 .  . The Number of visitors to the Tate Modern National amounted to approximately 5.83 in 2018 .

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

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

generated_template: According to a national pet owners survey , the templateYLabel[0] of templateYLabel[1] owned as a templateTitle[1] by households in the templateTitle[3] reached templateYValue[max] templateScale in templateXValue[idxmax(Y)] . The templateYLabel[0] of templateTitle[1] templateYLabel[1] has fluctuated over the past few years , hitting a low of templateYValue[min] templateScale in templateXValue[idxmin(Y)] . Americans love their pets Some 68 templateScale of all households in the templateTitle[3] owned at least one templateTitle[1] in templateXValue[max] , this is an templatePositiveTrend of over 10 templateScale since 1988 .
generated: According to a national pet owners survey , the Ticket of sales owned as a New by households in the Patriots reached 104 million in 2018 .  . The Ticket of New sales has fluctuated over the past few years , hitting a low of 93 million in 2010 .  . Americans love their pets Some 68 million of all households in the Patriots owned at least one New in 2018 , this is an increase of over 10 million since 1988 .

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

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

generated_template: The statistic shows the templateYLabel[0] templateTitle[4] prices of selected templateXValue[2] World templateXValue[2] templateTitle[7] at the templateXValue[0] templateXValue[2] templateTitle[9] templateTitle[10] . The templateXValue[0] game templateXValue[0] - 'Brazuca ' is sold at the templateXValue[2] templateTitle[9] shop for templateYValue[max] templateYLabel[2] templateYLabel[3] . templateXValue[2] Cup templateTitle[7] - additional information In a templateXValue[2] of social media and global connectivity , the templateXValue[2] Cup is templatePositiveTrend in popularity with every tournament , with over 26 templateScale Americans tuning in to watch the templateXValue[2] final .
generated: The statistic shows the Number 2010/11 prices of selected 2015/16 World 2017/18 at the 2017/18 2015/16 .  . The 2017/18 game - 'Brazuca ' is sold at the 2015/16 2017/18 shop for 76387 players .  . 2015/16 Cup 2017/18 - additional information In a 2015/16 of social media and global connectivity , the 2015/16 Cup is growing in popularity with every tournament , with over 26 million Americans tuning in to watch the 2015/16 final .

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 templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] franchise had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Franchise value of the Phoenix Suns of the National Basketball Association from 2003 to 2020 .  . In 2020 , the Phoenix Suns 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[idxmax(X)] templateYLabel[4] templateYLabel[5] templateYLabel[6] templateYLabel[7] in templateXValue[idxmax(Y)] . A ranking of the largest electric utilities companies in the templateTitle[0] can be found here as well as a ranking of global templateYLabel[1] templateTitle[4] in select countries .

generated_template: This statistic shows the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] from templateTitle[1] templateTitle[2] in the templateTitle[0] templatePositiveTrend by templateYValue[0] templateYValue[idxmax(X)] compared to the previous templateXLabel[0] .
generated: This statistic shows the U.S. Average electricity of average retail from 1990 to 2018 .  . In 2018 , Average from retail in the U.S. increased by 10.58 compared to the previous Year .

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

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

generated_template: Does it always rain in templateTitleSubject[0] ? Well , there is some truth in each stereotype ! Although it certainly does not templateYLabel[1] every day , there is a lot of rainfall in templateTitleSubject[0] . In templateXValue[max] , it rained templateYValue[idxmax(X)] out of 366 templateYLabel[2] , or over half of the templateYLabel[2] . And this was by no means a record : in templateXValue[4] , it rained templateYValue[max] templateYLabel[2] , or nearly 60 templateScale of all templateYLabel[2] .
generated: Does it always rain in Middle East ? Well , there is some truth in each stereotype ! Although it certainly does not percent every day , there is a lot of rainfall in Middle East .  . In 2015 , it rained 7 out of 366 percent , or over half of the percent .  . And this was by no means a record : in 2012 , it rained 7 percent , or nearly 60 percent of all percent .

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

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

generated_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] to templateTitle[4] templateTitle[5] in templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] to templateTitle[4] templateTitle[5] amounted to approximately templateYValue[min] templateScale templateYLabel[1] templateTitle[2] , the lowest amount in the provided time interval .
generated: This statistic shows the total Annual of profit annual to 2009 2016 in SE annual from 2009 to 2016 .  . In 2014 , the Annual of profit annual to 2009 2016 amounted to approximately 101.88 million profit annual , the lowest amount in the provided time interval .

Example 81:
titleEntities: {'Subject': ['Germany'], 'Date': ['2000', '2018']}
title: Revenue from used cars in Germany from 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['84.7', '82.1', '84.62', '77.85', '69.78', '66.81', '62.98', '66.32', '56.54', '51.65', '53.11', '52.6', '55.95', '55.44', '52.22', '55.65', '54.03', '59.92', '59.0']

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

generated_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the templateXValue[max] fiscal templateXLabel[0] , templateTitleSubject[0] generated around templateYValue[max] templateScale templateYLabel[2] in templateYLabel[0] , up from templateYValue[1] templateScale templateYLabel[2] the previous templateXLabel[0] .
generated: This statistic shows Germany from Revenue the fiscal Year of 2000 to the fiscal Year of 2018 .  . In the 2018 fiscal Year , Germany generated around 84.7 billion euros in Revenue , up from 82.1 billion euros the previous Year .

Example 82:
titleEntities: {'Subject': ['Number M A', 'Europe'], 'Date': ['2014', '2015']}
title: Number of M & A deals in Europe 2014 to 2015
X_Axis['Month']: ['Jun_15', 'May_15', 'Apr_15', 'Mar_15', 'Feb_15', 'Jan_15', 'Dec_14', 'Nov_14', 'Oct_14', 'Sep_14', 'Aug_14', 'Jul_14', 'Jun_14', 'May_14', 'Apr_14', 'Mar_14']
Y_Axis['Number', 'of', 'deals']: ['1222', '1030', '1107', '1166', '1097', '1076', '1235', '1152', '1167', '1222', '912', '1253', '1171', '1070', '1185', '1062']

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

generated_template: Over the end of templateXValue[0] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateTitle[4] a templateTitle[5] of templateYValue[max] templateScale , up from templateYValue[4] templateScale templateYLabel[4] templateYLabel[5] in the previous year . The magazine reported its highest templateTitle[1] templateYLabel[0] in the previous year , templateTitleSubject[0] amounted to more than templateYValue[5] templateScale templateTitleSubject[0] dollars . templateTitleSubject[0] is a Swiss multinational service , but it also has paid tiers known as templateTitleSubject[0] Silver and templateTitleSubject[0] Gold .
generated: Over the end of Jun 15 , the Number deals of Number M Number M A deals Europe a 2014 of 1253 million , up from 1097 million deals in the previous year .  . The magazine reported its highest M Number in the previous year , Number M A amounted to more than 1076 million Number M A dollars .  . Number M A is a Swiss multinational service , but it also has paid tiers known as Number M A Silver and Number M A Gold .

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] templatePositiveTrend almost twofold from templateXValue[min] to templateXValue[max] . The peak was reached in templateXValue[1] , when templateTitleSubject[0] 's annual templateYLabel[0] amounted to approximately templateYValue[max] templateScale templateYLabel[2] . In templateXValue[max] the templateYLabel[0] templateNegativeTrend to templateYValue[0] templateScale templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale people were accessing the templateTitle[2] in templateTitleSubject[0] . This figure is projected to grow to templateYValue[idxmax(X)] templateScale in templateXValue[idxmax(Y)] .
generated: This statistic shows the Turnover of million euros in Bulgari from 2011 to 2017 .  . In 2011 , 96.4 million people were accessing the 2011 in Bulgari .  . This figure is projected to grow 177.0 million in 2016 .

Example 84:
titleEntities: {'Subject': ['Washington Wizards', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price Washington Wizards ( NBA ) games 2015/16
X_Axis['Year']: ['2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['51.21', '32.7', '30.31', '32.24', '23.64', '24.52', '27.21', '29.14', '30.89', '46.83']

gold: This graph depicts the average ticket price for Washington Wizards games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 46.83 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[last] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[last] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] The templateTitleSubject[0] are a franchise of the National Basketball Association ( templateTitleSubject[1] ) which joined the league as the New Jersey templateTitleSubject[0] in 1976 as part of the ABA-NBA merger .
generated: 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 .  . Washington Wizards The Washington Wizards are a franchise of the National Basketball Association ( NBA ) which joined the league as the New Jersey Washington Wizards in 1976 as part of the ABA-NBA merger .

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 templateYLabel[0] templateYLabel[1] of mobile templateYLabel[2] actively used by mobile internet users in templateTitle[4] templateTitle[5] as of 2014 . During a templateXValue[0] survey , it was found that mobile internet users in templateXValue[1] used an templateYLabel[0] of templateYValue[max] mobile templateYLabel[2] on a monthly basis .
generated: This statistic shows the Average working of mobile hours actively used by mobile internet users in hours per as of 2014 .  . During a Singapore survey , it was found that mobile internet users in India used an Average of 44 mobile hours on a monthly basis .

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

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

generated_template: Light online fashion retailer templateTitleSubject[0] is one of the fastest-growing companies not just in Germany , but in Europe . While its active customer base expands with each templateXLabel[0] , templateTitleSubject[0] 's templateTitle[0] templateYLabel[0] income suffered a major blow in the financial templateXLabel[0] templateXValue[max] , going from templateYValue[1] templateScale to templateYValue[idxmax(X)] templateScale templateYLabel[3] between templateXValue[1] and templateXValue[max] . A great turnaround in templateXValue[4] templateTitleSubject[0] delivered its sunniest results in templateXValue[3] , when its templateYLabel[0] income equaled templateYValue[max] templateScale templateYLabel[3] as displayed in this chart .
generated: Light online fashion retailer Nintendo is one of the fastest-growing companies not just in Germany , but in Europe .  . While its active customer base expands with each Year , Nintendo 's Sales income suffered a major blow in the financial Year 2018 , going from 7.27 million to 6.4 million units between 2017 and 2018 .  . A great turnaround in 2014 Nintendo delivered its sunniest results in 2015 , when its Sales income equaled 13.95 million units as displayed in this chart .

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] templateScale people accessed the templateYLabel[3] through templateYLabel[1] devices . In templateXValue[max] , this figure is projected to reach about templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Number of mobile internet users in 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 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[idxmax(X)] people were employed by the company worldwide , down from templateYValue[1] a templateXLabel[0] earlier .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] worldwide . In templateXValue[max] , templateTitleSubject[0] employed templateYValue[0] templateYValue[idxmax(X)] .
generated: This statistic shows the Number of employees Penguin Random House from 2005 to 2018 worldwide .  . In 2018 , Penguin Random House employed 10351 .

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

generated_template: The statistic shows the total templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] generated a templateTitle[0] templateYLabel[0] of roughly templateYValue[idxmax(X)] templateScale templateYLabel[2] . Together with Nike and Adidas , templateTitleSubject[0] is templateYValue[16] of the largest sporting goods manufacturers in the world .
generated: The statistic shows the total Revenue of United Kingdom office revenue United in the Kingdom from 2000 to 2019 .  . In 2019 , United Kingdom generated a Cinema Revenue of roughly 1251.84 million GBP .  . Together with Nike and Adidas , United Kingdom is 742.0 of the largest sporting goods manufacturers in the world .

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

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

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateYLabel[0] of templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale units of templateTitle[3] templateTitle[4] were sold worldwide .
generated: This statistic shows the CPI UAE CPI Consumer of 2012 2017 from 2012 to 2017 .  . In 2012 , 116.78 million units of 2012 2017 were sold worldwide .

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 templateTitleDate[min] to templateTitleDate[max] , a comparison of the company templateTitle[1] quarterly templateYLabel[1] templateTitle[4] the yearly Christmas templateTitle[6] . In templateTitleDate[max] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of that templateXLabel[0] amounted to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: In the fourth templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] , up from templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] in the corresponding templateXLabel[0] of the corresponding templateXLabel[0] of the preceding year . As of the fourth templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] accounted for more than 20 templateScale templateYLabel[3] templateYLabel[4] .
generated: In the fourth Quarter of 2019 , Amazon 's Net income amounted to 3268 million U.S. dollars , up from 3000 million U.S. dollars in the corresponding Quarter of the corresponding Quarter of the preceding year .  . As of the fourth Quarter of 2019 , Amazon accounted for more than 20 million U.S. dollars .

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] templateScale 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 templateScale supported its templateTitle[1] , compared to 23 templateScale who opposed it . Despite templateTitle[2] disasters occurring as recently as 2011 , when a tsunami hit a power plant in Fukushima , Japan , approximately 34 templateScale 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 % supported its food , compared to 23 % who opposed it .  . Despite restaurant disasters occurring as recently 2011 , when a tsunami hit a power plant in Fukushima , Japan approximately 34 % 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] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[3] .

generated_template: This statistic depicts templateTitle[1] templateTitle[2] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateTitleSubject[0] templateTitle[1] templateTitle[2] amounted to about templateYValue[idxmin(X)] templateScale templateYLabel[1] templateYLabel[2] .
generated: This statistic depicts papaya production worldwide from 2000 to 2017 .  . In 2000 , Global papaya production amounted to about 7.25 million metric tons .

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 templateScale templateYLabel[2] . The figure represented an templatePositiveTrend of over 50 templateScale templateYLabel[2] compared to the previous templateXLabel[0] , when the templateTitle[2] registered a revenue of templateYValue[1] templateScale templateYLabel[2] . An templateTitleSubject[0] success story Founded in 1921 in Florence as a leather goods manufacturer , templateTitle[4] has grown to become one of the leading players in the global luxury goods market and ranked third on the list of the most valuable luxury brands worldwide in templateXValue[max] .

generated_template: The templateYLabel[0] of the templateTitleSubject[0] luxury brand templateTitle[4] templateTitle[5] has templatePositiveTrend twofold over the period surveyed , templatePositiveTrend from roughly templateYValue[min] templateScale templateYLabel[2] in templateXValue[idxmin(Y)] to templateYValue[max] templateScale templateYLabel[2] in the templateXLabel[0] templateXValue[idxmax(Y)] . Despite the steady templatePositiveTrend in templateYLabel[0] during the period considered , the templateTitle[3] reported a net loss of approximately 25 templateScale 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 Revenues of the Italian luxury brand Gucci 2011 has increased twofold over the period surveyed , rising from roughly 157.41 million euros in 2011 to 267.63 million euros in the Year 2018 .  . Despite the steady increase in Revenues during the period considered , the Guccio reported a net loss of approximately 25 million euros in 2018 .  . Worldwide recognition Founded in 1978 Milan , Gucci 2011 is one of the leading international company design houses .

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 templateScale templateYLabel[3] templateYLabel[4] .

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

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

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

generated_template: The statistic presents the templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[6] during the first quarter of templateTitleDate[0] . During that quarter , it was found that templateYValue[max] templateScale of internet templateYLabel[3] in templateXValue[0] averaged a connection speed of templateTitle[3] templateTitleSubject[0] or more , placing the templateXLabel[0] also in first place in a global ranking of highest average internet speeds . The global templateYLabel[1] templateYLabel[2] templateTitle[6] rate is 45 templateScale .
generated: The statistic presents the Countries most the Snapchat Audience of size millions 2020 during the first quarter of 2020 .  . During that quarter , it was found that 101.25 millions of internet millions in United States averaged a connection speed of users Snapchat or more , placing the Country also in first place in a global ranking of highest average internet speeds .  . The global size millions 2020 rate is 45 millions .

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] templateScale 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] 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] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] - additional information The 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] templatePositiveTrend from templateYValue[idxmin(X)] templateScale US templateYLabel[4] in templateXValue[idxmin(Y)] to templateYValue[idxmax(X)] templateScale US templateYLabel[4] in templateXValue[idxmax(Y)] .

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] templatePositiveTrend from templateYValue[idxmin(X)] templateScale to templateYValue[idxmax(X)] templateScale .
generated: The statistic shows the Market of cap billion NASDAQ a subscription in the 1999 in and 2013 .  . In the presented time period , the Market of cap billion NASDAQ grew from 1.16 billion to 2.54 billion .

Example 99:
titleEntities: {'Subject': ['Armenia'], 'Date': ['2019']}
title: Unemployment rate in Armenia 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['17.71', '17.71', '17.83', '17.62', '18.26', '17.5', '16.18', '17.31', '18.44', '19.01', '18.74', '16.37', '9.81', '10.28', '10.8', '11.22', '11.23', '11.52', '11.44', '11.25', '11.2']

gold: This statistic shows the unemployment rate in Armenia from 1999 to 2019 . In 2019 , the unemployment rate in Armenia was estimated to be 17.71 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was estimated to be templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Armenia from 1999 to 2019 .  . In 2019 , the Unemployment rate in Armenia was at approximately 17.71 % .

Example 100:
titleEntities: {'Subject': ['Peru'], 'Date': ['2015']}
title: Median age of the population in Peru 2015
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2015', '2010', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960', '1955', '1950']
Y_Axis['Median', 'age', 'in', 'years']: ['39.4', '38.0', '36.8', '35.6', '34.2', '32.6', '31.0', '27.5', '25.5', '24.1', '22.7', '21.2', '20.1', '19.2', '18.5', '17.9', '17.6', '17.7', '18.2', '18.7', '19.2']

gold: This statistic shows the median age of the population in Peru from 1950 to 2050  .The median age is the age that divides a population into two numerically equal groups ; that is , half the people are younger than this age and half are older . It is a single index that summarizes the age distribution of a population . In 2015 , the median age of the population of Peru was 27.5 years .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] .The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of the templateTitle[2] of templateTitleSubject[0] was templateYValue[7] templateYLabel[2] .

generated_template: 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 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'], '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] templateScale of templateYLabel[1] stated that templateXValue[16] and templateXValue[16] is essential for the templateTitleSubject[0] templateTitle[3] .

generated_template: This statistic shows the results of a survey , conducted in 2018 in the country , on templateTitleSubject[1] ' templateXValue[8] templateTitleSubject[0] templateTitle[2] resolutions templateTitle[5] templateTitleDate[0] . During the survey , templateYValue[2] templateScale of templateYLabel[1] said their templateTitle[3] templateTitle[5] templateTitleDate[0] is to templateXValue[2] , while templateYValue[3] templateScale would like to templateXValue[3] .
generated: This statistic shows the results of a survey , conducted in 2018 the country , on Americans ' Common good Americans American resolutions 2017 .  . During the survey , 55 % of respondents said their Dream 2017 is to Equality , while 54 % would like to Security .

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

gold: The statistic above shows the attendance at performing arts events in the United States from 2003 to 2013 . In year 2013 , approximately 73.54 million people attended a performing arts event at least once . Performing arts – additional information Performing arts are any type of art form in which a performer physically conveys an artistic piece ; this includes dance , opera , symphony and theatre performances .
gold_template: The statistic above shows the templateYLabel[0] at templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXLabel[0] templateXValue[max] , approximately templateYValue[idxmax(X)] templateScale 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: This graph depicts the templateTitle[3] templateYLabel[0] at templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale people attended a templateTitle[0] templateTitle[1] concert at least once . The templateTitle[1] concert industry – additional information In 2016 , Beyonce and Guns ' N ' templatePositiveTrend were among the most successful templateTitle[1] tours in North America , generating 169.4 templateScale templateTitleSubject[0] dollars and 130.8 templateScale templateTitleSubject[0] dollars , respectively in gross revenue .
generated: This graph depicts the events Attendance at performing arts in the 2003 from to 2013 .  . In 2013 , 73.54 millions people attended a Attendance performing concert at least once .  . The performing concert industry – additional information In 2016 , Beyonce and Guns ' N rose were among the most successful performing tours in North America , generating 169.4 millions U.S. dollars and 130.8 millions U.S. dollars , respectively in gross revenue .

Example 103:
titleEntities: {'Subject': ['British Telecommunications BT', 'ARPU'], 'Date': []}
title: British Telecommunications ( BT ) : consumer ARPU Q1 2014/15-Q1 2019/20
X_Axis['Quarter']: ['Q1_2019', 'Q4_2018', 'Q3_2018', 'Q2_2018', 'Q1_2018', 'Q4_2017', 'Q3_2017', 'Q2_2017', 'Q1_2017', 'Q4_2016', 'Q3_2016', 'Q2_2016', 'Q1_2016', 'Q4_2015', 'Q3_2015', 'Q2_2015', 'Q1_2015', 'Q4_2014', 'Q3_2014', 'Q2_2014', 'Q1_2014']
Y_Axis['Average', 'revenue', 'per', 'user', 'in', 'GBP', 'per', 'month']: ['37.9', '38.8', '39.6', '38.3', '37.9', '38.9', '37.7', '37.8', '37.6', '36.7', '37.3', '37.4', '35.0', '37.1', '36.6', '35.6', '34.9', '34.6', '34.2', '33.6', '33.2']

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

generated_template: In the fourth templateXLabel[0] of templateXValue[0] , the templateTitle[3] of templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to more than templateYValue[max] templateScale . The most recent figure constitutes an templatePositiveTrend of about 8.6 templateScale compared to fourth templateXLabel[0] of the previous year . The Japanese e-commerce company is headquartered in Tokyo , where it was founded in 1997 .
generated: In the fourth Quarter of Q1 2019 , the consumer of Average revenue per amounted to more than 39.6 % .  . The most recent figure constitutes an increase of about 8.6 % compared to fourth Quarter of the previous year .  . The Japanese e-commerce company is headquartered in Tokyo , where it was founded in 1997 .

Example 104:
titleEntities: {'Subject': ['Regal Entertainment Group'], 'Date': ['2006', '2017']}
title: Regal Entertainment Group 's net income 2006 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['112.3', '170.5', '153.2', '105.2', '157.6', '142.2', '40.1', '77.6', '95.5', '112.2', '360.4', '104.3']

gold: This statistic contains income data for the Regal Entertainment Group from 2006 to 2017 . In 2017 , REG reported a net profit of 112.3 million U.S dollars .
gold_template: This statistic contains templateYLabel[1] data for the templateTitleSubject[0] Group from templateXValue[min] to templateXValue[max] . In templateXValue[max] , REG reported a templateYLabel[0] profit of templateYValue[idxmax(X)] templateScale U.S templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[4] and templateTitle[5] templateYLabel[2] in the templateTitle[6] templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] were spent templateYLabel[0] templateYLabel[1] on templateTitle[4] and templateTitle[5] in the templateYLabel[3] by templateTitle[0] institutions .
generated: This statistic shows the Regal net and income million in the 2006 Net income from 2006 to 2017 .  . In 2017 , 112.3 U.S. dollars were spent Net income on net and income in the U.S. by Regal institutions .

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] templatePositiveTrend by around templateYValue[2] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] had templatePositiveTrend by around templateYValue[max] templateScale 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 had increased by around 4.05 % 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] templateScale of the templateYLabel[1] templateTitle[1] templateXValue[0] templateTitle[2] templateXValue[2] templateTitle[4] templateXLabel[0] the templateTitle[0] .

generated_template: This statistic depicts templateTitle[2] templateTitle[3] templateTitle[1] the templateTitle[0] by templateTitleSubject[0] construction firms in templateTitleDate[0] . The survey revealed that templateYValue[max] templateScale of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] templateTitle[2] the templateTitle[0] . Residential construction involves the building and selling of both individual and multi-family dwellings .
generated: This statistic depicts garage door used the Most by U.S. construction firms in 2018 .  . The survey revealed that 50 % of the respondents used Liftmaster Brand garage the Most .  . Residential construction involves the building and selling of both individual and multi-family dwellings .

Example 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] templateScale in templateXValue[6] , expected to rise to nearly 44 templateScale the following templateXLabel[0] before declining to templateYValue[12] templateScale by templateXValue[max] . A major factor in this is the legacy of poor fiscal discipline left by the economic depression from 1998 to 2002 . What causes templateYLabel[0] ? templateYLabel[0] is a rise in price levels for all goods .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Argentina from 2004 to 2018 , with projections up until 2024 .  . In 2018 , the average Inflation rate in Argentina amounted to about 34.28 % compared to the previous Year .

Example 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] templateScale believed that the templateTitleSubject[0] templateTitle[1] will templateXValue[3] templateXValue[1] in templateTitleDate[0] .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among Americans aged 16 and older regarding the templateTitle[0] they are looking for in a close templateTitle[2] . This statistic only shows the top five answers to that question . templateYValue[max] templateScale of the templateYLabel[1] stated a close templateTitle[2] has to be loyal .
generated: This statistic shows the results of a 2018 survey among Americans aged 16 and older regarding the Tourism they are looking for in a close growth .  . This statistic only shows the top five answers to that question .  . 56 % of the respondents stated a close growth has to be loyal .

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] templateScale 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] templateScale 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 templateScale of the people surveyed in templateTitleSubject[0] claim that most religious leaders are involved in acts of templateXValue[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateXValue[2] on templateTitle[3] templateTitle[4] in the templateTitle[5] as forecasted by professional chefs for templateTitleDate[0] . During the survey , templateYValue[1] templateScale of the templateYLabel[1] stated that templateXValue[1] was a `` hot trend '' for templateTitle[3] templateTitle[4] in templateTitleDate[0] .
generated: This statistic shows the Chile public in Corruption on country 's in the main as forecasted by professional chefs for 2018 .  . During the survey , 8.8 % of the respondents stated that Unemployment was a `` hot trend '' for country 's in 2018 .

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

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

generated_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] 49ers franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[idxmax(X)] .
generated: This graph depicts the total Regular season Home attendance of the Los Angeles Chargers 49ers franchise of the National Football League from 2006 to 2019 .  . In 2019 , the Regular season Home attendance of the franchise was 177755 .

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

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

generated_template: The templateTitle[0] templateTitle[1] templateTitle[2] for the templateTitleSubject[0] in templateXValue[max] was templateYValue[idxmax(X)] templateScale , an templatePositiveTrend of 0.01 templateScale when compared with templateXValue[1] . In templateXValue[min] , the share of templateYLabel[1] who were members of a templateTitle[0] templateTitle[1] was templateYValue[idxmin(X)] templateScale , nine templateScale more than templateXValue[max] . Uniting the unions In 2016/17 one the biggest templateTitle[0] Unions in the templateTitleSubject[0] was Unite , with a membership of over 1.28 templateScale people .
generated: The Annual CAC 40 for the CAC in 2019 was 5978.06 million , an increase of 0.01 % when compared with 2018 .  . In 1995 , the share of points who were members of a Annual CAC was 1872.0 % , nine % more than 2019 .  . Uniting the unions In 2016/17 one the biggest Annual Unions in the CAC was Unite , with a membership of over 1.28 million people .

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] templateScale .

generated_template: This statistic shows the templateTitle[0] of templateTitleSubject[0] travelers in the templateTitle[4] as of templateTitleSubject[0] templateTitleDate[0] . templateTitleSubject[0] travelers were characterized most by their desire to explore with an templateYLabel[0] templateXValue[2] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] of templateYValue[max] in the templateXValue[0] category . In 2015 , there was around 66 templateScale people considered to be templateTitleSubject[0] in the templateTitle[4] .
generated: This statistic shows the Video of U.S. travelers in the installed as of U.S. 2017 .  . U.S. travelers were characterized most by their desire to explore with an Installed Xbox 360 millions of 21 in the Xbox One S category .  . In 2015 , there was around 66 millions people considered to be U.S. in the installed .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] stood at around templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Capital expenditure of U.S. chemical industry in the 2004 between and 2018 .  . In 2018 , the Capital expenditure of U.S. chemical industry stood at around 33200 million .

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 templateScale ) . In templateXValue[2] , templateYValue[2] templateScale people played templateTitle[1] online templateTitle[5] templateTitle[6] .

generated_template: The statistic shows the number of members of different etnicities or races within the templateYLabel[1] in the templateTitle[5] . The term templateYLabel[1] refers to the age group templateTitle[3] to templateTitle[4] here . In templateTitleDate[0] , templateYValue[max] templateScale of the templateYLabel[1] in the templateTitle[5] were templateXValue[0] Whites .
generated: The statistic shows the number of members different etnicities or races within the players in the for .  . The term players refers to the age group who to play here .  . In 2006 , 22.2 % of the players in the for were September 2006 Whites .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] a templateTitle[3] templateTitle[4] of templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[7] in the templateTitle[8] from templateXValue[min] to templateXValue[max] . templateTitle[6] templateTitle[7] had a templateYLabel[0] templateYLabel[1] of templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] templateTitle[2] a templateTitle[3] templateTitle[4] in templateXValue[idxmin(Y)] .
generated: This statistic shows the Average price for a full set of gel toenails nail in the salons from 2009 to 2014 .  . toenails nail had a Average price of 34.01 U.S. dollars for a full set in 2009 .

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] templateScale templateYLabel[2] were accessing the templateXValue[0] templateXValue[1] on a templateYLabel[0] basis . The app 's reach is usage penetration is particularly strong in markets outside of the country and it is one of the templateTitleSubject[0] templateTitle[1] templateXValue[3] social templateTitle[5] worldwide . In 2014 , social network templateXValue[1] acquired the templateXValue[3] app for 19 templateScale U.S. dollars .

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

Example 117:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2017', '2019']}
title: Inflation rate ( CPI ) in the United Kingdom ( UK ) 2017 to 2019
X_Axis['Month']: ["Nov_'19", "Oct_'19", "Sep_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Apr_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17", "Nov_'17"]
Y_Axis['Inflation', 'rate']: ['1.5', '1.5', '1.7', '1.7', '2.1', '2', '2', '2.1', '1.9', '1.9', '1.8', '2.1', '2.3', '2.4', '2.4', '2.7', '2.5', '2.4', '2.4', '2.4', '2.5', '2.7', '3', '3', '3.1']

gold: The Consumer Price Index ( CPI ) rate of the United Kingdom in November 2019 was 1.5 percent , which together with the previous month , was the lowest rate recorded in this two year period . Between November 2017 and November 2019 the CPI rate was at it 's highest in November of 2017 , when an inflation rate of 3.1 percent was recorded .
gold_template: The Consumer Price Index ( templateTitle[2] ) templateYLabel[1] of the templateTitleSubject[0] in 2019 was templateYValue[min] templateScale , which together with the previous templateXLabel[0] , was the lowest templateYLabel[1] recorded in this templateYValue[min] year period . Between 2017 and 2019 the templateTitle[2] templateYLabel[1] was at it 's highest in of templateTitleDate[min] , when an templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale was recorded .

generated_template: This statistic shows the templateScale templateYLabel[3] in the previous templateYLabel[0] of the templateTitle[1] templateTitle[0] in templateTitleSubject[0] from 2018 to 2020 . In 2020 , the templateTitle[1] templateTitle[0] in templateTitleSubject[0] templatePositiveTrend by approximately 1.14 templateScale . 2018 saw the highest templateTitle[1] in this period , with an templateTitle[1] templateTitle[0] of nearly templateYValue[max] templateScale in comparison to the previous templateYLabel[0] .
generated: This statistic shows the percentage rate in the previous Inflation of the rate Inflation in United Kingdom from 2018 to 2020 .  . In 2020 , the rate Inflation in United Kingdom increased by approximately 1.14 % .  . 2018 saw the highest rate in this period , with an rate Inflation of nearly 3.1 % 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] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] for enterprise resource planning ( templateTitleSubject[0] ) templateTitle[3] from templateXValue[min] to templateXValue[4] , and a templateTitle[0] for templateXValue[max] . By templateXValue[max] , templateTitle[1] revenues for templateTitleSubject[0] templateTitle[3] are predicted to reach templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] , this is in line with other estimates for the templateTitle[1] templateTitleSubject[0] market . templateTitleSubject[0] templateTitle[3] is business process management templateTitle[3] designed to ease or automate many of the back office functions relating to human resources , services , and technology .
generated: This statistic shows the spending for enterprise resource planning ( Global ) sponsorships from 2011 to 2013 , and a Global for 2017 .  . By 2017 , spending revenues for Global sponsorships are predicted to reach 5.75 billion U.S. dollars , this is in line with other estimates for the spending Global market .  . Global sponsorships is business process management sponsorships designed to ease or automate many of the back office functions relating to human resources , services and technology .

Example 119:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2018']}
title: Participants in indoor soccer in the U.S. from 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'participants', 'in', 'millions']: ['5.23', '5.4', '5.12', '4.81', '4.53', '4.8', '4.9', '4.34', '4.93', '4.91', '4.74', '4.24', '4.7']

gold: This statistic shows the number of participants in indoor soccer in the United States from 2006 to 2018 . In 2018 , the number of participants ( aged six years and older ) in indoor soccer amounted to approximately 5.23 million . All in all , the number of soccer players in the United States seems to be on the rise .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] templateTitle[2] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] templateTitle[2] amounted to approximately templateYValue[idxmax(X)] templateScale . All in all , the templateYLabel[0] of templateTitle[2] players in the templateTitle[3] seems to be on the rise .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] in the templateTitle[2] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] amounted to approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Number of participants in indoor the soccer U.S. 2006 to 2018 .  . In 2018 , the Number of participants ( aged six years and older ) in indoor amounted to approximately 5.23 millions .

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[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 templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of fresh cucumbers 2000 in the U.S. from 2000 to 2018 .  . According to the report , the U.S. Per capita consumption of fresh cucumbers 2000 amounted to 8.0 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[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] on advertising and promotion .

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] reached templateYValue[5] templateScale .
generated: This statistic presents the Ad of expenditure million in Mattel from 2013 to 2019 .  . In 2015 , the Ad of expenditure million in Mattel reached 733.2 million .

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

gold: The statistic shows the estimated number of illegal immigrants in the U.S. in 2015 , by country of origin . As of January 2015 , about 6.58 million illegal immigrants from Mexico were living in the United States .
gold_template: The statistic shows the estimated number of templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] in templateTitleDate[0] , by templateXLabel[0] of templateTitle[0] . As of 2015 , about templateYValue[max] templateScale 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] templateScale 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 thousands 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 templateScale of online consumers in the templateTitle[0] who are templateTitleSubject[0] templateTitle[2] members . During the measured period in 2018 , it was found that templateYValue[2] templateScale 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] in templateTitleDate[0] on how long the templateYLabel[1] thought the feeling of templateTitle[3] could last in a templateTitle[4] . According to templateYValue[max] templateScale of templateYLabel[1] , the feeling of templateTitle[3] in a templateTitle[4] can last a templateXValue[last] .
generated: 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 % 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[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[min] templateScale .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[0] templateScale .
generated: This statistic displays the Unemployment rate in Texas from 1992 to 2018 .  . In 2018 , Unemployment in Texas was 3.9 % .

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] templateScale templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] in the country in templateXValue[max] templateXValue[idxmin(Y)] This figure is a templateNegativeTrend from templateXValue[min] levels , when the templateYLabel[0] of templateYLabel[1] was over templateYValue[13] templateScale . Arrest rate in the U.S .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateTitle[2] in the templateTitle[0] was templateYValue[min] templateYValue[idxmax(X)] of every 1,000 templateYLabel[6] . This is a significant templateNegativeTrend from templateXValue[min] , when templateYLabel[0] templateYLabel[1] was at templateYValue[idxmin(X)] deaths out of every 1,000s templateYLabel[6] . What is templateYLabel[0] templateYLabel[1] ? The templateYLabel[0] templateYLabel[1] templateYLabel[2] is the number of deaths of babies under the age of one templateYLabel[3] 1,000 templateYLabel[5] templateYLabel[6] .
generated: In 2018 , the Number arrests in the USA was 10310960 of every 1,000 arrests .  . This is a significant decrease from 1990 , when Number arrests was at 14195100 deaths out of every 1,000s arrests .  . What is Number arrests ? The Number arrests is the number of deaths babies under the age of one arrests 1,000 .

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: templateXValue[0] is the most automobile registrations in templateTitleDate[0] : More than templateYValue[max] templateScale such vehicles were templateYLabel[1] in the most populous templateTitleSubject[0] federal templateXLabel[0] . templateXValue[0] also leads in terms of annual car sales with almost templateYValue[26] templateScale cars sold in templateTitleDate[0] . Additionally , templateXValue[0] residents also purchased about 715,000 crossover and sport-utility vehicles ( CUVs and SUVs ) , some 221,270 pickups , and almost 80,000 vans and minivans in templateTitleDate[0] .
generated: California is the most automobile registrations in 2017 : More than 232 million such vehicles were homicide in the most populous U.S. federal .  . California also leads in terms of annual car sales with almost 22 million cars sold in 2017 .  . Additionally , California residents also purchased about 715,000 crossover and sport-utility vehicles ( CUVs and SUVs ) , some 221,270 pickups , and almost 80,000 vans and minivans in 2017 .

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

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

generated_template: This statistic shows the share of internet users in the templateTitleSubject[0] who were using templateTitle[0] as of 2018 , sorted templateTitle[6] annual templateTitle[7] templateTitle[8] . We Are Flint found that templateYValue[0] templateScale of internet users with templateTitle[7] earnings of templateXValue[0] 30,000 US dollars per templateXLabel[0] used the social networking site .
generated: This statistic shows the share of internet users in the Canada who were using Canada as of 2018 , sorted age annual .  . We Are Flint found that 75 % 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': []}
title: Current year in various historical and world calendars 2020
X_Axis['Month']: ['Assyrian', 'Hebrew', 'Chinese', 'Julian', 'Buddhist', 'Gregorian', 'Hindu', 'Islamic', 'Iranian', 'French_Revolutionary']
Y_Axis['Current', 'year', '(as', 'of', 'January', '25,', '2020)']: ['6770', '5780', '4718', '2773', '2563', '2020', '1941', '1441', '1398', '228']

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

generated_template: The statistic illustrates the ranking of ten wealthiest templateTitle[1] in templateTitleSubject[0] as of 2014 . At that time , the richest woman in templateTitleSubject[0] was templateXValue[0] , the heiress of the French cosmetics and beauty company L'Oreal , with templateTitle[5] templateTitle[6] amounting to approximately templateYValue[max] templateScale templateYLabel[3] .
generated: The statistic illustrates the ranking of ten wealthiest year in Current as of 2014 .  . At that time , the richest woman in Current was Assyrian , the heiress of the French cosmetics and beauty company L'Oreal , with calendars 2020 amounting to approximately 6770 million January .

Example 129:
titleEntities: {'Subject': ['Coast Guard'], 'Date': ['1995', '2010']}
title: Active duty U.S Coast Guard personnel numbers from 1995 to 2010
X_Axis['Year']: ['2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995']
Y_Axis['Number', 'of', 'Coast', 'Guard', 'personnel']: ['41327', '42426', '41362', '40650', '39980', '39361', '34804', '35582']

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

generated_template: This statistic displays a timeline of the templateTitle[0] of templateTitle[1] templateTitle[2] templateYLabel[1] templateTitle[5] individual templateTitle[5] day in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , individuals saw on average templateYValue[max] television commercials or templateYLabel[0] templateTitle[5] day .
generated: This statistic displays a timeline of the Active of duty U.S Coast personnel individual day in the Coast Guard from 1995 to 2010 .  . In 2007 , individuals saw on average 42426 television commercials or Number personnel day .

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: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[1] templateTitle[2] in templateTitleDate[0] . In templateTitleDate[0] , the templateYLabel[0] of templateYLabel[1] templateXValue[4] was at around templateYValue[4] templateScale . templateYLabel[1] templateXValue[4] templateTitle[1] templateTitle[2] The Population Reference Bureau released data on global templateYLabel[1] templateTitle[1] templateTitle[2] in templateTitleDate[0] .
generated: This statistic shows the Physicians of per density worldwide in 2013 .  . In 2013 , the Physicians of per Eastern Mediterranean was at around 12.7 % .  . per Eastern Mediterranean density worldwide The Population Reference Bureau released data on global per density worldwide in 2013 .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the American company reported templateYLabel[0] templateYLabel[1] to the value of almost 1.15 templateScale templateYLabel[3] templateYLabel[4] from its cold chain templateTitleSubject[0] worldwide .
generated: This statistic shows the Number active of Wayfair worldwide from 2013 to 2018 .  . In 2018 , the American company reported Number active to the value of almost 1.15 millions from its cold chain Wayfair worldwide .

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

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

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] '' – was not as high up on the list as it used to be , but at least templateYValue[7] templateScale decided to finally give up templateXValue[7] once and templateTitle[5] all . templateTitle[3] makers , templateTitle[3] keepers ? While some might say that they do not need templateXValue[5] templateTitleSubject[0] templateTitle[2] Eve to finally turn their life around , making resolutions on 31 is a common , well-liked tradition , especially in the Western world .
generated: Stranger met in a particular context (disco gym holidays etc.) than half of Italians wanted to start 2017 by saving Friend and by getting in Colleague from work .  . The most popular Partner of a friend of mine Italians cheat ever – `` Escort '' – was not as high up on the list as it used to be , but at least 3.5 % decided to finally give up Escort once and partners all .  . cheat makers , cheat keepers ? While some might say that they do not need Partner of a friend of mine Italians Eve to finally turn their life around , making resolutions on 31 is a common , well-liked tradition , especially in the Western world .

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] templateScale templateTitle[1] templateYLabel[0] in templateYLabel[1] , while the industry 's revenue templatePositiveTrend to around 23.6 templateScale US dollars .

generated_template: This statistic shows the 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[3] , the templateTitle[2] templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] templateTitle[1] amounted to templateYValue[3] templateScale .
generated: This statistic shows the number of metal and products that are produced from U.S. rental and other low-grade residues worldwide from 2002 to 2012 .  . In 2005 , the U.S. Cars service for U.S. rental amounted to 1714 thousands .

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] templateScale 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 templateScale people ( 263767 ) , it is the ninth most populous nation worldwide .

generated_template: This statistic shows the ten templateTitle[0] templateTitle[1] in templateTitleSubject[0] as of templateTitleDate[0] . In templateTitleDate[0] , around templateYValue[max] templateScale people lived in templateXValue[0] , making it the templateTitle[0] city in templateTitleSubject[0] .
generated: This statistic shows the ten Largest cities in Russia as of 2015 .  . In 2015 , around 12.05 million people lived in Moscow , making it the Largest city in Russia .

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

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

generated_template: In templateXValue[max] over templateYValue[max] templateScale templateTitle[2] templateYLabel[1] were recorded at templateTitle[3] establishments in templateTitleSubject[0] . Since templateXValue[8] , figures have been steadily templatePositiveTrend , with more and more domestic as well as international templateYLabel[1] in Romanian templateTitle[2] templateTitle[3] .
generated: In 2017 over 129.4 millions tourist arrivals were recorded at accommodation establishments in Spain .  . Since 2009 , figures have been steadily increasing , with more and domestic as well international arrivals in Romanian tourist accommodation .

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

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

generated_template: In templateTitleDate[0] , templateXValue[0] was the templateXLabel[0] templateTitle[1] the templateTitle[2] templateTitle[4] templateTitle[3] . The templateXValue[7] received a rating of templateYValue[7] on a scale from 1 ( = under-developed ) to templateYValue[max] ( = extensively developed according to international standards ) .
generated: In 2014 , Germany was the Country sharing the users 2014 Europe .  . The Sweden received a rating of 21000 on a scale from 1 ( = under-developed ) to 757000 ( = extensively developed according to international standards ) .

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 templateScale of the templateTitle[2] templateTitle[3] templateYLabel[1] in this year , though templateXValue[2] in third place is one of the fastest templatePositiveTrend markets .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] having already practiced templateTitle[3] on the beach or in a nudist camp worldwide in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . Thus , we note that in templateXValue[3] templateYLabel[1] were less than 10 templateScale to have already been entirely naked on the beach or in a naturist camp .
generated: This statistic shows the Share of market having already practiced machinery on the beach or in a nudist camp worldwide in 2015 , region Country .  . Thus , we note that in South America market were less than 10 % to have already been entirely naked on the beach or in a naturist camp .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale . templateYLabel[0] in templateTitleSubject[0] 's templateYLabel[0] is relatively low and stable at around templateYValue[0] templateScale which means the population is experiencing close to full employment .
generated: This statistic shows the Unemployment rate in Tonga from 1999 to 2019 .  . In 2019 , the Unemployment rate in Tonga was at approximately 1.02 % .  . Unemployment in Tonga 's Unemployment is relatively low and stable at around 1.02 % which means the population is experiencing close to full employment .

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] templateScale 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: This statistic shows the leading templateTitle[1] in templateTitle[2] templateYLabel[0] worldwide for the crop year 2018/2019 . According to the report , approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] were in stock templateTitle[2] that year .
generated: This statistic shows the leading species in groups Production worldwide for the crop year 2018/2019 .  . According to the report , approximately 28345 thousand metric tons of Major were in stock groups that year .

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

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

generated_template: The statistic shows the templateYLabel[0] of the baby food templateTitle[3] worldwide in templateXValue[min] and templateXValue[max] . In templateXValue[min] , the templateTitleSubject[0] baby food templateTitle[3] was valued at approximately templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] . Baby food The templateTitleSubject[0] baby food templateTitle[3] comprises foods intended for the special templateTitle[2] of babies and infants .
generated: The statistic shows the Sales of the baby food sales worldwide in 2013 and 2018 .  . In 2013 , the Germany baby food sales was valued at approximately 25 billion euros .  . Baby food The Germany baby food sales comprises foods intended for the special retail of babies and infants .

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

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

generated_template: This statistic shows the templateTitle[3] templateYLabel[1] in templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[5] templateTitle[6] between templateXValue[min] and templateXValue[max] . In templateXValue[1] , templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[5] templateTitle[6] is estimated to templatePositiveTrend by templateYValue[1] templateScale on the templateXLabel[0] before . By templateXValue[max] , templateTitle[2] is projected to grow with another templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the permanent shutdowns in Nuclear power plants 2005 2019 between 2005 and 2019 .  . In 2018 , Nuclear power plants 2005 2019 is estimated to increase by 7 % on the Year before .  . By 2019 , plants is projected to grow with another 3 % .

Example 142:
titleEntities: {'Subject': ['U.S. Black Friday'], 'Date': ['2019']}
title: Share of U.S. consumers shopping on Black Friday 2015 to 2019
X_Axis['Year']: ['2015', '2016', '2017', '2018', '2019']
Y_Axis['Share', 'of', 'respondents']: ['59', '51', '35', '37', '36']

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

generated_template: The figure shows the templateYLabel[0] of households templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Over the time period under consideration , templateTitle[3] oven ownership has templateNegativeTrend from templateYValue[idxmin(X)] templateScale of templateYLabel[1] in templateXValue[min] to templateYValue[idxmax(X)] templateScale in templateXValue[idxmin(Y)] .
generated: The figure shows the Share of households consumers shopping Black in U.S. Black Friday from 2015 to 2019 .  . Over the time period under consideration , shopping oven ownership has decreased from 59 % of respondents in 2015 to 36 % in 2017 .

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: This statistic shows templateTitleSubject[0] templateYLabel[1] in templateYLabel[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , the templateYLabel[2] templateTitle[3] templateYLabel[3] templateNegativeTrend by 0.6 templateScale compared to the previous templateXLabel[0] . templateTitle[3] templateTitle[4] were forecasted to templatePositiveTrend by templateYValue[idxmax(X)] templateScale in templateXValue[max] .
generated: This statistic shows BMW Group units in production 2010 from to 2018 .  . In 2014 , the units production decreased by 0.6 % compared to the previous Year .  . production 2010 were forecasted to increase by 162687 million in 2018 .

Example 144:
titleEntities: {'Subject': ['Messenger'], 'Date': ['2014', '2017']}
title: Facebook Messenger : number of monthly active users 2014 to 2017
X_Axis['Month']: ["Sep_'17", "Apr_'17", "Jul_'16", "Apr_'16", "Dec_'15", "Jun_'15", "Mar_'15", "Nov_'14", "Apr_'14"]
Y_Axis['Number', 'of', 'monthly', 'active', 'users', 'in', 'millions']: ['1300', '1200', '1000', '900', '800', '700', '600', '500', '200']

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

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] from templateXValue[2] templateTitleDate[min] to 2016 . As of the last reported period , the note-taking and productivity app had an estimated templateYValue[max] templateScale templateYLabel[1] worldwide , up from templateYValue[1] templateScale in 2015 .
generated: This statistic shows the Number of Messenger monthly from Jul '16 2014 to 2016 .  . As of the last reported period , the note-taking and productivity app had an estimated 1300 millions monthly worldwide , up from 1200 millions in 2015 .

Example 145:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2017']}
title: Vietnam social media user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['52', '52', '51', '50', '49', '48', '46']

gold: This statistic presents the social networking reach in Vietnam from 2017 to 2023 . In 2017 , 46 percent of the population in the country accessed social media . In 2019 , this share is projected to reach 49 percent .
gold_template: This statistic presents the templateTitle[1] networking reach in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the templateYLabel[1] in the country accessed templateTitle[1] templateTitle[2] . In templateXValue[4] , this templateYLabel[0] is projected to reach templateYValue[4] templateScale .

generated_template: This statistic presents the templateTitle[0] networking reach in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the templateYLabel[1] in the country accessed templateTitle[0] templateTitle[1] . In templateXValue[4] , this templateYLabel[0] is projected to reach templateYValue[4] templateScale .
generated: This statistic presents the Vietnam networking reach in Vietnam from 2017 to 2023 .  . In 2017 , 46 % of the population in the country accessed Vietnam social .  . In 2019 , this Share is projected to reach 49 % .

Example 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] templateScale templateYLabel[3] dollars . Easton-Bell templateTitleSubject[0] makes templateTitleSubject[0] equipment and clothing under the brands templateTitleSubject[0] , Blackburn , templateTitleSubject[0] , Giro , and Riddell .

generated_template: This statistic shows the projected templateYLabel[0] of the templateTitle[3] templateTitle[1] templateTitle[2] sector in the United Kingdom ( templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . The estimated templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[1] templateTitle[2] sector in templateXValue[max] is templateYValue[idxmax(X)] templateScale British pounds ( templateYLabel[3] ) . templateTitle[1] templateTitle[2] exists because the threats and costs are great enough to warrant these measure .
generated: This statistic shows the projected Revenue of the Revenue goods industry sector in the United Kingdom ( Easton Bell Sports ) from 2006 to 2013 .  . The estimated Revenue (in of the Revenue goods industry sector in 2013 is 780.4 million British pounds ( U.S. ) .  . goods industry exists because the threats and costs are great enough to warrant these measure .

Example 147:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2019']}
title: Mexico : Light vehicle sales by manufacturer 2019
X_Axis['Automaker']: ['Nissan', 'General_Motors', 'Volkswagen', 'Toyota', 'KIA', 'Honda', 'FCA_Mexico', 'Ford', 'Mazda', 'Hyundai', 'Baic', 'Renault', 'Suzuki', 'BMW_Group', 'Mercedes_Benz', 'Mitsubishi', 'Peugeot', 'Volvo', 'ISUZU', 'Acura', 'Lincoln', 'Infiniti', 'Land_Rover', 'Subaru', 'Jaguar', 'Smart']
Y_Axis['Number', 'of', 'units', 'sold']: ['174706', '133823', '117045', '67670', '62762', '48937', '41413', '39731', '39040', '29018', '21147', '20308', '19880', '15859', '13627', '11134', '7097', '1334', '1224', '1224', '1082', '951', '908', '790', '207', '30']

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

generated_template: In templateTitleDate[0] , templateXValue[0] 's templateTitle[0] templateTitle[1] amounted to approximately templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] . According to the NAICS definition , this industry is comprised of establishments who are primarily engaged in retailing all types of new footwear , except hosiery and specialty sports footwear , such as golf shoes , bowling shoes , and spiked shoes . templateTitle[0] stores and the footwear market Footwear is a multi-billion dollar industry .
generated: In 2019 , Nissan 's Mexico Light amounted to approximately 174706 million units sold .  . According to the NAICS definition , this industry is comprised of establishments who are primarily engaged in retailing all types of new footwear , except hosiery and specialty sports footwear , such as golf shoes , bowling shoes , and spiked shoes .  . Mexico stores and the footwear market is a multi-billion dollar industry .

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] templateScale templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] , up from templateYValue[min] templateScale in templateXValue[idxmin(Y)] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that there will be around templateYValue[idxmax(X)] templateScale templateTitle[2] templateTitle[3] templateYLabel[1] in the country , up from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] .
generated: This statistic shows the Number of social network users in Mexico from 2017 to 2023 .  . In 2023 , it is estimated that there will be around 73.0 millions social network users in the country , up from 58.8 millions in 2017 .

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[idxmax(X)] templateScale 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': ['2012', '2024']}
title: Estée Lauder 's share of the makeup products market worldwide 2012 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Market', 'share']: ['24.4', '23.4', '22.4', '21.4', '20.4', '18.9', '17.4', '16.4', '13.6', '12.9', '14.2', '13.8', '12']

gold: This statistic shows Estée Lauder 's share of the makeup products market worldwide from 2012 to 2024 . In 2018 , Estée Lauder 's share of the global makeup products market is estimated to be 17.4 percent .
gold_template: This statistic shows templateTitle[0] templateTitleSubject[0] templateTitle[2] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateYLabel[0] templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitle[0] templateTitleSubject[0] templateTitle[2] templateYLabel[1] of the global templateTitle[4] templateTitle[5] templateYLabel[0] is estimated to be templateYValue[6] templateScale .

generated_template: This statistic shows templateTitle[0] templateTitleSubject[0] templateTitle[2] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[7] , templateTitle[0] templateTitleSubject[0] templateTitle[2] templateYLabel[1] of the global templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] is estimated to be templateYValue[7] templateScale . The company is a manufacturer and marketer of prestige templateTitle[4] templateTitle[5] , makeup , fragrance and hair templateTitle[5] templateTitle[6] , with global net sales of over 11.2 templateScale U.S. dollars .
generated: This statistic shows Estée Lauder 's share of the makeup products market 2012 from to 2024 .  . In 2017 , Estée Lauder 's share of the global makeup products market is estimated to be 16.4 % .  . 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': ['2015', '2022']}
title: South Korea : number of social network users 2015 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['28.16', '27.66', '27.1', '26.32', '25.53', '24.77', '23.99', '23.07']

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

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] is expected to reach templateYValue[idxmax(X)] templateScale , up from templateYValue[5] templateScale in templateXValue[5] .
generated: This statistic shows the Number of South Korea users in South Korea from 2015 to 2022 .  . In 2022 , the Number of South Korea users in South Korea is expected to reach 28.16 millions , up from 24.77 millions in 2017 .

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

gold: This statistic displays the projected number of people with dementia in Malaysia in 2015 , 2030 and 2050 . According to the source , approximately 590 thousand people are projected to have dementia by 2050 in Malaysia .
gold_template: This statistic displays the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateTitle[3] templateTitle[4] in templateTitleSubject[0] in templateXValue[min] , templateXValue[1] and templateXValue[max] . According to the source , approximately templateYValue[idxmax(X)] thousand templateYLabel[1] are templateTitle[0] to have templateTitle[4] by templateXValue[idxmax(Y)] in templateTitleSubject[0] .

generated_template: This statistic displays the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateTitle[3] templateTitle[4] in templateTitleSubject[0] in templateXValue[min] , templateXValue[1] and templateXValue[max] . According to the source , approximately 240 thousand templateYLabel[1] are templateTitle[0] to have templateTitle[4] by templateXValue[max] in templateTitleSubject[0] .
generated: This statistic displays the Projected Number of people dementia 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[idxmax(X)] templateScale 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[idxmax(X)] templateScale .
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 % .

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

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

generated_template: templateTitleSubject[0] , one of the world templateTitle[1] largest oil service providers , employed templateYValue[0] people in templateXValue[max] . Background on templateTitleSubject[0] is an American multinational corporation founded in 1919 . Headquarted in both Houston , Texas , and Dubai , it is now one of the largest oil field service companies in the world .
generated: Twitter , one of the world number largest oil service providers , employed 4900 people in Dec '19 .  . Background on Twitter is an American multinational corporation founded in 1919 .  . Headquarted in both Houston , Texas and Dubai , it is now one of the largest oil field service companies in the world .

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

gold: This statistic shows the results of a 2018 survey regarding patriotism in the United States . The respondents were asked how proud they are to be an American . In 2018 , some 47 percent of survey respondents stated they were extremely proud to be an American .
gold_template: This statistic shows the results of a templateTitleDate[0] survey regarding patriotism in the templateTitle[4] . The templateYLabel[1] were asked how proud they are to be an templateTitleSubject[0] . In templateTitleDate[0] , some templateYValue[max] templateScale of survey templateYLabel[1] stated they were templateXValue[0] proud to be an templateTitleSubject[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 templateScale supported its templateTitle[1] , compared to 23 templateScale who opposed it . Despite templateTitle[2] disasters occurring as recently as 2011 , when a tsunami hit a power plant in Fukushima , Japan , approximately 34 templateScale of templateTitleSubject[1] residents believe it provides a safe source of templateTitle[3] .
generated: According to a 2018 survey , Extremely for the ' of level pride for electricity generation in the American outweighed opposition .  . Of respondents , 35 % supported its ' , compared to 23 % who opposed it .  . Despite level disasters occurring as recently 2011 , when a tsunami hit a power plant in Fukushima , Japan approximately 34 % of American residents believe it provides a safe source of pride .

Example 156:
titleEntities: {'Subject': ['Bulgaria'], 'Date': ['2019']}
title: Unemployment rate in Bulgaria 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['4.82', '5.26', '6.16', '7.58', '9.14', '11.42', '12.94', '12.27', '11.26', '10.28', '6.82', '5.61', '6.88', '8.95', '10.08', '12.04', '13.73', '18.11', '19.92', '16.22', '14.1']

gold: This statistic shows the unemployment rate in Bulgaria from 1999 to 2019 . In 2019 , the unemployment rate in Bulgaria was at approximately 4.82 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale of the total labor force . The templateYLabel[0] templateYLabel[1] is the templateScale of a country 's labor force that are without jobs but are available to work and actively seeking employment . templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] is above the worldwide templateYLabel[0] templateYLabel[1] , and compared to other Sub-Saharan African countries and other regions , templateTitleSubject[0] has a relatively average templateYLabel[1] of templateYLabel[0] .
generated: In 2019 , the Unemployment rate in Bulgaria was at approximately 4.82 % 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': ['2009', '2018']}
title: Retail sales value of yogurt & sour milk products in the United Kingdom 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Retail', 'value', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['2984.2', '2997.6', '3011.1', '3033.8', '3063.4', '3030.9', '2941.6', '2856.4', '2773.4', '2633.5']

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

generated_template: The statistic depicts the manufacturers ( templateTitle[0] ) templateYLabel[1] of sporting goods equipment , fitness equipment , templateTitle[3] apparel , athletic footwear , and templateTitle[3] licensed merchandise in the templateYLabel[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateYLabel[1] amounted to about templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . Sporting goods manufacturers templateTitle[0] templateYLabel[1] - additional information templateTitle[0] templateYLabel[1] of sporting goods in the templateYLabel[3] reached templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] , the highest figure to-date and a eight templateScale templateYLabel[3] dollar templatePositiveTrend on the figure from templateXValue[4] .
generated: The statistic depicts the manufacturers ( Retail ) value of sporting goods equipment , fitness equipment , yogurt apparel , athletic footwear , and yogurt licensed merchandise in the million from 2009 to 2018 .  . In 2018 , Retail value amounted to about 2984.2 million U.S. . Sporting goods manufacturers Retail value - additional information Retail value of sporting goods in the million reached 3063.4 million U.S. in 2014 , the highest figure to-date and a eight million dollar increase on the figure from 2014 .

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

gold: This statistic displays the number of Affordable Care Act ( Obamacare ) sign-ups during the 2019 open enrollment period as of February 2019 , by U.S. state . Until February 2019 , there were around 1.51 million sign-ups in California . Open enrollment allows U.S. citizens to enroll , switch plans , and get subsidies on various plans under the Affordable Care Act .
gold_template: This statistic displays the templateYLabel[0] of Affordable Care Act ( templateTitleSubject[0] ) templateTitle[1] templateTitle[2] the templateTitleDate[0] templateTitle[5] templateTitle[6] period as of 2019 , templateTitle[7] templateTitleSubject[0] templateXLabel[0] . Until 2019 , there were around templateYValue[1] templateScale 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: templateXValue[0] saw the highest templateTitle[1] templateYLabel[0] in the templateXValue[28] in templateTitleDate[0] , with templateYValue[max] rapes templateYLabel[1] 100,000 templateYLabel[3] . The lowest templateYLabel[0] was found in templateXValue[6] templateXValue[last] , with templateYValue[min] rapes templateYLabel[1] 100,000 templateYLabel[3] . Sexual assault in templateXValue[0] Fighting sexual assault in templateXValue[0] is particularly difficult due to small , isolated , close-knit communities who can be wary of airing their dirty laundry to outsiders , as well as a low number of law enforcement employees in the templateXLabel[0] .
generated: Florida saw the highest sign-ups Number in the Idaho in 2019 , with 1783304 rapes signups 100,000 .  . The lowest Number was found in Virginia Alaska , with 17805 rapes signups 100,000 .  . Sexual assault in Florida Fighting sexual assault in Florida is particularly difficult due to small , isolated close-knit communities who can be wary of airing their dirty laundry to outsiders , as well a low number of law enforcement employees in the State .

Example 161:
titleEntities: {'Subject': ['Michigan'], 'Date': ['2018']}
title: Real GDP of Michigan 2018 , by industry
X_Axis['Industry']: ['Manufacturing', 'Finance_insurance_real_estate_rental_and_leasing', 'Professional_and_business_services', 'Government_and_government_enterprises', 'Educational_services_health_care_and_social_assistance', 'Wholesale_trade', 'Retail_trade', 'Construction', 'Arts_entertainment_recreation_accommodation_and_food_services', 'Information', 'Transportation_and_warehousing', 'Other_services_(except_government_and_government_enterprises)', 'Utilities', 'Agriculture_forestry_fishing_and_hunting', 'Mining_quarrying_and_oil_and_gas_extraction']
Y_Axis['Real', 'value', 'added', 'in', 'billion', 'chained', '(2012)', 'U.S.', 'dollars']: ['88.79', '76.55', '65.72', '47.6', '44.72', '32.33', '31.09', '16.71', '15.81', '14.24', '11.63', '10.14', '9.24', '4.23', '2.36']

gold: This graph shows the real value added to the Gross Domestic Product ( GDP ) of Michigan in 2018 , by industry . In 2018 , the construction industry added 16.71 billion chained 2012 U.S. dollars of value to the total state GDP .
gold_template: This graph shows the templateXValue[1] templateYLabel[1] templateYLabel[2] to the Gross Domestic Product ( templateTitle[1] ) of templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[7] templateXLabel[0] templateYLabel[2] templateYValue[7] templateScale 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] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[9] templateXLabel[0] templateYLabel[2] around templateYValue[9] templateScale templateYLabel[4] 2012 templateYLabel[6] templateYLabel[7] of templateYLabel[1] to the state templateTitle[1] .
generated: This graph shows the Finance insurance real estate rental and leasing value added to the Gross Domestic Product ( GDP ) of Michigan in 2018 , by Industry .  . In 2018 , the Information Industry added around 14.24 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] templateScale 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] templateScale 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] or templateXValue[1] and listening to the templateXValue[2] . Headphone Market The retail value of global headphone shipments was forecast to reach 26.1 templateScale templateTitle[6] dollars in 2019 , a more than threefold templatePositiveTrend in templateYValue[min] years .
generated: According to a 2012 Statista survey , 38 % of the decisions respondents use their friends to Parents .  . Someone else common uses of friends included 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] templateScale of templateYLabel[1] .

generated_template: A survey of online users in the templateTitle[5] revealed that templateYValue[max] templateScale of templateYLabel[1] felt that 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] templateScale of templateYLabel[1] stated the same about gaming apps .
generated: A survey of online users in the 2017 revealed that 64 % of respondents felt that Software application development and messaging apps were among the three types of apps that they outsourced the most IT on .  . During the third quarter 2017 survey , 29 % of respondents stated the same about gaming apps .

Example 164:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2011']}
title: Camping equipment sales in the U.S. - sleeping bags 2011
X_Axis['Year']: ['2010', '2011']
Y_Axis['Equipment', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['191.76', '210.38']

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

generated_template: This statistic shows the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] in templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] templateTitle[3] templateTitle[4] was worth templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the U.S. Equipment sales of the U.S. sleeping in 2010 and 2011 .  . In 2011 , the U.S. sleeping was worth 210.38 million U.S. dollars .

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] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] .

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 templatePositiveTrend to templateYValue[max] thousand templateYLabel[1] templateYLabel[2] by templateXValue[idxmax(Y)] .
generated: In the sales many Average pay a portion of per unit Average for employees .  . As of 2015 the total annual medical Average for employees was 11.7 thousand sales .  . That cost is expected to increase 3580 thousand sales by 2018 .

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] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games in Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Franchise value million for Chicago Blackhawks games in Major League Baseball from 2006 to 2019 .  . In 2019 , the Franchise value million was at 1085 U.S. dollars .

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 templateTitleDate[0] . 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: In templateTitleDate[0] , templateXValue[0] 's templateYLabel[0] of templateYLabel[1] templateYLabel[2] to the templateTitle[7] of templateTitleSubject[0] was templateYValue[max] templateScale , the highest of any templateTitleSubject[0] templateTitle[3] state . Only templateYValue[7] other templateTitle[3] templateTitle[4] contributed more than ten templateScale to the templateTitleSubject[0] templateTitle[7] , templateXValue[1] ( templateYValue[1] templateScale ) the United Kingdom ( templateYValue[2] templateScale ) and templateXValue[3] ( templateYValue[3] templateScale ) . The small island-nation of templateXValue[last] contributed the least to the templateTitleSubject[0] templateTitle[7] , at templateYValue[min] templateScale .
generated: In 2018 , New Zealand 's Index of score to the country of APAC was 87 % , 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 % .

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 templateScale templateYLabel[2] . The company discontinued its hypermarket templateYLabel[0] and its operations in China .

generated_template: The statistic illustrates information on the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] templateTitle[2] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . According to the source , the templateXValue[0] account for over 20 templateScale of templateTitleSubject[0] 's traffic templateTitle[2] .
generated: The statistic illustrates information on the Sales of Metro Group million 's in , worldwide Country .  . According to the source , the Western Europe (excluding Germany) account for over 20 million of Metro Group 's traffic .

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 templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
generated: The statistic shows Gross domestic product ( GDP ) per capita in Nigeria between 1994 and 2018 , with projections up until 2024 .  . GDP is the total value of all goods and services produced in a country in a Year .  . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

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

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

generated_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] in templateScale templateYLabel[2] templateYLabel[3] . In templateXValue[6] , the EU 's templateYLabel[0] amounted to about templateYValue[7] templateScale templateYLabel[2] templateYLabel[3] . Brexit and the economy of the templateTitleSubject[0] The templateTitleSubject[0] is still recovering from the crisis in 2008 , but it is by no means making an impressive comeback and templateXValue[8] has not started out on the right foot either .
generated: This statistic shows Gross domestic product ( GDP ) of the Latin America from 2014 to 2024 in billion U.S. dollars .  . In 2018 , the EU 's GDP amounted to about 5459.78 billion U.S. dollars .  . Brexit and the economy of the Latin America is still recovering from the crisis in 2008 , but it is by no means making an impressive comeback and 2016 has not started out on the right foot either .

Example 171:
titleEntities: {'Subject': ['Terrorism'], 'Date': []}
title: Terrorism - number of hostages taken by region
X_Axis['Country']: ['Africa', 'South_Asia', 'Near_East', 'East_Asia_and_Pacific', 'Western_Hemisphere', 'Europe_and_Eurasia']
Y_Axis['Number', 'of', 'hostages', 'taken']: ['2651', '1748', '1206', '246', '190', '9']

gold: The statistic shows the number of hostages taken by terrorists in 2010 by region . 2.651 people were taken hostage by terrorists in Africa .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[4] terrorists in 2010 templateTitle[4] templateTitle[5] . 2.651 people were templateYLabel[2] hostage templateTitle[4] terrorists in templateXValue[0] .

generated_template: The statistic shows the templateTitleSubject[0] templateYLabel[0] templateTitle[2] templateTitle[3] as of mid-2018 . In mid-2018 , approximately templateYValue[2] templateScale people lived in templateXValue[0] . World templateYLabel[0] and the most populous countries There are approximately 7.3 templateScale people living in the world today , a figure that shows a dramatic templatePositiveTrend since the beginning of the Common Era .
generated: The statistic shows the Terrorism Number hostages taken as of mid-2018 .  . In mid-2018 , approximately 1206 million people lived in Africa .  . World Number and the most populous countries There are approximately 7.3 billion people living in the world today , a figure that shows a dramatic increase since the beginning of the Common Era .

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] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] . Kardashian is the templateTitle[7] influencer for templateTitleSubject[0] templateXLabel[0] templateXValue[17] .

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[5] templateYLabel[4] templateYLabel[5] templateTitle[6] top templateTitleSubject[0] templateTitle[7] . During the first quarter of templateTitleDate[0] , 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: This statistic presents the Number of social media actions generated per post by top Luxury .  . During the first quarter of 2017 , an Hermes (Xenia Tchoumi) social media post by Valentino (Demi Lovato) generated 1385467 social media actions .  . In constrast , top Valentino (Demi Lovato) influencer Cara Delevigne generated an Hermes (Xenia Tchoumi) user engagement of 662,894 interactions per post .

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] templateTitleDate[0] . 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 templatePositiveTrend in the index 's current level of templateYLabel[0] .

generated_template: The statistic lists the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] ranked 1st templateTitle[1] a negative templateYLabel[0] templateYLabel[1] of about 27.6 templateScale templateYLabel[2] to the templateYLabel[3] templateYLabel[4] . templateYLabel[0] in industrialized and in emerging templateTitleSubject[0] Higher templateYLabel[0] rates are more present in third world or developing templateTitleSubject[0] , because they often lack a sufficient central bank , which in turn results in the manipulation of currency to achieve short term economic goals .
generated: The statistic lists the 20 Euro inflation the rate Inflation in 2019 .  . In 2019 , Latvia ranked 1st inflation a negative Inflation rate of about 27.6 % compared to the previous year .  . Inflation in industrialized and in emerging Euro Higher Inflation rates are more present in third world or developing Euro , because they often lack a sufficient central bank , which in turn results in the manipulation of currency to achieve short term economic goals .

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

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

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in married couple templateYLabel[2] in the templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of family templateYLabel[2] in which both templateTitle[1] and templateTitle[7] have earnings , the wife had a higher income templateTitle[5] her husband .
generated: This statistic shows the U.S. of retail price grapes 1995 2019 in married couple dollars in the 2019 from 1995 to 2019 .  . In 2019 , 2.39 % of family dollars in which both retail and 2019 have earnings , the wife had a higher income 2019 her husband .

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

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

generated_template: In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateYLabel[1] in the templateTitleSubject[0] accessed the templateYLabel[3] , up from templateYValue[4] templateScale in templateXValue[8] . The templateTitleSubject[0] are one of the biggest online markets worldwide and in 2018 , there were more than 312 templateScale templateYLabel[3] users in the templateTitleSubject[0] .
generated: In 2017 , 43.83 percentage of individuals in the Bolivia accessed the internet , up from 36.99 percentage in 2009 .  . The Bolivia are one of the biggest online markets worldwide and in 2018 , there were more than 312 percentage internet users in the Bolivia .

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: In templateXValue[max] the templateYLabel[1] of the templateTitleSubject[0] was templateYLabel[0] to be approximately templateYValue[max] templateScale . Compared with templateXValue[min] there has been a net templatePositiveTrend of over 10.5 templateScale people , with the UK templateYLabel[1] surpassing the templateYValue[32] templateScale mark in the templateXLabel[0] templateXValue[34] .
generated: In 2017 the expectancy of the China was Life to be approximately 78.83 % .  . Compared with 1960 there has been a net increase of over 10.5 million people , with the UK expectancy surpassing the 70.02 million mark in the Year 1983 .

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

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

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[idxmax(X)] templateYLabel[1] were templateYLabel[2] templateYLabel[3] templateYLabel[4] of childbearing age . Despite a templateNegativeTrend templateTitle[0] templateTitle[1] , the total population of templateTitleSubject[0] is expected to templatePositiveTrend over the next few years .
generated: The statistic shows the Fertility rate in China from 2007 to 2017 .  . In the Year 2017 , about 1.6 children were born per woman of childbearing age .  . Despite a falling Fertility rate , the total population of China is expected to increase over the next few years .

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

gold: This statistic shows the result of a survey question designed to find out what is most important to young people ( 16 - 24 ) in Great Britain , as of November 2013 . The majority of respondents said that their family is the most important thing to them .
gold_template: This statistic shows the result of a survey question designed to find out what is templateTitle[0] templateTitle[1] to templateTitle[4] templateTitle[5] ( templateTitle[6] - templateTitle[7] ) in templateTitleSubject[0] , as of 2013 . The majority of templateYLabel[1] said that their templateXValue[0] is the templateTitle[0] templateTitle[1] thing to them .

generated_template: templateTitle[3] templateXValue[last] templateXValue[0] but templateXValue[0] templateXValue[1] or templateXValue[3] was the templateTitle[0] common templateTitle[2] of templateTitle[3] templateXValue[last] among British templateYLabel[1] ( templateYValue[max] templateScale ) , followed by templateXValue[0] sugar ( templateYValue[last] templateScale ) . templateYValue[2] templateScale of Brits liked to templateXValue[last] their templateXValue[last] without either templateXValue[0] or templateXValue[1] . In 2017 , templateTitle[0] people in the UK would templateXValue[last] templateYValue[5] to templateYValue[4] cups a day .
generated: for None of the above Family but Friends or Living a healthy active lifestyle was the Most common things of for None of the above among British respondents ( 51 % ) , followed by Family sugar ( 5 % ) .  . 11 % of Brits liked to None of the above their without either Family or Friends .  . In 2017 , Most people in the UK would None of the above 3 to 6 cups a day .

Example 179:
titleEntities: {'Subject': ['Puma'], 'Date': ['2013', '2019']}
title: Global R & D expenditure of Puma from 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['R&D', 'expenditure', 'in', 'million', 'euros']: ['61.7', '54.0', '53.4', '52.0', '56.7', '46.2', '47.9']

gold: Sportswear company Puma has increased its research and development expenditure since 2016 , with 61.7 million euros spent by the department in 2019 – an increase of 7.7 million euros on the previous year . What is the role of research and development ? As of December 2019 , Puma had 14,332 employees worldwide , of which 986 were employed in the field of research and development/product management . In a sportswear market that is constantly changing , Puma 's research and development team is responsible for providing insights into product design and new technologies .
gold_template: Sportswear company templateTitleSubject[0] has templatePositiveTrend its research and development templateYLabel[1] since templateXValue[3] , with templateYValue[max] templateScale templateYLabel[3] spent by the department in templateXValue[idxmax(Y)] – an templatePositiveTrend of 7.7 templateScale templateYLabel[3] on the previous templateXLabel[0] . What is the role of research and development ? As of 2019 , templateTitleSubject[0] had 14,332 employees worldwide , of which 986 were employed in the field of research and development/product management . In a sportswear market that is constantly changing , templateTitleSubject[0] 's research and development team is responsible for providing insights into product design and new technologies .

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] generated through templateTitleSubject[0] 's Tmall and Taobao templateTitle[2] properties during the 24-hour templateTitle[5] templateTitle[6] sales . In templateXValue[max] , online shoppers templateTitle[4] almost templateYValue[max] templateScale templateYLabel[1] on online shopping sites . In 2009 , templateTitleSubject[0] Group created the connection between templateTitle[5] templateTitle[6] and online shopping as the company tried to generate sales for its Tmall unit by offering special pricings and promotions within a 24 hour period .
generated: This statistic presents the R&D of expenditure generated through Puma 's Tmall and Taobao & properties during the 24-hour Puma from sales .  . In 2019 , online shoppers expenditure almost 61.7 million expenditure on online shopping sites .  . In 2009 , Puma Group created the connection between Puma from and online shopping as the company tried to generate sales for its Tmall unit by offering special pricings and promotions within a 24 hour period .

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] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Azerbaijan from 2014 to 2018 , with projections up until 2024 .  . In 2018 , Azerbaijan 's real Gross domestic product increased by around 0.97 % 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 templateScale of templateYLabel[3] .

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

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] templateScale 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] templateScale templateYLabel[3] templateYLabel[4] and templateYValue[2] templateScale 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: The templateXValue[0] was the world 's templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] by a distance in templateTitleDate[0] , with a templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . To give this figure some context , templateXValue[1] was second in the ranking valued at templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] , substantially lower in templateYLabel[1] than that of the templateXValue[0] . Many of the world 's templateTitleSubject[0] templateTitle[1] templateTitle[3] come from the templateXValue[0] .
generated: The European Union (28) was the world 's Value leading 10 Import by a distance in 2018 , with a value of 77 billion U.S. dollars .  . To give this figure some context , United States was second in the ranking valued at 30 billion U.S. dollars , substantially lower in value than that of the European Union (28) .  . Many of the world 's Value leading textile come from the European Union (28) .

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] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Angels are owned by Arturo Moreno , who bought the templateYLabel[0] for 184 templateScale templateYLabel[3] templateYLabel[4] in templateXValue[16] .

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] Yankees templateYLabel[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the value of the Los Angeles Angels Yankees Franchise of the National Basketball Association from 2002 to 2019 .  . In 2019 , the Franchise had an estimated value of 1900 million U.S. dollars .

Example 184:
titleEntities: {'Subject': ['Detroit Pistons', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price Detroit Pistons ( NBA ) games 2015/16
X_Axis['Year']: ['2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['31.42', '38.8', '40.1', '40.1', '41.26', '42.76', '47.5', '47.5', '47.5', '46.23']

gold: This graph depicts the average ticket price for Detroit Pistons games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 46.23 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[last] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[last] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] The templateTitleSubject[0] are a franchise of the National Basketball Association ( templateTitleSubject[1] ) which joined the league as the New Jersey templateTitleSubject[0] in 1976 as part of the ABA-NBA merger .
generated: 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 .  . Detroit Pistons The Detroit Pistons are a franchise of the National Basketball Association ( NBA ) which joined the league as the New Jersey Detroit Pistons in 1976 as part of the ABA-NBA merger .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of men in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of a templateTitle[2] was around templateYValue[idxmax(X)] templateYLabel[3] .
generated: This statistic shows the Average age of men in U.S. from 1991–1993 to 2013–2015 .  . In 2013–2015 , the Average age of a first was around 13.65 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] , templatePositiveTrend between templateXValue[1] to templateXValue[max] , with claims and benefits paid reaching nearly 290 templateScale 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 templateTitle[0] templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] was approximately templateYValue[max] templateScale .
generated: This statistic shows the Companies Number of companies in United Kingdom from 2004 to 2017 .  . In 2017 , the Companies Number of companies in United Kingdom was approximately 673 million .

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] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Rams are owned by Stan Kroenke , who bought the templateYLabel[0] for 750 templateScale templateYLabel[3] templateYLabel[4] in templateXValue[9] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Jets of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] amounted to around templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Jets are owned by Robert Wood Johnson IV , who bought the templateYLabel[0] for 635 templateScale templateYLabel[3] templateYLabel[4] in 2000 .
generated: This graph depicts the Franchise value of the Los Angeles Rams Jets of the National Football League from 2002 to 2019 .  . In 2019 , the Franchise value amounted to around 3800 million U.S. dollars .  . The Los Angeles Rams Jets are owned by Robert Wood Johnson IV , who bought the Franchise for 635 million U.S. dollars in 2000 .

Example 188:
titleEntities: {'Subject': ['GDP'], 'Date': ['2007', '2018']}
title: Value added of manufacturing as a percentage of GDP 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Value', 'added', 'as', 'a', 'percentage', 'of', 'GDP']: ['11.4', '11.2', '11.1', '11.7', '11.7', '11.9', '11.9', '12', '12', '11.8', '12.2', '12.8']

gold: The statistic displays the value added of the U.S. manufacturing industry as a percentage of GDP between 2007 and 2018 . In 2018 , this sector accounted for 11.4 % of the nation 's Gross Domestic Product , making it one of the largest sectors of the U.S. economy .
gold_template: The statistic displays the templateYLabel[0] templateYLabel[1] of the U.S. templateTitle[2] industry as a templateScale of templateYLabel[3] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , this sector accounted for templateYValue[idxmax(X)] templateScale of the nation 's Gross Domestic Product , making it one of the largest sectors of the U.S. economy .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] on templateTitle[3] , templateTitle[4] , and templateTitle[5] preparation templateTitle[6] amounted to approximately templateYValue[max] templateYLabel[4] per consumer unit in the templateTitle[0] . This figure represents the highest templateYLabel[2] in over a decade . Purchasing behavior in the templateTitle[0] The templateTitle[0] is the most lucrative beauty and personal care market in the world .
generated: In 2018 , the Value added percentage on , GDP and 2007 preparation 2018 amounted to approximately 12.8 GDP per consumer unit in the Value .  . This figure represents the highest percentage in over a decade .  . Purchasing behavior in the Value is the most lucrative beauty and personal care market in the world .

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

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

generated_template: This statistic displays the proportion of individuals who currently smoke cigarettes , cigars , cigarillos or a pipe in selected European countries in templateTitleDate[0] . In this period , templateXValue[0] had the highest proportion of templateTitle[1] in the EU with templateYValue[max] templateScale . Cigarette prices differ across templateTitleSubject[0] : the most expensive templateXLabel[0] in which to purchase cigarettes is the templateXValue[26] ( UK ) .
generated: This statistic displays the proportion of individuals who currently smoke cigarettes , cigars cigarillos or a pipe in selected European countries in 2013 .  . In this period , Belgium had the highest proportion of engines in the EU with 85 % .  . Cigarette prices differ across Search : the most expensive Country in which to purchase cigarettes is the Indonesia ( UK ) .

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

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

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] templateScale of people declared they templateXValue[6] for the templateTitle[0] time in that templateTitle[6] range . Approximately templateYValue[3] templateScale of young adults templateXValue[6] their templateTitle[0] templateTitle[1] templateTitle[2] in their early 20s .
generated: A survey conducted in 2015 reveals that most Italians Must check devices with parents/leave in common area before going to bed their U.S. parental digital What online accounts he/she can have the behavior of What he/she can post online for others to see and .  . More specifically , about 79 % of people declared they Must check devices with parents/leave in common area before going to bed for the U.S. time in that behavior range .  . Approximately 74 % of young adults Must check devices with parents/leave in common area before going to bed their U.S. parental digital in their early 20s .

Example 191:
titleEntities: {'Subject': ['Chained'], 'Date': ['2000', '2019']}
title: Chained consumer price index of all urban consumers 2000 to 2019
X_Axis['December', 'value']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Chained', 'Consumer', 'Price', 'Index', '(1999=100)']: ['144.73', '141.7', '139.55', '137.22', '134.79', '134.21', '133.51', '131.77', '129.84', '126.14', '124.54', '121.56', '121.3', '117.0', '114.4', '111.2', '107.8', '106.0', '103.9', '102.6']

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

generated_template: The statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] . In templateXValue[max] , around templateYValue[max] templateScale people of templateTitleSubject[0] templateTitle[1] were produced in the templateTitle[2] .
generated: The statistic shows the 20 Chained consumer the price index all in 2000 .  . In 2019 , around 144.73 million people of Chained consumer were produced in the price .

Example 192:
titleEntities: {'Subject': ['Texas'], 'Date': ['1990', '2018']}
title: Texas - Median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['59785', '59295', '58146', '56473', '53875', '53027', '51926', '49047', '47266', '47475', '46490', '46053', '43307', '41422', '41397', '39271', '40149', '40860', '38609', '38688', '35783', '35075', '33072', '32039', '30755', '28727', '27953', '27733', '28228']

gold: This statistic shows the median household income in Texas from 1990 to 2018 . In 2018 , the median household income in Texas amounted to 59,785 U.S. dollars .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYValue[idxmax(X)] templateYLabel[4] .
generated: This statistic shows the household income in Texas from 1990 to 2018 .  . In 2018 , the household income in Texas amounted to 59785 dollars .

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

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

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

Example 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 templateScale templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a state surplus ; a negative value , a state deficit . In templateXValue[6] , the state deficit of templateTitleSubject[0] was at around 29.98 templateScale templateYLabel[3] .
generated: 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 at around 29.98 billion euros .

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 templatePositiveTrend 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 templatePositiveTrend by more than five templateYLabel[0] templateYLabel[1] by the second half of templateXValue[11] .

generated_template: From templateXValue[17] to templateXValue[7] , household templateTitle[0] templateTitle[1] saw an overall templatePositiveTrend , peaking at templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh in the second half of templateXValue[7] . This templateNegativeTrend in the following years , but by the second half of templateXValue[1] templateTitle[1] had templatePositiveTrend to templateYValue[1] templateYLabel[0] templateYLabel[1] . On average , the templateTitleSubject[1] pays some of the highest templateTitle[0] templateTitle[1] in Europe .
generated: From 2010 S2 to 2015 S2 , household Electricity prices saw an overall increase , peaking at 30.88 Euro cents per kWh in the second half of 2015 S2 .  . This decreased in the following years , but by the second half of 2018 S2 prices had increased to 30.0 Euro cents .  . On average , the Germany pays some of the highest Electricity prices in Europe .

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] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic depicts the templateTitleSubject[0] ' real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateTitle[5] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] refers to the total market value of all goods and services that are produced within a country per templateXLabel[0] . It is an important indicator of the economic strength of a country .
generated: The statistic depicts the Cyprus ' real Gross domestic product ( GDP ) growth rate from 2014 to 2018 , with projections up until 2024 .  . GDP refers to the total market value of all goods and services that are produced within a country per Year .  . It is an important indicator of the economic strength of a country .

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

gold: An average of 15.4 million viewers tuned in to watch Thursday Night Football in the United States during the 2019 NFL season , an increase of half a million on the previous year . Ratings for Monday Night Football also increased in 2019 , with the average number of viewers rising to 12.6 million . Prime-time packages receive ratings boost Thursday Night Football is part of the prime-time television schedule that also includes Monday Night Football and Sunday Night Football – these NFL games are broadcast across the entire United States by one major national network .
gold_template: An templateTitle[4] of templateYValue[max] templateScale templateYLabel[0] tuned in to watch templateTitle[1] templateTitle[2] templateTitle[3] in the country during the templateXValue[max] templateXValue[idxmax(Y)] season , an templatePositiveTrend of half a templateScale on the previous templateXLabel[0] . Ratings for Monday templateTitle[2] templateTitle[3] also templatePositiveTrend in templateXValue[max] , with the templateTitle[4] number of templateYLabel[0] templatePositiveTrend to 12.6 templateScale . 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: This statistic presents the templateYLabel[0] of templateYLabel[1] templateTitle[2] of templateTitle[3] templateTitle[4] in templateXValue[min] , witha forecast until templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale templateYLabel[1] templateTitle[4] were templateTitle[2] of templateTitle[3] .
generated: This statistic presents the Viewers of millions Night of Football average in 2015 , witha forecast until 2019 .  . In 2015 , 12.43 millions average were Night of Football .

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

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

generated_template: This statistic shows the templateYLabel[3] of templateTitle[0] templateYLabel[1] to templateYLabel[2] ( templateTitle[0] penetration ) on the European domestic templateTitle[0] market between templateXValue[min] and templateXValue[max] . The templateYLabel[3] is used as an indicator of templateTitle[0] sector development . In templateXValue[max] , the templateYLabel[1] templateYLabel[3] to templateYLabel[2] was templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the millions) of U.S. enplanements to (in ( U.S. penetration ) on the European domestic U.S. market between 2004 and 2018 .  . The millions) is used as an indicator of U.S. sector development .  . In 2018 , the enplanements millions) to (in was 777.91 million .

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

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

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] templateScale templateYLabel[3] templateYLabel[4] in an optimistic scenario .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[5] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] amounted to reach templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Market size of the driving sensor components in the market between 2015 and 2030 .  . In 2030 , the Market size of driving sensor components global in the market amounted to reach 25.0 billion U.S. dollars .

Example 202:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: U.S. per capita consumption of oat products 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['4.8', '4.8', '4.6', '4.6', '4.5', '4.5', '4.7', '4.8', '4.7', '4.7', '4.7', '4.7', '4.7', '4.6', '4.7', '4.7', '4.5', '4.5', '4.4']

gold: The timeline shows the per capita consumption of oat products in the United States from 2000 to 2018 . The U.S. per capita consumption of oat products amounted to 4.8 pounds in 2018 .
gold_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[idxmax(Y)] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] ( shelled ) in the templateTitle[5] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] amounted to templateYValue[max] templateYLabel[3] in templateXValue[1] .
generated: The timeline shows the Per capita consumption of oat ( shelled ) in the products from 2000 to 2018 .  . The U.S. Per capita consumption of oat amounted to 4.8 pounds in 2017 .

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: This statistic presents the templateTitle[0] templateTitleSubject[0] templateTitle[1] templateTitle[2] on templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . With some 842,000 templateYLabel[0] templateYLabel[1] , templateXValue[0] was ranked first that year . The total number of templateYLabel[0] templateYLabel[1] was more than 8.7 templateScale units in templateTitleDate[0] .
generated: This statistic presents the Wealth U.S. UHNW U.S. UHNW on Number Ultra-High-Net-Worth in 2014 .  . With some 842,000 Number Ultra-High-Net-Worth , New York was ranked first that year .  . The total number of Ultra-High-Net-Worth was more than 8.7 million units in 2014 .

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] templateScale templateYLabel[3] templateYLabel[4] . Following closely behind was templateXValue[1] 's tour , which made templateYValue[1] templateScale templateYLabel[4] in templateYLabel[1] and sold over 1.15 templateScale 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] templateScale 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 million 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[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Samoa from 1999 to 2019 .  . In 2019 , the Unemployment rate in Samoa was at approximately 8.46 % .

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: This statistic shows the amount of templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[1] in templateTitleDate[0] , distinguished templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , templateTitle[1] investors from the templateXValue[0] invested templateYValue[max] templateScale templateYLabel[2] in templateTitleSubject[1] .
generated: This statistic shows the amount of Number H & in H M 2019 , distinguished openings Country .  . In 2019 , H investors from the Total invested 375 % store in H M .

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

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

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[3] , templateYLabel[1] and templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] people worked either part-time or full-time as templateTitle[1] templateTitle[4] , templateYLabel[1] or templateTitle[3] . Included are solo templateYLabel[1] without employees working part-time or full-time in their artistic trade .
generated: This statistic shows the Production of veneer U.S. , million and 2003 in the 2016 from 2003 to 2016 .  . In 2016 , 1082 people worked either part-time or full-time as veneer 2003 , million or U.S. . Included are solo million without employees working part-time or full-time in their artistic trade .

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] templateScale templateTitle[1] templateTitle[2] templateYLabel[0] as of templateXLabel[0] two templateXValue[0] .

generated_template: Over the end of templateXValue[0] , the templateYLabel[0] of templateTitle[0] templateTitle[1] ( including noodles ) in the UK amounted to 164 templateScale . This represents an templatePositiveTrend of over 20 templateScale compared to the previous templateXLabel[0] of the previous year . templateTitle[0] templateTitle[1] in the templateTitleSubject[0] The templateTitle[5] is a measure of utilized goods and personal services .
generated: Over the end of Q2 2016 , the Users of Number mobile ( including noodles ) in the UK amounted to 164 millions .  . This represents an increase of over 20 millions compared to the previous Quarter of the previous year .  . Number mobile in the United Kingdom Kingdom is a measure of utilized goods and personal services .

Example 209:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2018']}
title: Number of participants in sailing in the U.S. 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'participants', 'in', 'millions']: ['3.75', '3.97', '4.1', '4.1', '3.92', '3.92', '3.96', '3.73', '3.87', '4.34', '4.23', '3.79', '3.39']

gold: This statistic shows the number of participants in sailing in the United States from 2006 to 2018 . In 2018 , there were approximately 3.75 million participants in sailing in the U.S. , down from 3.97 million the previous year .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[2] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[idxmax(X)] templateScale templateYLabel[1] in templateTitle[2] in the templateTitleSubject[0] , down from templateYValue[1] templateScale 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[idxmax(X)] templateScale .
generated: This statistic shows the Number of participants in sailing 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] templateScale templateYLabel[2] in templateXValue[idxmax(Y)] . The templateYLabel[0] of French tourists in their country has templatePositiveTrend since the beginning of the 2010s and has already reached nearly templateYValue[1] templateScale templateYLabel[2] in templateXValue[1] . Similarly , the templateYLabel[0] of international tourists in templateTitleSubject[0] is estimated to grow in the coming years .

generated_template: This statistic shows the 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 templatePositiveTrend , reaching templateYValue[2] templateScale templateYLabel[2] in templateXValue[2] .
generated: This statistic shows the France 2012 of Domestic and travel to 2028 in France from 2012 to 2018 , with a forecast for 2028 .  . Over this period , the 2012 of the Domestic and travel industry to 2028 in France has increased , reaching 112.3 billion euros in 2017 .

Example 211:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1950', '2017']}
title: Deaths by unintentional drug overdose in the U.S. 1950 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1990', '1980', '1970', '1960', '1950']
Y_Axis['Death', 'rate', 'per', '100,000', 'population']: ['20.1', '18.2', '14.8', '13.1', '12.2', '11.5', '11.6', '10.6', '10.3', '10.2', '9.9', '9.2', '8.0', '7.2', '6.7', '6.1', '4.9', '4.5', '4.4', '3.9', '3.7', '3.5', '3.4', '2.3', '1.9', '2.8', '1.7', '2.5']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] templateTitle[3] templateTitle[4] were living below the templateYLabel[0] line in the templateTitle[5] . templateYLabel[0] is the state of one who lacks a certain amount of material possessions or money .
generated: This statistic shows the Death rate of U.S. drug overdose in the U.S. from 1950 to 2017 .  . In 2017 , 20.1 % of U.S. drug overdose were living below the Death line in the U.S. . Death is the state of one who lacks a certain amount of material possessions or money .

Example 212:
titleEntities: {'Subject': ['Number'], 'Date': ['2016']}
title: Number of deaths caused by majors droughts worldwide up to 2016
X_Axis['Country', 'and', 'Year']: ['China_(1928)', 'Bangladesh_(1943)', 'India_(1942)', 'India_(1965)', 'India_(1900)', 'Soviet_Union_(1921)', 'China_(1920)', 'Ethiopia_(May_1983)', 'Sudan_(April_1983)', 'Ethiopia_(December_1973)']
Y_Axis['Number', 'of', 'deaths']: ['3000000', '1900000', '1500000', '1500000', '1250000', '1200000', '500000', '300000', '150000', '100000']

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

generated_template: In templateTitleDate[0] , 'Un templateXValue[0] Paris ' from Guillaume templateXValue[0] was the bestselling book templateXLabel[0] in templateTitleSubject[0] . templateXValue[0] needs no introduction in templateTitleSubject[0] , a country where 24 templateScale of the inhabitants considered themselves as fervent readers . Guillaume templateXValue[0] and Elena templateXValue[8] : templateTitle[0] sellers `` Astérix templateXValue[2] Transitalique '' was published in both print and digital formats and templateYLabel[1] close to 1.6 templateScale copies in 2017 in templateTitleSubject[0] .
generated: In 2016 , 'Un China (1928) Paris ' from Guillaume China (1928) was the bestselling book Country in Number .  . China (1928) needs no introduction in Number , a country where 24 % of the inhabitants considered themselves as fervent readers .  . Guillaume China (1928) and Elena Sudan (April 1983) : Number sellers `` Astérix India (1942) Transitalique '' was published in both print and digital formats and deaths close to 1.6 million copies in 2017 Number .

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] templateScale 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[min] to the second templateXLabel[0] of templateTitleDate[max] . As of the last reported templateXLabel[0] , templateYValue[0] templateScale 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 percentage 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: From templateXValue[17] to templateXValue[7] , household templateTitle[0] templateTitle[1] saw an overall templatePositiveTrend , peaking at templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh in the second half of templateXValue[7] . This templateNegativeTrend in the following years , but by the second half of templateXValue[1] templateTitle[1] had templatePositiveTrend to templateYValue[1] templateYLabel[0] templateYLabel[1] . On average , the templateTitleSubject[1] pays some of the highest templateTitle[0] templateTitle[1] in Europe .
generated: From 2010 S2 to 2015 S2 , household Electricity prices saw an overall increase , peaking at 16.5 Euro cents per kWh in the second half of 2015 S2 .  . This decreased in the following years , but by the second half of 2018 S2 prices had increased to 15.11 Euro cents .  . On average , the Latvia pays some of the highest Electricity prices in Europe .

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: 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[3] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[3] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .
generated: This statistic shows the Electricity prices for household end users in Luxembourg semi-annually from 2010 S2 to 2019 S1 .  . In the second half of 2017 S2 , the average Electricity price for households was 16.18 Euro cents per kWh .

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

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

generated_template: There were templateYValue[0] reported templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateXValue[max] . This was a slight templatePositiveTrend compared to the previous templateXLabel[0] , but an templatePositiveTrend 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 233 reported road deaths in Switzerland 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 217:
titleEntities: {'Subject': ['Walmart'], 'Date': ['2006', '2019']}
title: Walmart 's operating income worldwide 2006 to 2019
X_Axis['Fiscal', 'year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Operating', 'income', 'in', 'billion', 'U.S.', 'dollars']: ['21.96', '20.44', '22.76', '24.11', '27.15', '26.87', '27.73', '26.49', '25.51', '23.97', '22.77', '21.92', '20.5', '18.69']

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

generated_template: templateTitleSubject[0] posted templateYLabel[0] templateYLabel[1] of around templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[max] , down from templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[1] . In the second time period , operating income of the bank templatePositiveTrend from 63.6 templateScale templateYLabel[3] templateYLabel[4] to 71 templateScale templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] bank templateTitleSubject[0] is a British banking and services company and one of the major players on the largest banking market worldwide .
generated: Walmart posted Operating income of around 21.96 billion U.S. dollars in 2019 , down from 20.44 billion U.S. dollars in 2018 .  . In the second time period , operating income of the bank increased from 63.6 billion U.S. dollars to 71 billion U.S. dollars .  . Walmart bank Walmart is a British banking and services company and one of the major players on the largest banking market worldwide .

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

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

generated_template: In templateXValue[max] , the templateTitleSubject[0] 's global templateTitle[0] amounted to templateYValue[0] templateYValue[idxmax(X)] templateYLabel[2] . The templateTitleSubject[0] Ltd. is a Swiss company which primarily manufactures watches . templateTitleSubject[0] The templateTitleSubject[0] was formed in 1983 through the merging of two Swiss watch manufacturers : ASUAG and SSIH .
generated: In 2018 , the BNP Paribas 's global Return amounted to 8.2 equity .  . The BNP Paribas Ltd. is a Swiss company which primarily manufactures watches .  . BNP Paribas The BNP Paribas was formed in 1983 through the merging of two Swiss watch manufacturers : ASUAG and SSIH .

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] templatePositiveTrend about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Slovakia from 1994 to 2018 , with projections up until 2024 .  . In 2018 , the average Inflation rate in Slovakia amounted to about 2.53 % compared to the previous 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 templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
generated: The statistic shows Gross domestic product ( GDP ) per capita in 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 positive change is an indicator 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 2019 , some templateYValue[max] templateScale of surveyed males had consumed templateTitle[0] or templateTitle[1] in the templateTitle[3] three templateTitle[5] in templateTitleSubject[0] . In comparison , templateYValue[min] templateScale 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 shows the templateYLabel[0] of adults in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateTitle[7] . During that period of time , templateYValue[max] templateScale of female templateYLabel[1] stated that they used the social networking site .
generated: This statistic shows the Share of adults in the Canada who were using Marijuana as of 2019 , sorted Canada by .  . During that period of time , 18.4 % of female respondents stated that they used the social networking site .

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] templateYLabel[2] templateYLabel[3] of templateTitle[5] templateTitle[6] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[2] week was templateYValue[idxmax(X)] templateYLabel[3] long .
generated: This statistic shows the Average price U.S. dollars of salons U.S. in the Average from 2006 to 2019 .  . In 2019 , the Average U.S. week was 29.91 dollars long .

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] templateScale templateYLabel[2] in templateXValue[max] . This figure represents a templateNegativeTrend compared to the peak reached by the templateTitle[3] in templateXValue[3] , when the templateYLabel[0] reported amounted to templateYValue[2] templateScale templateYLabel[2] . The reduction in templateYLabel[0] coincided with lower profits for the templateTitle[3] during the same period .

generated_template: In templateXValue[max] , the templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] Group reported a templateYLabel[0] of almost templateYValue[min] templateScale templateYLabel[2] . Despite the impressive figure , the templateYLabel[0] of templateXValue[idxmin(Y)] represented a templateNegativeTrend 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] templateScale templateYLabel[2] .
generated: In 2018 , the Italian fashion company Giorgio Group reported a Turnover of almost 1275 million euros .  . Despite the impressive figure , the Turnover of 2011 represented a decrease for Giorgio compared to the previous years .  . Indeed , in 2011 , the first Year considered in this graph , the Turnover of the company amounted to approximately 1702 million euros .

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

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

generated_template: This statistic shows the templateYLabel[0] in the templateYLabel[2] of automotive templateYLabel[1] per liter in templateTitle[3] templateTitle[4] for the period between 2018 and 2019 . The templateYLabel[2] changes exclude value added taxes from prices for automotive templateYLabel[1] . In 2019 , the templateYLabel[1] templateYLabel[2] per liter in the templateXValue[5] templateXValue[6] was 2.2 seven templateScale lower than it was in 2019 .
generated: This statistic shows the Value in the U.S. of automotive million per liter in Cup most for the period between 2018 and 2019 .  . The U.S. changes exclude value added taxes from prices for automotive million .  . In 2019 , the million U.S. per liter in the Costa Rica Peru was 2.2 seven million lower than it was in 2019 .

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] templateScale templateYLabel[2] ( or about 47.8 templateScale US dollars ) in templateYLabel[0] .

generated_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the templateXValue[max] fiscal templateXLabel[0] , templateTitleSubject[0] generated around templateYValue[max] templateScale templateYLabel[2] ( or about 27 templateScale U.S. dollars ) in templateYLabel[0] .
generated: This statistic shows Vinci Group revenue Group Revenue from the fiscal Year of 2009 to the fiscal Year of 2018 .  . In the 2018 fiscal Year , Vinci Group revenue generated around 43.52 billion euros ( or about 27 billion U.S. dollars ) in Revenue .

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] templateScale templateYLabel[2] . This is expected to templatePositiveTrend in templateXValue[1] to templateYValue[1] templateScale templateYLabel[2] .

generated_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 templatePositiveTrend , reaching templateYValue[2] templateScale templateYLabel[2] in templateXValue[2] .
generated: This statistic presents the Portugal 2012 of International and tourism to 2028 in Portugal from 2012 to 2018 , with a forecast for 2028 .  . Over this period , the 2012 of the International and tourism industry to 2028 in Portugal has increased , reaching 18.1 billion euros in 2017 .

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

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

generated_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateYLabel[0] templateYLabel[1] of the over-the-counter and templateTitle[8] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[5] , templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateYLabel[0] templateYLabel[1] of the templateTitle[6] over-the-counter and templateTitle[8] templateYLabel[0] is estimated to be templateYValue[6] templateScale .
generated: This statistic shows Johnson Johnson & 's Market share of the over-the-counter and market worldwide from 2013 to 2021 .  . In 2018 , Johnson Johnson & 's Market share of the care over-the-counter and market is estimated to be 3.85 % .

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] templateScale in all templateYLabel[2] .

generated_template: This statistic shows the estimated templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] partner was templateXValue[0] , accounting templateTitle[3] templateYValue[max] templateScale of all templateYLabel[2] .
generated: This statistic shows the estimated Main export partners Iran in 2017 .  . In 2017 , Iran 's Main export partner was Japan , accounting Iran 27.5 % 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[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

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

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

gold: Reddit is one of the world 's most popular websites and as of October 2019 , the United States generated 49.57 percent of desktop traffic to the forum site . Reddit in the United States In August 2019 , Reddit accounted for over less than one percent of social media website traffic in the United States , still ranking the platform one of the leading social networks based on visits . Founded in 2005 , Reddit is a discussion website which enables users to aggregate news by posting links and let other users vote and comment on them .
gold_template: Reddit is one of the world 's most popular websites and as of October templateTitleDate[0] , the templateXValue[0] generated templateYValue[max] templateScale of desktop templateYLabel[1] to the forum site . Reddit in the templateXValue[0] In 2019 , Reddit accounted for over less than one templateScale 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] accounted for templateYValue[max] templateScale of desktop templateYLabel[1] to the visual blogging site during this period of time .
generated: This statistic represents the regional Distribution of Reddit.com traffic in the last three months .  . As of October 2019 , the United States accounted for 49.57 % of desktop traffic to the visual blogging site during this period of time .

Example 231:
titleEntities: {'Subject': ['Green Bay Packers', 'NFL'], 'Date': ['2001', '2018']}
title: Revenue of the Green Bay Packers ( NFL ) 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['456', '434', '421', '391', '347', '299', '282', '276', '259', '242', '232', '218', '197', '194', '189', '138', '152', '132']

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

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

Example 232:
titleEntities: {'Subject': ['NFL'], 'Date': ['2001', '2018']}
title: Revenue of the Baltimore Ravens ( NFL ) 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['438', '417', '403', '378', '345', '304', '292', '279', '262', '255', '240', '226', '205', '201', '192', '172', '155', '148']

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

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] templateTitle[1] are owned by James Pohlad , who bought the franchise for 44 templateScale templateYLabel[2] templateYLabel[3] in 1984 .
generated: The statistic depicts the Revenue of the NFL Baltimore from 2001 to 2018 .  . In 2018 , the Revenue of the Major League Baseball franchise amounted to 438 million U.S. dollars.The NFL Baltimore are owned by James Pohlad , who bought the franchise for 44 million U.S. dollars in 1984 .

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] templatePositiveTrend on an annual basis . As of templateXValue[max] , it amounted to around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: templateTitleSubject[0] is a multinational pharmaceutical company based out of Basel , Switzerland . In templateXValue[max] , the company saw its greatest templateYLabel[0] of templateYLabel[1] in recent history with templateYValue[idxmax(X)] templateYLabel[1] working for the company . This is an templateNegativeTrend from templateXValue[9] when the company had over 125,000 templateYLabel[1] .
generated: Denmark is a multinational pharmaceutical company based out of Basel , Switzerland .  . In 2018 , the company saw its greatest GDP of million in recent history with 2245954 million working for the company .  . This is an decrease from 2017 when the company had over 125,000 million .

Example 234:
titleEntities: {'Subject': ['Malawi'], 'Date': ['2000']}
title: Malawi : internet penetration 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'individuals', 'using', 'the', 'internet']: ['13.78', '11.47', '5.3', '5.83', '5.05', '4.35', '3.33', '2.26', '1.07', '0.7', '0.97', '0.43', '0.38', '0.35', '0.28', '0.22', '0.16', '0.13']

gold: This statistic gives information on the internet penetration in Malawi from 2000 to 2017 . In 2017 , 13.78 percent of the population accessed the internet , up from 0.13 percent in 2000 . In 2015 , Malawi 's population amounted to 18.11 million inhabitants .
gold_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the population accessed the templateYLabel[3] , up from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] . In templateXValue[2] , templateTitleSubject[0] 's population amounted to 18.11 templateScale inhabitants .

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

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

gold: The statistic shows the infant mortality rate in South Sudan from 2008 to 2018 . In 2018 , the infant mortality rate in South Sudan was at about 63.7 deaths per 1,000 live births .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[min] templateYValue[idxmax(X)] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Infant mortality rate in 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] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] . In templateXValue[max] , total templateYLabel[0] templateNegativeTrend just above templateYValue[min] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the total 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] templateScale 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] templateScale of templateTitle[1] streamed on templateTitleSubject[0] templateTitle[3] in templateTitleDate[0] was via a connected TV .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[2] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , about templateYValue[1] templateScale of templateYLabel[1] templateYLabel[2] in the templateTitle[2] .
generated: This statistic shows the Share of time spent in the watching in 2017 , device Platform .  . In 2017 , about 15 % of time spent in the watching .

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] templateScale 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] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateYValue[0] templateScale 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 2019 , sorted group .  . During that period of time , 81 % 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 2019 , it was found that templateYValue[4] templateScale 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] templateScale .

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] templateScale of templateYLabel[1] used templateXValue[0] , while templateYValue[1] templateScale used templateXValue[1] . The least used framework was templateXValue[last] with only templateYValue[min] templateScale of templateYLabel[1] reporting to use it .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] among workers templateTitle[7] in templateTitleDate[0] . templateXValue[5] the survey , templateYValue[max] templateScale of templateYLabel[1] said templateXValue[10] salary , benefits and financial templateXValue[0] would influence them to choose one job over another . Employers are increasingly trying to attract employees templateXValue[4] wellness templateXValue[0] .
generated: This statistic shows the Most utilized frameworks among developers worldwide 2019 among workers 2019 in .  . React Native the survey , 49.9 % of respondents said Apache Spark salary , benefits and financial Node.js would influence them to choose one job over another .  . Employers are increasingly trying to attract employees Unity 3D wellness 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] templateScale templateYLabel[3] . That same templateXLabel[0] , templateTitleSubject[0] generated 27.55 templateScale 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] amounted to templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] . The global templateTitle[0] templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] was 3.92 templateScale templateYLabel[2] templateYLabel[3] .
generated: In 2019 , the SAP Net to businesses affected by a profit 2006 in the SAP amounted to 4083 3321 million euros , up from 4083 million euros in the previous Year .  . The global SAP Net profit 2006 was 3.92 million euros .

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

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

generated_template: The statistic presents the ranking of ten wealthiest templateTitle[1] in templateTitleSubject[0] as of 2014 . At that time , the richest woman in templateTitleSubject[0] was templateXValue[0] , the heiress of the French cosmetics and beauty company L'Oreal , with templateTitle[5] templateTitle[6] amounting to approximately templateYValue[max] templateScale templateYLabel[3] .
generated: The statistic presents the ranking of ten wealthiest selling in France as of 2014 .  . At that time , the richest woman in France was FIFA 19 , the heiress of the French cosmetics and beauty company L'Oreal , with gaming platforms amounting to approximately 1353.4 thousands .

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

Example 245:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1992', '2017']}
title: Luggage and leather goods store sales in the U.S. from 1992 to 2017
X_Axis['Year']: ['17', '16', '15', '14', '13', '12', '11', '10', '9', '8', '7', '6', '5', '4', '3', '2', '1', '0', '99', '98', '97', '96', '95', '94', '93', '92']
Y_Axis['Billion', 'U.S.', 'dollars']: ['33.92', '33.97', '34.14', '33.83', '33.21', '32.32', '31.25', '27.78', '27.46', '30.91', '32.77', '31.96', '30.13', '29.28', '27.17', '26.27', '25.2', '26.7', '25.38', '22.79', '21.12', '21.63', '20.28', '19.02', '17.59', '16.12']

gold: This timeline shows jewelry , luggage , and leather goods store sales in the United States from 1992 to 2017 . In 2017 , U.S. jewelry , luggage , and leather goods store sales amounted to about 33.92 billion U.S. dollars .
gold_template: This timeline shows jewelry , templateTitle[0] , and templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] templateTitle[6] templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[1] jewelry , templateTitle[0] , and templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] .

generated_template: This timeline shows templateTitle[0] and templateTitle[0] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[1] templateTitle[0] and templateTitle[0] templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] . According to the NAICS definition , companies operating in this sector are retailing templateTitle[0] and templateTitle[0] templateTitle[2] from fixed point-of-sale locations.The templateTitle[0] Industry in the templateTitleSubject[0] StatesIn 2012 , the global apparel market was valued at 1.7 templateScale templateYLabel[1] templateYLabel[2] and was estimated to employ approximately 75 templateScale people , making it templateXValue[16] of the largest industries in the world .
generated: This timeline shows Luggage and goods store sales in the U.S. from 1992 to 2017 .  . In 2017 , U.S. Luggage and goods store sales amounted to about 33.92 billion U.S. dollars .  . According to the NAICS definition , companies operating in this sector are retailing Luggage and 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 templateNegativeTrend from templateXValue[min] to templateXValue[max] , sinking from templateYValue[idxmin(X)] to templateYValue[idxmax(X)] units . Such a templateNegativeTrend in the templateYLabel[0] of templateYLabel[1] in the country might be due to the financial crisis of the years 2007 and 2008 , which hit templateTitleSubject[0] quite harshly also in the following years . During the same period , the templateYLabel[0] of bank branches also templateNegativeTrend steadily , and , as a consequence , the population size per bank branch templatePositiveTrend from 1,734 in 2008 to 2,067 in templateXValue[2] .

generated_template: In templateXValue[max] , the templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] Group reported a templateYLabel[0] of almost templateYValue[min] templateScale templateYLabel[2] . Despite the impressive figure , the templateYLabel[0] of templateXValue[idxmin(Y)] represented a templateNegativeTrend 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] templateScale templateYLabel[2] .
generated: In 2018 , the Italy banks 2011 2018 Group reported a Number of almost 505 million 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 million banks .

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

gold: In 2018 , the global trade value of goods exported throughout the world amounted to approximately 19.5 trillion U.S. dollars . In comparison , this figure stood at around 6.45 trillion U.S. dollars in 2000 . The rise in the value of goods exported around the world reflects developments in international trade , globalization , and advances in technology .
gold_template: In templateXValue[max] , the global templateTitleSubject[0] value of goods exported throughout the world amounted to approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . In comparison , this figure stood at around templateYValue[14] templateScale 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: This statistic shows that the templateTitle[0] templateYLabel[0] of templateYLabel[1] ( ALOS ) in templateTitleSubject[0] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[14] , a hospital templateYLabel[1] in the templateTitle[3] had an templateTitle[0] templateYLabel[0] of templateYValue[14] templateYLabel[2] . Since then , there was no significant change in the templateYLabel[0] of templateYLabel[1] .
generated: This statistic shows that the Trade Export of volume ( ALOS ) in Trade 1950 2018 from 1950 to 2018 .  . In 2000 , a hospital volume in the worldwide had an Trade Export of 6452.32 billion .  . Since then , there was no significant change in the Export of volume .

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] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the total global templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the British retailer generated approximately templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[0] .
generated: The statistic shows the total global Spending of Research development from 2010 to 2019 .  . In 2010 , the British retailer generated approximately 1911 million U.S. dollars of Research .

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 templateScale ( on average , women had 33 templateScale less opportunities than men in templateXValue[last] ) .

generated_template: In templateTitleDate[0] , templateXValue[0] was the templateTitleSubject[0] American templateXLabel[0] with the highest templateTitle[4] templateTitle[5] templateTitle[6] 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[4] templateTitle[5] templateTitle[6] of 54 templateScale ( on average , women 's income in templateXValue[last] was estimated to be 54 templateScale lower than men 's ) .
generated: In 2020 , Nicaragua was the Latin America American Country with the highest index 2020 by Index , with 0.81 points .  . Guatemala , on the other hand , had the worst score in the region with 0.67 points , which shows a index 2020 by of 54 % ( on average , women 's income in Guatemala was estimated to be 54 % lower than men '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[idxmax(X)] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] borne by one templateYLabel[4] while being of child-bearing age . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Fertility rate in Nepal from 2007 to 2017 The Fertility rate is the average Number of children borne by one woman while being of child-bearing age .  . In 2017 , the Fertility rate in Nepal amounted to 1.97 children per woman .

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

gold: This statistic shows the price development of rare earth oxide gadolinium oxide globally from 2009 to 2025 . It is expected that the price of gadolinium oxide will reach some 28,055 U.S. dollars per metric ton in 2017 .
gold_template: This statistic shows the templateYLabel[0] development of templateTitle[1] templateTitleSubject[0] templateTitle[3] templateTitleSubject[1] templateTitle[3] templateTitle[7] from templateTitleDate[min] 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 displays the templateYLabel[0] of rare earth templateTitle[2] templateTitle[1] templateTitle[2] templateTitle[4] from templateXValue[min] to templateXValue[max] . It is expected that the templateYLabel[0] of templateTitle[1] templateTitle[2] will reach some templateYValue[8] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[8] .
generated: This statistic displays the Price of rare earth gadolinium from 2010 to 2025 .  . It is expected that the Price of rare earth will reach some 28473 U.S. dollars per metric ton in 2018 .

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] templateScale 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 illustrates the templateXLabel[0] of templateTitle[1] in templateTitle[6] homes in templateTitleDate[0] . In that year , templateYValue[max] templateScale templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] had templateXValue[2] templateTitle[1] .
generated: The statistic illustrates 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[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYValue[idxmax(X)] templateYLabel[4] .
generated: This statistic shows the household income in Illinois from 1990 to 2018 .  . In 2018 , the household income in Illinois amounted to 70145 dollars .

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

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

generated_template: The statistic shows the average 25 templateTitle[0] at the templateTitle[6] templateTitle[8] templateTitle[9] templateTitleDate[0] in templateTitleSubject[0] according to their current templateYLabel[0] / templateTitle[4] templateYLabel[1] . templateXValue[0] of Argentina is the templateTitle[2] valued player , templateTitle[1] a templateTitle[4] templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] .
generated: The statistic shows the average 25 Number at the 2019 in U.S. according to their current Number / firms professionals .  . Deloitte of Argentina is the leading valued player , professionals a firms Number professionals of 73855 thousand professionals .

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

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

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the templateScale of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
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 % 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[idxmin(X)] templateScale people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Number of mobile internet users in Singapore from 2017 to 2023 .  . In 2017 , 4.3 millions people accessed the internet through their mobile phone .  . In 2023 , this figure is projected to amount 4.9 millions mobile phone internet users .

Example 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] templateScale 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 templatePositiveTrend 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] templateScale of the templateTitle[4] templateTitle[5] templateTitle[6] .
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 % of the gross domestic product .

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

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

generated_template: In templateTitleDate[0] , templateXValue[0] was ranked the leading templateTitle[0] market based on the templateTitle[0] templateTitleSubject[0] templateYLabel[0] , with an templateYLabel[0] templateYLabel[1] of templateYValue[max] . The templateTitle[0] templateTitleSubject[0] templateYLabel[0] measures on-the-ground trade templateTitle[0] templateTitleSubject[0] . The templateTitle[0] market in templateXValue[0] According to the templateTitle[0] templateTitleSubject[0] templateYLabel[0] , templateXValue[0] had the highest templateTitleSubject[0] in terms of infrastructure , customs and templateTitle[0] competence .
generated: In , Somalia was ranked the leading Terrorism market based on the Terrorism Number , with an Number kidnappings of 2527 .  . The Terrorism Terrorism Number measures on-the-ground trade Terrorism .  . The Terrorism market in Somalia According to the Terrorism Number , Somalia had the highest Terrorism in terms of infrastructure , customs and Terrorism competence .

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] templateScale of the templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[7] in templateYLabel[2] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[3] ) , with projections up until templateXValue[max] . The data relate to the entire Turkish state and include government templateYLabel[1] and the templateYLabel[1] of the provinces , municipalities , communes as well as social security . In templateXValue[6] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to around templateYValue[6] templateScale of the templateYLabel[3] .
generated: This statistic shows the National debt of Pakistan from 2014 to 2017 in relation to the gross domestic product ( GDP ) , with projections up until 2024 .  . The data relate to the entire Turkish state and include government debt and the debt of the provinces , municipalities communes as well social security .  . In 2018 , Pakistan 's National debt amounted to around 71.69 % 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] templateScale 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] templateScale 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 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 Azerbaijan from 1994 to 2024 .  . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year .  . GDP is an important indicator of a country 's economic power .

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] templateScale templateYLabel[2] , followed by templateXValue[1] with templateYValue[1] templateScale templateYLabel[2] produced .

generated_template: The statistic provides information on the templateYLabel[0] of adults templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] most of the time in selected countries templateTitle[3] as of 2019 . During the survey , templateYValue[30] templateScale of templateYLabel[1] from the templateXValue[19] templateXValue[30] stated that they trusted templateYLabel[4] most of the time , compared to templateYValue[3] templateScale of templateYLabel[1] in templateXValue[4] . The templateXValue[19] templateXValue[30] was among the several countries where less than templateYValue[27] templateScale of survey templateYLabel[1] considered templateYLabel[4] templateYLabel[5] generally trustworthy , ranking close to templateXValue[31] , templateXValue[32] and templateXValue[28] .
generated: The statistic provides information on the Volume of adults tonnes most of the time in selected countries Europe as of 2019 .  . During the survey , 10.08 thousand of from the Bosnia and Herzegovina Czechia stated that they trusted tonnes most of the time , compared to 1329.76 thousand of in Poland .  . The Bosnia and Herzegovina Czechia was among the several countries where less than 11.76 thousand of survey thousand considered tonnes generally trustworthy , ranking close to Slovenia , Latvia and Malta .

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 templateScale 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] templatePositiveTrend by templateYValue[idxmin(X)] templateScale in comparison to the previous templateXLabel[0] . The rate of templateTitle[1] platform templateYLabel[0] continued in all of the following years and the templateYLabel[0] rate reached templateYValue[idxmax(X)] templateScale in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateTitle[3] templateYLabel[0] held by templateTitleSubject[0] as a templateScale of templateTitle[1] templateTitle[2] templateYLabel[1] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] are forecast to account for templateYValue[idxmax(X)] templateScale of templateTitle[1] templateTitle[2] PC templateYLabel[1] in templateXValue[idxmax(Y)] , up from just templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] . In templateXValue[3] , Apple 's MacBook Air templateYLabel[1] are expected to total 8.4 templateScale devices .
generated: This statistic shows the worldwide Growth held by Growth as a percentage of crowdfunding platforms number from 2008 to 2012 .  . Growth are forecast to account for 60 % of crowdfunding platforms PC number in 2012 , up from just 38 % in 2008 .  . In 2009 , Apple 's MacBook Air number are expected to total 8.4 million devices .

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] templateScale templateYLabel[2] templateYLabel[3] , templatePositiveTrend from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[1] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] and flash templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[4] templateTitle[2] approximately templateYValue[last] templateScale templateYLabel[2] templateYLabel[3] worth of property and crop templateYLabel[0] across the templateTitle[5] .
generated: This statistic shows the Fresh Production worldwide 1990 and flash 1990 in the 2018 from 1990 to 2018 .  . In 2018 , 1990 production approximately 868.09 million metric tons worth of property and crop Production across the 2018 .

Example 265:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2013', '2019']}
title: Internet retail sales value trend monthly in the United Kingdom ( UK ) 2013 to 2019
X_Axis['Month']: ['Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16', 'Jun_16', 'May_16', 'Apr_16', 'Mar_16', 'Feb_16', 'Jan_16', 'Dec_15', 'Nov_15', 'Oct_15', 'Sep_15', 'Aug_15', 'Jul_15', 'Jun_15', 'May_15', 'Apr_15', 'Mar_15', 'Feb_15', 'Jan_15', 'Dec_14', 'Nov_14', 'Oct_14', 'Sep_14', 'Aug_14', 'Jul_14', 'Jun_14', 'May_14', 'Apr_14', 'Mar_14', 'Feb_14', 'Jan_14', 'Dec_13', 'Nov_13', 'Oct_13', 'Sep_13', 'Aug_13', 'Jul_13', 'Jun_13', 'May_13', 'Apr_13', 'Mar_13', 'Feb_13', 'Jan_13']
Y_Axis['Percentage', 'change']: ['12.4', '11.8', '11.6', '13.3', '13', '12.9', '12', '14.2', '16.1', '15.8', '21', '13', '16.5', '15.3', '13.6', '9', '10.1', '9.6', '13.6', '19', '20.1', '20.1', '17.1', '22.1', '24.3', '22.2', '16.1', '30.1', '28.4', '30', '25.8', '23.6', '17.8', '17.2', '19.6', '12.9', '11.6', '12.4', '13.8', '8.4', '14.9', '11.6', '14.4', '8.7', '13.2', '13.9', '9.4', '14.3', '12.5', '11.4', '19.3', '11.1', '19.7', '12', '10.1', '13.7', '14.8', '15', '16.6', '15', '9.1', '11.9', '11', '13.5', '17.5', '17.8', '17.4', '21.7', '10.2', '18.1', '10.7', '13.9', '18.8', '12.9', '12.8']

gold: This statistic displays the monthly trend of the value of retail internet sales in the United Kingdom ( UK ) from January 2013 to March 2019 . In March 2019 , retail sales increased by 12.4 percent .
gold_template: This statistic displays the templateTitle[5] templateTitle[4] of the templateTitle[3] of templateTitle[1] templateTitle[0] templateTitle[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from 2013 to 2019 . In 2019 , templateTitle[1] templateTitle[2] templatePositiveTrend by templateYValue[0] templateScale .

generated_template: Between 2014 and 2019 , the lowest templateYLabel[0] of templateTitle[0] raindays occurred in 2014 , when just templateYValue[18] templateYLabel[1] recorded 1mm or more of templateTitle[2] . The highest templateYLabel[0] of raindays was recorded in of the same year , at templateYValue[max] . Fourth quarter the wettest The wettest periods of the year tend to be the start and the end .
generated: Between 2014 and 2019 , the lowest Percentage of Internet raindays occurred in 2014 , when just 13.6 change recorded 1mm or more of sales .  . The highest Percentage of raindays was recorded in of the same year , at 30.1 . Fourth quarter the wettest periods of the year tend to be the start and the end .

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[idxmax(X)] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] among the templateTitleSubject[0] population was about templateYValue[0] templateYValue[idxmax(X)] .
generated: The statistic shows the Life expectancy at birth in the Germany from 2007 to 2017 .  . In 2017 , the average Life expectancy at birth among the Germany population was about 80.99 .

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[idxmax(X)] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] of land area .

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

Example 268:
titleEntities: {'Subject': ['European Union'], 'Date': ['2009', '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[idxmax(X)] 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 templateScale euros in templateXValue[1] .

generated_template: The statistic depicts the manufacturers ( templateTitle[0] ) templateYLabel[1] of sporting goods equipment , fitness equipment , templateTitle[3] apparel , athletic footwear , and templateTitle[3] licensed merchandise in the templateYLabel[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateYLabel[1] amounted to about templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . Sporting goods manufacturers templateTitle[0] templateYLabel[1] - additional information templateTitle[0] templateYLabel[1] of sporting goods in the templateYLabel[3] reached templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] , the highest figure to-date and a eight templateScale templateYLabel[3] dollar templatePositiveTrend on the figure from templateXValue[4] .
generated: The statistic depicts the manufacturers ( Number ) companies of sporting goods equipment , fitness equipment , manufacturers apparel , athletic footwear , and manufacturers licensed merchandise in the companies from 2009 to 2018 .  . In 2018 , Number companies amounted to about 171072 thousand companies .  . Sporting goods manufacturers Number companies - additional information Number companies of sporting goods in the companies reached 191459 million companies in 2010 , the highest figure to-date and a eight million companies dollar increase on the figure from 2014 .

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

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

generated_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] templateTitle[4] club of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[0] received a salary of templateYValue[max] templateScale 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[idxmax(X)] templateScale in the first quarter of templateXValue[max] in France . The templateYLabel[0] templateYLabel[1] refers to the amount of money , expressed as a templateScale or ratio , that a person deducts from his disposable personal income .

generated_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] to templateTitle[4] templateTitle[5] in templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] to templateTitle[4] templateTitle[5] amounted to approximately templateYValue[min] templateScale templateYLabel[1] templateTitle[2] , the lowest amount in the provided time interval .
generated: This statistic shows the total Savings of rate savings to 2010 2017 in French from 2010 to 2017 .  . In 2014 , the Savings of rate savings to 2010 2017 amounted to approximately 14 % rate savings , the lowest amount in the provided time interval .

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] templateScale templateYLabel[3] templateYLabel[4] .

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

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

generated_template: The statistic presents the templateTitleSubject[0] templateXValue[5] templateTitle[2] templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateYLabel[0] . With templateYLabel[0] amounting to over 16 templateScale templateYLabel[2] templateYLabel[3] in templateTitleDate[0] , templateXValue[0] McLennan templateXValue[0] topped the list of templateTitleSubject[0] global templateXValue[5] templateTitle[2] globally . templateXValue[5] templateTitle[2] – additional information An templateXValue[5] broker ( or templateXValue[5] agent ) is an templateXValue[5] specialist offering consulting templateXValue[8] both to individuals and commercial businesses .
generated: The statistic presents the U.S. Synchrony Financial financial service in 2018 , U.S. Revenue .  . With Revenue amounting to over 16 billion U.S. dollars in 2018 , Fannie Mae McLennan topped the list of U.S. global Synchrony Financial financial globally .  . Synchrony Financial financial – additional information An Synchrony Financial broker ( or Synchrony Financial agent ) is an Synchrony Financial specialist offering consulting Ally Financial both to individuals and commercial businesses .

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 templateScale 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] templateNegativeTrend by 1.3 templateScale from the previous templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] as listed by the National Crime Information Center ( NCIC ) from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] templateNegativeTrend slightly compared to previous years , with templateYValue[idxmax(X)] cases .
generated: This statistic shows the Percentage of change exports trade as listed by the National Crime Information Center ( NCIC ) from 1990 to 2019 .  . In 2019 , the Percentage of change exports trade decreased slightly compared to previous years , with -1.3 cases .

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] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[1] .

generated_template: This statistic shows the number of templateTitle[1] templateYLabel[0] per100,000 templateYLabel[3] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[idxmax(X)] templateYLabel[3] of templateYLabel[0] were consumed in the templateTitle[1] .
generated: This statistic shows the number of cement Consumption per100,000 tons worldwide from 2006 to 2016 .  . In 2016 , approximately 2395070 tons of Consumption were consumed in the cement .

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 templateScale British Pounds . During this year the government has templateYLabel[1] templateYValue[max] templateScale 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] templateScale pounds , the second highest templateTitle[3] templateTitle[8] in this fiscal year .

generated_template: This statistic shows the average templateYLabel[0] of templateTitle[4] of in the templateTitle[1] and templateTitle[2] templateTitle[3] in the United Kingdom ( templateTitleSubject[0] ) in templateTitleDate[0] , templateTitle[7] templateTitle[8] . On average , templateXValue[4] and templateXValue[3] templateXValue[4] templateXValue[0] were templateYValue[4] templateYLabel[1] old .
generated: This statistic shows the average Amount of United in the public and sector spending in the United Kingdom ( United Kingdom ) in , by function .  . On average , Defense and Other (including EU transactions Defense Social protection were 52 budgeted old .

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 2016 . According to the source , templateXValue[0] , the founder and chairman as well as CEO of Tencent , was templateYLabel[1] templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in the measured period . Overall , Tencent reported 70.84 templateScale Chinese yuan revenue from its online games in templateTitleDate[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[0] of templateYLabel[5] templateYLabel[2] in the templateTitle[6] as of templateTitle[7] templateTitleDate[0] . templateXValue[0] had the templateTitle[2] templateYLabel[0] of templateYLabel[5] templateYLabel[2] as templateYValue[max] templateScale of the templateXLabel[0] 's total templateYLabel[2] were templateYLabel[5] . In 2015 , the templateYLabel[5] restaurant chain Chipotle templateYLabel[5] Grill had 351 locations in templateXValue[2] , the templateTitle[2] number Chipotle templateYLabel[2] in the country .
generated: This statistic shows the Video game the industry Net of dollars billion in the 2016 as of 2016 .  . Ma Huateng (Tencent) had the industry Net of dollars billion as 21.9 billion of the Entrepreneur 's total billion were dollars .  . In 2015 , the dollars restaurant chain Chipotle dollars Grill had 351 locations in Shi Yuzhu (Giant Interactive) , the industry number Chipotle billion in the country .

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[idxmax(X)] templateScale .

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

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] templateScale .

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] of the templateYLabel[1] templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) adjusted for Purchasing Power Parity ( PPP ) from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] of templateYLabel[1] templateYLabel[2] ( in relation to PPP dollars ) amounted to about templateYValue[6] templateScale .
generated: 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 global GDP ( in relation to PPP dollars ) amounted to about 1.36 % .

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] templateScale of the templateTitleSubject[0] population was at templateTitle[1] of templateTitle[0] , defined by Statistics templateTitleSubject[0] as 60 templateScale of the average disposable income in the country . People with an income lower than this are considered poor . In templateXValue[max] , this definition matched nearly templateYValue[max] templateScale of the Belgians .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[1] is measured by the templateYLabel[0] of templateYLabel[1] templateYLabel[4] in the templateYLabel[3] templateYLabel[4] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[4] lived in cities .
generated: This statistic shows the Poverty of risk in Belgium from 2008 to 2018 .  . risk is measured by the At-risk-of-poverty of rate in the rate .  . In 2018 , 16.4 % of Belgium 's rate lived in cities .

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] templateScale . It was followed by templateXValue[1] , which was used by templateYValue[1] templateScale of templateYLabel[1] that year .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitle[2] in templateTitle[3] in the templateTitle[4] as forecasted by professional chefs templateXValue[0] templateTitleDate[0] . During the survey , templateYValue[max] templateScale of the templateYLabel[1] stated that templateXValue[0] ( e.g . iPad ) templateXValue[0] , templateXValue[0] and templateXValue[0] was the hottest templateTitle[2] trend in templateTitle[3] in templateTitleDate[0] .
generated: This statistic shows the Most popular in mobile payment in the services as forecasted by professional chefs Swish 2017 .  . During the survey , 75 % of the respondents stated that Swish ( e.g . iPad ) Swish , and Swish was the hottest mobile trend in payment 2017 .

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 templateScale . In the second templateXLabel[0] of templateXValue[0] , lab-created templateTitle[2] cost some 15.7 templateScale less than naturally occurring templateTitle[2] .

generated_template: The statistic illustrates the growth of templateTitle[1] templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] from the first templateXLabel[0] of templateXValue[last] to the fourth templateXLabel[0] of templateXValue[0] . In the second templateXLabel[0] of templateXLabel[0] , the social network 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic illustrates the growth of synthetic Price difference in the Q1 from the first Quarter of Q1 2016 to the fourth Quarter of Q3 2017 .  . In the second Quarter of , the social network 's Price difference amounted to -15.3 percent .

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

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

generated_template: The statistic shows the total templateTitleSubject[0] templateYLabel[0] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . The total templateYLabel[0] is the templateYLabel[1] that divides a templateTitle[3] value of 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 shows the total Capital Spending in the industry from 2000 to 2020 .  . The total Spending is the billion that divides a industry value of two numerically equal groups ; that is , half the people are younger than this billion and half are older .  . It is a single index that summarizes the billion distribution of a semiconductor .

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: Federally qualified templateTitleSubject[0] ( templateYLabel[1] ) are community-based templateTitleSubject[0] that are funded in part templateTitle[7] HRSA , a sector of Medicare and Medicaid , to treat underserved patients in their communities . As of templateTitleDate[0] templateXValue[4] had the largest templateYLabel[0] of templateTitleSubject[0] centers with templateYValue[max] templateTitleSubject[0] throughout the templateXLabel[0] . templateXValue[33] templateXValue[34] had the fewest templateYLabel[0] of templateTitleSubject[0] centers with just templateYValue[34] templateTitleSubject[0] throughout the templateXLabel[0] .
generated: Federally qualified U.S. ( forcible ) are community-based U.S. that are funded in part 2018 HRSA , a sector of Medicare and Medicaid , to treat underserved patients in their communities .  . As of 2018 New York had the largest Number of U.S. centers with 15505 U.S. throughout the State .  . Nebraska Alaska had the fewest Number of U.S. centers with just 1192 U.S. throughout the State .

Example 285:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019', '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[idxmax(Y)] . The templateTitle[5] has forecast that a templateNegativeTrend in templateYLabel[3] templateYLabel[0] will occur through templateXValue[max] , reaching templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] of templateTitle[0] templateTitle[1] equivalent templateTitle[3] templateYLabel[5] . This forecast is driven by templatePositiveTrend consumption of natural gas due to lower cost and templateYLabel[0] with higher efficiency .

generated_template: This statistic shows the estimated templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] to templateTitle[4] templateTitle[5] in templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] to templateTitle[4] templateTitle[5] amounted to approximately templateYValue[min] templateScale templateYLabel[1] templateTitle[2] , the lowest amount in the provided time interval .
generated: This statistic shows the estimated Emissions of metric emissions per to person U.S. in from 2019 to 2050 .  . In 2050 , the Emissions of metric emissions per to person U.S. amounted to approximately 12.5 billion metric emissions , the lowest amount in the provided time interval .

Example 286:
titleEntities: {'Subject': ['North America'], 'Date': ['2011', '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 shows the projected templateTitle[0] templateTitle[1] templateTitleSubject[0] templateYLabel[0] templateYLabel[1] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , it is estimated that the templateTitle[0] templateTitle[1] templateTitleSubject[0] industry will have a templateYLabel[0] templateYLabel[1] of around templateYValue[3] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the projected Online gaming North America Data volume in the North from 2011 to 2016 .  . In 2012 , it is estimated that the Online gaming North America industry will have a Data volume of around 37 million petabytes .

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] templateScale of templateYLabel[1] from the templateXValue[8] said that they had used templateTitleSubject[0] templateTitle[3] templateTitle[4] .

generated_template: This statistic shows the templateTitle[2] considered to be the templateTitleSubject[0] templateTitle[1] templateTitle[3] in templateTitleDate[0] , based on a survey conducted in six templateTitle[2] . Of templateYLabel[1] , templateYValue[max] templateScale viewed templateXValue[0] as a templateXLabel[0] with templateTitle[1] people , with the United Kingdom ( templateXValue[1] ) following at templateYValue[1] templateScale . In 2016 a similar report on templateTitleSubject[0] reputable templateTitle[2] also found templateXValue[0] to have one of the best reputations templateTitle[3] , next to Sweden .
generated: This statistic shows the usage considered to be the YouTube for in 2017 , based on a survey conducted in six usage .  . Of respondents , 97 % viewed Mexico as a Country with YouTube people , with the United Kingdom ( Brazil ) following at 95 % .  . In 2016 a similar report on YouTube reputable usage also found Mexico to have one of the best reputations for , next to Sweden .

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[idxmax(X)] templateScale .

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

Example 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] templateScale 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] , accounting templateTitle[3] templateYValue[max] templateScale 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 % 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] templateScale templateYLabel[4] dollars .

generated_template: The templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] aged templateTitle[4] templateYLabel[2] in the templateTitleSubject[0] has gradually templatePositiveTrend since the 1960s . Now templateTitleSubject[0] in the templateTitle[6] aged templateTitle[4] can expect to live templateYValue[22] more templateYLabel[2] on average . Women aged templateTitle[4] templateYLabel[2] can expect to live around 20.6 more templateYLabel[2] on average .
generated: The Television revenue for NCAA aged TV/television (in in the NCAA has gradually increased since the 1960s .  . Now NCAA in the 2013 aged TV/television can expect to live 112.44 more (in on average .  . Women aged TV/television (in can expect to live around 20.6 more (in on average .

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[idxmax(X)] templateScale .

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

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] ' 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 percentage of Connecticut ' 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[max] , templateTitleSubject[0] had an average of templateYValue[max] templateScale templateTitle[1] templateYLabel[2] templateYLabel[3] via templateYLabel[1] . These accounted for 59 templateScale of all templateTitle[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic gives information on the consolidated number of monthly templateYLabel[0] templateYLabel[2] templateYLabel[3] ( templateTitleSubject[1] ) across templateYLabel[1] 's online shopping properties from the fourth templateXLabel[0] of 2016 to the fourth templateXLabel[0] of templateTitleDate[0] . As of the last templateXLabel[0] of templateTitleDate[0] , templateYLabel[1] 's Chinese e-commerce properties had been accessed by around templateYValue[max] templateScale templateYLabel[2] .
generated: This statistic gives information on the consolidated number of monthly Number visiting members ( LinkedIn ) across mobile 's online shopping properties from the fourth Quarter of 2016 to the fourth Quarter of 2013 .  . As of the last Quarter of 2013 , mobile 's Chinese e-commerce properties had been accessed by around 63 millions visiting .

Example 294:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2020', '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] templateScale 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 templateScale templateYLabel[1] templateYLabel[2] each : The templateXValue[1] , templateXValue[2] , and templateXValue[3] templateTitle[1] templateYValue[1] templateScale , templateYValue[2] templateScale , and templateYValue[3] templateScale templateYLabel[1] templateYLabel[2] respectively .

generated_template: templateYLabel[1] are a popular choice in Europe when it comes to pet ownership , although we do n't see them outside as often as their canine friends . As shown in this statistic , templateXValue[0] and templateXValue[1] are the two countries leading the list of cat owners in the EU , with the former 's narrow win . While templateXValue[0] also ranks as the top EU templateXLabel[0] with the highest templateYLabel[0] of pet dogs , templateYLabel[1] still win in templateTitleSubject[0] households .
generated: Facebook are a popular choice in Europe when it comes to pet ownership , although we do n't see them outside as often their canine friends .  . As shown in this statistic , India and United States are the two countries leading the list of cat owners in the EU , with the former 's narrow win .  . While India also ranks as the top EU Country with the highest Number of pet dogs , Facebook still win in Facebook households .

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[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] are owned by Ken Kendrick , who bought the franchise for 238 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[14] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] templateTitle[1] are owned by James Pohlad , who bought the franchise for 44 templateScale templateYLabel[2] templateYLabel[3] in 1984 .
generated: The statistic depicts the Revenue of the Arizona Diamondbacks 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 Diamondbacks are owned by James Pohlad , who bought the franchise for 44 million U.S. dollars in 1984 .

Example 296:
titleEntities: {'Subject': ['Frequency'], '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] templateTitleDate[0] . During the survey , templateYValue[2] templateScale of templateYLabel[1] said that they ate templateTitle[2] templateTitle[3] templateXValue[2] per templateXValue[1] .

generated_template: This statistic presents the templateTitle[2] of templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] as of 2018 . During an online survey conducted by We Are Flint , it was found that templateYValue[0] templateScale of internet users in the templateTitleSubject[1] accessed the messenger app templateXValue[1] templateXValue[2] days .
generated: This statistic presents the fast of Frequency eating in the Frequency as of 2018 .  . During an online survey conducted by We Are Flint , it was found that 5.2 % of internet users in the Frequency accessed the messenger app A few times per week About once per week days .

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 templateScale of templateXValue[0] users in the templateTitle[0] who have a templateXValue[0] media templateTitle[4] as of 2018 . According to the findings , templateYValue[max] templateScale of templateYLabel[1] reported that templateXValue[0] of their templateXValue[0] accounts were templateXValue[0] , while templateYValue[3] templateScale of templateYLabel[1] stated the opposite saying templateXValue[3] of their templateXValue[0] accounts were templateXValue[0] at templateXValue[0] .

generated_template: This statistic shows the distribution of templateTitle[1] beliefs in templateXValue[1] in templateTitleDate[0] . Approximately 36 templateScale of people claimed that they were a templateXValue[3] denomination , with the majority of those belonging to the templateXValue[1] of templateXValue[1] . On the templateXValue[3] hand , over half of the respondent group said that they did n't identify themselves as having a templateXValue[last] .
generated: This statistic shows the distribution of social beliefs in Yes a few of my social meda accounts are private 2018 .  . Approximately 36 % of people claimed that they were a No none of my social media accounts are private denomination , with the majority of those belonging to the Yes a few of my social meda accounts are private of .  . On the No none of my social media accounts are private hand , over half of the respondent group said that they did n't identify themselves as having a Don't know/No opinion .

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 templateScale 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] templateScale templateYLabel[2] templateYLabel[3] of templateYLabel[0] .

generated_template: The statistic depicts templateTitleSubject[0] 's total templateYLabel[0] between templateXValue[min] and templateXValue[max] . templateTitleSubject[0] Oil Field Services Ltd is one of the world templateTitle[2] largest templateTitleSubject[0] oil and natural gas service companies . In templateXValue[max] , the company brought in a total templateYLabel[0] of around templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts Newmont Mining 's total Revenue between 2007 and 2018 .  . Newmont Mining Oil Field Services Ltd is one of the world 's largest Newmont Mining oil and natural gas service companies .  . In 2018 , the company brought in a total Revenue of around 7253 million U.S. dollars .

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

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

generated_template: In the fourth templateXLabel[0] of templateTitleDate[max] , revenues of AWS templatePositiveTrend templateYValue[min] templateScale compared to the preceding templateXLabel[0] . AWS is one of templateTitleSubject[0] 's strongest revenue segments , generating 25.66 templateScale U.S. dollars in 2018 net sales , up from 17.46 templateScale U.S. dollars in 2017 . AWS ranks as one of the most popular public cloud infrastructure and platform templateTitleSubject[0] running applications worldwide in 2018 , ahead off Azure and Google cloud templateTitleSubject[0] .
generated: In the fourth Quarter of 2019 , revenues of AWS increased 548.6 billion 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[idxmax(X)] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: The graph shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The graph shows the Fertility rate in Brazil from 2007 to 2017 .  . In 2017 , the Fertility rate in Brazil amounted to about 1.74 Number children born per .

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 templateTitleDate[min] and templateTitleDate[max] , templateTitle[7] templateTitle[8] . In templateXValue[last] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] is estimated to be templateYValue[min] templateScale in that period .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] templateTitle[5] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[0] templateTitle[2] templateXLabel[0] of templateTitleSubject[0] templateTitle[5] , with templateYValue[max] templateScale of templateTitleSubject[0] 's templateYLabel[0] coming from Germany.IKEAIKEA is an internationally known home furnishing retailer . It has grown rapidly since it was founded in 1943 .
generated: This statistic shows the Annual growth global generic of Annual 2009 in .  . In that year , Rest of world was the Annual global Country of Annual 2009 , with 19 % of Annual 's Average coming from Germany.IKEAIKEA is an internationally known home furnishing retailer .  . It has grown rapidly since it was founded in 1943 .

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[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[0] templateScale .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[0] templateScale .
generated: This statistic displays the Unemployment rate in Pennsylvania from 1992 to 2018 .  . In 2018 , Unemployment in Pennsylvania was 4.3 % .

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 templateTitle[2] 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] templateScale 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] templateScale units in templateXValue[max] .

generated_template: The statistic illustrates the templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] in the templateTitleSubject[0] amounted to reach templateYValue[idxmax(X)] templateScale .
generated: The statistic illustrates the Unit shipments of kitchen/laundry appliances in the unit from 2005 to 2017 .  . In 2017 , Unit shipments of kitchen/laundry appliances in the U.S. amounted to reach 64.61 millions .

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[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmin(Y)] and templatePositiveTrend to about templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateTitle[0] and templateTitle[1] templateYLabel[0] of templateTitle[3] , templateTitle[4] and templateTitle[5] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . templateTitle[0] and templateTitle[1] templateYLabel[0] of templateTitle[3] , templateTitle[4] and templateTitle[5] in the templateYLabel[2] were forecasted to reach templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmin(Y)] .
generated: This statistic shows the U.S. and tea Sales of total , wholesale and value in the 1990 from to 2018 .  . U.S. and tea Sales of total , wholesale and value in the U.S. were forecasted to reach 12.66 billion U.S. dollars in 1990 .

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] templateScale of the country 's templateTitle[3] templateTitle[4] templateTitle[5] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to 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] templateScale 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 % 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 2017 survey period , templateYValue[max] templateScale 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: According to a templateTitleDate[0] Statista survey , templateYValue[max] templateScale 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] or templateXValue[1] and listening to the templateXValue[2] . Headphone Market The retail value of global headphone shipments was forecast to reach 26.1 templateScale templateTitle[6] dollars in 2019 , a more than threefold templatePositiveTrend in templateYValue[min] years .
generated: According to a 2017 Statista survey , 72 % of the according respondents use their U.S. to Hacking by foreign governments .  . Interfering with elections by hacking the counting of ballots common uses of U.S. included Securing confidential intelligence reports or and listening to the Securing citizen records (ex. IRS filings) .  . Headphone Market The retail value of global headphone shipments was forecast to reach 26.1 billion according dollars in 2019 , a more than threefold increase in 8 years .

Example 307:
titleEntities: {'Subject': ['U.S.'], 'Date': ['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[idxmax(X)] 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] templateScale 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 templateNegativeTrend from templateYValue[max] templateScale of templateTitle[5] templateTitle[6] who templateTitle[2] the drug to be easy to obtain in the templateXLabel[0] templateXValue[idxmax(Y)] . templateTitleSubject[0] is a hallucinogenic drug that can alter the user 's thoughts , feelings , and perception of reality .
generated: In 2019 , around 32 % 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 % of 1995 2019 who refueling the drug to be easy to obtain in the Year 1997 .  . 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] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the 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] templateScale templateYLabel[2] templateYLabel[3] in templateTitle[2] and templateTitle[3] .

generated_template: This statistic reveals the templateYLabel[0] of Japanese templateTitle[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] Inc. is a U.S. drug retailing company , with three major segments : Pharmacy Services , Retail/LTC , and Corporate . templateTitleSubject[0] is headquartered in Woonsocket , Rhode Island .
generated: This statistic reveals the Expenditure of Japanese Teva from 2006 to 2019 .  . Teva Inc. is a U.S. drug retailing company , with three major segments : Pharmacy Services , Retail/LTC and Corporate .  . Teva is headquartered in Woonsocket , Rhode Island .

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

Example 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 templateScale templateYLabel[2] templateXValue[6] templateYLabel[4] templateYLabel[5] .

generated_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 82.16 templateScale templateYLabel[2] templateXValue[6] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the gross domestic product ( GDP ) of New Brunswick from 2000 to 2018 .  . In 2018 , New Brunswick 's GDP was 82.16 million chained 2012 Canadian dollars .

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: In templateTitleDate[0] , templateXValue[0] was the city with the largest templateYLabel[0] of tourists staying at least templateYValue[8] night templateYLabel[2] capita . That same year , international overnight visitor spending in templateXValue[0] reached 28.5 templateScale US dollars . The city , which is the largest and most populous city in the United Arab Emirates , was also templateYValue[8] of the most expensive holiday destinations in the world in 2018 .
generated: In 2014 , China was the city with the largest Number of tourists staying at least 59300 night employees capita .  . That same year , international overnight visitor spending in China reached 28.5 billion US dollars .  . The city , which is the largest and most populous city in the United Arab Emirates , was also 59300 of the most expensive holiday destinations in the world in 2018 .

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] templatePositiveTrend its templateTitle[2] templateTitle[3] templateTitle[4] ( templateYLabel[0] ) from templateYValue[min] templateScale in templateXValue[idxmin(Y)] to templateYValue[0] templateScale in templateXValue[max] . templateYLabel[0] are calculated by the total number of seats multiplied by the total distance traveled . When describing the airline industry , people consider templateYLabel[0] as a measure of capacity .

generated_template: This statistic shows templateTitle[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateTitle[0] templateYLabel[1] numbers to the templateTitleSubject[0] templatePositiveTrend by 6.3 templateScale , while it was expected to grow by templateYValue[0] templateYValue[idxmax(X)] in templateXValue[max] . By 2020 , the templateTitleSubject[0] is expected to have around 4.3 templateScale templateYLabel[2] of templateTitle[0] tourism .
generated: This statistic shows Southwest billions in the Southwest Airlines from 2011 to 2019 .  . In 2016 , Southwest billions numbers to the Southwest Airlines grew by 6.3 billions , while it was expected to grow by 157.25 in 2019 .  . By 2020 , the Southwest Airlines is expected to have around 4.3 billions of Southwest tourism .

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[idxmax(X)] 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 templateXValue[max] . Since templateXValue[min] , this figure had been steadily templatePositiveTrend up until the templateXLabel[0] templateXValue[10] in which it also reached its peak with around 213 thousand templateTitle[1] templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitle[7] . From templateXValue[10] on , the templateYLabel[0] of casino templateTitle[5] templateTitle[6] in templateTitleSubject[0] has been going down continuously .
generated: In the U.S. state of U.S. , there were approximately 52 thousand recalls children and 's products U.S. 2001 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 templateScale of the total templateXLabel[1] living in templateXLabel[0] areas in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , templateYValue[max] templateScale of the total templateXLabel[1] of templateTitleSubject[0] was living in templateXLabel[0] areas .

generated_template: While nearly two-thirds of templateTitleSubject[0] remains rural , templateTitle[0] has grown by seven templateScale from templateXValue[last] to templateXValue[0] . Political reforms in the 1980s created greater economic stability in templateTitleSubject[0] in the decade that followed and led to higher templateTitle[0] . The templatePositiveTrend 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 the decade that followed and led to higher Urbanization .  . The increasing total) of Botswana , as well 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[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale . templateTitleSubject[0] 's population and economy The Republic of templateTitleSubject[0] is a sovereign state archipelago in Southeast Asia , composed of more than 17,000 islands .
generated: This statistic shows the Unemployment rate in Sudan from 1999 to 2019 .  . In 2019 , the Unemployment rate in Sudan was at approximately 13 % .  . Sudan 's population and economy The Republic of Sudan is a sovereign state archipelago in Southeast Asia , composed of more than 17,000 islands .

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[idxmax(X)] templateScale .

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

Example 318:
titleEntities: {'Subject': ['Google'], 'Date': ['2017', '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 2013 , templateTitleSubject[0] acquired social traffic app templateXValue[5] for templateYValue[4] templateScale templateYLabel[2] templateYLabel[3] . The internet templateXLabel[0] 's most expensive acquisition was templateXValue[0] in 2011 , tallying templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the member states of the European Union templateTitle[2] the highest templateYLabel[0] templateTitle[4] templateTitle[5] templateYLabel[1] in templateTitleDate[0] . In the templateXValue[0] ( UK ) , approximately templateYValue[max] templateScale templateYLabel[1] were reported from the templateTitle[4] templateTitle[5] segment , making the UK Europe 's leading member state in terms of templateTitle[4] templateTitle[5] templateYLabel[1] . The dawn of templateTitle[4] payments The practice of paying without cash has its roots in the templateXValue[0] States and was first used in the 1940s as a means of paying for automobile fuel .
generated: The statistic shows the member states of the European Union acquisitions the highest Price Google 2017 million in 2017 .  . In the Motorola Mobility (2012) ( UK ) , approximately 12500.0 million were reported from the Google 2017 segment , making the UK Europe 's leading member state in terms of Google 2017 million .  . The dawn of Google payments The practice of paying without cash has its roots in the Motorola Mobility (2012) States and was first used in the 1940s as a means of paying for automobile fuel .

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] 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] 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 templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in Malawi 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 321:
titleEntities: {'Subject': ['UK'], 'Date': ['2012', '2019']}
title: Number of female directors in FTSE 100 companies UK 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'female', 'directors']: ['292', '264', '259', '244', '233', '205', '169', '141']

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

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the report , there were 1.58 templateScale templateYLabel[1] who had personal wealth of over 10 templateScale yuan in templateTitleSubject[0] in templateXValue[1] , templateNegativeTrend from 1.61 templateScale in the previous templateXLabel[0] .
generated: This statistic presents the Number of female at UK from 2012 to 2019 .  . According to the report , there were 1.58 million female who had personal wealth of over 10 billion yuan in UK 2018 , decreased from 1.61 million in the previous Year .

Example 322:
titleEntities: {'Subject': ['Louis Cardinals'], 'Date': ['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] templateScale templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitleSubject[0] are owned by William DeWitt Jr. , who bought the templateYLabel[0] for 150 templateScale templateYLabel[3] templateYLabel[4] in 1996 .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Dee and Jimmy Haslam .
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 Dee and Jimmy Haslam .

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] templateScale of templateYLabel[1] stated that they think having an templateTitle[5] is templateXValue[0] , while 48 templateScale considered it templateXValue[0] templateXValue[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitleSubject[0] in the templateTitle[4] as of templateTitle[5] templateTitleDate[0] . During the survey , templateYValue[max] templateScale of Millennial templateYLabel[1] said they used templateXValue[0] for templateTitle[0] templateTitle[1] .
generated: This statistic shows the Americans ' moral of Americans in the towards as of abortion 2018 .  . During the survey , 49 % of Millennial respondents said they used Morally acceptable for Americans ' .

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 templateScale templateYLabel[4] .

generated_template: This statistic displays the proportion of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] only ( excludes templateYLabel[1] templateYLabel[2] templateYLabel[3] both templateYLabel[4] templateYLabel[5] and eyeglasses ) in templateTitle[6] templateTitleSubject[0] templateTitle[8] in templateTitleDate[0] . In this year , templateXValue[1] , templateXValue[2] and templateXValue[0] had the highest proportion of templateYLabel[1] wearing templateYLabel[4] templateYLabel[5] with approximately templateYValue[max] templateScale doing so . This was followed by templateXValue[3] and templateXValue[4] with templateYValue[3] templateScale of the respective populations wearing templateYLabel[4] templateYLabel[5] .
generated: This statistic displays the proportion of budgetary balances billion euros only ( excludes budgetary balances billion both euros and eyeglasses ) in member EU state in 2017 .  . In this year , Greece Romania and Poland had the highest proportion of budgetary wearing euros with approximately 8.57 billion doing so .  . This was followed by Hungary and Czech Republic with 3.14 billion of the respective populations wearing euros .

Example 325:
titleEntities: {'Subject': ['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 templateNegativeTrend by 1.9 templateScale in templateTitleSubject[0] over a 12-week templateXLabel[2] templateXLabel[3] 12 , templateTitleDate[max] 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 templateScale .

generated_template: The statistic shows the templateScale of templateTitle[0] that involved templateTitle[2] in the templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , about templateYValue[1] templateScale of templateYLabel[1] were committed with use of templateTitle[2] in templateXValue[1] . A ranking of the total number of templateTitle[0] templateTitle[5] templateTitleSubject[0] templateXLabel[0] can be found here .
generated: The statistic shows the percentage of Waitrose that involved growth in the year-on-year in 2015 , Britain 12 .  . In 2015 , about - percentage of growth were committed with use of growth in 16 Jun 19 .  . A ranking of the total number of Waitrose Britain Great Britain 12 can be found here .

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[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the poverty rate in Louisiana from 2000 to 2018 .  . In 2018 , about 18.6 percentage of Louisiana 's population lived below the poverty 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] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the highest templateTitle[0] templateTitle[1] was in templateTitleSubject[0] stood at templateYValue[max] templateYLabel[0] templateYLabel[1] 100,000 residents .
generated: This statistic shows the Business climate in June 2019 , Months .  . In 2019 , the highest Business climate was in June stood at 64.7 Index points 100,000 residents .

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 2017 . During the survey , templateYValue[max] templateScale of the templateYLabel[1] stated they most often read the templateXValue[0] Version of the templateXValue[10] . During the same survey , 32 templateScale of templateYLabel[1] stated that they had never read the templateXValue[10] , whilst 16 templateScale stated that they read the templateXValue[10] every day .

generated_template: The statistic shows which templateTitle[1] templateTitle[2] are templateTitleSubject[0] to U.S. survey templateYLabel[1] templateTitle[3] templateTitle[4] a templateTitle[5] templateXValue[10] in templateTitleDate[0] . During the survey , templateYValue[1] templateScale of templateYLabel[1] cited templateXValue[1] as templateTitleSubject[0] templateTitle[3] templateTitle[4] a templateTitle[5] templateXValue[10] .
generated: The statistic shows which Bible version are Bible U.S. to U.S. survey respondents U.S. 2017 a New American Bible in 2017 .  . During the survey , 13 % of respondents cited New International Version as Bible U.S. U.S. 2017 a New American Bible .

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] 2017 to 2018 . The templateYLabel[0] is composed of several different indices , including an assessment of one 's personal financial situation . In 2018 , Brazil 's templateTitle[0] templateTitle[1] was at templateYValue[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[last] points .
generated: This statistic shows the Consumer confidence index ( June ) of the 2019 from 2018 to 2019 .  . In 2019 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 templateTitleSubject[0] Education templateTitle[0] templateXValue[0] ranking for templateTitleDate[0] , sorted templateTitle[3] templateYLabel[0] templateYLabel[1] . templateXValue[0] was placed first as the templateXValue[0] with the highest templateYLabel[0] templateYLabel[1] worldwide , at templateYValue[max] .
generated: This statistic shows the Canada Education Forestry fishing mining quarrying oil and gas ranking for 2019 , sorted hours Average usual .  . Forestry fishing mining quarrying oil and gas was placed first as the Forestry fishing mining quarrying oil and gas with the highest Average usual worldwide , at 45.0 .

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] templateScale . Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitleSubject[0] templateTitle[6] templateTitle[7] templateYLabel[1] templateNegativeTrend by templateDelta[0,4] templateScale .

generated_template: In templateTitleDate[0] , there were about templateYValue[0] templateScale templateYLabel[1] ( here classified as people living in a country in which they were not born in ) in templateTitleSubject[0] out of a total population of about 5.7 templateScale . templateYLabel[1] made up a significant share of the population and play a crucial role in templateTitleSubject[0] , where the birth rate was among the lowest in the world . templateTitleSubject[0] 's immigrant population The immigrant population of templateTitleSubject[0] made up slightly more than 40 templateScale of the total population of the country in 2018 .
generated: In 2004 , there were about 27.3 % market ( here classified as people living in a country in which they were not born in ) U.S. out of a total population of about 5.7 % .  . market made up a significant share of the population and play a crucial role in U.S. , where the birth rate was among the lowest in the world .  . U.S. 's immigrant population The immigrant population of U.S. made up slightly more than 40 % of the total population of the country in 2018 .

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 templateTitleDate[0] , Azerbaijan exported roughly templateYValue[max] templateScale templateYLabel[2] of templateTitle[2] templateTitle[3] to templateTitleSubject[0] , establishing itself as the major templateTitle[2] templateTitle[3] supplier for the templateXLabel[0] . templateXValue[1] followed with templateYValue[1] templateScale templateYLabel[2] . The amount of templateTitle[2] templateTitle[3] templateTitle[4] from other suppliers was lower , during the first quarter of templateTitleDate[0] .

generated_template: The statistic shows the 20 templateTitle[0] templateTitle[1] the templateTitle[2] templateTitle[3] of the templateYLabel[1] templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[2] ) based on Purchasing Power Parity ( PPP ) in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[4] had an estimated templateYLabel[0] of about templateYValue[4] templateScale of the templateYLabel[1] templateYLabel[2] .
generated: The statistic shows the 20 Italy volume the crude oil of the volume Q1 2018 by ( tons ) based on Purchasing Power Parity ( PPP ) in 2018 .  . In 2018 , Saudi Arabia had an estimated Import of about 1825182 % of the volume tons .

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[idxmax(X)] templateScale in templateXValue[max] .

generated_template: The templateYLabel[0] of templateYLabel[1] of the templateTitleSubject[0] of templateTitleSubject[0] ( NAR ) has steadily templatePositiveTrend since templateXValue[7] , reaching a membership of templateYValue[max] templateScale in templateXValue[idxmax(Y)] , an all-time high . This trend also reflects the recovery of the property market after the financial crisis of 2007 - templateXValue[min] , as the volume of home sales began to climb from templateXValue[8] . The NAR is a North American trade templateTitleSubject[0] for real estate workers formed in 1908 and currently based in Chicago , Illinois .
generated: The Return of equity the HSBC of ( NAR ) has steadily increased since 2012 , reaching a membership of 10.9 % in 2011 , an all-time high .  . This trend also reflects the recovery of the property market after the financial crisis of 2007 - 2009 , as the volume of home sales began to climb from 2011 .  . The NAR is a North American trade HSBC for real estate workers formed in 1908 and currently based in Chicago , Illinois .

Example 334:
titleEntities: {'Subject': ['FourFourTwo', '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 total templateYLabel[0] of templateTitle[1] and templateTitle[2] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the total templateYLabel[0] of templateTitle[1] and templateTitle[2] in the templateTitle[3] amounted to templateYValue[1] templateScale templateYLabel[2] .
generated: This statistic shows the total AIR of FourFourTwo and magazine in the United from 2006 to 2016 .  . In 2015 , the total AIR of FourFourTwo and magazine in the United amounted to 469 thousands .

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

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

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , athletes from templateYValue[idxmax(X)] templateYLabel[2] participated in the templateTitleSubject[0] in Rio de Janeiro , Brazil .
generated: This statistic shows the Passenger of journeys millions in the Manchester Metrolink from 1992 to 2019 .  . In 2019 , athletes from 8.1 millions participated in the Manchester Metrolink in Rio de Janeiro , Brazil .

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] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] , when it was just recovering from a slump below the 0-percent-mark in templateXValue[9] . Political turmoil begets economic turmoil In templateXValue[10] , after a coup d'etat following months of political crisis , the Thai military took over the country , and the senate and government were dissolved . As a result , templateTitleSubject[0] 's economy experienced a sudden downturn , GDP growth and templateYLabel[0] slumped , while unemployment , which is usually delayed in reflecting economic struggles , has been templatePositiveTrend ever since .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Thailand from 2004 to 2018 , with projections up until 2024 .  . In 2018 , the average Inflation rate in Thailand amounted to about 1.06 % 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 templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
generated: The statistic shows Gross domestic product ( GDP ) per capita in 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 positive change is an indicator 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] templateScale 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: In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateYLabel[1] were committed templateTitle[1] templateTitle[2] in the templateTitle[3] . While this figure has remained relatively steady since templateXValue[min] , it has been templatePositiveTrend since templateXValue[2] . Firearms in the templateTitle[3] Firearms in the templateTitle[3] have become a large part of American culture .
generated: In 2018 , 6.1 thousands of hectoliters were committed wine produced in the Portugal .  . While this figure has remained relatively steady since 2006 , it has been increasing since 2016 .  . Firearms in the Portugal Firearms in the Portugal have become a large part of American culture .

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

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 2017 , 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 2017 .

generated_template: As of templateTitleDate[0] , there were 52 templateTitle[2] templateYLabel[1] templateTitle[4] templateTitle[5] worldwide , with the majority being built in templateXValue[0] . Comparatively , there were 186 templateTitle[2] templateYLabel[1] shut down permanently as of that year . The templateXValue[3] templateXValue[9] had permanently shut down 37 templateTitle[2] facilities as of 2020 .
generated: As of 2017 , there were 52 platforms offerings 2017 by worldwide , with the majority being built in Wefunder .  . Comparatively , there were 186 platforms offerings shut down permanently as of that year .  . The uFunding Jumpstart Micro had permanently shut down 37 platforms facilities as of 2020 .

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[idxmin(X)] templateYLabel[3] templateYLabel[4] and templatePositiveTrend to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] by templateXValue[max] .

generated_template: According to a survey conducted by the Organization for Economic Cooperation and Development ( OECD ) , the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] steadily templateNegativeTrend between the years templateXValue[min] and templateXValue[4] , going from templateYValue[idxmin(X)] to templateYValue[min] within twelve years . Nevertheless , this trend was abruptly reverted during the first templateXLabel[0] of the Hollande Presidency : the templateYLabel[0] of templateYLabel[1] jumped from templateYValue[min] in templateXValue[idxmin(Y)] to templateYValue[max] in templateXValue[idxmax(Y)] . The templateYLabel[0] of Public templateYLabel[1] in templateTitleSubject[0] have templatePositiveTrend during Hollande 's presidency During the first templateXLabel[0] of the former French president François Hollande Presidency , the templateYLabel[0] of publicly owned templateYLabel[1] in templateTitleSubject[0] also templatePositiveTrend at an abrupt pace , going from 928 in templateXValue[4] to 1,458 templateYLabel[1] in templateXValue[3] .
generated: According to a survey conducted by the Organization for Economic Cooperation and Development ( OECD ) , the Value of per in U.S. steadily decreased between the years 2001 and 2013 , going from 100 to 92 within twelve years .  . Nevertheless , this trend was abruptly reverted during the first Year of the Hollande Presidency : the Value of per jumped from 92 in 2002 to 221 in 2012 .  . The Value of Public per in U.S. have increased during Hollande 's presidency During the first Year of the former French president François Hollande Presidency , the Value of publicly owned per in U.S. also increased at an abrupt pace , going from 928 in 2013 to 1,458 per in 2014 .

Example 342:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '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 templateScale 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 templateScale of templateYLabel[1] templatePositiveTrend continuously over the remainder of the period to a peak in templateXValue[2] .

generated_template: The total templateTitle[0] expenditure in templateTitleSubject[0] in templateTitleDate[0] accounted for approximately templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's Gross Domestic Product ( templateYLabel[1] ) that templateXLabel[0] . This was the result of the steepest templatePositiveTrend in the past ten years and was the first time templateTitle[0] templateTitle[1] as a share of the templateYLabel[1] exceeded templateYValue[max] templateScale . This share saw a continuous templatePositiveTrend over the past decade , indicating that as the templateYLabel[1] templatePositiveTrend , templateTitle[0] templateTitle[1] templatePositiveTrend at an even faster rate .
generated: The total United expenditure in United Kingdom 2000 accounted for approximately 35.2 percentage of United Kingdom 's Gross Domestic Product ( GDP ) that Year .  . This was the result of the steepest increase in the past ten years and was the first time United Kingdom as a share of the GDP exceeded 86.5 percentage .  . This share saw a continuous increase over the past decade , indicating that as the GDP grew , United Kingdom grew at an even faster rate .

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

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] templateScale templateYLabel[2] by the end of templateXValue[0] .

generated_template: templateTitleSubject[0] generated a templateYLabel[0] of templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in the fourth templateXLabel[0] of templateXValue[0] , the multinational templateXLabel[0] of templateXValue[0] . templateTitleSubject[0] templateTitle[1] global revenue templatePositiveTrend to 260.17 templateScale in the fourth templateXLabel[0] of templateXValue[0] . Founded in 1976 in California , templateTitleSubject[0] has risen in Minneapolis .
generated: Information Technology generated a Revenue of 13.54 billion euros in the fourth Quarter of Q3 2019 , the multinational Quarter of Q3 2019 .  . Information Technology Technology global revenue grew to 260.17 billion in the fourth Quarter of Q3 2019 .  . Founded in 1976 California , Information Technology has risen in Minneapolis .

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] templateScale 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: In the third templateXLabel[0] of templateXValue[0] , templateTitleSubject[0] announced that over templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] were using the social networking platform to promote their products and services , up from templateYValue[2] templateScale templateYLabel[3] in the first templateXLabel[0] of the previous year . templateTitleSubject[0] advertising revenue templateTitleSubject[0] generates the vast majority of its revenues through advertising . In 2018 , the social network 's ad revenue amounted to over 55 templateScale U.S. dollars , compared to merely 825 templateScale payments and other fees revenue .
generated: In the third Quarter of Q4 2016 , U.S. announced that over 1 % growth were using the social networking platform to promote their products and services , up from 0.9 % growth in the first Quarter of the previous year .  . U.S. advertising revenue U.S. generates the vast majority of its revenues through advertising .  . In 2018 , the social network 's ad revenue amounted to over 55 million U.S. dollars , compared to merely 825 million payments and other fees revenue .

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 2018 . During the measured period , the templateXValue[0] accounted for templateYValue[max] templateScale of templateYLabel[1] audiences . templateXValue[1] was ranked second with a templateYValue[1] templateScale templateYLabel[0] .

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] templateYLabel[3] spent on templateTitle[1] apps templateTitle[7] smartphone users in Southeast Asian countries in 2016 . During the survey period , it was found that smartphone users in templateXValue[3] spent an templateYLabel[0] of 66 templateYLabel[4] every day on templateTitle[1] apps .
generated: This statistic presents the Share Twitter users spent on user apps 2018 smartphone users in Southeast Asian countries in 2016 .  . During the survey period , it was found that smartphone users in United Kingdom spent an Share of 66 users every day on user apps .

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: This statistic represents the distribution of templateTitleSubject[0] templateYLabel[0] templateYLabel[1] in templateTitleSubject[1] in templateTitleDate[0] , broken down templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to about templateYValue[max] templateScale templateYLabel[3] .
generated: This statistic represents the distribution of India Domestic market in India 2018 , broken down 2018 Airline .  . In 2018 , India 's Domestic market amounted to about 39.7 % share .

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] templateScale of the population in templateTitle[5] areas in templateXValue[7] belonged to templateYLabel[1] templateYLabel[2] .

generated_template: The statistic shows the average templateTitle[0] for templateTitleSubject[0] templateXLabel[0] as of 2019 , by templateXLabel[1] . The templateXValue[13] - 800 model had a list templateYLabel[0] of templateYValue[18] templateScale templateYLabel[2] templateYLabel[3] . The templateTitleSubject[0] templateXValue[5] - templateXValue[14] is the company 's most expensive model Along with Airbus , Embraer , and Bombardier Aerospace , templateTitleSubject[0] is one of the world 's four largest templateXLabel[0] manufacturers .
generated: The statistic shows the average Share for China Province as of 2019 , by Province .  . The National total - 800 model had a list Share of 22.16 % minorities .  . The China Sichuan - Guangxi is the company 's most expensive model Along with Airbus , Embraer and Bombardier Aerospace , China is one of the world 's four largest Province manufacturers .

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 shows the templateTitle[0] of passenger templateTitle[1] templateTitle[2] templateTitle[3] the templateTitleSubject[0] in templateTitleDate[0] , with a breakdown templateTitle[6] templateXLabel[0] . templateTitle[5] templateYLabel[0] of passenger templateTitle[1] from templateXValue[3] stood at around templateYValue[3] templateScale templateYLabel[1] in templateTitleDate[0] .
generated: This statistic shows the Top of passenger 5 origin countries the Canada in 2016 , with a breakdown Canada Country .  . admitted Number of passenger 5 from Congo stood at around 1644 million refugees in 2016 .

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: The statistic ranks male professional tennis templateTitle[4] ( active and retired ) templateTitle[5] the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[8] since the beginning of the Open Era in templateTitleDate[min] . templateXValue[0] has templateTitle[8] templateYLabel[1] templateYValue[1] times .
generated: The statistic ranks male professional tennis major ( active and retired ) supermarkets the Volume of tonnes UK since the beginning of the Open Era in 2016 .  . Tesco has UK tonnes 35832 times .

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] templateYValue[idxmax(X)] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYValue[idxmax(X)] templateYLabel[4] .
generated: This statistic shows the Median Household income in North Carolina from 1990 to 2018 .  . In 2018 , the Median Household income in North Carolina amounted to 53764 53369 dollars .

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 templateScale of their templateYLabel[1] templateTitle[1] were women .

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] templateScale of the total people working for templateTitleSubject[0] .
generated: This statistic shows the worldwide of employees at Michelin agricultural company in 2018 , employees Region .  . In Asia (excl. India) North America , the company employed approximately 15259 % of the total people working for Michelin .

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[min] to templateTitleDate[max] , in templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . The templateTitle[4] of templateTitle[3] is expected to templatePositiveTrend to templateYValue[0] templateYLabel[0] templateYLabel[1] in templateXValue[0] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] of templateYLabel[1] enrolled in Canadian colleges in templateTitle[7] , templateTitle[4] templateTitle[5] templateTitle[6] . In the academic templateXLabel[0] templateTitle[7] , around templateYValue[max] templateYLabel[1] aged between templateXValue[0] and templateXValue[1] templateXValue[0] were enrolled in Canadian colleges .
generated: This statistic shows the Kingdom U.S. of dollars enrolled in Canadian colleges in dollars , price forecast U.S. . In the academic Year dollars , around 71.3 dollars aged between 2023/24 and 2022/23 2023/24 were enrolled in Canadian colleges .

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[idxmax(X)] templateScale templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] .

generated_template: In templateXValue[max] over templateYValue[max] templateScale tourists arrived in different templateTitle[3] establishments in the Czech Republic ( officially templateTitleSubject[0] ) . The templateYLabel[0] of tourists visiting templateTitleSubject[0] has templatePositiveTrend over the years , with the largest templateYLabel[0] of international tourists coming from Germany – in templateXValue[2] , approximately 1.9 templateScale visitors arrived from Germany alone . templateTitleSubject[0] – the perfect destination for beer lovers One of templateTitleSubject[0] 's attractions for tourists is its beer .
generated: In 2018 over 5.49 millions tourists arrived in different accommodation establishments in the Czech Republic ( officially Slovakia ) .  . The Number of tourists visiting Slovakia has increased over the years , with the largest Number of international tourists coming from Germany – in 2016 , approximately 1.9 millions visitors arrived from Germany alone .  . Slovakia – the perfect destination for beer lovers One of Slovakia 's attractions for tourists is its beer .

Example 355:
titleEntities: {'Subject': ['Great Britain'], '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] templateScale templateYLabel[3] in templateXValue[idxmax(Y)] . This was the peak since the beginning of the reporting period in templateXValue[min] and the first time figures exceeded templateYValue[max] templateScale units . Following templatePositiveTrend demand , the templateTitle[2] Development Association expects templateYLabel[0] volumes to templatePositiveTrend further in the coming years , with companies within the industry seeking to invest in greater templateYLabel[0] capacity .

generated_template: This statistic shows the templateYLabel[0] of adults in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the American company reported templateYLabel[0] templateYLabel[1] to the value of almost 1.15 templateScale templateYLabel[3] templateYLabel[4] from its cold chain templateTitleSubject[0] worldwide .
generated: This statistic shows the Production of adults in Great Britain from 2013 to 2018 .  . In 2018 , the American company reported Production level to the value of almost 1.15 million bricks from its cold chain Great Britain worldwide .

Example 356:
titleEntities: {'Subject': ['Ecuador'], 'Date': ['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 templateNegativeTrend lately . In templateXValue[max] , approximately templateYValue[idxmax(X)] templateScale of the Ecuadorian templateYLabel[1] was living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] templateScale in 2005.Still , social inequality remains a challenge in templateTitleSubject[0] and Latin America as a whole .

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[idxmax(X)] templateScale .
generated: This statistic shows the Percentage of population in poverty headcount in the ratio 3.20 2005 to 2017 .  . In 2017 , the Percentage of population ( aged six years and older ) in poverty headcount amounted to approximately 8.7 percentage .

Example 357:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1950', '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] templateScale of the American templateYLabel[2] was 65 years old or over ; a figure which is expected to reach templateYValue[max] templateScale by templateXValue[idxmax(Y)] . This is a significant templatePositiveTrend from templateXValue[min] , when only templateYValue[min] templateScale 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: The statistic shows the templateTitle[1] of templateTitle[2] templateTitleSubject[0] as templateScale of total e-mail templateYLabel[2] from templateXValue[min] to templateXValue[max] . In the most recently reported period , templateTitleSubject[0] messages accounted for templateYValue[idxmax(X)] templateScale of e-mail templateYLabel[2] worldwide , down from templateYValue[9] templateScale in templateXValue[9] .
generated: The statistic shows the seniors of percentage U.S. as percentage of total e-mail population from 1950 to 2050 .  . In the most recently reported period , U.S. messages accounted for 22 percentage of e-mail population worldwide , down from 10 percentage in 1970 .

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

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

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

Example 359:
titleEntities: {'Subject': ['Uruguay'], 'Date': ['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 templateNegativeTrend recently . In templateXValue[max] , approximately templateYValue[idxmax(X)] templateScale of Uruguayans was living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] templateScale of the country 's templateYLabel[1] in 2006.Still , social inequality remains a challenge in Latin America as a whole .

generated_template: The templateTitle[2] rate in the templateTitleSubject[0] has been templateNegativeTrend in the past few years . In templateXValue[max] , approximately templateYValue[idxmax(X)] templateScale of the Dominicans were living on less than templateTitle[5] templateTitle[6] templateTitle[7] per templateTitle[8] , almost three-times lower than templateXValue[min] , when templateYValue[idxmin(X)] templateScale of the country 's templateYLabel[1] was estimated to live in templateTitle[2] .
generated: The headcount rate in the Uruguay has been decreasing in the past few years .  . In 2017 , approximately 0.4 percentage of the Dominicans were living on less than U.S. dollars day per 2006 , almost three-times lower than 2006 , when 3.7 percentage of the country 's population was estimated to live in headcount .

Example 360:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017', '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 templateScale of annual IT templateTitle[5] of templateTitle[1] businesses in the templateTitle[0] . During the 2017 survey period , templateYValue[last] templateScale of C-level templateYLabel[1] stated that templateTitle[3] templateTitle[4] accounted for templateXValue[last] 10 templateScale of their annual IT templateTitle[5] .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among Americans on the templateTitle[0] of templateTitle[1] templateTitle[2] . The survey shows that templateYValue[min] templateScale of Americans consider themselves experts at templateTitle[1] templateTitle[2] . templateYValue[max] templateScale of Americans stated that they have templateXValue[3] templateXValue[last] to use templateTitle[2] .
generated: This statistic shows the results of a 2017 survey among Americans on the U.S. of consumer business .  . The survey shows that 10 % of Americans consider themselves experts at consumer business .  . 21 % of Americans stated that they have 6 to 8 More than 10 to use business .

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[idxmax(X)] .

generated_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] 49ers franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[idxmax(X)] .
generated: This graph depicts the total Regular season Home attendance of the Denver Broncos 49ers franchise of the National Football League from 2006 to 2019 .  . In 2019 , the Regular season Home attendance of the franchise was 607497 .

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] templateScale over the templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the 20 templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[0] ) in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[17] ranked 4th templateTitle[1] an estimated templateYLabel[0] templateYLabel[1] of about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateYLabel[4] . templateYLabel[0] around the world templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[0] ) is an indicator of the monetary value of all goods and services produced by a nation in a specific time period .
generated: This statistic shows the 20 Forecast gross the domestic growth of the GDP growth euro ( GDP ) in 2020 .  . In 2020 , Belgium ranked 4th gross an estimated GDP growth of about 2.83 % compared to the previous year .  . GDP around the world GDP growth euro ( GDP ) is an indicator of the monetary value of all goods and services produced by a nation in a specific time period .

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] templateScale 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[max] templateScale 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 57.21 % 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] templateScale templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] amounted to an estimated templateYValue[7] templateScale templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] is a country constantly plagued by war and terrorism .
generated: This statistic shows the Total population of Ireland from 2014 to 2017 , with projections up until 2024 .  . In 2018 , Ireland 's Total population amounted to an estimated 4.83 millions Inhabitants .  . population of Ireland is a country constantly plagued by war and terrorism .

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

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

generated_template: The statistic presents the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] as of 2019 , ranked templateTitle[5] templateTitle[6] templateYLabel[0] . According to the estimates , Johan Sundstein , a templateXLabel[0] from Denmark , also known as templateXValue[0] , earned templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] throughout his recorded templateTitle[1] gaming career . templateTitle[1] gaming – additional information Video gaming is no longer a pastime of purported home-dwellers or arcade-loitering individuals ; the professionalization of this activity has now validated it as a career choice for talented templateTitle[2] to earn templateScale and sign lucrative sponsorship deals .
generated: The statistic presents the Netherlands brands 2018 as of 2019 , ranked by Buzz .  . According to the estimates , Johan Sundstein , a Platform from Denmark , also known as Samsung , earned 47.7 % score throughout his recorded brands gaming career .  . brands gaming – additional information Video gaming is no longer a pastime of purported home-dwellers or arcade-loitering individuals ; the professionalization of this activity has now validated it as a career choice for talented Netherlands to earn millions and sign lucrative sponsorship deals .

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] templateScale templateYLabel[4] templateYLabel[5] of templateYLabel[1] templateYLabel[2] .

generated_template: This graph shows the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[2] in 2010 , sorted templateTitle[5] templateTitle[6] , in templateXLabel[3] templateXLabel[4] of templateXLabel[0] templateXLabel[1] templateXLabel[2] . In 2010 , there were templateYValue[1] templateYLabel[1] templateYLabel[2] operating within the templateTitle[2] with between templateXValue[0] and templateXValue[1] templateXLabel[3] templateXLabel[4] of templateXLabel[0] templateXLabel[1] templateXLabel[2] .
generated: This graph shows the total of retail sales in the sales in 2010 , sorted malls 2005 , in square feet of Gross leasable area .  . In 2010 , there were 388.6 retail sales operating within the sales with between Less than 100001 and 100001 to 200000 square feet of Gross leasable area .

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] templateScale 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[7] , with projections up until templateXValue[max] . In templateXValue[7] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] amounted to an estimated templateYValue[7] templateScale templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] is a country constantly plagued by war and terrorism .
generated: This statistic shows the Total population of Russia from 2014 to 2017 , with projections up until 2024 .  . In 2017 , Russia 's Total population amounted to an estimated 146.9 millions Inhabitants .  . population of Russia is a country constantly plagued by war and terrorism .

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 2017 , sorted templateTitle[8] templateTitle[9] . According to the source , templateYValue[max] templateScale of templateXValue[1] who subscribe to online video or music subscriptions had a templateTitleSubject[0] or templateTitleSubject[0] subscription as of 2017 .

generated_template: At the beginning of templateTitleDate[0] , templateYValue[0] templateScale 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 % 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': ['2004', '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 templatePositiveTrend by more than 15 templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Italy from 2004 to 2019 .  . In 2019 , the Unemployment rate in Italy was at 28.9 % .

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: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[1] templateTitle[2] in templateTitleDate[0] . In templateTitleDate[0] , the templateYLabel[0] of templateYLabel[1] templateXValue[4] was at around templateYValue[4] templateScale . templateYLabel[1] templateXValue[4] templateTitle[1] templateTitle[2] The Population Reference Bureau released data on global templateYLabel[1] templateTitle[1] templateTitle[2] in templateTitleDate[0] .
generated: This statistic shows the Surface of area Nordic in 2017 .  . In 2017 , the Surface of area Iceland was at around 103000 % .  . area Iceland area Nordic The Population Reference Bureau released data on global area Nordic in 2017 .

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[idxmin(X)] templateScale of the Brazilian templateYLabel[1] accessed templateTitle[3] from their templateTitle[1] device . This figure is expected to grow to templateYValue[idxmax(X)] templateScale in templateXValue[idxmax(Y)] .

generated_template: This statistic gives information on the templateTitle[1] templateTitle[3] rate in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the Singaporean templateYLabel[1] were using the templateTitle[1] . In templateXValue[max] , this figure is projected to grow to templateYValue[idxmax(X)] templateScale .
generated: This statistic gives information on the mobile internet rate in Brazil from 2017 to 2023 .  . In 2017 , 41 % of the Singaporean population were using the mobile .  . In 2023 , this figure is projected to grow 51 % .

Example 372:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '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[idxmax(X)] templateYLabel[3] in templateXValue[idxmin(Y)] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[6] and templateTitle[7] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[6] and templateTitle[7] templateTitle[5] amounted to approximately templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of fresh nectarines and 2000 peaches in the U.S. from 2000 to 2018 .  . According to the report , the U.S. Per capita consumption of fresh nectarines and 2000 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[idxmax(X)] templateYLabel[3] in templateXValue[max] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] ( shelled ) in the templateTitle[5] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] amounted to templateYValue[max] templateYLabel[3] in templateXValue[1] .
generated: The timeline shows the Per capita consumption of fresh ( shelled ) in the carrots from 2000 to 2018 .  . The U.S. Per capita consumption of fresh amounted to 9.4 pounds in 2017 .

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 2011 . templateXValue[last] reported a templateYLabel[0] of templateYValue[min] templateScale 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 2018 and 2019 . 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] . In 2019 , the templateYLabel[1] templateYLabel[2] per liter in the U.S. was templateYValue[max] templateScale higher than it was in 2018 .
generated: This statistic shows the Debt in the euros of billion per liter in nations 2011 for the period between 2018 and 2019 .  . 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 2019 , the billion euros per liter in the U.S. was 11.8 billion higher than it was in 2018 .

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: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Gross domestic product ( GDP ) in Syria from 1984 to 2010 .  . In 2010 , the Gross domestic product was 60.04 billion U.S. .

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 2018 , 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] templatePositiveTrend 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 templateScale of total e-mail templateYLabel[2] from templateXValue[min] to templateXValue[max] . In the most recently reported period , templateTitleSubject[0] messages accounted for templateYValue[idxmax(X)] templateScale of e-mail templateYLabel[2] worldwide , down from templateYValue[9] templateScale in templateXValue[9] .
generated: The statistic shows the average of housing Spain as percentage of total e-mail per from 2007 to 2018 .  . In the most recently reported period , Spain messages accounted for 1613 % of e-mail per worldwide , down from 1447 % 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] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] is a U.S.-based company that manufactures and sells boats and marine engines . The company previously made and sold sporting and fitness equipment and bowling & billiards equipment .

generated_template: The timeline shows templateTitleSubject[0] templateTitle[1] templateTitle[3] templateYLabel[0] since templateXValue[min] . In templateXValue[max] , the local review and search site templateTitle[1] templateYLabel[0] amounted to over templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , an templatePositiveTrend from 952.8 templateScale templateYLabel[2] templateYLabel[3] compared the previous templateXLabel[0] .
generated: The timeline shows Brunswick Corporation revenue Corporation since 2007 .  . In 2019 , the local review and search site revenue amounted to over 5671.2 million U.S. dollars , an increase from 952.8 million U.S. dollars compared 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] 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] 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': ['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] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: In templateXValue[max] , templateTitleSubject[0] reported a templateYLabel[0] templateYLabel[1] figure of about templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] . In general , annual templateYLabel[0] templateYLabel[1] figures report a company 's revenues minus expenses over the course of a templateXLabel[0] and are highly affected by both expenses and corporate strategy . Changes between years are often quite variable as they include the expenses incurred in the form of large long-term investments in the expense column in addition to the usual yearly operating costs .
generated: In 2019 , Southwest Airlines reported a Net income figure of about 2300 million U.S. dollars .  . In general , annual Net income figures report a company 's revenues minus expenses over the course of a Year and are highly affected by both expenses and corporate strategy .  . Changes between years are often quite variable as they include the expenses incurred in the form of large long-term investments in the expense column in addition to the usual yearly operating costs .

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: The total templateYLabel[0] of templateTitle[1] templateYLabel[1] fluctuated in the past years in templateTitleSubject[0] . Since templateXValue[5] , the templateYLabel[0] of templateTitle[1] templateYLabel[1] templatePositiveTrend generally . As of templateXValue[max] , there were approximately templateYValue[0] thousand templateTitle[1] templateYLabel[1] registered .
generated: The total Number of births fluctuated in the past years in Belgium .  . Since 2013 , the Number of births increased generally .  . As of 2018 , there were approximately 117800 thousand births registered .

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[idxmax(Y)] .
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 2007 .

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] templateScale 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] of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[7] templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[idxmax(X)] templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] members , as compared to templateYValue[14] in templateXValue[14] .
generated: This statistic shows the Consumption of wine Germany million hectoliters 2018 2000 to 2018 .  . In 2018 , there were 20.0 Consumption wine Germany million hectoliters members , as compared to 20.0 in 2001 .

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 2017 , sorted templateTitle[7] templateTitle[8] . According to the source , templateYValue[max] templateScale of templateXValue[last] who subscribe to service templateTitle[4] had a templateTitle[2] or templateTitle[3] subscription as of 2017 .

generated_template: At the beginning of templateTitleDate[0] , templateYValue[0] templateScale 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 % 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[idxmin(X)] templateScale . In templateXValue[max] , the templateScale of the templateYLabel[1] above the age of templateYLabel[4] was forecasted to reach templateYValue[idxmax(X)] templateScale .

generated_template: This statistic displays the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per day in the templateTitleSubject[0] between templateXValue[min] and templateTitleDate[max] , templateTitle[8] templateTitle[9] . In this templateYValue[min] templateXLabel[0] period , individuals aged between 11 and 18 templateXValue[0] old templateTitle[3] templateYValue[max] templateYLabel[1] of templateTitle[1] templateTitle[2] per day .
generated: This statistic displays the Share of aging population Thailand per day in the Thailand between 2015 and 2035 , .  . In this 10.6 Year period , individuals aged between 11 and 18 2035 old Thailand 22.8 population of aging population per day .

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: The statistic illustrates the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateTitleDate[max] . In templateTitleDate[max] , about templateYValue[max] templateYLabel[1] in the templateTitle[4] were committed templateTitle[2] templateTitle[3] .
generated: The statistic illustrates the Number of new published U.S. in the category from 2002 to 2013 .  . In 2013 , about 53590 new in the category were committed published U.S. .

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 12 , 2017 , Puerto Rican singer templateXValue[0] released his Spanish-language music video `` templateXValue[0] '' featuring templateXValue[0] , and the rest is history . In of the same year , the video became the most-viewed templateTitleSubject[0] video of templateTitle[4] templateTitle[5] and as of 2019 , the video still holds the top spot with over templateYValue[max] templateScale 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: The templateTitle[4] templateTitle[5] is an annual templateTitle[3] prize presented by France templateTitle[3] . The award , voted for by templateTitle[3] journalists , is given to the male player who was deemed to have played the best templateTitle[3] over the previous 12 months . Unsurprisingly , templateYValue[min] of the greatest footballers of all time , templateXValue[0] and templateXValue[1] , top the list of all-time templateTitle[1] .
generated: The all time is an annual videos prize presented by France videos .  . The award , voted for by videos journalists , is given to the male player who was deemed to have played the best videos over the previous 12 months .  . Unsurprisingly , 2.97 of the greatest footballers of all time , Luis Fonsi - Despacito ft. Daddy Yankee and Ed Sheeran - Shape of You , top the list of all-time viewed .

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] templateTitleDate[0] , distinguished templateTitle[5] major templateXLabel[0] . In templateTitle[7] templateTitleDate[0] , the templateXValue[3] templateXLabel[0] of templateTitleSubject[0] contributed about templateYValue[3] templateScale templateYLabel[4] templateYLabel[5] to the total templateYLabel[4] templateYLabel[0] .

generated_template: This statistic shows the 20 templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[0] ) in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[3] ranked 4th templateTitle[1] an estimated templateYLabel[0] templateYLabel[1] of approximately templateYValue[3] templateScale templateYLabel[2] to the templateYLabel[3] templateYLabel[4] . templateYLabel[0] around the world templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[0] ) is an indicator of the monetary value of all goods and services produced by a nation in a specific time period .
generated: This statistic shows the 20 Canada Gross the Domestic million of the GDP by industry ( GDP ) in 2019 .  . In 2019 , Construction ranked 4th Gross an estimated GDP million of approximately 142530 million chained to the 2012 Canadian .  . GDP around the world GDP by industry ( GDP ) is an indicator of the monetary value of all goods and services produced by a nation in a specific time period .

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 templateScale templateYLabel[3] .

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] of the templateYLabel[1] templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) adjusted for Purchasing Power Parity ( PPP ) from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] of templateYLabel[1] templateYLabel[2] ( in relation to PPP dollars ) amounted to about templateYValue[6] templateScale .
generated: The statistic shows Japan balance Budget of the balance 2024 ( trillion ) adjusted for Purchasing Power Parity ( PPP ) from 2014 to 2024 .  . In 2018 , Japan balance Budget of balance trillion ( in relation to PPP dollars ) amounted to about -17.64 trillion .

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] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows total templateYLabel[2] templateTitle[0] templateYLabel[0] templateTitle[2] the Supplemental Nutrition Assistance Program ( templateTitle[3] , formerly called templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . In templateXValue[2] , about templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] were spent templateTitle[2] the Supplemental Nutrition Assistance Program .
generated: The statistic shows total U.S. Global Spending golf the Supplemental Nutrition Assistance Program ( sponsorships , formerly called Global ) from 2010 to 2016 .  . In 2014 , about 1.65 billion U.S. dollars were spent golf the Supplemental Nutrition Assistance Program .

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] templateNegativeTrend by around templateYValue[4] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

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

Example 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[idxmax(X)] templateYLabel[3] templateYLabel[4] was spent on each templateYLabel[2] in templateTitle[1] elementary and secondary templateTitle[2] in the academic templateXLabel[0] of templateXValue[idxmax(Y)] .

generated_template: The templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] aged templateTitle[4] templateYLabel[2] in the templateTitleSubject[0] has gradually templatePositiveTrend since the 1960s . Now templateTitleSubject[0] in the templateTitle[6] aged templateTitle[4] can expect to live templateYValue[22] more templateYLabel[2] on average . Women aged templateTitle[4] templateYLabel[2] can expect to live around 20.6 more templateYLabel[2] on average .
generated: The Expenditures per for U.S. aged expenditure pupil in the U.S. has gradually increased since the 1960s .  . Now U.S. in the pupil aged expenditure can expect to live 5767 more pupil on average .  . Women aged expenditure pupil can expect to live around 20.6 more pupil on average .

Example 392:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1987', '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] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale templateYLabel[2] were in the templateTitle[3] .
generated: The statistic shows the U.S. of exports trade goods world 1987 in the 2019 from 1987 to 2019 .  . In 2019 , about 1645.53 billion U.S. were in the goods .

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] templateScale templateYLabel[2] in templateYLabel[0] .

generated_template: This statistic shows the templateTitle[5] templateYLabel[0] of templateTitleSubject[0] market mergers and acquisitions ( templateTitleSubject[0] templateTitle[3] A ) templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , based on the values of disclosed templateTitle[4] , the templateTitleSubject[0] 's templateTitleSubject[0] templateTitle[3] A deals amounted to templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the 2016 Revenue of UEFA European Championships market mergers and acquisitions ( UEFA European Championships Championships A ) 2016 from 1992 Sweden to 2016 France .  . In 1996 England , based on the values of disclosed 1992 , the UEFA European Championships 's Championships A deals amounted to 1390.9 million euros .

Example 394:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2005']}
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[idxmax(X)] templateScale 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: This statistic gives information on the templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] as of 2019 . During this period of time , it was found that templateYValue[max] templateScale of templateTitle[3] templateTitle[4] in the Latin American country were aged between 25 and 34 templateXValue[0] .
generated: This statistic gives information on the book reading of by young in United Kingdom as of 2019 .  . During this period of time , it was found that 50.6 % of by young in the Latin American country were aged between 25 and 34 2015 .

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[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This timeline depicts the templateYLabel[3] merchant wholesalers ' templateYLabel[0] templateYLabel[1] on templateTitle[2] , piece goods , and notions from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] on templateTitle[2] , piece goods , and notions in templateYLabel[3] templateTitle[4] was templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This timeline depicts the dollars merchant wholesalers ' Expenditure billion on musical , piece goods , and notions from 1999 to 2013 .  . In 2013 , the Expenditure billion on musical , piece goods , and notions in dollars U.S. was 5.52 billion dollars .

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[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic presents the proportion of cars templateYLabel[2] a templateYLabel[3] templateYLabel[4] system installed in templateXValue[min] and templateXValue[max] . The templateYLabel[0] of templateYLabel[3] templateYLabel[4] system equipped cars templatePositiveTrend from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] to templateYValue[idxmax(X)] templateScale in templateXValue[idxmax(Y)] .
generated: This statistic presents the proportion of cars billion a U.S. dollars system installed in 2014 and 2019 .  . The Retail of U.S. dollars system equipped cars increased from 4.5 billion in 2014 to 53.2 billion in 2019 .

Example 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] templateScale 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: The statistic shows the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] ( UK ) from templateTitleDate[min] to templateTitleDate[max] . The largest single year on year templatePositiveTrend came from 1919 to 1920 , not long after the end of the First World War , when templateYLabel[2] templatePositiveTrend by 300,647 . Despite the lower level of population , from templateTitleDate[min] to 1914 the templateYLabel[0] of templateYLabel[1] templateYLabel[2] was consistently above templateYValue[0] templateScale .
generated: The statistic shows the total Population of million inhabitants in the China ( UK ) from 2018 to .  . The largest single year on increase came from 1919 to 1920 , not long after the end of the First World War , when inhabitants increased by 300,647 .  . Despite the lower level of population , from 2018 to 1914 the Population of million inhabitants was consistently above 113.46 million .

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] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleDate[0] , templateTitle[3] templateXLabel[0] . In templateTitleDate[0] , there were templateYValue[max] templateYLabel[1] templateYLabel[2] reported in templateXValue[0] . According to the source , a total of 8,093 templateYLabel[1] templateYLabel[2] occurred worldwide , resulting in 32,836 deaths and 1,888 injuries in templateTitleDate[0] .
generated: The statistic shows the Production of value million in 2016 , value Country .  . In 2016 , there were 3434.02 value million reported in China mainland .  . According to the source , a total of 8,093 value million occurred worldwide , resulting in 32,836 deaths and 1,888 injuries in 2016 .

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 templateScale templateYLabel[2] templateYLabel[3] . That templateXLabel[0] , Alphabet 's total templateTitleSubject[0] segment templateYLabel[0] amounted to over 160.74 templateScale US templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] amounted to about templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] The templateTitleSubject[0] is the largest sporting goods manufacturer across Europe and the second largest templateTitle[5] – only behind long-term competitor Nike .
generated: This statistic shows the Revenue of Google worldwide from 2001 to 2019 .  . In 2019 , Google network Revenue amounted to about 21.55 billion U.S. dollars .  . Google The Google is the largest sporting goods manufacturer across Europe and the second largest 2001 – only behind long-term competitor Nike .

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

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

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

Example 401:
titleEntities: {'Subject': ['United States'], 'Date': ['2017', '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[last] templateScale templateYLabel[2] templateYLabel[3] ( or about 326 templateScale euros ) .

generated_template: This statistic represents the European templateTitle[0] templateYLabel[0] from templateXValue[min] through templateXValue[max] . In templateXValue[max] , approximately templateYValue[0] templateScale tires were produced here . In terms of sales , Bridgestone and Michelin are ranked as the leading templateTitle[0] manufacturers .
generated: This statistic represents the European Mechanical Revenue from 2006 through 2017 .  . In 2017 , approximately 340.0 billion tires were produced here .  . In terms of sales , Bridgestone and Michelin are ranked as the leading Mechanical manufacturers .

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] templateScale in templateXValue[6] . Rates are forecast to remain stable in templateXValue[7] and rise by one templateScale point in templateXValue[max] .

generated_template: This statistic shows templateTitle[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateTitle[0] templateYLabel[1] numbers to templateTitleSubject[0] templatePositiveTrend by templateYValue[idxmin(X)] templateScale over the previous templateXLabel[0] . templateTitle[0] visitors to the region was forecast to grow by templateYValue[idxmax(X)] templateScale in templateXValue[max] .
generated: This statistic shows Hotel rate in Rome from 2011 to 2019 .  . In 2011 , Hotel rate numbers to Rome increased by 67 % over the previous Year .  . Hotel visitors to the region was forecast to grow by 71 % 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 statistic shows the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[2] in 2010 , sorted templateTitle[5] templateTitle[6] , in templateXLabel[3] templateXLabel[4] of templateXLabel[0] templateXLabel[1] templateXLabel[2] . In 2010 , there were templateYValue[max] templateYLabel[1] templateYLabel[2] operating within the templateTitle[2] with between templateXValue[0] and templateXValue[1] templateXLabel[3] templateXLabel[4] of templateXLabel[0] templateXLabel[1] templateXLabel[2] .
generated: This statistic shows the total Number of workers in the employment in 2010 , sorted UK 2018 , in Industry of .  . In 2010 , there were 139352 workers operating within the employment with between Music creators and Music retail Industry of .

Example 404:
titleEntities: {'Subject': ['Play Q4'], 'Date': ['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] templateScale mobile templateYLabel[2] were templateYLabel[1] , representing a 4.07 templateScale templatePositiveTrend compared to the previous templateXLabel[0] .

generated_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateTitle[4] templateYLabel[2] in the templateTitle[0] templateTitleSubject[0] app store . As of the third templateXLabel[0] of templateTitleDate[0] , approximately templateYValue[max] mobile templateTitle[4] templateYLabel[2] were templateYLabel[1] , representing a 3.88 templateScale templatePositiveTrend compared to the previous templateXLabel[0] .
generated: This statistic gives information on the Number of available apps in the Google Play Q4 app store .  . As of the third Quarter of 2019 , approximately 3849865 mobile apps were available , representing a 3.88 % increase compared to the previous Quarter .

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] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[5] , with projections through templateXValue[max] . In templateXValue[5] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[5] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Lithuania from 2014 to 2019 , with projections through 2024 .  . In 2019 , Lithuania 's real Gross domestic product increased by around 3.41 % 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[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYValue[idxmax(X)] templateYLabel[4] .
generated: This statistic shows the household income in Wisconsin from 1990 to 2018 .  . In 2018 , the household income in Wisconsin amounted to 63451 62629 dollars .

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[idxmax(X)] templateScale 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 represents templateTitleSubject[0] 's templateYLabel[0] and templateYLabel[1] templateYLabel[2] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , the company incurred around templateYValue[idxmax(X)] templateScale templateYLabel[4] templateYLabel[5] in templateYLabel[0] and templateYLabel[1] templateYLabel[2] .
generated: This statistic represents Royal Dutch Shell 's Costs and million U.S. from the fiscal Year of 2010 to the fiscal Year of 2018 .  . In the fiscal Year of 2018 , the company incurred around 208 million dollars in Costs and million U.S. .

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] templateScale templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] amounted to an estimated templateYValue[7] templateScale templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] is a country constantly plagued by war and terrorism .
generated: This statistic shows the Total population of Pakistan from 2014 to 2018 , with projections up until 2024 .  . In 2018 , Pakistan 's Total population amounted to an estimated 197.26 millions Inhabitants .  . population of Pakistan is a country constantly plagued by war and terrorism .

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] templateScale templateYLabel[3] templateYLabel[4] , and is forecasted to reach to about templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] by 2025 .

generated_template: The statistic shows the templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] ( VPN ) templateYLabel[0] worldwide , from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateTitleSubject[0] VPN templateYLabel[0] is forecast to reach templateYValue[4] templateScale templateYLabel[3] templateYLabel[4] . templateTitle[1] templateTitle[2] networks are designed to extend a templateTitle[3] securely from a templateTitle[2] location , such as a business or home , across a public templateTitle[3] , as if the templateTitle[3] were directly linked .
generated: The statistic shows the size of the sexual wellness market ( VPN ) Market worldwide , from 2017 to 2023 .  . In 2018 , the Global VPN Market is forecast to reach 29.42 billion U.S. dollars .  . sexual wellness networks are designed to extend a market securely from a wellness location , such as a business or home , across a public market , as if the market were directly linked .

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

Example 411:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2008', '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] templateScale . This is a templateNegativeTrend 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] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at templateYValue[min] templateScale .
generated: This statistic shows the Unemployment rate in Belgium from 2008 to 2018 .  . In 2018 , the Unemployment rate in Belgium was at 6 % .

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 2017 survey , templateYValue[max] templateScale 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: 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 templateScale supported its templateTitle[1] , compared to 23 templateScale who opposed it . Despite templateTitle[2] disasters occurring as recently as 2011 , when a tsunami hit a power plant in Fukushima , Japan , approximately 34 templateScale of templateTitleSubject[1] residents believe it provides a safe source of templateTitle[3] .
generated: According to a 2017 survey , To meet people who share my interests or hobbies for the user of reasons for electricity generation in the U.S. outweighed opposition .  . Of respondents , 35 % supported its user , compared to 23 % who opposed it .  . Despite reasons disasters occurring as recently 2011 , when a tsunami hit a power plant in Fukushima , Japan approximately 34 % of U.S. residents believe it provides a safe source of for .

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 templateScale of the templateYLabel[0] templateYLabel[1] worldwide in that year .

generated_template: This statistic shows the templateYLabel[1] of disposable income spent on templateTitle[1] consumed in templateTitleDate[0] , broken down by templateTitle[5] templateTitle[6] . U.S. consumers spent templateYValue[min] templateScale of their disposable income on templateTitle[1] at templateTitle[2] , compared to a templateTitle[1] templateYLabel[0] templateYLabel[1] of templateYValue[1] templateScale in templateXValue[1] .
generated: This statistic shows the value of disposable income spent on global consumed in 2012 , broken down by country .  . U.S. consumers spent 1.71 % of their disposable income on global at seeds , compared to a global Market value of 22.15 % in China .

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

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

generated_template: This statistic shows total domestic consumption templateYLabel[0] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . Over this period , templateTitle[0] templateTitle[1] in the templateTitleSubject[1] templatePositiveTrend , reaching approximately over templateYValue[max] templateScale British pounds in templateXValue[idxmax(Y)] . Household consumption templateYLabel[0] looks at the overall templateTitle[1] on templateTitle[0] goods and services of a wide variety .
generated: This statistic shows total domestic consumption Expenditure in the United Kingdom ( ) from 2005 to 2018 .  . Over this period , Expenditure beer in the United Kingdom increased , reaching approximately over 5631 million British pounds in 2018 .  . Household consumption Expenditure looks at the overall beer on Expenditure goods and services of a wide variety .

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] templatePositiveTrend up to more than templateYValue[max] thousand as of templateXValue[idxmax(Y)] . By templateXValue[max] , the templateYLabel[1] of templateYLabel[2] in Europe had templateNegativeTrend to approximately templateYValue[0] thousand .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] individuals spent watching templateTitle[2] per day in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The data refers to templateTitle[2] owners in metropolitan templateTitleSubject[0] aged templateYValue[max] years and older . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] in templateTitleSubject[0] came to templateYValue[0] templateYLabel[1] and 36 templateYLabel[2] , six minute less than one templateXLabel[0] earlier and a continuous slight templateNegativeTrend since templateXValue[3] .
generated: This statistic shows the European Total individuals spent watching numbers per day in European from 2005 to 2018 .  . The data refers to numbers owners in metropolitan European aged 420200 years and older .  . In 2018 , the European ATM numbers 2005 Total in European came to 406532 number and 36 ATMs , six minute less than one Year earlier and a continuous slight decrease since 2015 .

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[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale . templateYLabel[0] in templateTitleSubject[0] 's templateYLabel[0] is relatively low and stable at around templateYValue[0] templateScale which means the population is experiencing close to full employment .
generated: This statistic shows the Unemployment rate in Bosnia-Herzegovina from 1999 to 2019 .  . In 2019 , the Unemployment rate in Bosnia-Herzegovina was at approximately 21.22 % .  . Unemployment in Bosnia-Herzegovina 's Unemployment is relatively low and stable at around 21.22 % which means the population is experiencing close to full employment .

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] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

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

Example 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 2017 in leading online markets . During the measured period , templateTitleSubject[0] accounted for templateYValue[3] templateScale of templateYLabel[1] templateYLabel[2] in templateXValue[3] . The Microsoft-owned platform accounted for templateYValue[0] templateScale of templateYLabel[1] templateYLabel[2] templateXValue[0] .

generated_template: Multiple factors can play a role in the decision to invest in templateTitle[7] templateTitle[8] . To calculate how prosperous templateTitleSubject[0] templateTitle[2] are on the templateTitle[8] market , the Urban Land Institute considered transport connectivity , forecasted templateTitle[7] templateTitle[8] returns , a city 's economic performance , availability of assets/opportunities for new development , market size and liquidity , regulatory environment , digital connectivity , attractiveness to talent , city leadership , housing affordability , and affordability of space for new/small/growing businesses . Leading templateTitleSubject[0] templateTitle[2] for development and investment According to the ranking , the leading five templateTitleSubject[0] templateTitle[2] templateTitle[4] templateTitle[5] templateTitle[6] for templateTitleDate[0] are templateXValue[0] , templateXValue[1] , templateXValue[2] , templateXValue[3] and templateXValue[4] .
generated: Multiple factors can play a role in the decision to invest in country .  . To calculate how prosperous Bing search are on the country market , the Urban Land Institute considered transport connectivity , forecasted country returns , a city 's economic performance , availability of assets/opportunities for new development , market size and liquidity , regulatory environment , digital connectivity , attractiveness to talent , city leadership , housing affordability , and affordability of space for new/small/growing businesses .  . Leading Bing search for development and investment According to the ranking , the leading five Bing search share 2017 by for 2017 are Worldwide , United States Brazil , Canada and Latin America .

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 templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in 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']}
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 templateXValue[max] . In templateXValue[7] , the templateTitleSubject[0] templateYLabel[0] templateTitle[2] templateTitle[3] stood at templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] .

generated_template: This statistic depicts the templateTitleSubject[0] annual templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[6] templateXValue[min] through templateXValue[max] . In templateXValue[7] , the templateTitleSubject[0] templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] stood at templateYValue[7] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] templateYLabel[6] .
generated: This statistic depicts the Average annual prices for zinc worldwide 2014 through 2025 .  . In 2018 , the Average Price for zinc worldwide stood at 2922 nominal U.S. dollars per metric ton .

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[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at templateYValue[0] templateScale .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[0] templateScale .
generated: This statistic displays the Unemployment rate in Georgia from 1992 to 2018 .  . In 2018 , Unemployment in Georgia was 3.9 % .

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] templateScale templateYLabel[2] .

generated_template: This statistic shows the templateTitle[5] templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The season runs from 1 to 30 of the following templateXLabel[0] . The templateYLabel[0] of the templateXValue[0] season was templateYValue[0] templateScale templateYLabel[2] .
generated: This statistic shows the Big Revenue of the European from the 2006/07 season to the 2019/20 season .  . The season runs from 1 to 30 of the following Year .  . The Revenue of the 2019/20 season was 17.95 billion euros .

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: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of a templateTitle[2] is an index that divides the templateTitle[2] into two equal groups : half of the templateTitle[2] is older than the templateYLabel[0] templateYLabel[1] and the other half younger . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] 's templateTitle[2] was templateYValue[7] templateYLabel[2] .
generated: This statistic shows the Median age of the population 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] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .

generated_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] templatePositiveTrend , reaching around templateYValue[2] templateScale templateYLabel[2] in templateXValue[2] .
generated: This statistic presents the tourism GDP of Total and contribution to Saudi in Saudi Arabia from 2012 to 2018 , with a forecast for 2028 .  . Over this period , the GDP of the Total and contribution industry to Saudi in Saudi Arabia grew , reaching around 240.9 billion Saudi in 2017 .

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 templatePositiveTrend templateXValue[4] templateXLabel[0] in the templateTitle[5] was templateXValue[0] , which generated templateYLabel[0] of templateYValue[0] templateScale 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] in the templateTitle[4] in templateTitleDate[0] . In that year , templateXValue[0] was the country 's top selling templateTitle[1] templateTitle[2] in the templateTitle[4] , with templateYLabel[0] that amounted to about templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic presents the Revenue of the Revenue fastest-growing private security in the companies in 2018 .  . In that year , Netizen was the country 's top selling fastest-growing private in the companies , with Revenue that amounted to about 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] templateScale of templateYLabel[3] .

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a trade deficit . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] trade deficit amounted to around 7.23 templateScale 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 % 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[idxmax(X)] templateTitle[1] templateTitle[2] U.S. templateTitle[3] personnel committed suicide .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In calendar templateXLabel[0] templateXValue[1] , the average templateTitle[1] templateYLabel[0] templateYLabel[1] one templateYLabel[2] of templateTitle[0] templateTitle[2] was about templateYValue[1] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the U.S active duty Number suicides in the suicides from 1990 to 2010 .  . In calendar Year 2009 , the average active Number suicides one of U.S duty was about 302 suicides .

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[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] Canada company from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the health templateTitle[1] chain is expected to generate a templateYLabel[0] of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] . In templateXValue[1] , the templateTitleSubject[0] based company operated 307 clubs .
generated: The statistic depicts the Revenue of the Malaysia Canada 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[max] . As of that period , the mobile messaging app announced more than templateYValue[0] templateScale templateYLabel[1] templateTitle[3] templateYLabel[2] . In October templateTitleDate[min] , templateTitleSubject[0] had also reported 560 templateScale registered templateYLabel[2] worldwide .

generated_template: In the fourth templateXLabel[0] of templateTitleDate[max] , templateTitleSubject[0] 's templateTitle[1] templateYLabel[0] templateYLabel[1] amounted to more than templateYValue[max] templateScale templateYLabel[3] . This would be slightly as an templatePositiveTrend of over 8.6 templateScale compared to fourth templateXLabel[0] of the previous year . The Japanese e-commerce company is headquartered in Tokyo , where it was founded in 1997 .
generated: In the fourth Quarter of 2016 , LINE 's number monthly amounted to more than 220.0 millions .  . This would be slightly as an increase of over 8.6 millions compared to fourth Quarter of the previous year .  . The Japanese e-commerce company is headquartered in Tokyo , where it was founded in 1997 .

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[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Liberia from 1999 to 2019 .  . In 2019 , the Unemployment rate in Liberia was at approximately 2.03 % .

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[idxmax(X)] templateScale .

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

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 2018 , the Spanish newspaper sold on average templateYValue[min] thousand copies daily .

generated_template: This statistic shows the number of templateYLabel[1] on the official templateTitleSubject[0] Facebook page from 2010 to templateXValue[0] . The page had a total of templateYValue[last] templateScale templateYLabel[1] in 2019 .
generated: This statistic shows the number of Circulation on the official El Pais Facebook page from 2010 to July 2017-June 2018 .  . The page had a total of 435433 % Circulation in 2019 .

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: In templateTitleDate[0] , hip-hop and rap templateTitle[0] accounted for templateYValue[max] templateScale of total templateTitle[0] templateYLabel[1] in the templateTitle[3] , more than double the templateScale of R templateXValue[8] B templateTitle[0] sales . Other highly popular genres included templateXValue[1] and templateXValue[2] templateTitle[0] , whereas just templateYValue[10] templateScale of all templateTitle[0] sold in the templateTitleSubject[0] in templateTitleDate[0] was templateXValue[10] . Why are some genres more popular than others ? Whilst templateTitle[0] is a highly subjective medium in terms of the listener 's taste and preferences , the top genres in terms of templateYLabel[1] tend not to fluctuate heavily .
generated: In 2016 , hip-hop and rap Best accounted for 97.19 percentage of total Best Percentage in the based , more than double the percentage of R Skoda Citigo MkI B Best sales .  . Other highly popular genres included Land Rover Discovery MkIV and Renault Kadjar MkI Best , whereas just 92.26 percentage of all Best sold in the Great Britain in 2016 was Toyota Land Cruiser MkVII .  . Why are some genres more popular than others ? Whilst Best is a highly subjective medium in terms of the listener 's taste and preferences , the top genres in terms of Percentage tend not to fluctuate heavily .

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 templateScale templatePositiveTrend on the previous templateXLabel[0] . By templateXValue[3] the templateYLabel[0] of individuals had templatePositiveTrend to almost templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] thousand templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . During the time under consideration , the templateYLabel[0] templateYLabel[1] remained relatively stable . The highest templateYLabel[1] occurred in templateXValue[min] , with templateYValue[idxmin(X)] abortions templateYLabel[2] thousand templateYLabel[4] .
generated: This statistic shows the Number illegal entries thousand thousands in EU from 2009 to 2018 .  . During the time under consideration , the Number illegal remained relatively stable .  . The highest illegal occurred in 2009 , with 104.6 abortions entries thousand thousands .

Example 435:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
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[idxmin(X)] templateScale in revenues in templateXValue[idxmax(Y)] . The figure is estimated to templateNegativeTrend to $ templateYValue[idxmax(X)] templateScale by templateXValue[idxmin(Y)] .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] had a size of templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the U.S. e-learning industry revenue U.S. size 2016 from to 2021 .  . In 2016 , the U.S. e-learning industry revenue U.S. had a size of 20.85 billion U.S. dollars .

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 2018 , 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 templateScale attendees .

generated_template: This statistic gives information on the templateTitleSubject[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateXValue[7] destination and accommodation templateTitle[5] in 2016 , based on templateScale of templateYLabel[1] . During this period of time , it was found that templateYValue[max] templateScale of all templateYLabel[1] were directed to templateTitle[1] leader templateXValue[0] . The templateXValue[7] website 's templateTitleDate[0] revenue amounted to 1.48 templateScale templateTitleSubject[0] dollars .
generated: This statistic gives information on the North America cinema circuits of North B & B Theatres destination and accommodation 2018 in 2016 , based on percentage of screens .  . During this period of time , it was found that 8218 % of all screens were directed to cinema leader AMC Theatres .  . The B & B Theatres website 's 2018 revenue amounted to 1.48 billion North America dollars .

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] templateScale of templateYLabel[1] cut off templateXValue[last] cable or satellite templateXValue[1] because it templateXValue[5] templateXValue[0] .

generated_template: According to a templateTitleDate[0] Statista survey , templateYValue[max] templateScale 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] or templateXValue[1] and listening to the templateXValue[2] . Headphone Market The retail value of global headphone shipments was forecast to reach 26.1 templateScale templateTitle[6] dollars in 2019 , a more than threefold templatePositiveTrend in templateYValue[min] years .
generated: According to a 2017 Statista survey , 86.7 % of the 2017 respondents use their for to Price - too expensive .  . I share a friend/family member's login to watch shows on their cable/satellite provider's app common uses of for included I use an internet streaming service such as Netflix Hulu Amazon Video etc. or and listening to the I use an antenna to get the basic channels on my TV .  . Headphone Market The retail value of global headphone shipments was forecast to reach 26.1 billion 2017 dollars in 2019 , a more than threefold increase in 0.9 years .

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 templateScale of templateYLabel[1] templateYLabel[2] that take templateTitle[0] supplements , distributed templateTitle[5] templateTitle[6] . The statistic is based on a survey conducted in 2018 . Among templateYLabel[1] adult males , some templateYValue[min] templateScale reported taking templateTitle[0] supplements .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[1] who were using templateTitleSubject[0] as of 2019 , sorted templateTitle[6] templateTitle[7] . During that period of time , templateYValue[max] templateScale of female and templateYValue[min] templateScale of male respondents stated that they used the photo sharing app .
generated: This statistic shows the Percentage of adults in the U.S. who were using U.S. as of 2019 , sorted gender 2018 .  . During that period of time , 77 percentage of female and 73 percentage of male respondents stated that they used the photo sharing app .

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] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .

generated_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] 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[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Rwanda from 2008 to 2018 .  . Urbanization means the Share of urban population in the total population of a country .  . In 2018 , 17.21 % of Rwanda 's total population lived in urban areas and cities .

Example 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] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] , and forecasted to templateNegativeTrend to templateYValue[min] templateScale metric tones by templateXValue[idxmin(Y)] .

generated_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] templatePositiveTrend , reaching around templateYValue[3] templateScale templateYLabel[2] in templateXValue[2] .
generated: This statistic presents the 2013 2020 of Production and pork to 2020 in Production from 2013 to 2019 , with a forecast for 2020 .  . Over this period , the 2020 of the Production and pork industry to 2020 in Production increased , reaching around 112.07 thousand carcass in 2018 .

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] templateScale templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] amounted to approximately templateYValue[idxmax(X)] templateYLabel[1] templateTitle[3] templateTitle[4] in templateXValue[max] , down from templateYValue[1] templateYLabel[1] the previous templateXLabel[0] . templateTitle[1] templateTitle[2] in templateTitleSubject[0] - additional information templateTitle[1] templateTitle[2] , most often classed as a dairy product , comes in a variety of different flavors and is usually made from templateTitle[2] or milk , with added natural or artificial flavorings , sweeteners and colorings .
generated: This statistic shows the Bonus of Champions League total performance in UEFA Champions League from 2005/06 to 2017/18 .  . Champions League Bonus in UEFA Champions League amounted to approximately 437.1 payments total performance in 2017/18 , down from 1396.13 payments the previous Year .  . Champions League in UEFA Champions League - additional information Champions League , most often classed as a dairy product , comes in a variety of different flavors and is usually made from League or milk , with added natural or artificial flavorings , sweeteners and colorings .

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: This statistic depicts the research and development ( templateTitle[2] templateTitle[3] templateTitle[4] ) templateYLabel[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company invested approximately templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in research and development . templateTitleSubject[0] was an agricultural company specialized on genetically engineered seeds .
generated: This statistic depicts the research and development ( R & D ) Expenditure of Syngenta from 2009 to 2018 .  . In 2018 , the company invested approximately -1300 million U.S. dollars in research and development .  . Syngenta was an agricultural company specialized on genetically engineered seeds .

Example 444:
titleEntities: {'Subject': ['The Cheesecake Factory'], 'Date': ['2009', '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] templateYValue[idxmax(X)] , up from templateYValue[1] the previous templateXLabel[0] .

generated_template: This statistic shows information on the templateYLabel[0] templateYLabel[1] development of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] generated roughly templateYValue[max] templateScale templateYLabel[3] in revenues , an templatePositiveTrend compared to the previous templateXLabel[0] at templateYValue[1] templateScale templateYLabel[3] . Revenues templatePositiveTrend from templateYValue[min] templateScale templateYLabel[3] in templateXValue[idxmin(Y)] .
generated: This statistic shows information on the Number restaurants development of The Cheesecake Factory from 2009 to 2018 .  . In 2018 , The Cheesecake Factory generated roughly 201 million restaurants in revenues , an increase compared to the previous Year at 199 million restaurants .  . Revenues increased from 149 million restaurants in 2010 .

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[idxmax(X)] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] had reached about templateYValue[idxmax(X)] 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] templateScale templateYLabel[3] templateYLabel[4] in the templateTitleDate[0] MLS season .

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] 's Oasis III with a templateYLabel[0] templateYLabel[1] of templateYValue[max] and an expected templateXLabel[6] date of 2016 .
generated: This statistic shows the Major League Soccer Toronto FC Soccer Name ranked by Portland Timbers to LA Galaxy , 2019 Operating income/loss .  . The largest ship Name was Atlanta United 's Oasis III with a Operating income/loss of 7 and an expected Name date of 2016 .

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] templateScale templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] amounted to an estimated templateYValue[7] templateScale templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] is a country constantly plagued by war and terrorism .
generated: This statistic shows the Total population of Eritrea from 2014 to 2017 , with projections up until 2024 .  . In 2017 , Eritrea 's Total population amounted to an estimated 5.93 millions Inhabitants .  . population of Eritrea is a country constantly plagued by war and terrorism .

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] templateScale of golf players will be between templateXValue[2] and templateXValue[3] templateXValue[1] old .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[1] a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateYValue[2] templateTitle[7] in the templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] templateYLabel[1] had attended a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateXLabel[0] .
generated: This statistic shows the Share of average forecasted a distribution golfers 2020 at least once in the by 18 group in the group from 20 or younger to 50 to 60 years .  . In 50 to 60 years , 42 % of Europe average had attended a distribution golfers 2020 at least once in the by Year .

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 templateScale . As of this time an templateYLabel[0] templateYValue[1] templateScale templateYLabel[2] in the whole of templateXValue[1] were hard of templateYLabel[4] , templateYLabel[3] templateYValue[5] templateScale of these templateYLabel[2] located in templateXValue[5] .

generated_template: In templateTitleDate[0] , the templateXValue[0] , followed templateTitle[5] ( the leading 15 countries from ) templateXValue[1] Europe , had that largest templateTitle[2] of pharmaceutical templateYLabel[2] globally . For comparison , templateXValue[10] had templateYValue[min] of the lowest single shares among the listed countries . The data shows that the templateXValue[0] held templateYValue[max] templateScale of templateYLabel[1] pharmaceutical templateYLabel[2] .
generated: In 2015 , the Global , followed vs ( the leading 15 countries from ) Europe , had that largest hearing of pharmaceutical people globally .  . For comparison , Poland had 0.8 of the lowest single shares among the listed countries .  . The data shows that the Global held 328.0 % of number pharmaceutical people .

Example 450:
titleEntities: {'Subject': ['earth'], 'Date': ['2010']}
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 the templateYLabel[1] rate of the global 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] templateScale in templateXValue[9] , reaching 42 templateScale U.S. dollars in size .
generated: This statistic shows the U.S. rate of the global earth oxide holmium from 2010 to 2025 .  . The earth oxide holmium is rare to grow by 38 % in 2019 , reaching 42 billion U.S. dollars in size .

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 2020 . During the measured period , the largest templateTitle[4] templateTitle[5] presence of the brand was on templateXValue[0] with templateYValue[max] templateScale templateYLabel[1] , as opposed to its templateYValue[min] templateScale follower base on templateXValue[last] .

generated_template: This statistic gives information on the templateTitle[1] templateTitle[2] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] as of October templateTitleDate[0] . During this period of time , templateYValue[min] templateScale of Indian templateYLabel[1] templateYLabel[2] were templateXValue[0] .
generated: This statistic gives information on the followers Michael of followers millions in Michael Kors as of October 2020 .  . During this period of time , 3.5 millions of Indian followers millions were Facebook .

Example 452:
titleEntities: {'Subject': ['Foursquare'], 'Date': ['2010', '2014']}
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 2010 and October templateTitleDate[max] . As of that templateXLabel[0] , the social check-in app community had accumulated over templateYValue[max] templateScale templateYLabel[2] worldwide .

generated_template: This statistic illustrates the templateYLabel[0] to population templateYLabel[1] in the templateTitle[0] amongst different ethnic groups in templateTitleDate[0] . In templateTitleDate[0] , about templateYValue[2] templateScale of the templateXValue[2] community was employed . In total , around templateYValue[5] templateScale of all Americans were employed at this time .
generated: This statistic illustrates the Number to population registered in the Number amongst different ethnic groups in 2010 .  . In 2010 , about 15 millions of the Mar '12 community was employed .  . In total , around 45 millions of all Americans were employed at this time .

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 templatePositiveTrend for the past decade and amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] . An templatePositiveTrend in templateYLabel[0] corresponded with templatePositiveTrend demand for templateTitle[0] templateTitle[1] in the templateTitleSubject[0] , particularly after the templateXValue[10] Recession . templateTitle[0] templateTitle[1] becomes competitive Since the early 2000s , the price of coal had been going up , and templatePositiveTrend more rapidly following the templateXValue[10] Recession , which affected the cost of crude oil to an even greater degree .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] due to templateTitle[0] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were a total of templateYValue[idxmax(X)] templateYLabel[1] reported due to templateTitle[0] in the templateTitle[1] .
generated: The statistic shows the Production of billion due to Natural in the gas from 1998 to 2018 .  . In 2018 , there were a total of 831.8 billion reported due to 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 templateScale of adult templateTitle[6] templateTitle[7] in the templateTitle[5] who have used a templateTitle[0] templateTitle[1] or templateTitle[2] as of 2019 . According to the findings , only templateYValue[0] templateScale of templateYLabel[1] stated that they were templateXValue[0] using a templateTitle[0] templateTitle[1] or templateTitle[2] , while in comparison templateYValue[max] templateScale 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] templateScale 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 % 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[idxmax(X)] .

generated_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] 49ers franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[idxmax(X)] .
generated: This graph depicts the total Regular season Home attendance of the Atlanta Falcons 49ers franchise of the National Football League from 2006 to 2019 .  . In 2019 , the Regular season Home attendance of the franchise was 572811 .

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] templateScale templateYLabel[2] to the templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[1] in templateTitleDate[0] , templateTitle[3] templateXLabel[0] of templateXValue[0] . In templateTitleDate[0] , the FBI recorded templateYValue[7] templateScale of templateXValue[7] .
generated: This statistic shows the Inflation of rate compared in the rate in 2019 , industrialized Country of Russia .  . In 2019 , the FBI recorded 1.17 % of France .

Example 457:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017', '2020']}
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 2014 to 2020 . templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[3] amounted to templateYValue[0] templateScale as of 30 , templateTitleDate[max] . The smaller the templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[3] , the cheaper the templateTitle[2] is .

generated_template: In the period between templateTitleDate[min] and templateTitleDate[max] , the wettest months tended to be at the start and ends of the year . The greatest measurement of rain recorded was in 2015 when there was approximately templateYValue[max] templateYLabel[2] of templateYLabel[1] . The lowest level was in 2014 , which experienced surprisingly minimal rain .
generated: In the period between 2017 and 2020 , the wettest months tended to be at the start and ends of the year .  . The greatest measurement of rain recorded was in 2015 when there was approximately 4.96 rate of .  . The lowest level was in 2014 , which experienced surprisingly minimal rain .

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 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 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 2016 . templateTitleSubject[0] templateYLabel[1] to templateXValue[0] templatePositiveTrend by templateYValue[max] templateScale 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: In templateTitleDate[0] , templateXValue[0] had the templateTitleSubject[0] templateTitle[1] residential templateYLabel[2] market templateTitle[4] , with an templateYLabel[0] templateYLabel[2] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] . The government of templateXValue[0] provide public templateTitle[2] for lower-income residents and almost 45 templateScale of the templateXValue[0] population lived in public permanent templateTitle[2] in 2017 .
generated: In 2016 , Kenya had the Luxury destinations residential growth market travel , with an Year-over-year growth travel of 59 % growth .  . The government of Kenya provide public largest for lower-income residents and almost 45 % of the Kenya population lived in public permanent largest in 2017 .

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[idxmax(X)] templateTitle[3] templateYLabel[1] templateYLabel[2] would open in templateXValue[max] .

generated_template: This statistic shows the templateTitle[3] of the templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] and visualises the predicted 'ageing templateTitle[6] ' _ . Over the 20 templateXLabel[0] period , the templateYLabel[1] templateYLabel[2] is expected to templatePositiveTrend by 1.7 years , the largest templatePositiveTrend predicted between templateXValue[2] and templateXValue[1] at templateDelta[2,1] years .
generated: This statistic shows the new of the hotel rooms in the Europe ( ) from 2012 to 2016 and visualises the predicted 'ageing opening ' _ .  . Over the 20 Year period , the hotel rooms is expected to increase by 1.7 years , the largest increase predicted between 2014 and 2015 at 4727 years .

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

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] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateScale templateYLabel[3] templateYLabel[4] . For comparison , the Greek templateYLabel[1] amounted to approximately 360 templateScale euros that same templateXLabel[0] .
generated: The statistic shows the National debt of Switzerland from 2014 to 2018 , with projections up until 2024 .  . In 2018 , the National debt of Switzerland amounted to around 280.14 billion U.S. dollars .  . For comparison , the Greek debt amounted to approximately 360 billion euros 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[idxmin(X)] templateScale of the templateYLabel[1] users accessed the templateTitle[3] through their templateTitle[1] device . This figure is projected to grow to 59percent in templateXValue[max] .

generated_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[idxmax(X)] templateScale of the Indian templateYLabel[1] will be accessing templateTitle[1] networks , up from templateYValue[5] templateScale 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 % of the Indian population will be accessing mobile networks , up from 47 % 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 templatePositiveTrend since templateXValue[min] , reaching templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] . The templateTitleSubject[0] Corporation is a templateYLabel[2] medical technology company headquartered in Kalamazoo , Michigan .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitleSubject[0] Company from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitleSubject[0] Company generated a total amount of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in annual templateYLabel[0] .
generated: This statistic shows the Expenses of Stryker Company from 2011 to 2019 .  . In 2019 , Stryker Company generated a total amount of 971 million U.S. dollars in annual Expenses .

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 templateScale templateYLabel[2] of templateTitle[0] oranges produced . The Spanish templateTitle[2] was more than two times the templateTitle[2] of templateXValue[1] , the second largest producer of oranges . The other three producers in the EU produced less than one templateScale templateYLabel[2] during this year .

generated_template: This statistic shows the population distribution of templateTitleSubject[0] templateYLabel[1] living abroad as of templateTitleDate[0] , templateTitle[5] templateTitle[6] . templateTitle[5] the end of that year , around templateYValue[2] templateScale templateTitleSubject[0] nationals who were living templateTitle[1] were in templateXValue[2] .
generated: This statistic shows the population distribution of European Union 1,000 living abroad as of , Union 2016/17 .  . Union the end of that year , around 920 thousand European Union nationals who were living orange were in Greece .

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] templateNegativeTrend from templateYValue[idxmin(X)] in templateXValue[idxmax(Y)] to templateYValue[min] in templateXValue[idxmin(Y)] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] & templateTitleSubject[0] templateYLabel[1] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the cruise company templateTitleSubject[0] carried approximately templateYValue[max] templateYValue[idxmax(X)] templateYLabel[1] on its ships ..
generated: This statistic shows the Number of China & persons 2018 from 1990 to 2018 .  . In 2018 , the cruise company China carried approximately 3.5 3.03 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] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] Clippers franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Phoenix Suns Clippers 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 .

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] templateScale in exports .

generated_template: This statistic shows an estimate of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] as of templateTitleDate[0] , templateTitle[6] templateTitle[7] . During this year , it was projected that the templateTitle[1] templateTitle[2] in templateXValue[0] would hold a templateYValue[0] templateScale templateYLabel[0] of the entire templateTitle[2] . templateTitle[1] industry statistics – additional information The entire templateTitle[1] industry is expected to grow over the coming years , with worldwide revenues projected to reach 1.79 templateScale U.S. dollars in 2022. templateTitle[1] is no longer just a form of entertainment , it is becoming a profession for many players .
generated: This statistic shows an estimate of the Brazil important export Share as of 2017 , Brazil 2017 .  . During this year , it was projected that the important export in China would hold a 21.8 % Share of the entire export .  . important industry statistics – additional information The entire important industry is expected to grow over the coming years , with worldwide revenues projected to reach 1.79 trillion U.S. dollars in 2022. important is no longer just a form of entertainment , it is becoming a profession for many players .

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 templateScale of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] templateTitle[4] templateXLabel[0] in 2010 . templateYValue[max] templateScale of templateYLabel[1] templateYLabel[2] were located in templateXValue[0] .

generated_template: According to the latest available data , templateXValue[0] was the templateXLabel[0] with the highest templateTitle[4] templateTitle[5] templateYLabel[0] in the templateTitleSubject[0] , followed by templateXValue[2] and templateXValue[3] . Although templateXValue[1] has the highest teen templateTitle[5] templateYLabel[0] , California is the templateXLabel[0] with the highest number of templateTitle[4] pregnancies . In templateTitleDate[0] , there were almost 57,000 templateTitle[4] pregnancies in California .
generated: According to the latest available data , Iran was the Country with the highest by country Percentage in the Stuxnet , followed by India and Azerbaijan .  . Although Indonesia has the highest teen country Percentage , California is the Country with the highest number of by pregnancies .  . In , there were almost 57,000 by pregnancies in California .

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 , templateTitleDate[0] was templateXValue[2] war drama film ' templateXValue[0] ' _ , with a templateYValue[max] templateScale templateYLabel[2] dollar templateTitle[9] templateTitle[5] studio Universal Pictures . Universal also spent templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] on templateTitleSubject[0] promotion of 'Dolittle ' _ .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] from the templateTitle[1] from 2014 to templateTitleDate[max] . According to the report , approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[2] were exported from the templateTitleSubject[0] during the fiscal templateXLabel[0] templateXValue[idxmax(Y)] .
generated: 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 1917 .

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 2020 , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[min] templateScale of templateTitleSubject[0] templateTitle[3] were templateXValue[0] and templateYValue[max] templateScale were templateXValue[last] .

generated_template: This statistic gives information on the templateTitle[1] of templateTitleSubject[0] templateYLabel[1] worldwide as of 2020 , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[min] templateScale of templateTitleSubject[0] templateTitle[3] were templateXValue[0] and templateYValue[max] templateScale were templateXValue[last] .
generated: This statistic gives information on the distribution of LinkedIn users worldwide as of 2020 , sorted by gender .  . During the survey period , 43 percentage of LinkedIn audiences were Female and 57 percentage 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] templateScale British Pounds in templateYLabel[0] templateYLabel[1] .

generated_template: In the academic templateXLabel[0] templateXValue[0] there were approximately templateYValue[min] thousand templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] , around 900 fewer than there were in the templateXValue[last] academic templateXLabel[0] . During templateXValue[1] templateTitle[1] templateYLabel[1] had the worst teacher to pupil ratio in the UK , with around 22 children for every one teacher .
generated: In the academic Year 2018/19 there were approximately 953.0 thousand League payments in the Premier League , around 900 fewer than there were in the 2010/11 academic Year .  . During 2017/18 League payments had the worst teacher to pupil ratio in the UK , with around 22 children for every one teacher .

Example 473:
titleEntities: {'Subject': ['Canada'], 'Date': ['2000', '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[idxmax(X)] arsons templateYLabel[2] 100,000 templateYLabel[4] in templateTitleSubject[0] in templateXValue[idxmin(Y)] .

generated_template: This statistic shows the templateTitle[0] farmland templateTitle[1] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[14] , templateTitle[4] in the templateTitle[3] occupied a templateTitle[0] templateTitle[1] of approximately 913 templateScale templateYLabel[2] . By templateXValue[max] , this amount templateNegativeTrend to 899 templateScale templateYLabel[2] .
generated: This statistic shows the Canada farmland reported in the rate from 2000 to 2018 .  . In 2014 , 2000 in the rate occupied a Canada reported of approximately 913 million per .  . By 2018 , this amount dropped to 899 million per .

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[idxmax(X)] templateYValue[idxmax(X)] .

generated_template: This statistic shows the total templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] is a health care company headquartered in Minnetonka , Minnesota . The total templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] the templateTitleSubject[0] , as of 31 , templateXValue[max] , was about templateYValue[idxmax(X)] templateTitle[1] worldwide templateYValue[idxmax(X)]
generated: This statistic shows the total Number of Oil 's number Marathon Oil from 2010 to 2018 .  . The Marathon Oil is a health care company headquartered in Minnetonka , Minnesota .  . The total Number of employees 's number the Marathon Oil , as of 31 , 2018 was about 2400 Oil worldwide 2400 . 

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 2016 . During the survey period , video sharing templateXLabel[0] templateXValue[0] was templateTitle[0] templateTitle[1] among templateTitleSubject[0] templateTitle[5] with a templateYValue[max] templateScale usage rate . templateXValue[2] was ranked third with templateYValue[2] templateScale reporting that they accessed the photo sharing app .

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 social in the networks as of October 2016 .  . During that month , YouTube was ranked first with almost 91 monthly active networks users .  . YouTube Snapchat was ranked third in terms of U.S. but came first in regards of mobile-only users .

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 templateScale of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . In templateTitleDate[0] , templateYValue[min] templateScale of Americans templateXValue[0] to templateXValue[0] old volunteered at least once during the templateXLabel[0] .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateYLabel[0] of templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , templateYValue[2] templateScale units of templateTitle[3] templateTitle[4] were sold worldwide .
generated: This statistic shows the U.S. population Percentage of U.S. 2015 from 65 years and over to 55 to 64 years .  . In 25 to 34 years , 28.9 percentage units of U.S. 2015 were sold worldwide .

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 templateYLabel[0] volume of oranges in the templateTitle[0] from templateXValue[10] to templateXValue[max] . According to the report , templateTitleSubject[0] templateYLabel[0] of oranges amounted to approximately templateYValue[min] templateScale templateYLabel[2] in templateXValue[idxmin(Y)] , down from templateYValue[1] templateScale templateYLabel[2] the previous templateXLabel[0] .
generated: This statistic shows the Production volume of oranges in the U.S. from 2008 to 2018 .  . According to the report , U.S. Production of oranges amounted to approximately 181.4 thousand tons in 2002 , down from 437.6 thousand tons the previous Year .

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] templateScale templateYLabel[1] templateYLabel[2] . Total templateTitle[7] of templateYLabel[1] templateTitle[1] templateTitle[2] templateTitle[3] amounted to about 1.25 templateScale templateYLabel[1] templateYLabel[2] in templateTitleDate[0] .

generated_template: The statistic shows the templateXLabel[0] templateTitle[1] programs with the most templateYLabel[1] in templateTitleDate[0] . In that year , the templateXValue[0] government donated some templateYValue[1] templateScale templateYLabel[3] dollars in templateYLabel[1] templateYLabel[2] .
generated: The statistic shows the Company baby programs with the most U.S. in 2016 .  . In that year , the Private label government donated some 416.2 million dollars in U.S. dollars .

Example 479:
titleEntities: {'Subject': ['Los Angeles Rams'], 'Date': ['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[idxmax(X)] . The franchise moved from St. Louis to templateTitleSubject[0] before the templateXValue[3] templateTitle[1] .

generated_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] 49ers franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[idxmax(X)] .
generated: This graph depicts the total Regular season Home attendance of the Los Angeles Rams 49ers franchise of the National Football League from 2006 to 2019 .  . In 2019 , the Regular season Home attendance of the franchise was 498605 .

Example 480:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000']}
title: Estimated U.S. clothing and clothing accessories per capita sales 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'sales', 'in', 'U.S.', 'dollars']: ['795', '804', '797', '786', '774', '763', '734', '689', '667', '709', '734', '714', '680', '650', '616', '599', '587', '594']

gold: The timeline shows the estimated clothing and clothing accessories per capita sales in the United States from 2000 to 2017 . In 2017 , clothing and clothing accessories sales amounted to 795 U.S. dollars per capita .
gold_template: The timeline shows the templateTitle[0] templateTitle[2] and templateTitle[2] templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] and templateTitle[2] templateTitle[4] templateYLabel[2] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] templateYLabel[0] templateYLabel[1] .

generated_template: The timeline shows the templateTitle[0] templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[1] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] templateYLabel[2] amounted to about templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] templateYLabel[0] templateYLabel[1] .
generated: The timeline shows the Estimated clothing Per capita sales in the U.S. capita 2000 to 2017 .  . In 2017 , clothing sales amounted to about 795 U.S. dollars Per capita .

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: Federally qualified templateTitleSubject[0] ( templateYLabel[1] ) are community-based templateTitleSubject[0] that are funded in part templateTitle[7] HRSA , a sector of Medicare and Medicaid , to treat underserved patients in their communities . As of templateTitleDate[0] templateXValue[4] had the largest templateYLabel[0] of templateTitleSubject[0] centers with templateYValue[max] templateTitleSubject[0] throughout the templateXLabel[0] . templateXValue[33] templateXValue[34] had the fewest templateYLabel[0] of templateTitleSubject[0] centers with just templateYValue[34] templateTitleSubject[0] throughout the templateXLabel[0] .
generated: Federally qualified U.S. ( aggravated ) are community-based U.S. that are funded in part state HRSA , a sector of Medicare and Medicaid , to treat underserved patients in their communities .  . As of 2018 Tennessee had the largest Number of U.S. centers with 105412 U.S. throughout the State .  . Mississippi Alaska had the fewest Number of U.S. centers with just 4391 U.S. throughout the State .

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 templateScale 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] templateScale 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 templatePositiveTrend interests in mobile health technologies .

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] templateScale 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 2012 among adult Americans , on whether they believe the glass is half full or half empty .  . 84 percentage of respondents said they consider themselves optimists .  . The use and for of the American people use is defined as a mental attitude or worldview that favors a positive outcome , while for favors a negative outcome or prediction .

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] templateScale templateYLabel[3] templateYLabel[4] . templatePositiveTrend crop prices lead to templatePositiveTrend templateTitle[2] demands and has been especially noted in recent years in South Asia .

generated_template: This statistic represents the templateTitleSubject[0] templateTitle[1] templateTitle[2] insoles templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale units of templateTitleSubject[0] templateTitle[1] templateTitle[2] are expected to be consumed globally .
generated: This statistic represents the Forecast sulfur fertilizer insoles Total worldwide from 2014 to 2020 .  . In 2014 , 61.44 million units of Forecast sulfur fertilizer are expected to be consumed globally .

Example 484:
titleEntities: {'Subject': ['Smartphone'], 'Date': ['2016']}
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] templateScale and is forecast to further grow by several hundred templateScale 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 templateScale user mark . templateYLabel[0] unit sales levelling off In the past five years , about 1.4 templateScale smartphones were sold templateTitle[2] annually , reflecting stagnation in the templateYLabel[0] market during the last few years .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] templateTitle[5] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] was forecasted to templatePositiveTrend by templateYValue[2] templateScale in templateXValue[3] and a further templateYValue[2] templateScale in templateXValue[2] templateTitle[1] templateTitle[2] - additional information templateTitle[1] in the 21st century is no longer limited to a small geographical area , as internet communication and almost universal access to templateTitle[2] and accommodation allow for easy national and transnational movement between companies , their partners , customers , suppliers or distributors . Although multinational corporations and templateTitle[1] templateTitle[2] have a long history , they have reached unprecedented levels in the modern era and seem to be templatePositiveTrend every templateXLabel[0] .
generated: This statistic shows the Smartphone users worldwide 2016 users 2021 from 2016 to 2021 .  . Smartphone users worldwide 2016 was forecasted to increase by 3.2 billions in 2018 and a further 3.2 billions in 2019 users worldwide - additional information users in the 21st century is no longer limited to a small geographical area , as internet communication and almost universal access to worldwide and accommodation allow for easy national and transnational movement between companies , their partners , customers suppliers or distributors .  . Although multinational corporations and users worldwide have a long history , they have reached unprecedented levels in the modern era and seem to be growing every Year .

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 2019 , photo sharing platform templateTitleSubject[0] reported templateYValue[max] templateScale templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[2] worldwide , up from templateYValue[1] templateScale global templateYLabel[1] in 2018 . templateTitleSubject[0] is a feature of the app allowing templateTitle[2] post photo and video sequences that disappear 24 hours after being posted . templateTitleSubject[0] usageInstagram has over templateYValue[max] templateScale monthly templateTitle[1] templateTitle[2] and is one of the most popular social networks worldwide .

generated_template: This statistic shows a timeline with the amount of monthly templateYLabel[2] templateTitle[0] templateTitleSubject[0] templateYLabel[3] worldwide as of templateXValue[last] templateTitleDate[max] . As of that templateXLabel[0] , the mobile messaging app announced more than templateYValue[max] templateScale templateYLabel[1] templateYLabel[3] , up from over templateYValue[1] templateScale in of the previous year . Mobile data from 2017 shows that templateTitle[0] templateYLabel[3] in the country are a highly engaged audience with an average of 186 monthly usage sessions .
generated: This statistic shows a timeline with the amount of monthly millions Daily Instagram Stories millions worldwide as of Oct '16 19 .  . As of that Month , the mobile messaging app announced more than 500 millions DAU , up from over 400 millions in of the previous year .  . Mobile data from 2017 shows that Daily millions in the country are a highly engaged audience with an average of 186 monthly usage sessions .

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] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateScale templateYLabel[3] templateYLabel[4] . For comparison , the Greek templateYLabel[1] amounted to approximately 360 templateScale 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 of 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: In templateTitleDate[0] , templateXValue[0] was the largest templateTitle[1] templateTitle[2] templateXLabel[0] in the world , with templateYValue[max] templateScale people were produced in the templateXValue[0] . The majority of templateTitleSubject[0] templateTitle[1] fuels is derived from fossil fuels . templateXValue[0] 's templateTitleSubject[0] templateTitle[1] mix has shifted from a dominant use of coal to an templatePositiveTrend of natural gas and renewable sources .
generated: In 2017 , El Salvador was the largest America & Country in the world , with 61.8 million people were produced in the El Salvador .  . The majority of Latin America Caribbean America fuels is derived from fossil fuels .  . El Salvador 's Latin America Caribbean America mix has shifted from a dominant use of coal to an increase of natural gas and renewable sources .

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 presents and estimate of templateTitleSubject[0] 's templateYLabel[1] from the first templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . In the fourth templateXLabel[0] of templateTitleDate[max] , the photo sharing app is projected to generate templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in global revenues , up from an templateYLabel[0] templateYValue[6] templateScale templateYLabel[3] templateYLabel[4] in the second templateXLabel[0] of 2019 .
generated: This statistic presents and estimate of Moscow 's per from the first Quarter of 2019 to the fourth Quarter of 2019 .  . In the fourth Quarter of 2019 , the photo sharing app is projected to generate 760 million meter euros in global revenues , up from an Cost 613 million 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] templateScale to communicating back to their consumers on templateTitle[5] templateTitle[6] , while the templateXValue[2] industry reported in templateYValue[2] templateScale .

generated_template: According to the most recent data gathered in 2019 , the movie with the templateTitle[0] templateTitle[1] weekend at the templateTitleSubject[0] box templateYLabel[1] is Marvel creation ‘ templateXValue[5] : templateXValue[0] ' _ , which grossed a record breaking templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] on its first weekend of screening . ‘ templateXValue[0] ' was not the only movie from The templateXValue[5] series to perform impressively at the templateYLabel[0] templateYLabel[1] on its templateTitle[1] weekend . ‘ templateXValue[5] : templateXValue[1] ' ranked second , though its templateYLabel[2] was almost one hundred templateScale templateYLabel[4] templateYLabel[5] lower , emphasizing how successful ‘ templateXValue[0] ' really was .
generated: According to the most recent data gathered in 2019 , the movie with the Average U.S. weekend at the U.S. box response is Marvel creation ‘ Internet/Technology : Utilities ' _ , which grossed a record breaking 18 % rate on its first weekend of screening .  . ‘ Utilities ' was not the only movie from The Internet/Technology series to perform impressively at the Average response on its U.S. weekend .  . ‘ Internet/Technology : Retail ' ranked second , though its rate was almost one hundred percent rate lower , emphasizing how successful ‘ Utilities ' really was .

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 templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per day in the templateTitleSubject[0] between templateTitleDate[min] and templateTitleDate[max] , templateTitle[8] templateTitle[9] . In this templateYValue[min] templateXLabel[0] period , individuals aged between 11 and 18 templateXValue[0] old templateTitle[3] templateYValue[max] templateYLabel[1] of templateTitle[1] templateTitle[2] per day .
generated: This statistic shows the U.S. of rice import volume per day in the U.S. between 2013/14 and 2017/18 , .  . In this 755 Year period , individuals aged between 11 and 18 2013/14 old volume 787 thousand of rice import per day .

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 templateTitleDate[0] . During the survey period , it was found that templateYValue[max] templateScale 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: This statistic illustrates 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] templateScale , closely followed by templateXValue[1] at templateYValue[1] templateScale . On the opposite side , templateXValue[last] is used by four templateScale of templateYLabel[1] .
generated: This statistic illustrates the results of a survey about the most top active social media in UK the third quarter of 2018 .  . The most used active social Platform in UK was Youtube at 80 % , closely followed by Facebook at 78 % .  . On the opposite side , Imgur is used by four percent of respondents .

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[min] to templateTitleDate[max] . The earthquake templateTitle[1] occurred on templateXValue[0] , templateXValue[0] in templateXValue[0] caused approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] worth of templateYLabel[0] and is the costliest earthquake on record .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitle[4] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , about templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of templateXValue[0] were produced in the templateTitle[0] .
generated: This statistic shows the Earthquakes that caused Damage in the economic in 1900 , U.S. Date, .  . In 1900 , about 30000 million U.S. dollars of January 17 1994 Los Angeles were produced in the Earthquakes .

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[idxmax(X)] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . See the templateTitle[0] of templateTitleSubject[0] for comparison .

generated_template: The templateTitle[0] templateTitle[1] in templateTitleSubject[0] was templateYValue[max] people templateYLabel[1] templateYLabel[2] templateYLabel[3] ( 47.24 templateYLabel[1] templateYLabel[2] mile ) in templateXValue[idxmax(Y)] . This number has been slowly templatePositiveTrend for the past ten years . Higher templateTitle[0] templateTitle[1] is associated with urbanization , but not necessarily economic growth .
generated: The Population density in Italy was 206.67 people per square kilometer ( 47.24 per square mile ) in 2014 .  . This number has been slowly increasing for the past ten years .  . Higher Population density is associated with urbanization , but not necessarily economic growth .

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 2014 , asking the templateYLabel[1] where they usually templateTitle[3] their shampoo and/or templateTitle[6] . Some templateYValue[3] templateScale of templateTitleSubject[0] adults indicated that they buy their shampoo/conditioner templateXValue[3] .

generated_template: A survey of online users in the templateTitle[5] revealed that templateYValue[max] templateScale of templateYLabel[1] felt that 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] templateScale of templateYLabel[1] stated the same about gaming apps .
generated: A survey of online users in the shampoos revealed that 62 % 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 % 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] templateScale templateYLabel[2] in templateTitleDate[0] . templateXValue[1] templateXValue[0] came in second at templateYValue[1] templateScale templateYLabel[2] of sunflowers . templateTitle[3] products There are several products that are derived from sunflowers .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitle[3] in the templateTitle[6] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In templateTitleDate[0] , about templateYValue[2] templateScale of the templateXValue[2] community was employed . In total , around templateYValue[5] templateScale of all Americans were employed at this time .
generated: This statistic shows the Leading U.S. Production of sunflower in the 2019 in , 2019 State .  . In 2019 , about 102630 thousand of the Minnesota community was employed .  . In total , around 53925 thousand of all Americans were employed at this time .

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 2016 , 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: templateXValue[0] was the templateTitle[0] templateTitle[1] templateTitle[2] page on templateTitle[4] in the templateTitleSubject[0] ( templateXValue[0] ) as of templateTitle[8] templateTitleDate[0] , attracting templateYValue[max] templateScale 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] : more than just e-commerce templateXValue[0] has enjoyed significant growth in the templateXValue[0] market in recent years .
generated: Søren Bjerg (Bjergsen) was the Leading eSports pro page on Twitter in the Twitter ( Søren Bjerg (Bjergsen) ) as of number 2016 , attracting 604 thousands Number Twitter .  . This was more than double that of Enrique Cedeño Martínez (xPeke) , the company with the second highest number of Twitter .  . Søren Bjerg (Bjergsen) : more than just e-commerce Søren Bjerg (Bjergsen) has enjoyed significant growth in the Søren Bjerg (Bjergsen) market in recent years .

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 templatePositiveTrend by nearly one templateScale in this time period . In templateXValue[min] , there were approximately templateYValue[idxmin(X)] templateScale templateYLabel[0] in templateTitleSubject[0] and by the end of templateXValue[max] the Swedish templateTitle[0] reached templateYValue[idxmax(X)] templateScale people .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the estimated templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateScale templateYLabel[0] .
generated: This statistic shows the Population Sweden of from 2009 to 2019 .  . In 2013 , the estimated Population Sweden of amounted to approximately 9.64 millions Inhabitants .

Example 498:
titleEntities: {'Subject': ['Canada'], 'Date': ['2027']}
title: Projected increase of the national defense budget in Canada fiscal years 2018 to 2027
X_Axis['Year']: ['2026-2027', '2025-2026', '2024-2025', '2023-2024', '2022-2023', '2021-2022', '2020-2021', '2019-2020', '2018-2019', '2017-2018']
Y_Axis['Annual', 'funding', 'increase', 'in', 'million', 'Canadian', 'dollars']: ['2300', '2020', '1751', '1495', '1250', '1016', '793', '580', '377', '184']

gold: This statistic shows the projected increase over baseline spending for the national defense budget of Canada between the fiscal years 2018 to 2027 . In fiscal year 2027 , the defense budget for Canada is projected to be 2.3 billion Canadian dollars greater than 2026 budget .
gold_template: This statistic shows the templateTitle[0] templateYLabel[2] over baseline spending for the templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleSubject[0] between the templateTitle[6] templateTitle[7] templateTitleDate[min] to templateTitleDate[max] . In templateTitle[6] templateXLabel[0] templateTitleDate[max] , the templateTitle[3] templateTitle[4] for templateTitleSubject[0] is templateTitle[0] to be templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] greater than 2026 templateTitle[4] .

generated_template: This statistic illustrates the templateTitle[0] templateTitle[1] templateYLabel[0] of liquid templateTitle[3] ( including semi-skimmed , skimmed , whole templateTitle[3] , low templateScale fat templateTitle[3] and other templateTitle[3] ) in templateTitleSubject[0] from the 52 weeks ending on the 18th of October templateXValue[last] to the 52 weeks ending on the 7th of October templateXValue[0] . In the 52 weeks ending October templateXValue[0] , the templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitle[3] was templateYValue[0] templateYLabel[1] templateYLabel[2] liter .
generated: This statistic illustrates the Projected increase Annual of liquid defense ( including semi-skimmed , skimmed whole defense , low million fat defense and other defense ) in Canada from the 52 weeks ending on the 18th of October 2017-2018 to the 52 weeks ending on the 7th of October 2026-2027 .  . In the 52 weeks ending October 2026-2027 , the Projected increase Annual of defense was 2300 funding increase liter .

Example 499:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2015']}
title: Purchasing power change in the Netherlands 2015 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Year', 'on', 'year', 'percentage', 'change']: ['1.3', '1.6', '0.3', '0.3', '2.6', '1']

gold: In 2018 , the purchasing power in the Netherlands increased for the fourth year in a row . Purchasing power growth slowed down considerably though in 2017 and 2018 in comparison to 2015 and especially 2016 . In the next two years , the purchasing power was forecast to increase further .
gold_template: In templateXValue[2] , the templateTitle[0] templateTitle[1] in the templateTitleSubject[0] templatePositiveTrend for the fourth templateXLabel[0] in a row . templateTitle[0] templateTitle[1] growth slowed down considerably though in templateXValue[3] and templateXValue[2] in comparison to templateXValue[min] and especially templateXValue[4] . In the next templateYValue[1] years , the templateTitle[0] templateTitle[1] was forecast to templatePositiveTrend further .

generated_template: This statistic shows the templateScale of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] in templateXValue[min] and templateXValue[4] , with a forecast to templateXValue[max] . In templateXValue[min] , templateYLabel[1] templateYLabel[2] made in templateTitleSubject[0] amounted to roughly templateYValue[max] templateScale .
generated: This statistic shows the percentage of year percentage in Netherlands 2015 and 2016 , with a forecast to 2020 .  . In 2015 , year percentage made in Netherlands amounted to roughly 2.6 percentage .

Example 500:
titleEntities: {'Subject': ['General Motors'], 'Date': ['1999', '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] templateScale 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[idxmax(X)] templateScale 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 6.64 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: 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 U.S. customer in the satisfaction as of October 2019 .  . During that month , Pinterest was ranked first with almost 80 monthly active satisfaction users .  . Pinterest Wikipedia was ranked third in terms of ACSI but came first in regards of mobile-only users .

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 templateScale of templateTitle[1] templateTitle[2] owners in the templateTitle[0] who templateTitle[6] apps at least once a month or more as of 2018 , sorted templateTitle[9] templateTitle[10] templateTitle[11] . During the survey period , it was found that templateYValue[1] templateScale 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] templateScale 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 % 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[idxmax(X)] 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[idxmin(Y)] .

generated_template: In templateXValue[max] , around templateYValue[idxmax(X)] templateScale templateYLabel[3] of templateYLabel[0] were consumed by templateTitleSubject[0] templateTitle[1] , compared to a high of templateYValue[max] templateScale templateYLabel[3] in templateXValue[idxmax(Y)] . While the templateYLabel[1] of airline templateYLabel[0] in the templateTitle[0] has templatePositiveTrend in recent years , it is yet to surpass the levels seen prior to the templateXValue[11] recession . Commercial templateTitle[1] The above figures include all commercial air carriers based in the templateTitleSubject[0] who carry cargo and/or passengers on domestic or international flights , and with annual revenue of over 20 templateScale templateTitleSubject[0] dollars .
generated: In 2019 , around 69 million aircraft of Number were consumed by Boeing 737 , compared to a high of 1208 million aircraft in 2013 .  . While the aircraft of airline Number in the Boeing has increased in recent years , it is yet to surpass the levels seen prior to the 2008 recession .  . Commercial 737 The above figures include all commercial air carriers based in the Boeing who carry cargo and/or passengers on domestic or international flights , and with annual revenue of over 20 billion Boeing dollars .

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: This statistic represents the templateYLabel[0] templateYLabel[1] templateYLabel[3] spent on templateTitle[1] apps templateTitle[7] smartphone users in Southeast Asian countries in 2016 . During the survey period , it was found that smartphone users in templateXValue[3] spent an templateYLabel[0] of 66 templateYLabel[4] every day on templateTitle[1] apps .
generated: This statistic represents the Area thousand hectares spent on countries apps 2017 smartphone users in Southeast Asian countries in 2016 .  . During the survey period , it was found that smartphone users in Colombia spent an Area of 66 hectares every day on countries apps .

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[max] , social network templateTitleSubject[0] 's total revenues amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] , the majority of which were generated through advertising . The company announced over seven templateScale active advertisers on templateTitleSubject[0] during the third templateXLabel[0] of templateTitleDate[max] . During that fiscal period , the company 's net income was 7.35 templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The templateTitleSubject[0] Times templateTitleSubject[0] 's templateTitle[6] templateYLabel[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the final templateXLabel[0] of templateTitleDate[max] , up templateTitle[8] 263.5 templateScale in the corresponding templateXLabel[0] of templateXValue[4] . The templateTitleSubject[0] ' subscription templateYLabel[0] generally grows steadily over the course of each year . However , for the most part , the company sees a small templateYLabel[0] templateNegativeTrend in the third templateXLabel[0] , which has been an ongoing and consistent trend for the company over the last few years .
generated: The Facebook Times 's 2019 Revenue amounted to 21082 million U.S. dollars in the final Quarter of 2019 , up 2019 263.5 million in the corresponding Quarter of Q4 '18 .  . The Facebook ' subscription Revenue generally grows steadily over the course of each year .  . However , for the most part , the company sees a small Revenue decrease in the third Quarter , which has been an ongoing and consistent trend for the company over the last few years .

Example 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[last] templateScale of consumers preferred to use templateXValue[last] templateTitle[3] making purchases at templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: This statistic shows the preferred templateTitle[3] templateTitle[4] for templateTitle[1] templateTitle[2] according to internet users in the templateTitle[0] in templateTitleDate[0] . During the survey period , it was found that templateYValue[1] templateScale of templateYLabel[1] most frequently used a templateXValue[1] templateXValue[0] to pay for templateTitle[1] purchases .
generated: This statistic shows the preferred when shopping for type preference according to internet users in the Payment in 2018 .  . During the survey period , it was found that 44 % of respondents most frequently used a Debit card Credit card to pay for type purchases .

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: This statistic depicts the total templateTitle[2] templateTitle[3] in the templateTitle[1] in templateTitleDate[0] , templateTitle[4] templateYLabel[0] of templateYLabel[1] . In templateTitleDate[0] , about templateYValue[max] templateScale of the templateXValue[2] community was employed . In total , around templateYValue[5] templateScale of all Americans were employed at this time .
generated: This statistic depicts the total expenditure per in the Easter in 2019 , capita Average of expenditure .  . In 2019 , about 47.97$ % of the Gifts community was employed .  . In total , around 8.73$ million of all Americans were employed at this time .

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[idxmin(X)] templateScale of the templateYLabel[1] in templateTitleSubject[0] were accessing the templateTitle[1] . This figure is projected to grow to templateYValue[idxmax(X)] templateScale by templateXValue[idxmax(Y)] .

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

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 templatePositiveTrend in comparison to the previous templateXLabel[0] , but significantly lower than for example in templateXValue[min] and templateXValue[7] , when the templateYLabel[0] templateYLabel[1] was templateYValue[idxmin(X)] and templateYValue[7] respectively . It was also considerably lower than in neighboring country the Netherlands , which in templateXValue[max] had a positive templateYLabel[0] templateYLabel[1] of over 86,000 .

generated_template: This statistic shows templateTitleSubject[0] templateYLabel[1] in templateYLabel[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , the templateYLabel[2] templateTitle[3] templateYLabel[3] templateNegativeTrend by 0.6 templateScale compared to the previous templateXLabel[0] . templateTitle[3] templateTitle[4] were forecasted to templatePositiveTrend by templateYValue[idxmax(X)] templateScale in templateXValue[max] .
generated: This statistic shows Belgium balance in 2010 2018 from 2010 to 2018 .  . In 2014 , the balance 2010 fell by 0.6 % compared to the previous Year .  . 2010 2018 were forecasted to increase by 50180 million in 2018 .

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 2019 , 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 of the previous templateXLabel[0] .

generated_template: The statistic above presents templateYLabel[0] data for the templateTitleSubject[0] templateTitle[2] templateTitle[3] and sound templateTitle[4] industry from templateXValue[min] to templateXValue[max] . In 2019 , this industry employed an estimated templateYValue[0] templateYValue[idxmax(X)] people , down slightly from templateYValue[1] recorded in the previous templateXLabel[0] .
generated: The statistic above presents Employment data for the U.S. publishing industries and sound 2001 industry from 2001 to 2019 .  . In 2019 , this industry employed an estimated 733.7 people , down slightly from 730.5 recorded in the previous Year .

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] templateScale .

generated_template: This statistic shows the size of the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[2] , with projections up until templateXValue[max] . In templateXValue[2] , templateTitleSubject[0] had a templateYLabel[0] templateYLabel[1] of around templateYValue[3] templateScale people .
generated: This statistic shows the size of the Employed persons in Switzerland from 2010 to 2018 , with projections up until 2020 .  . In 2018 , Switzerland had a Employed persons of around 5.01 millions people .

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[idxmax(X)] 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[1] templateYLabel[2] by one templateYLabel[4] while being of child-bearing age . In templateXValue[max] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] amounted to templateYValue[min] templateYValue[idxmax(X)] 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 children born by one woman while being of child-bearing age .  . In 2017 , Afghanistan 's Fertility rate 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] templateScale of the templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] templateTitle[1] the greatest earthquake occurrence templateTitle[5] templateTitleDate[min] to templateTitleDate[max] . During this period , about templateYValue[2] templateScale templateYLabel[2] in templateXValue[0] were templateYLabel[1] by templateTitle[6] .
generated: This statistic shows the National of debt relation in GDP debt the greatest earthquake occurrence gross 2018 to .  . During this period , about 99.31 % relation in Japan were debt by domestic .

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 templateScale templatePositiveTrend from the templateYLabel[0] that took place in the preceding year .

generated_template: This statistic shows templateTitleSubject[0] templateYLabel[1] templateYLabel[2] templateTitle[3] templateTitle[4] of templateTitle[5] in templateTitleDate[0] . templateXValue[0] templateXLabel[0] comprised the largest templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleDate[0] with templateYValue[max] templateScale . templateXLabel[0] templateTitle[3] templateXValue[last] had the smallest templateYLabel[0] at just templateYValue[min] templateScale .
generated: This statistic shows United Kingdom transplants Kingdom UK of 2018/19 in .  . England Country comprised the largest Number of transplants in with 779 % .  . Country Kingdom Northern Ireland had the smallest Number at just 34 % .

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 templateTitleSubject[0] 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: In templateTitleDate[0] , templateXValue[0] was the city with the highest templateYLabel[0] of tourists staying at least templateYValue[8] night templateYLabel[2] capita . That same year , international overnight visitor spending in templateXValue[0] reached 28.5 templateScale US dollars . The city , which is the largest and most populous city in the United Arab Emirates , was also templateYValue[8] of the most expensive holiday destinations in the world in 2018 .
generated: In 2017 , Afghanistan was the city with the highest Child of tourists staying at least 69.5 night first capita .  . That same year , international overnight visitor spending in Afghanistan reached 28.5 billion US dollars .  . The city , which is the largest and most populous city in the United Arab Emirates , was also 69.5 of the most expensive holiday destinations in the world in 2018 .

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 2016 . Durign the survey , templateYValue[max] of templateYLabel[1] stated templateTitleSubject[0] templateTitle[4] did a templateXValue[0] of potraying templateXValue[0] .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among adult Cubans living in templateTitleSubject[0] on their opinion of templateTitle[1] templateTitle[2] , their former President . templateYValue[max] templateScale of templateYLabel[1] stated they have a very or somewhat templateXValue[1] opinion of templateTitle[1] templateTitle[2] .
generated: This statistic shows the results of a 2016 survey among adult Cubans living in Hollywood on their opinion of ethnic minorities , their former President .  . 38 % of respondents stated they have a very or somewhat Give into stereotypes when portraying racial minorities opinion of ethnic minorities .

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[idxmax(X)] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] to one templateYLabel[4] while being of child-bearing age . In templateXValue[max] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] amounted to templateYValue[min] templateYValue[idxmax(X)] 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 , Russia 's Fertility rate amounted to 1.42 1.76 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] templateScale templateYLabel[3] templateYLabel[4] . Popular templateTitleSubject[0] titles include Candy Crush Saga , Farm Ville Saga as well as Bubble Witch Saga .

generated_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] ' templateYLabel[0] templateYLabel[1] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic depicts the Operating income of King from 2010 to 2018 .  . In 2018 , King ' Operating income amounted to 750.0 million U.S. dollars .

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[min] and templateTitleDate[max] , about templateYValue[7] templateYLabel[1] in templateXValue[4] templateXValue[7] were templateYLabel[2] in templateTitle[5] programs .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] templateYLabel[2] 100,000 templateYLabel[4] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] templateTitle[2] in templateXValue[4] was templateYValue[4] templateYLabel[2] 100,000 residents , as compared to templateYValue[46] in templateXValue[1] templateXValue[46] .
generated: This statistic shows the Number children U.S. enrolled 100,000 in the U.S. in 2017 , by State .  . In 2017 , the Number children U.S. in New York was 121572 enrolled 100,000 residents , as compared to 71 in California Guam .

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 2019 , excluding London . As of 2019 , 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 templateScale templateTitle[4] of templateXLabel[1] templateXLabel[2] in the templateTitle[2] in templateTitleDate[0] . In templateTitleDate[0] , templateYValue[3] templateScale of templateXLabel[3] private templateYLabel[2] had an templateXLabel[0] templateXLabel[2] between templateXValue[3] and templateXValue[3] templateXLabel[3] templateXLabel[4] . templateXLabel[2] and poverty The largest proportion of citizens , at templateYValue[max] templateScale , earn an templateXLabel[0] templateXLabel[1] templateXLabel[2] between templateXValue[4] to templateXValue[4] templateXLabel[3] templateXLabel[4] .
generated: This statistic shows the percentage office of City in the estate in 2019 .  . In 2019 , 425 % of City private square had an City between Edinburgh and City .  . City and poverty The largest proportion of citizens , at 468 % , earn an City between Birmingham to City .

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[idxmin(Y)] , 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: This statistic shows that the templateTitle[0] templateYLabel[0] of templateYLabel[1] ( ALOS ) in templateTitleSubject[0] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[14] , a hospital templateYLabel[1] in the templateTitle[3] had an templateTitle[0] templateYLabel[0] of templateYValue[14] templateYLabel[2] . Since then , there was no significant change in the templateYLabel[0] of templateYLabel[1] .
generated: This statistic shows that the Number of drive-in ( ALOS ) in U.S. 1995 from to 2019 .  . In 2005 , a hospital drive-in in the sites had an Number of 401 cinema .  . Since then , there was no significant change in the Number of drive-in .

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[idxmax(X)] children were born templateYLabel[1] 1,000 of templateYLabel[3] templateYLabel[4] .

generated_template: In templateXValue[max] , there were about templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] in the templateTitle[4] with a templateTitle[2] mother . This is an templatePositiveTrend from templateXValue[min] levels , when there were about templateYValue[min] templateScale templateYLabel[1] templateYLabel[2] with a templateTitle[2] mother . templateTitle[2] parenthood The typical family is comprised of two parents and at least one child .
generated: In 2018 , there were about 14.8 % per 1,000 in the 1990 with a Hispanics mother .  . This is an increase from 1990 levels , when there were about 14.8 % per 1,000 with a Hispanics mother .  . Hispanics parenthood The typical family is comprised of two parents and at least one child .

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 templatePositiveTrend in templateTitleSubject[0] during this period . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of Greek templateYLabel[1] had templateTitle[1] templateTitle[2] .

generated_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . During the most recently reported period , it was templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] in templateXValue[1] , up from templateYValue[1] templateScale in the previous templateXLabel[0] .
generated: This statistic gives information on the Share of households in the Greece from 2007 to 2018 .  . During the most recently reported period , it was 76 million households in the Greece in 2017 , up from 71 million in the previous 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[idxmax(X)] templateScale templateYLabel[2] templateYLabel[0] in universities in templateTitleSubject[0] .

generated_template: The statistic shows the templateYLabel[1] generated by templateTitleSubject[0] , templateTitleSubject[1] , and templateTitle[2] for the fiscal years templateXValue[min] to templateXValue[max] . templateTitleSubject[0] , templateTitleSubject[1] , and templateTitle[2] reported a record templateYLabel[1] of over templateYValue[3] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[3] , up templateTitle[5] templateYValue[4] templateScale in the previous templateXLabel[0] .
generated: The statistic shows the university generated by South Korea , and university for the fiscal years 2011 to 2018 .  . South Korea , South Korea and university reported a record university of over 2.11 millions in 2015 , up Korea 2.13 millions in the previous Year .

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: This statistic shows the templateScale of templateTitle[0] that involved templateTitle[2] in the templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , about templateYValue[max] templateScale of templateYLabel[1] were committed with use of templateTitle[2] in templateXValue[1] . A ranking of the total number of templateTitle[0] templateTitle[5] templateTitleSubject[0] templateXLabel[0] can be found here .
generated: This statistic shows the percentage of Worlds that involved most in the dangerous in 2018 , by City .  . In 2018 , about 138.26 % of rate were committed with use of most in Acapulco - Mexico .  . A ranking of the total number of Worlds by City can be found here .

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 shows the templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitle[2] between 1982 and 26 , 2020 , templateTitle[4] templateXLabel[0] and ethnicity of the shooter ( s ) . Between 1982 and 2020 , templateYValue[max] out of 118 templateTitle[0] templateTitle[1] were initiated templateTitle[4] templateXValue[0] templateTitle[3] . The Las Vegas strip massacre in 2017 had the highest templateYLabel[0] of victims between 1982 and templateTitleDate[0] , with 58 people killed , and over 500 injured .
generated: This statistic shows the Share of Leading internet in the traffic between 1982 and 26 , 2020 worldwide Category and ethnicity of the shooter ( s ) .  . Between 1982 and 2020 , 57.7 out of 118 Leading internet were initiated worldwide Video categories .  . The Las Vegas strip massacre in 2017 had the highest Share of victims between 1982 and 2018 , with 58 people killed , and over 500 injured .

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] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the 25 templateTitle[0] templateTitle[1] in the templateTitle[2] in templateTitleDate[0] , by templateYLabel[1] . In templateTitleDate[0] , about templateYValue[max] templateScale people were living in templateXValue[0] County , templateXValue[0] . Additional information on urbanization in the templateTitle[2] Urbanization is defined as the process by which cities grow or by which societies become more urban .
generated: This statistic shows the 25 Golf-Association executives in the ' in 2012 , by million .  . In 2012 , about 4.58 million people were living in Tim Finchem (PGA Tour commissioner & CEO) 2013 County , Tim Finchem (PGA Tour commissioner & CEO) 2013 .  . Additional information on urbanization in the ' Urbanization is defined as the process by which cities grow or by which societies become more urban .

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 templatePositiveTrend . According to the templateTitle[0] there will be roughly over templateYValue[max] templateScale of people living in templateTitleSubject[0] by templateXValue[idxmax(Y)] .

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[1] , the templateTitle[2] templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] templateTitle[1] amounted to templateYValue[3] templateScale .
generated: This statistic shows the number of metal and products that are produced from Denmark population and other low-grade residues worldwide from 2019 to 2029 .  . In 2028 , the growth Number inhabitants for Denmark population amounted to 6.02 % .

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: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitle[2] between 1982 and 26 , 2020 , templateTitle[4] templateXLabel[0] and ethnicity of the shooter ( s ) . Between 1982 and templateTitleDate[0] , templateYValue[max] out of 118 templateTitle[0] templateTitle[1] were initiated templateTitle[4] templateXValue[0] templateTitle[3] . The Las Vegas strip massacre in 2017 had the highest templateYLabel[0] of victims between 1982 and templateTitleDate[0] , with 58 people killed , and over 500 injured .
generated: This statistic shows the Number of Piracy actual in the attempted between 1982 and 26 , 2020 worldwide Country and ethnicity of the shooter ( s ) .  . Between 1982 and 2019 , 35 out of 118 Piracy actual were initiated worldwide Nigeria attacks .  . The Las Vegas strip massacre in 2017 had the highest Number of victims between 1982 and 2019 , with 58 people killed , and over 500 injured .

Example 530:
titleEntities: {'Subject': ['U.S.'], '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[idxmax(X)] templateScale templateYLabel[2] of templateTitle[0] templateTitle[1] were produced . templateTitle[0] templateTitle[1] is a templateTitle[0] , low-calorie dessert , which is often served in a large variety of flavors .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] 100,000 population in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] homicides templateTitle[1] templateTitle[2] templateYLabel[1] 100,000 population were committed in the templateTitle[4] .
generated: This statistic shows the Frozen yogurt production million 100,000 population in the 2006 from to 2018 .  . In 2018 , about 50.2 homicides yogurt production million 100,000 population were committed in the 2006 .

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 templateScale . The peak , in templateXValue[min] , was followed in templateXValue[1] by the lowest templateYLabel[0] templateYLabel[1] of this period .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] templateTitle[3] templateTitle[4] in the templateTitle[5] templateTitle[6] templateXValue[min] to templateXValue[max] . In the 12 months leading up to 2014 , templateYLabel[2] travelers spent an templateYLabel[0] of templateYValue[1] templateYLabel[2] templateYLabel[3] templateTitle[3] templateTitle[4] .
generated: This statistic shows the Turnout Assembly rate turnout rates in the 1999 2011 to .  . In the 12 months leading up to 2014 , rate travelers spent an Turnout of 38.2 rate turnout rates .

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] templateScale of templateYLabel[1] aged 25 to templateYValue[1] stated they templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] to a templateTitle[7] at some time during templateTitle[8] life .

generated_template: This statistic shows the templateYLabel[0] of American templateTitle[1] templateTitle[2] templateTitle[3] ever templateTitle[4] templateTitle[5] in templateTitle[6] templateTitle[7] , sorted templateTitle[8] templateXLabel[0] templateXLabel[1] . The findings were acquired in early templateTitleDate[0] and published in the Journal of Sexual Medicine , a publication on sexual behavior in the templateTitle[0] , in 2010 . templateYValue[max] templateScale of templateYLabel[1] aged 25 to 29 stated they templateTitle[3] templateTitle[4] templateTitle[5] at some time during templateTitle[6] life .
generated: This statistic shows the Share of American women who have ever given oral in sex male , sorted their Age group .  . The findings were acquired in early and published in the Journal of Sexual Medicine , a publication on sexual behavior in the U.S. , in 2010 .  . 89 % of respondents aged 25 to 29 stated they have given oral at some time during sex life .

Example 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[idxmax(X)] templateScale , an templatePositiveTrend compared to the previous templateXLabel[0] at templateYValue[1] templateScale . This templateYLabel[0] has only been templatePositiveTrend in recent years . Considering current German population numbers stand at almost 83 templateScale , such a high templateYLabel[0] of templateYLabel[1] templateYLabel[2] is significant in itself and also for predicting future trends on digitalization and online connectivity in the country .

generated_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] templateScale of the Chilean population accessed the templateYLabel[3] , up from templateYValue[16] templateScale in templateXValue[16] . In templateXValue[max] , templateYValue[max] templateScale of the Chilean population acceded to templateYLabel[3] .
generated: This statistic gives information on the users in Germany from 2001 to 2018 .  . In 2018 , 84 % of the Chilean population accessed the users , up from 41.7 % in 2002 .  . In 2018 , 84 % of the Chilean population acceded to users .

Example 534:
titleEntities: {'Subject': ['GDP'], 'Date': ['1990']}
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] templateScale of its gross domestic product ( templateYLabel[2] ) . This is an templatePositiveTrend from templateYValue[last] templateScale 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 templateScale 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] , there were about templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] in the templateTitle[4] with a templateTitle[2] mother . This is an templatePositiveTrend from templateXValue[min] levels , when there were about templateYValue[min] templateScale templateYLabel[1] templateYLabel[2] with a templateTitle[2] mother . templateTitle[2] parenthood The typical family is comprised of two parents and at least one child .
generated: In 2017 , there were about 12.06 percentage GDP in the 1990 with a percentage mother .  . This is an increase from 1990 levels , when there were about 9.04 percentage GDP with a percentage mother .  . percentage parenthood The typical family is comprised of two parents and at least one child .

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 templateScale 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 templateScale of ultra-rich templateYLabel[2] ) and templateXValue[1] ( 1.3 templateScale templateYLabel[2] ) .

generated_template: In templateTitleDate[0] , templateXValue[0] was ranked the leading templateTitle[0] market based on the templateTitle[0] templateTitleSubject[0] templateYLabel[0] , with an templateYLabel[0] templateYLabel[1] of templateYValue[max] . The templateTitle[0] templateTitleSubject[0] templateYLabel[0] measures on-the-ground trade templateTitle[0] templateTitleSubject[0] . The templateTitle[0] market in templateXValue[0] According to the templateTitle[0] templateTitleSubject[0] templateYLabel[0] , templateXValue[0] had the highest templateTitleSubject[0] in terms of infrastructure , customs and templateTitle[0] competence .
generated: In 2014 , Germany was ranked the leading Millionaire market based on the Millionaire Europe Number , with an Number millionaire of 1433985 .  . The Millionaire Europe Number measures on-the-ground trade Millionaire Europe .  . The Millionaire market in Germany According to the Millionaire Europe Number , Germany had the highest Europe in terms of infrastructure , customs and Millionaire competence .

Example 536:
titleEntities: {'Subject': ['CVS Health'], 'Date': ['2012']}
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] of the templateTitle[3] templateTitle[4] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitle[5] templateYLabel[0] of templateTitleSubject[0] was around templateYValue[6] templateScale templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Health Share of the share retail of CVS Health from 2012 to 2025 .  . In 2019 , the prescriptions Share of CVS Health was around 34.55 % retail 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: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of a templateTitle[2] is an index that divides the templateTitle[2] into two equal groups : half of the templateTitle[2] is older than the templateYLabel[0] templateYLabel[1] and the other half younger . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] 's templateTitle[2] was templateYValue[7] templateYLabel[2] .
generated: This statistic shows the Median age of the population in Zimbabwe 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 Zimbabwe 's population was 18.4 years .

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] templateScale 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 templateScale templateYLabel[2] templateYLabel[3] . This means that every day the market was open , the templateTitle[0] amount of templateYLabel[2] government templateTitle[6] bought and sold amounted to half a templateScale templateYLabel[2] templateYLabel[3] in that templateXLabel[0] . What are templateTitle[5] templateTitle[6] ? templateTitle[5] templateTitle[6] are templateYLabel[2] government debt , bonds sold to finance the templateTitle[4] government .
generated: 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 ? are British government debt , bonds sold to finance the 2011 government .

Example 539:
titleEntities: {'Subject': ['Births'], 'Date': ['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 2018 , 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: This statistic shows the templateTitle[1] templateYLabel[0] of templateYLabel[1] enrolled in Canadian colleges in templateTitle[7] , templateTitle[4] templateTitle[5] templateTitle[6] . In the academic templateXLabel[0] templateTitle[7] , around templateYValue[max] templateYLabel[1] aged between templateXValue[0] and templateXValue[1] templateXValue[0] were enrolled in Canadian colleges .
generated: This statistic shows the number of children enrolled in Canadian colleges in 2018 , mother 2018 .  . In the academic Year 2018 , around 25038 children aged between 15 to 19 years and 20 to 24 years 15 to 19 years were enrolled in Canadian colleges .

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] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] The 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 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 .  . Orlando Magic The Orlando Magic are a professional basketball franchise of the National Basketball Association ( NBA ) , entering the league as an expansion team in 1968 .

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 templateScale templateXLabel[3] dollars amounted to templateYValue[min] templateYLabel[1] .

generated_template: The total templateYLabel[0] of templateTitle[2] templateXLabel[1] templateYLabel[1] on the U.S. crowdfunding platform templateTitleSubject[0] amounted to 291,825 up to 11 , templateTitleDate[0] . The templateYLabel[0] of templateYLabel[1] that were templateXValue[last] templateXLabel[0] templateXLabel[1] was templateYValue[last] up to this point . Crowdfunding failure Putting yourself , your ideas and your templateYLabel[1] out into the world and subjecting them to the possibility of public scrutiny is not the easiest pill to swallow for a lot of people , and failure can be hard to accept .
generated: The total Number of funding raised projects on the U.S. crowdfunding platform Kickstarter amounted to 291,825 up to 11 , 2019 .  . The Number of projects that were More than 1M+ Money raised was 385 up to this point .  . Crowdfunding failure Putting yourself , your ideas and your projects out into the world and subjecting them to the possibility of public scrutiny is not the easiest pill to swallow for a lot of people , and failure can be hard to accept .

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] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] templateYLabel[4] of land area .

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

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: 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 templateXValue[max] , templateTitle[0] templateYLabel[0] of templateTitle[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] was about templateYValue[1] kilowatts templateYLabel[3] hour .
generated: This statistic represents the Household Consumption of electricity per capita in Indonesia from the Year 2000 to 2016 .  . In 2016 , Household Consumption of electricity per capita in Indonesia was about 333.3 kilowatts per hour .

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 2019 , ranked by templateTitle[1] of templateYLabel[0] . According to the findings , the templateTitle[4] templateXLabel[1] templateXValue[0] had recorded in a total of templateYValue[max] templateScale likes on templateTitleSubject[0] , and ranking second was templateXValue[1] with templateYValue[1] templateScale page likes .

generated_template: This statistic shows the largest templateTitle[2] ( CPA ) templateTitle[3] in the templateTitle[1] in templateTitleDate[0] , in terms of templateYLabel[1] templateYLabel[0] . According to the rankings , templateXValue[0] templateXValue[7] was the largest templateTitle[2] templateXLabel[0] in the templateTitle[1] with templateYLabel[1] templateYLabel[0] of templateYValue[max] templateScale templateYLabel[1] templateYLabel[4] . templateTitle[0] templateTitle[2] templateTitle[3] - additional information templateXValue[0] was the templateTitle[0] CPA templateXLabel[0] in the templateTitle[1] in templateTitleDate[0] with a templateYLabel[0] of templateYValue[max] templateScale templateYLabel[1] templateYLabel[4] in the templateXLabel[2] templateXLabel[3] ending , templateTitleDate[0] .
generated: This statistic shows the largest followers ( CPA ) popular in the number in 2019 , in terms of millions Followers .  . According to the rankings , Louis Vuitton Ralph Lauren was the largest followers Designer in the number with millions Followers of 23.28 millions .  . Facebook followers popular - additional information Louis Vuitton was the Facebook CPA Designer in the number in 2019 with a Followers of 23.28 millions in the Brand ending , 2019 .

Example 545:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['1999']}
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] templateScale British pounds was spent on templateTitle[0] templateTitle[1] and templateTitle[2] products . templateYLabel[0] templatePositiveTrend during the period under consideration to approximately templateYValue[max] templateScale British pounds in templateYLabel[0] in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Over this period , the templateYLabel[1] templateYLabel[0] templatePositiveTrend to its peak to an approximate total of templateYValue[max] thousand templateYLabel[3] in templateXValue[idxmax(Y)] .
generated: This statistic shows the Sales million GBP in United Kingdom from 1999 to 2017 .  . Over this period , the million Sales grew to its peak to an approximate total of 1720 thousand GBP in 2017 .

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] templateScale templateYLabel[3] .

generated_template: This statistic represents the templateTitle[0] templateTitleSubject[0] templateTitle[1] templateTitle[2] on templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . With some 842,000 templateYLabel[0] templateYLabel[1] , templateXValue[0] was ranked first that year . The total number of templateYLabel[0] templateYLabel[1] was more than 8.7 templateScale units in templateTitleDate[0] .
generated: This statistic represents the Leading U.S. prescriptions dispensed on Rx dispensed in 2014 .  . With some 842,000 Rx dispensed , Metformin HCI was ranked first that year .  . The total number of Rx dispensed was more than 8.7 million units in 2014 .

Example 547:
titleEntities: {'Subject': ['Europe'], 'Date': ['2000', '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[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . The total templateYLabel[0] templateTitle[2] of the templateTitle[3] abroad amounted to 5.95 templateScale templateYLabel[3] templateYLabel[4] in templateXValue[max] templateXValue[idxmax(Y)]

generated_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] was priced at templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] on the templateTitle[0] in templateXValue[1] .
generated: This statistic shows the Direct investment of Europe in the U.S. from 2000 to 2018 .  . According to the report , Europe was priced at 3.55 investments trillion U.S. dollars on the Direct in 2017 .

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] templateScale templateYLabel[3] dollars.The templateTitleSubject[0] are owned by David Tepper , who bought the templateYLabel[0] for about templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[1] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to around templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Paul Allen , who bought the templateYLabel[0] for 194 templateScale templateYLabel[3] templateYLabel[4] in 1997 .
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 Paul Allen , who bought the Franchise for 194 million U.S. dollars in 1997 .

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 templatePositiveTrend to around templateYValue[idxmax(X)] templateYLabel[1] British thermal units .

generated_template: This statistic illustrates a timeline of the templateTitle[0] of templateTitle[1] templateTitle[2] templateYLabel[1] templateTitle[5] individual templateTitle[5] day in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , individuals saw on average templateYValue[max] television commercials or templateYLabel[0] templateTitle[5] day .
generated: This statistic illustrates a timeline of the U.S. of production energy quadrillion forecast individual day in the U.S. from 2019 to 2050 .  . In 2045 , individuals saw on average 5.54 television commercials or Production forecast day .

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: The statistic shows the templateTitle[3] templateTitle[4] templateYLabel[2] rate of the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is expected to grow by templateYValue[2] templateScale from templateXValue[3] to templateXValue[2] .
generated: The statistic shows the China based million rate of the China Market value from 2008 to 2015 .  . The Market value is expected to grow by 380.8 million from 2009 to 2010 .

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 2013 , sorted by templateYLabel[0] of templateTitle[3] templateTitleSubject[0] templateYLabel[1] . During that month , templateXValue[2] accounted for templateYValue[2] templateScale of templateTitle[3] templateTitleSubject[0] templateYLabel[1] .

generated_template: This statistic gives a breakdown of templateTitle[0] 's revenue in the fiscal year of templateTitleDate[0] , templateTitle[2] templateXLabel[0] . The Montreal-headquartered company had templateTitleSubject[0] of approximately templateYValue[1] templateScale templateYLabel[1] templateYLabel[2] in its templateXValue[1] segment .
generated: This statistic gives a breakdown of Photo 's revenue in the fiscal year of 2013 , sites Platform .  . The Montreal-headquartered company had Photo of approximately 43 million uploads in its Facebook segment .

Example 552:
titleEntities: {'Subject': ['Nokia'], 'Date': ['1999', '2019']}
title: Nokia 's net sales 1999 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Net', 'sales', 'in', 'billion', 'euros']: ['23.32', '22.56', '23.15', '23.64', '12.5', '11.76', '12.71', '30.18', '38.66', '42.45', '40.98', '50.71', '51.06', '41.12', '34.19', '29.37', '29.53', '30.02', '31.19', '30.38', '19.77']

gold: In 2018 , Nokia had 22.5 billion euros in net sales , which is a small decrease from the year before . In the past decade , the company has made several directional shifts from handheld devices to network infrastructure in order to keep up with the quickly evolving electronics and telecommunications industry . Focus on B2B markets and products After selling its mobile and devices segment to Microsoft in 2014 , Nokia has focused on its network infrastructure business .
gold_template: In templateXValue[1] , templateTitleSubject[0] had 22.5 templateScale templateYLabel[3] in templateYLabel[0] templateYLabel[1] , which is a small templateNegativeTrend from the templateXLabel[0] before . In the past decade , the company has made several directional shifts from handheld devices to network infrastructure in order to keep up with the quickly evolving electronics and telecommunications industry . Focus on B2B markets and products After selling its mobile and devices segment to Microsoft in templateXValue[5] , templateTitleSubject[0] has focused on its network infrastructure business .

generated_template: This statistic shows the templateTitle[3] templateYLabel[0] templateYLabel[1] ( templateTitle[4] or standardized measure ) of the templateTitleSubject[0] to the templateTitle[1] templateTitle[2] ( EUR USD ) , according to the data from the European Central Bank , which cover the period from the introduction of the templateTitleSubject[0] in templateXValue[min] up until templateXValue[max] . The templateTitle[4] ( standardized ) measure is based on the calculation of many observations throughout the period in question . It is therefore different than an templateTitle[3] measure at point in time , which reflects concrete values as of end of the templateXLabel[0] .
generated: This statistic shows the sales Net ( 1999 or standardized measure ) of the Nokia to the 's net ( EUR USD ) , according to the data from the European Central Bank , which cover the period from the introduction of the Nokia in 1999 up until 2019 .  . The 1999 ( standardized ) measure is based on the calculation of many observations throughout the period in question .  . It is therefore different than an sales measure at point in time , which reflects concrete values as of end the Year .

Example 553:
titleEntities: {'Subject': ['Europe'], 'Date': ['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] templateScale templateYLabel[1] templateYLabel[2] in templateTitle[7] . The second largest retailer was also German . templateXValue[1] GmbH templateXValue[1] Ko .

generated_template: This statistic gives information on the templateTitle[0] templateTitle[1] templateTitle[2] networking templateTitle[4] in the templateTitle[5] as of 2019 , ranked by templateYLabel[0] templateYLabel[1] templateTitle[9] . During the measured period , mobile templateXValue[7] users spent an templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic gives information on the Leading food beverage networking Europe in the 2017 as of 2019 , ranked by Billion U.S. revenue .  . During the measured period , mobile Casino Guichard-Perrachon S.A. (France) users spent an Billion U.S. of 111.77 billion dollars .

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[idxmax(X)] templateScale templateYLabel[1] were templateYLabel[2] in nursery or kindergarten programs in the templateTitle[4] .

generated_template: This statistic shows the value of templateYLabel[0] templateYLabel[1] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . Home templateYLabel[1] value is calculated by subtracting the value of remaining mortgage debt from the market value of the real estate property . That means that the value of home templateYLabel[1] increases as the debtor pays off the mortgage .
generated: This statistic shows the value of Number children in the numbers from 1970 to 2018 .  . Home children value is calculated by subtracting the value of remaining mortgage debt from the market value of the real estate property .  . That means that the value of home children increases as the debtor pays off the mortgage .

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[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[0] templateScale .

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[0] templateScale .
generated: This statistic displays the Unemployment rate in North Carolina from 1992 to 2018 .  . In 2018 , Unemployment in North Carolina was 3.9 % .

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[min] to templateTitleDate[max] . National templateTitle[1] templateTitle[2] templateNegativeTrend to an average of templateYValue[0] templateScale people looking for work in templateTitleDate[max] . See the templateTitle[0] templateTitle[1] rate and the monthly templateTitle[1] rate for further information .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[2] templateYLabel[3] templateYLabel[4] for templateTitle[3] in the country of America from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateYLabel[0] change of the PPI amounted to templateYValue[0] templateScale . The PPI for templateTitle[3] stood at 199.8 in templateTitleDate[max] .
generated: This statistic shows the Unemployed millions of the millions for 1990 in the country of America from 1990 to 2019 .  . In 2019 , the Unemployed change of the PPI amounted to 6.0 millions .  . The PPI for 1990 stood at 199.8 in 2019 .

Example 557:
titleEntities: {'Subject': ['Arizona Coyotes'], 'Date': ['2005', '2019']}
title: Arizona Coyotes ' revenue 2005 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['102', '96', '98', '101', '92', '80', '67', '83', '70', '67', '66', '68', '67', '63']

gold: This graph depicts the annual National Hockey League revenue of the Arizona Coyotes from the 2005/06 season to the 2018/19 season . The revenue of the Arizona Coyotes amounted to 102 million U.S. dollars in the 2018/19 season .
gold_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .

generated_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .
generated: This graph depicts the annual National Hockey League Revenue of the Arizona Coyotes Coyotes from the 2005/06 season to the 2018/19 season .  . The Revenue of the Arizona Coyotes Coyotes 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 shows the largest 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 templateScale of the planet 's surface is covered with templateYLabel[2] .
generated: This statistic shows the largest 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 % 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] templateScale British pounds in templateYLabel[0] templateTitle[2] , up from templateYValue[max] templateScale the previous templateXLabel[0] .

generated_template: British Telecommunications ( templateTitleSubject[0] ) templateTitleSubject[0] generated 24.08 templateScale British pounds in templateXValue[max] , slightly up on the templateXLabel[0] prior , and 6.2 templateScale British pounds higher than the low of 17.84 templateScale British pounds recorded in templateXValue[max] . templateTitleSubject[0] revenue mix templateTitleSubject[0] have had to respond to the impact of the changing telecommunications landscape on their templateYLabel[0] streams . The volume of calls made in the United Kingdom ( UK ) has more than halved since the beginning of templateXValue[7] , affecting templateYLabel[0] , and templateYLabel[0] streams such as business network access have also templateNegativeTrend .
generated: British Telecommunications ( Hays ) generated 24.08 million British pounds in 2019 , slightly up on the Year prior , and 6.2 million British pounds higher than the low of 17.84 million British pounds recorded in 2019 .  . Hays revenue mix Hays have had to respond the impact of the changing telecommunications landscape on their Revenue streams .  . The volume of calls made in the United Kingdom ( UK ) has more than halved since the beginning of 2012 , affecting Revenue , and Revenue streams such as business network access have also dropped .

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] templateScale templateYLabel[2] in its templateXValue[2] segment . The company was formed templateTitle[2] the merger of Gaz de templateXValue[0] and Suez to GDF Suez and officially changed its name to templateTitleSubject[0] in 2015 .

generated_template: In the fiscal year of templateTitleDate[0] , templateXValue[0] was the largest templateYLabel[1] market for templateTitleSubject[0] . templateXValue[0] accounted for almost templateYValue[0] templateScale of Rolls-Royce , templateTitleSubject[0] , and MINI templateYLabel[1] . Chinese customers bought about 640,800 such vehicles in the templateTitleDate[0] fiscal year .
generated: In the fiscal year of 2018 , France was the largest billion market for Engie .  . France accounted for almost 24.98 billion of Rolls-Royce , Engie and MINI billion .  . Chinese customers bought about 640,800 such vehicles in the 2018 fiscal year .

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 26 , there was templateYValue[last] templateYLabel[2] templateYLabel[3] in the templateTitle[2] in templateXValue[max] . This is compared to templateYValue[last] templateYLabel[2] shooting in templateXValue[min] , templateYValue[last] in templateXValue[18] , and templateYValue[max] templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] . School templateYLabel[3] The templateTitle[2] sees the most school templateYLabel[3] in the world .

generated_template: Since templateXValue[min] the templateTitleSubject[0] templateTitle[2] templateTitle[3] for twins has risen significantly . As of templateXValue[max] the templateTitle[2] templateTitle[3] for twins in the templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[2] 1,000 templateYLabel[4] templateYLabel[1] . It is believed that an templatePositiveTrend in the templateYLabel[0] templateTitle[2] templateTitle[3] may be associated with an templatePositiveTrend use of vitro fertilization and other assisted reproductive technologies in recent years .
generated: Since 1982 the U.S. 1982 for twins has risen significantly .  . As of 2020 the U.S. 1982 for twins in the U.S. was 1 mass 1,000 shootings number .  . It is believed that an increase in the Total U.S. 1982 may be associated with an increased use of vitro fertilization and other assisted reproductive technologies in recent years .

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[idxmax(X)] templateScale people attended movies at the Canadian movie theater chain , down from templateYValue[1] templateScale visitors a templateXLabel[0] earlier .

generated_template: This statistic represents templateTitleSubject[0] 's templateYLabel[0] and templateYLabel[1] templateYLabel[2] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , the company incurred around templateYValue[idxmax(X)] templateScale templateYLabel[4] templateYLabel[5] in templateYLabel[0] and templateYLabel[1] templateYLabel[2] .
generated: This statistic represents Cineplex 's Attendance and millions from the fiscal Year of 2010 to the fiscal Year of 2018 .  . In the fiscal Year of 2018 , the company incurred around 69.27 millions in Attendance and millions .

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[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Angola from 1999 to 2019 .  . In 2019 , the Unemployment rate in Angola was at approximately 7.25 % .

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] templateScale templateYLabel[4] from its templateYLabel[0] sponsor Genting .

generated_template: The statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , the average templateYLabel[0] templateTitle[1] templateYLabel[1] amounted to templateYValue[max] templateScale templateYLabel[4] dollars .
generated: The statistic shows the Jersey Aston sponsorship in the Aston Villa from 2009/10 (Acorns) to 2015/16 (Intuit Quickbooks) .  . In 2015/16 (Intuit Quickbooks) , the average Jersey Aston sponsorship amounted to 8 million GBP dollars .

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] templateScale 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 , according to survey templateYLabel[1] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[4] that should be addressed in the run up to the templateTitleDate[0] United Kingdom ( templateTitleSubject[1] ) General templateTitle[2] ( as of 2014 ) . With templateYValue[max] templateScale of templateYLabel[1] , templateXValue[0] was considered to be the templateTitle[0] templateTitle[1] topic , followed by the National Health Service ( templateXValue[1] ) and the templateXValue[2] ( EU ) .
generated: This statistic shows , according to survey respondents , the Reasons for unreported against that should be addressed in the run up to the 2014 United Kingdom ( Wales ) General unreported ( as of 2014 ) .  . With 43 % of respondents , Trivial/no loss was considered to be the Reasons for topic , followed by the National Health Service ( Lack of police engagement ) and the Private/dealt with ourselves ( EU ) .

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 templateNegativeTrend down to templateYValue[last] 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 dropped down to 23 involving .

Example 567:
titleEntities: {'Subject': ['Burger King', 'EBITDA'], '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 templatePositiveTrend by templateYValue[max] templateScale .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . A declining tendency could be observed as the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateNegativeTrend from templateYValue[idxmin(X)] in templateXValue[idxmax(Y)] to 6,146 in templateXValue[max] .
generated: The statistic shows the EBITDA of margin in the EBITDA from 2011 to 2014 .  . A declining tendency could be observed as the EBITDA of margin decreased from 21.3 in 2013 to 6,146 in 2014 .

Example 568:
titleEntities: {'Subject': ['Golden State Warriors', '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] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] at games of the templateTitleSubject[0] templateTitle[4] templateTitle[5] from the templateXValue[last] season to the templateXValue[0] season . In 2018/19 , the templateYLabel[0] templateTitle[1] templateYLabel[1] at the games was at templateYValue[0] templateScale .
generated: The statistic shows the Gate receipts at games of the Golden State Warriors Warriors NBA from the 10/11 season to the 18/19 season .  . In 2018/19 , the Gate receipts at the games was at 178 million .

Example 569:
titleEntities: {'Subject': ['Subaru', 'United Kingdom', 'UK'], 'Date': ['2016', '2019']}
title: Subaru car sales in the United Kingdom ( UK ) 2016 to 2019
X_Axis['Month']: ['Dec_19', 'Nov_19', 'Oct_19', 'Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16']
Y_Axis['Units', 'sold']: ['708', '157', '88', '526', '52', '103', '244', '138', '101', '632', '74', '174', '193', '145', '135', '829', '100', '112', '265', '231', '153', '761', '62', '155', '246', '216', '99', '510', '44', '152', '202', '155', '123', '706', '48', '178', '330', '219', '256', '762', '69', '148']

gold: This statistic shows the monthly amount of cars sold by Subaru in the United Kingdom ( UK ) between July 2016 and December 2019 . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December 2019 , 708 new Subaru cars had been sold .
gold_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between 2016 and 2019 . Peaks in registration numbers were recorded in and of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In 2019 , templateYValue[0] new templateTitleSubject[0] cars had been templateYLabel[1] .

generated_template: In 2019 , templateTitleSubject[0] templateYLabel[1] templateYValue[0] vehicles in the templateTitleSubject[1] market . templateTitle[2] of the British luxury templateTitle[1] usually peaked in and of each year , as those were the months when the Driver & Vehicle Licensing Agency ( DVLA ) issued new registration plates . 2018 was the notable exception , as there had only been templateYValue[15] templateTitle[2] recorded .
generated: In 2019 , Subaru sold 708 vehicles in the United Kingdom market .  . sales of the British luxury car usually peaked in and of each year , as those were the months when the Driver & Vehicle Licensing Agency ( DVLA ) issued new registration plates .  . 2018 was the notable exception , as there had only been 829 sales recorded .

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 templateNegativeTrend In the templateTitleSubject[0] . During this period , the templateYLabel[0] of templateTitle[3] specialty stores has seen a templateNegativeTrend 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 templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the templateScale of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
generated: This statistic shows the Number of enterprises in United Kingdom from 2008 to 2018 and details the percentage of the entire enterprises , living in enterprises areas .  . In 2018 , 5690 % of the total enterprises in United Kingdom lived in cities .

Example 571:
titleEntities: {'Subject': ['Spain'], 'Date': ['2000', '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 templateNegativeTrend 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: As of templateXValue[max] , the templateYLabel[0] templateYLabel[1] wage of templateTitleSubject[0] was templateYValue[max] thousand templateYLabel[3] per templateXLabel[0] , a growth of just over 5.8 thousand templateYLabel[3] when compared with templateXValue[min] . From templateXValue[min] until templateXValue[11] , templateYLabel[2] templatePositiveTrend by less than a thousand templateYLabel[3] , with wage growth accelerating mainly in the period after templateXValue[8] . The 607 Euro templatePositiveTrend recorded between templateXValue[8] and templateXValue[7] was the largest wage rise seen during this period .
generated: As of 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 grew 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: The statistic shows the 20 templateTitleSubject[0] templateTitle[1] templateTitle[2] in the templateTitle[3] according to the Global Peace templateYLabel[0] 2018 . According to the Global Peace templateYLabel[0] templateTitleDate[0] , templateXValue[0] was the templateTitleSubject[0] templateTitle[1] templateXLabel[0] in the templateTitle[3] with an templateYLabel[0] value of 1.072 . Additional information on the Global Peace templateYLabel[0] The Global Peace templateYLabel[0] is an effort by the Institute for Economics and Peace to measure the degree of peacefulness in templateTitle[2] across the global and rank them accordingly .
generated: The statistic shows the 20 Countries ranked by in the number according to the Global Peace Number 2018 .  . According to the Global Peace Number , Canada was the Countries ranked Country in the number with an Number value of 1.072 .  . Additional information on the Global Peace Number The Global Peace Number is an effort by the Institute for Economics and Peace to measure the degree of peacefulness in by across the global and rank them accordingly .

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[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Guyana from 1999 to 2019 .  . In 2019 , the Unemployment rate in Guyana was at approximately 12.22 % .

Example 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 templateScale templateYLabel[3] templateYLabel[4] of templateTitleSubject[0] and melons were produced worldwide .

generated_template: The templateTitleSubject[0] templateYLabel[0] of templateTitle[3] templateTitle[4] in the country in templateXValue[max] was templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . This was a templateNegativeTrend from around templateYValue[2] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[2] . Global seafood market The global demand for seafood is on the rise .
generated: The Vegetables Production of volume 2000 in the country in 2018 was 1088.9 volume million metric tons .  . This was a decrease from around 1075.2 volume million metric tons in 2016 .  . Global seafood market The global demand for seafood is on the rise .

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 templatePositiveTrend trend in templateYLabel[1] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( including both foreign and domestic ) at templateTitle[3] in templateTitleSubject[0] amounted to approximately templateYValue[idxmax(X)] templateScale .

generated_template: In templateXValue[max] over templateYValue[max] templateScale tourists arrived in different templateTitle[3] establishments in the Czech Republic ( officially templateTitleSubject[0] ) . The templateYLabel[0] of tourists visiting templateTitleSubject[0] has templatePositiveTrend over the years , with the largest templateYLabel[0] of international tourists coming from Germany – in templateXValue[2] , approximately 1.9 templateScale visitors arrived from Germany alone . templateTitleSubject[0] – the perfect destination for beer lovers One of templateTitleSubject[0] 's attractions for tourists is its beer .
generated: In 2018 over 2.8 millions tourists arrived in different accommodation establishments in the Czech Republic ( officially Latvia ) .  . The Number of tourists visiting Latvia has increased over the years , with the largest Number of international tourists coming from Germany – in 2016 , approximately 1.9 millions visitors arrived from Germany alone .  . Latvia – the perfect destination for beer lovers One of Latvia 's attractions for tourists is its beer .

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 2013 . During the survey period , it was found that templateYValue[1] templateScale of templateTitle[2] games were 25 to 34 years old . The average templateTitle[0] of a templateTitle[2] gamer was 46.5 years .

generated_template: This statistic shows the share of templateTitleSubject[0] templateTitle[1] templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateTitle[6] templateTitle[7] . According to cases handled templateTitle[5] templateTitleSubject[0] harassment support templateTitle[7] WHOA , templateYValue[max] templateScale of reporting templateTitle[3] were between templateXValue[0] and templateYValue[2] years old .
generated: This statistic shows the share of U.S. distribution gamers in 2013 , .  . According to cases handled 2013 U.S. harassment support 2013 WHOA , 24 percentage of reporting gamers were between 18-24 and 19 years old .

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] templateScale 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 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 templateScale of templateYLabel[3] .
generated: The statistic shows NASA budget billion in U.S. to dollars 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 , NASA budget trade deficit amounted to around 7.23 billion of dollars .

Example 578:
titleEntities: {'Subject': ['Frequency'], '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] templateTitleDate[0] . During the survey , templateYValue[1] templateScale 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 templateTitleDate[0] survey among adult Cubans living in templateTitleSubject[0] on their opinion of templateTitle[1] templateTitle[2] , their former President . templateYValue[max] templateScale of templateYLabel[1] stated they have a very or somewhat templateXValue[1] opinion of templateTitle[1] templateTitle[2] .
generated: This statistic shows the results of a 2014 survey among adult Cubans living in Frequency on their opinion of making online , their former President .  . 52.8 % of respondents stated they have a very or somewhat Yes once or twice opinion of making online .

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] templateScale templateYLabel[1] . As of templateXValue[0] , templateTitleSubject[0] had templateYValue[max] templateScale templateYLabel[1] templateTitle[3] . templateTitleSubject[0] 's eSports success While the templateYLabel[0] of gamers playing templateTitleSubject[0] has templatePositiveTrend dramatically , so has the appeal of the game as an eSport .

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] templateScale templateYLabel[1] across the globe as of 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 Overwatch ? Having burst onto the scene in October 2016 , Overwatch has since become a 2018 phenomenon , amassing almost 40 millions players across the globe as of May 2018 .  . How did Overwatch become so big ? The reasons why Overwatch 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 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: This statistic shows the approximate 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[min] templateScale templateYLabel[1] enrolled for templateTitle[0] degrees in the templateTitle[2] .
generated: This statistic shows the approximate Number of enrollments in U.S. across the students from the academic Year 2008/09 to 2018/19 .  . In 2018/19 , around 98235 thousand students enrolled for Number degrees in the students .

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[idxmax(X)] templateYLabel[1] globally . This is a significant templatePositiveTrend 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: The statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , around templateYValue[0] templateScale templateTitle[1] templateYLabel[1] were templateYLabel[2] in templateTitleSubject[0] .
generated: The statistic shows the Production of copper metric in Canada 2018 , province Month .  . In 2018 , around 27456 million copper metric were tons in Canada .

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 displays the distribution of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] as of mid-2019 , templateTitleDate[0] , templateTitle[5] templateTitle[6] . templateTitle[5] the end of that year , around templateYValue[max] templateScale templateTitleSubject[0] nationals who were living templateTitle[1] were in templateXValue[0] .
generated: This statistic displays the distribution of Number CFPs as of mid-2019 , 2014 by region .  . by the end of that year , around 600 % Number nationals who were living crowdfunding were in Europe .

Example 584:
titleEntities: {'Subject': ['Manitoba', 'Canada'], 'Date': ['2000', '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] templateScale templateYLabel[2] templateXValue[6] templateYLabel[4] templateYLabel[5] .

generated_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 82.16 templateScale templateYLabel[2] templateXValue[6] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the gross domestic product ( GDP ) of Manitoba from 2000 to 2018 .  . In 2018 , Manitoba 's GDP was 82.16 million chained 2012 Canadian dollars .

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 2017 . During the survey period , templateYValue[1] templateScale 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: templateTitle[3] templateXValue[last] templateXValue[0] but templateXValue[0] templateXValue[1] or templateXValue[3] was the templateTitle[0] common templateTitle[2] of templateTitle[3] templateXValue[last] among British templateYLabel[1] ( templateYValue[max] templateScale ) , followed by templateXValue[0] sugar ( templateYValue[last] templateScale ) . templateYValue[2] templateScale of Brits liked to templateXValue[last] their templateXValue[last] without either templateXValue[0] or templateXValue[1] . In 2017 , templateTitle[0] people in the UK would templateXValue[last] templateYValue[5] to templateYValue[4] cups a day .
generated: online More than 300$ 0$ no expenses but Up to 25$ or Up to 75$ was the Average common value of online More than 300$ among British respondents ( 34 % ) , followed by 0$ no expenses sugar ( 0 % ) .  . 34 % of Brits liked to More than 300$ their without either 0$ no expenses or Up to 25$ .  . In 2017 , Average people in the UK would More than 300$ 6 to 14 cups a day .

Example 586:
titleEntities: {'Subject': ['New England Patriots'], 'Date': ['1960']}
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] is the templateTitle[0] templateYLabel[0] leader of the templateTitleSubject[0] templateTitle[4] with templateYValue[max] templateYLabel[1] .
generated: The statistic shows New England Patriots England players with the most Touchdowns scored in franchise history .  . Rob Gronkowski is the Career Touchdowns leader of the New England Patriots England with 80 scored .

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 templateScale of templateYLabel[1] templateYLabel[2] at the templateTitle[0] templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateXValue[2] , templateYValue[5] templateScale of available templateYLabel[1] were templateYLabel[2] for the templateTitle[0] templateTitleSubject[0] in Seoul , South Korea .
generated: This statistic shows the percentage of consumption metric at the Total U.S. from 2010/11 to 2018/19 .  . In 2016/2017 , 215636 % of available consumption were metric for the Total U.S. in Seoul , South Korea .

Example 588:
titleEntities: {'Subject': ['Texas'], 'Date': ['2000', '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[idxmax(X)] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[idxmax(X)] 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': ['2016']}
title: Disneyland Paris visitors spending per day 2006 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Average', 'spend', 'in', 'euros', '(excluding', 'VAT)']: ['54.0', '53.7', '50.7', '48.1', '46.4', '46.2', '45.3']

gold: This statistic displays daily expenditure per person at Disneyland Paris theme parks in France between 2006 and 2016 . Visitors spending includes entrance fee , catering , merchandise and other services in the theme parks . In the fiscal year 2016 , the average spending dipped to 54 euros ( before VAT ) .
gold_template: This statistic displays daily expenditure templateTitle[4] person at templateTitleSubject[0] theme parks in France between templateTitleDate[min] and templateXValue[max] . templateTitle[2] templateTitle[3] includes entrance fee , catering , merchandise and other services in the theme parks . In the fiscal templateXLabel[0] templateXValue[max] , the templateYLabel[0] templateTitle[3] dipped to templateYValue[max] templateYLabel[2] ( before VAT ) .

generated_template: This statistic depicts the templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateTitle[1] templateTitle[2] had templateYValue[2] templateYLabel[1] open throughout the templateTitleSubject[0] . templateTitle[1] templateTitle[2] is an American chain of variety templateYLabel[1] that operates in 45 templateTitle[5] templateTitleSubject[0] and the District of Columbia .
generated: This statistic depicts the Average of Paris visitors spend in the Disneyland Paris from 2010 to 2016 .  . In 2014 , Paris visitors had 50.7 spend open throughout the Disneyland Paris .  . Paris visitors is an American chain of variety spend that operates in 45 day Disneyland Paris and the District of Columbia .

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[2] templateScale of total templateTitle[0] templateYLabel[1] .
generated: The statistic above presents the distribution of U.S. winter in the oil in , 2005/06 Winter .  . In , 2017/18 U.S. winter accounted for 2.78 % of total 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 templateScale of templateYLabel[1] templateYLabel[2] to the templateTitle[1] over the period between 2014 and templateTitleDate[max] , templateTitle[4] templateXLabel[0] of templateXLabel[1] . In that period , some templateYValue[max] templateScale 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] in templateTitleDate[0] . In templateTitleDate[0] , templateYValue[3] children were born templateYLabel[2] 1,000 templateYLabel[4] who held a Bachelor 's templateXValue[2] and templateYValue[max] children were born templateYLabel[2] 1,000 templateYLabel[4] with a templateXValue[0] or templateXValue[last] templateXValue[2] .
generated: This statistic shows the Share U.S. nickel the Country origin of the child 's country in the 2015 in .  . In 2015 , 11 children were born imports 1,000 who held a Bachelor 's Australia and 41 children were born imports 1,000 with a Other or Canada Australia .

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] templatePositiveTrend by templateYValue[min] templateScale . templateTitleSubject[0] is an American sporting goods manufacturer , based in Baltimore , Maryland .

generated_template: This statistic shows the number of The templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[idxmax(X)] thousand people were sold in templateTitleSubject[0] . The templateYLabel[0] templateYLabel[1] occurred in templateTitleSubject[0] – additional information According to the graph at the lowest templateYLabel[0] templateYLabel[1] in templateXValue[max] .
generated: This statistic shows the number of The Revenue growth in Under Armour from 2009 to 2019 .  . In 2019 , approximately 1 thousand people were sold in Under Armour .  . The Revenue growth occurred in Under Armour – additional information According to the graph at the lowest Revenue growth in 2019 .

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[idxmax(X)] templateYLabel[2] occurred nationwide .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[0] was templateYValue[min] templateYValue[idxmax(X)] of every 1,000 templateYLabel[6] . This is a significant templateNegativeTrend from templateXValue[min] , when templateYLabel[0] templateYLabel[1] was at templateYValue[idxmin(X)] deaths out of every 1,000s templateYLabel[6] . What is templateYLabel[0] templateYLabel[1] ? The templateYLabel[0] templateYLabel[1] templateYLabel[2] is the number of deaths of babies under the age of one templateYLabel[3] 1,000 templateYLabel[5] templateYLabel[6] .
generated: In 2018 , the Number reported cases in the U.S. was 282061 of every 1,000 cases .  . This is a significant decrease from 1990 , when Number reported was at 639270 deaths out of every 1,000s cases .  . What is Number reported ? The Number reported cases is the number of deaths babies under the age of one cases 1,000 .

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] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Cincinnati Bengals , a franchise of the National Football League , from 2001 to 2018 .  . In 2018 , the Revenue of the Cincinnati Bengals was 380 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] templateScale templateYLabel[4] templateYLabel[5] . This was followed by 'Kabir templateXValue[1] ' at around templateYValue[1] templateScale templateYLabel[5] worth templateYLabel[0] templateYLabel[1] collection that year .

generated_template: This statistic presents the templateTitle[0] templateTitleSubject[0] templateTitle[1] templateTitle[2] on templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . With some 842,000 templateYLabel[0] templateYLabel[1] , templateXValue[0] was ranked first that year . The total number of templateYLabel[0] templateYLabel[1] was more than 8.7 templateScale units in templateTitleDate[0] .
generated: This statistic presents the Highest India grossing domestic on Box office in 2019 .  . With some 842,000 Box office , War was ranked first that year .  . The total number of Box office was more than 8.7 billion units in 2019 .

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] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Luxembourg from 1984 to 2018 , with projections up until 2024 .  . In 2018 , the average Inflation rate in Luxembourg amounted to about 2.02 % compared to the previous Year .

Example 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: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[4] templateTitle[5] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[5] with the most templateYLabel[1] with templateYValue[max] . In 2016 , the U.S. casino gaming market had revenues of 71.1 templateScale U.S. dollars .
generated: This statistic shows the Median of amount spend presents selected in 2015 .  . In that year , United Kingdom was the selected with the most amount with 420 .  . In 2016 , the U.S. casino gaming market had revenues of 71.1 billion U.S. dollars .

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[idxmax(X)] templateScale 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[idxmax(X)] templateScale 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] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateTitle[3] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Over nine years , the templateYLabel[0] of templateYLabel[1] templateTitle[3] templateYLabel[2] gradually diminished and reached templateYValue[idxmax(X)] in templateXValue[idxmin(Y)] .
generated: This statistic shows the total Gross of profit Goods million in Sporting Goods from 2006 to 2018 .  . Over nine years , the Gross of profit Goods million gradually diminished and reached 2437 in 2006 .

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[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYValue[idxmax(X)] templateYLabel[4] .
generated: This statistic shows the household income in Michigan from 1990 to 2018 .  . In 2018 , the household income in Michigan amounted to 60449 dollars .

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[idxmax(X)] templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , around templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] were recorded in templateTitleSubject[0] . In templateXValue[idxmin(Y)] templateTitleSubject[0] was the country with the third-most templateYLabel[1] templateYLabel[2] and the sixth-most fatalities due to templateTitle[0] worldwide .
generated: The statistic shows the Fan of Cost Index in NFL between 2006 and 2019 .  . In 2019 , around 540.52 Cost Index were recorded in NFL .  . In 2006 NFL was the country with the third-most Cost Index and the sixth-most fatalities due to Average worldwide .

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: This statistic shows the distribution of templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] as of templateTitleDate[0] , broken down by sector . In templateTitleDate[0] , approximately templateYValue[max] templateScale of the templateTitleSubject[0] 's templateYLabel[1] for templateTitle[1] was attributable to the templateXValue[0] transportation sector .
generated: This statistic shows the distribution of online value in the Global as of 2019 , broken down by sector .  . In 2019 , approximately 132.6 % of the Global 's value for online was attributable to the Macintosh transportation sector .

Example 603:
titleEntities: {'Subject': ['Russia'], '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] templateTitleDate[0] , 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] templateScale 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] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The templateTitle[4] templateTitle[5] is an annual templateTitle[3] prize presented by France templateTitle[3] . The award , voted for by templateTitle[3] journalists , is given to the male player who was deemed to have played the best templateTitle[3] over the previous 12 months . Unsurprisingly , templateYValue[min] of the greatest footballers of all time , templateXValue[0] and templateXValue[1] , top the list of all-time templateTitle[1] .
generated: The Russia CIS is an annual revenue prize presented by France revenue .  . The award , voted for by revenue journalists , is given to the male player who was deemed to have played the best revenue over the previous 12 months .  . Unsurprisingly , 828.02 of the greatest footballers of all time , Kholop and Perfect Man , top the list of all-time box .

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] templateScale of adults in templateXValue[17] suffered from templateYLabel[0] .

generated_template: As of templateTitleDate[0] , the states with the highest rates of templateTitle[0] included 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] . The overall templateTitle[1] of legal templateYLabel[1] in the templateTitle[2] has gradually templateNegativeTrend since the mid-1980s .
generated: As of 2017 , the states with the highest rates of COPD included Alaska , Arizona Arkansas , and California .  . As of that time , there were around 6.3 prevalence 1,000 in the State of Alaska .  . The overall prevalence of legal prevalence in the U.S. has gradually decreased since the mid-1980s .

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] templateScale templateYLabel[1] in templateTitleSubject[0] , up from nearly templateYValue[1] templateScale templateYLabel[1] a templateXLabel[0] earlier .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[1] a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateYValue[2] templateTitle[7] in the templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] templateYLabel[1] had attended a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateXLabel[0] .
generated: This statistic shows the Number of households number a households 2005 2017 at least once in the 2017 31.95 in the 2017 from 2005 to 2017 .  . In 2017 , 34.07 millions of Mexico households had attended a households 2005 2017 at least once in the 2017 Year .

Example 606:
titleEntities: {'Subject': ['Amazon Prime Day'], '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 templateXValue[idxmax(Y)] , the desktop templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateScale , which represented a 16 templateScale 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: This statistic presents the templateYLabel[0] templateYLabel[1] templateTitleSubject[0] templateYLabel[2] of the templateTitleSubject[0] Store from templateXValue[min] to templateXValue[max] . In the last reported templateXLabel[0] , customers spent an estimated templateYValue[idxmax(X)] templateScale templateYLabel[4] templateYLabel[5] on in-app purchases , subscriptions , and premium appsin the templateTitleSubject[0] store . Additional information The templateTitleSubject[0] was launched in 2008 to coincide with templateTitleSubject[0] 's iPhone 3G release .
generated: This statistic presents the Conversion rate Amazon Prime Day of the Amazon Prime Day Store from 2017 to 2019 .  . In the last reported Year , customers spent an estimated 13.5 % rate on in-app purchases , subscriptions and premium appsin the Amazon Prime Day store .  . Additional information The Amazon Prime Day was launched in 2008 to coincide with Amazon Prime Day 's iPhone 3G release .

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] templateScale 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: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . The templateYLabel[0] templateYLabel[1] is calculated using the price templatePositiveTrend of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the templateXLabel[0] .
generated: The statistic shows the Inflation rate in Saudi Arabia 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 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] templateScale of templateYLabel[1] stated they think templateTitle[5] is templateXValue[0] , while templateYValue[min] templateScale 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] templateScale of templateYLabel[1] stated they think templateTitle[5] or templateTitle[6] templateTitle[7] are templateXValue[0] , while templateYValue[min] templateScale said it templateXValue[2] on the situation .
generated: This statistic shows the moral stance of Americans regarding pornography or 2018 in .  . During this survey , 55 % of respondents stated they think pornography or 2018 are Morally acceptable , while 1 % 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 represents global countries at the end of year templateTitleDate[0] , templateTitle[5] templateYLabel[0] templateTitle[2] templateTitleSubject[0] templateYLabel[1] . templateXValue[0] 's templateYLabel[0] templateTitle[2] templateTitleSubject[0] ( photovoltaic ) templateYLabel[1] reached templateYValue[max] templateYLabel[2] at the end of templateTitleDate[0] . templateTitle[2] photovoltaic - additional information With the assistance of politically-driven subsidies , the templateTitle[2] photovoltaic ( templateTitleSubject[0] ) sector continues to attract investors , with investment volumes that eclipse the deals made in other markets , including the wind power and biomass energy sectors .
generated: This statistic represents global countries at the end of year 2019 , by Brand strongest BSI Strength .  . Singapore 's Brand strongest BSI ( photovoltaic ) Strength reached 90.5 Index at the end of 2019 .  . strongest photovoltaic - additional information With the assistance of politically-driven subsidies , the strongest photovoltaic ( BSI ) sector continues to attract investors , with investment volumes that eclipse the deals made in other markets , including the wind power and biomass energy sectors .

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

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 2019 , ordered templateTitle[5] templateXValue[last] templateXLabel[1] . At that time , there were templateYValue[2] templateXValue[2] templateTitle[1] in templateTitleSubject[0] . In templateXValue[last] , there were 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 2019 , 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 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 templateScale of respondents consult with a physician at least once a year .

generated_template: In templateTitleDate[0] , templateXValue[0] had the largest templateTitleSubject[0] population outside of Italy with almost templateYValue[max] templateScale people 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 had the largest Europe population outside of Italy with almost 60214 million people 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 2019 , templateTitle[4] templateYLabel[0] of templateYLabel[1] . templateXValue[0] RHF was ranked first with over templateYValue[max] thousand templateYLabel[1] , while templateXValue[1] was ranked second with templateYValue[1] thousand templateYLabel[1] .

generated_template: In templateTitleDate[0] , templateTitleSubject[0] was the fifth largest European country in terms of templateTitle[1] size , with an estimate of 59 templateScale templateYLabel[1] . The most populous templateTitle[5] of the country was templateXValue[0] , inhabited templateTitle[4] one-sixth of templateTitleSubject[0] 's citizens , followed templateTitle[4] templateXValue[1] and templateXValue[2] , which registered about 5.9 and templateYValue[2] templateScale templateYLabel[1] , respectively . Milan , the capital of Italian fashion , finance and commerce Milan , the capital of templateXValue[0] and the second most populous city of the country is known worldwide as the Mecca of Italian fashion , finance and commerce .
generated: In 2019 , Norway was the fifth largest European country in terms of companies size , with an estimate of 59 million employees .  . The most populous number of the country was Helse Sør-Øst RHF , inhabited by one-sixth of Norway 's citizens , followed by Telenor ASA and Aker ASA , which registered about 5.9 and 20753 % employees , respectively .  . Milan , the capital of Italian fashion , finance and commerce Milan , the capital of Helse Sør-Øst RHF and the second most populous city of the country is known worldwide as the Mecca of Italian fashion , finance and commerce .

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 templateScale templateYLabel[5] dollars in 2006 .

generated_template: This statistic shows a list of the templateTitle[1] that female residents rated as the templateTitleSubject[0] to templateTitle[4] templateTitle[5] in templateTitle[6] at templateTitle[7] . In templateXValue[0] , templateYValue[max] templateScale of templateTitle[3] surveyed said that they templateYLabel[3] templateYLabel[4] walking templateTitle[5] the streets at templateTitle[7] templateTitle[6] .
generated: This statistic shows a list of the industry that female residents rated as the U.S. to economic output in by at state .  . In California , 15.1 billion of total surveyed said that they (in billion walking output the streets at state by .

Example 615:
titleEntities: {'Subject': ['North America'], 'Date': ['2007', '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] templateScale templateYLabel[2] templateYLabel[3] are expected .

generated_template: The statistic above shows templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the women templateTitle[1] magazine generated a total templateTitle[2] templateYLabel[0] of almost templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] . Here you can find out how many templateTitle[2] pages the magazine sells annually .
generated: The statistic above shows North America printer cartridge Revenue in the North from 2007 to 2015 .  . In 2014 , the women printer magazine generated a total cartridge Revenue of almost 23628 million U.S. dollars .  . Here you can find out how many cartridge pages the magazine sells annually .

Example 616:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2000', '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 templatePositiveTrend . The peak was in templateXValue[0] at templateYValue[max] templateScale British pounds ( templateYLabel[4] ) .

generated_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] enrolled in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . There were roughly templateYValue[idxmax(X)] templateTitle[2] templateYLabel[1] templateYLabel[2] 100,000 templateYLabel[4] nationwide in templateTitleSubject[0] in templateXValue[idxmin(Y)] .
generated: This statistic shows the total Inheritance of tax enrolled in United Kingdom from 2000 to 2019 .  . There were roughly 2.2 United tax receipts 100,000 GBP nationwide in United Kingdom 2000/01 .

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] templateYValue[idxmax(X)] templateYLabel[3] .

generated_template: The statistic shows the total templateYLabel[0] of templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] in Indonesian territorial waters from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[idxmax(X)] templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] . templateTitle[4] templateTitle[5] in templateTitleSubject[0] templateTitle[4] templateTitle[5] in Indonesian waters spiked in templateXValue[4] , when templateYValue[max] templateYLabel[1] were reported .
generated: The statistic shows the total R&D of 's and R & D in Indonesian territorial waters from 2008 to 2019 .  . In 2019 , there were 140 's and R & D in LVMH Group .  . & D in LVMH Group & D in Indonesian waters spiked in 2015 , when 140 expenditure were reported .

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 templateTitleDate[0] 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 number of templateYLabel[1] on the official templateTitleSubject[0] Facebook page from 2010 to templateTitleDate[0] . The page had a total of templateYValue[last] templateScale templateYLabel[1] in 2019 .
generated: This statistic shows the number of offenders on the official U.S. Facebook page from 2010 to 2018 .  . The page had a total of 5099 thousand offenders in 2019 .

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] templateScale .

generated_template: This statistics presents the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] occurs when people are without work , it is also known as joblessness . In order that the prevalence of templateYLabel[0] can be measured , a calculation is made by the division of the number of unemployed individuals by all individuals currently in the labor force , this yields a templateScale templateYLabel[1] .
generated: This statistics presents the Unemployment rate in Panama from 2014 to 2018 , with projections up until 2024 .  . Unemployment occurs when people are without work , it is also known as joblessness .  . In order that the prevalence of Unemployment can be measured , a calculation is made by the division of the number of unemployed individuals by all individuals currently in the labor force , this yields a percentage rate .

Example 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] templatePositiveTrend from about templateYValue[min] thousand templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] in templateXValue[idxmin(Y)] to about templateYValue[0] thousand templateYLabel[1] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the templateScale of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
generated: This statistic shows the Number of students in Denmark from 2008 to 2018 and details the percentage of the entire students , living in students areas .  . In 2018 , 148551 % of the total students in Denmark lived in cities .

Example 621:
titleEntities: {'Subject': ['UK'], 'Date': ['2014', '2019']}
title: Monthly hours of sunlight in UK 2014 to 2019
X_Axis['Month']: ["Dec_'19", "Nov_'19", "Oct_'19", "Sep_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Apr_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17", "Nov_'17", "Oct_'17", "Sep_'17", "Aug_'17", "Jul_'17", "Jun_'17", "May_'17", "Apr_'17", "Mar_'17", "Feb_'17", "Jan_'17", "Dec_'16", "Nov_'16", "Oct_'16", "Sep_'16", "Aug_'16", "Jul_'16", "Jun_'16", "May_'16", "Apr_'16", "Mar_'16", "Feb_'16", "Jan_'16", "Dec_'15", "Nov_'15", "Oct_'15", "Sep_'15", "Aug_'15", "Jul_'15", "Jun_'15", "May_'15", "Apr_'15", "Mar_'15", "Feb_'15", "Jan_'15", "Dec_'14", "Nov_'14", "Oct_'14", "Sep_'14", "Aug_'14", "Jul_'14", "Jun_'14", "May_'14", "Apr_'14", "Mar_'14", "Feb_'14", "Jan_'14"]
Y_Axis['Number', 'of', 'hours']: ['46.2', '48.1', '87.9', '144.0', '173.6', '173.2', '160.8', '188.5', '168.9', '115.6', '100.6', '47.9', '37.6', '63.0', '113.2', '134.1', '147.4', '237.6', '239.9', '246.0', '132.6', '85.0', '95.6', '48.7', '45.3', '71.1', '72.7', '109.0', '155.5', '168.7', '155.7', '208.3', '158.0', '119.7', '55.0', '55.1', '40.7', '74.7', '105.3', '119.9', '181.7', '156.4', '136.5', '209.6', '160.8', '117.3', '84.9', '37.1', '29.2', '35.6', '91.2', '157.8', '148.8', '160.6', '189.7', '174.4', '212.9', '121.9', '76.0', '58.5', '57.1', '51.9', '82.8', '123.3', '171.0', '223.0', '178.4', '149.6', '144.9', '126.7', '75.0', '42.8']

gold: In the period of consideration , the total monthly hours of sunlight in the UK followed a similar pattern each year . The most notable change occurred in 2018 , when the hours of sunlight shot up in May , June and July to 246 , 240 and 238 hours respectively . Unsurprisingly it was the end of each year when sunlight hours were lowest .
gold_template: In the period of consideration , the total templateTitle[0] templateYLabel[1] of templateTitle[2] in the templateTitleSubject[0] followed a similar pattern each year . The most notable change occurred in 2018 , when the templateYLabel[1] of templateTitle[2] shot up in templateXValue[7] , and to templateYValue[max] , templateYValue[18] and templateYValue[17] templateYLabel[1] respectively . Unsurprisingly it was the end of each year when templateTitle[2] templateYLabel[1] were lowest .

generated_template: In the period between templateTitleDate[min] and templateTitleDate[max] , the wettest months tended to be at the start and ends of the year . The greatest measurement of rain recorded was in 2015 when there was approximately templateYValue[max] templateYLabel[2] of templateYLabel[1] . The lowest level was in 2014 , which experienced surprisingly minimal rain .
generated: In the period between 2014 and 2019 , the wettest months tended to be at the start and ends of the year .  . The greatest measurement of rain recorded was in 2015 when there was approximately 246.0 hours of .  . The lowest level was in 2014 , which experienced surprisingly minimal rain .

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] templateScale of the population in templateXValue[2] had templateYLabel[0] to templateTitle[4] .

generated_template: The statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[3] ranked 1st templateTitle[1] an estimated templateYLabel[0] templateYLabel[1] of about templateYValue[3] templateScale templateYLabel[2] to the templateYLabel[3] templateYLabel[4] . templateYLabel[0] in industrialized and in emerging templateTitleSubject[0] Higher templateYLabel[0] rates are more templateYLabel[2] .
generated: The statistic shows the 20 Countries lowest the access rate in 2017 .  . In 2017 , Dem. Republic of the Congo ranked 1st lowest an estimated Access rate of about 19.1 % rate to the rate .  . Access in industrialized and in emerging Countries Higher Access rates are more rate .

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 templateTitleDate[min] to the third templateXLabel[0] of templateTitleDate[max] . Automated transaction templateYLabel[2] ( ATMs ) are banking outlets designed for maximum access to banking transactions and information , outside of bank working hours and without the assistance of a clerk . It can be seen that the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] templatePositiveTrend between the first templateXLabel[0] of templateTitleDate[min] and the second templateXLabel[0] of 2016 , reaching a total of more than templateYValue[9] thousand as of the second templateXLabel[0] of 2016 .

generated_template: In the fourth templateXLabel[0] of templateTitleDate[max] , the templateTitle[3] of templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to more than templateYValue[max] templateScale . The most recent figure constitutes an templatePositiveTrend of about 8.6 templateScale compared to fourth templateXLabel[0] of the previous year . The Japanese e-commerce company is headquartered in Tokyo , where it was founded in 1997 .
generated: In the fourth Quarter of 2019 , the United of Number cash machines amounted to more than 70682 % .  . The most recent figure constitutes an increase of about 8.6 % compared to fourth Quarter of the previous year .  . The Japanese e-commerce company is headquartered in Tokyo , where it was founded in 1997 .

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] templateScale , the highest among the referenced templateTitleSubject[0] templateTitle[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[0] of templateYLabel[5] templateYLabel[2] in the templateTitle[6] as of templateTitle[7] templateTitleDate[0] . templateXValue[0] had the templateTitle[2] templateYLabel[0] of templateYLabel[5] templateYLabel[2] as templateYValue[max] templateScale of the templateXLabel[0] 's total templateYLabel[2] were templateYLabel[5] . In 2015 , the templateYLabel[5] restaurant chain Chipotle templateYLabel[5] Grill had 351 locations in templateXValue[2] , the templateTitle[2] number Chipotle templateYLabel[2] in the country .
generated: This statistic shows the Global operating the margin Operating of margin in the by as of company 2016 .  . Kraft Heinz had the margin Operating of margin as 21.9 % of the Company 's total margin were .  . In 2015 , the margin restaurant chain Chipotle margin Grill had 351 locations in General Mills , the margin number Chipotle margin in the country .

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] templateScale of the templateXValue[last] area in the templateTitle[5] in templateTitleDate[0] . In templateXValue[0] , Alaska , templateYValue[max] templateScale of the templateXValue[last] 's area was comprised of templateYLabel[1] in templateTitleDate[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] the largest number of templateTitle[3] templateYLabel[0] templateYLabel[1] templateYLabel[2] 10,000 templateYLabel[4] in the templateTitle[9] in templateTitleDate[0] . In templateXValue[1] , California , there were templateYValue[1] templateTitle[3] templateYLabel[0] templateYLabel[1] templateYLabel[2] 10,000 templateYLabel[4] in templateTitleDate[0] .
generated: This statistic shows the Cities largest the number of percentage Share parkland 10,000 in the 2018 in .  . In Fremont , California there were 49.4 percentage Share parkland 10,000 in 2018 .

Example 626:
titleEntities: {'Subject': ['Summer Olympic Games'], 'Date': ['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: In the templateXValue[0] United Kingdom General Election , templateYLabel[0] templateYLabel[1] was templateYValue[0] templateScale of eligible voters , a 1.5 templateScale drop compared with the previous general election in templateXValue[max] . Between templateXValue[27] and templateXValue[7] templateYLabel[0] templateYLabel[1] never templateNegativeTrend below 70 templateScale , but in templateXValue[6] it templateNegativeTrend to just templateYValue[6] templateScale . Since that low point , templateYLabel[0] templateYLabel[1] has gradually recovered and reached templateYValue[2] templateScale in the templateXValue[2] of templateXValue[2] , which is still some way off the peak of templateYValue[max] templateScale recorded in the templateXValue[idxmax(Y)] General Election .
generated: In the 2016 United Kingdom General Election , Number sports was 35 % of eligible voters , a 1.5 % drop compared with the previous general election in 2016 .  . Between 1900 and 1988 Number sports never fell below 70 percent , but in 1992 it fell to just 29 % .  . Since that low point , Number sports has gradually recovered and reached 34 % in the 2008 of , which is still some way off the peak of 35 % recorded in the 2016 General Election .

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[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the templateScale of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
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 % 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 templatePositiveTrend from templateYValue[min] templateYLabel[1] in the templateXLabel[0] templateXValue[idxmin(Y)] . templateYLabel[1] in templateTitleSubject[0] In templateXValue[1] , there were more male babies born than female babies , and overall templateYLabel[1] have been templatePositiveTrend since templateTitleDate[min] .

generated_template: This statistic shows the templateScale of templateYLabel[1] who use the templateTitle[0] in the templateTitle[5] from templateXValue[min] and templateXValue[max] . In the most recent measured templateXLabel[0] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] templateYLabel[1] were reported to use the templateTitle[0] at least occasionally , up from templateYValue[8] templateScale in templateXValue[8] .
generated: This statistic shows the percentage of births who use the Number in the 2019 from 2001 and 2019 .  . In the most recent measured Year , 382533 % of Canada births were reported to use the Number at least occasionally , up from 376951 % in 2011 .

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 templatePositiveTrend to templateYValue[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] 's meat templateYLabel[0] had been templatePositiveTrend 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] templateScale of the templateYLabel[1] stated they templateXValue[6] templateXValue[5] 200 templateXValue[0] away from templateTitle[4] templateXValue[6] , whereas templateYValue[0] templateScale said they templateXValue[6] templateXValue[0] or templateXValue[0] away from templateTitle[4] templateXValue[6] .

generated_template: Among slightly lower templateYLabel[0] of templateXValue[1] than templateXValue[0] access the templateTitle[2] via templateTitleSubject[0] templateTitle[1] in templateTitleSubject[1] , according to figures from the United Kingdom 's ( UK ) Office for National Statistics ( ONS ) . templateYValue[0] templateScale of templateXValue[0] aged 16 years or older reported accessing the templateTitle[2] this way , compared with templateYValue[1] templateScale of templateXValue[1] . Smartphone ownership As of 2018 , 95 templateScale of people aged 16 to 34 years owned a smartphone .
generated: Among slightly lower Percentage of 11 - 50 miles than 10 miles or less access the between via United States distance in United States , according to figures from the United Kingdom 's ( UK ) Office for National Statistics ( ONS ) .  . 21 percentage of 10 miles or less aged 16 years or older reported accessing the between this way , compared with 17 percentage of 11 - 50 miles .  . Smartphone ownership As of 2018 , 95 percentage of people aged 16 to 34 years owned a smartphone .

Example 631:
titleEntities: {'Subject': ['Armour'], '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] templateScale of templateYLabel[1] rated templateTitle[3] templateTitleSubject[0] 's templateTitle[1] as templateXValue[0] .

generated_template: This statistic shows the results of templateTitle[1] beliefs in templateXValue[1] in templateTitleDate[0] . Approximately 36 templateScale of people claimed that they were a templateXValue[3] denomination , with the majority of those belonging to the templateXValue[1] of templateXValue[1] . On the templateXValue[3] hand , over half of the respondent group said that they did n't identify themselves as having a templateXValue[last] .
generated: This statistic shows the results of quality beliefs in Somewhat positive 2014 .  . Approximately 36 % of people claimed that they were a Somewhat negative denomination , with the majority of those belonging to the Somewhat positive of .  . On the Somewhat negative hand , over half of the respondent group said that they did n't identify themselves as having a Extremely Negative .

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 2020 , templateYValue[max] templateScale of the participants stated that templateXValue[13] templateXValue[0] and a templateXValue[11] templateXValue[0] the government were the templateTitle[2] templateTitle[3] problems templateTitle[5] the templateTitle[6] .

generated_template: The 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] templateScale , closely followed by templateXValue[1] at templateYValue[1] templateScale . On the opposite side , templateXValue[last] is used by four templateScale of templateYLabel[1] .
generated: The statistic shows the results of a survey about the most important problem facing in U.S. the third quarter of 2020 .  . The most used important problem Response in U.S. was Dissatisfaction with government/Poor leadership at 28 % , closely followed by Immigration at 6 % .  . 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 templateScale of templateYLabel[1] that hired templateTitleSubject[0] templateTitle[4] templateTitle[5] templateXValue[min] to templateXValue[1] and then those templateYLabel[1] that were planning to hire templateTitleSubject[0] templateTitle[4] for templateXValue[max] . In templateXValue[max] , around templateYValue[idxmax(X)] templateScale of templateYLabel[1] in this survey said that they intended to hire templateTitleSubject[0] templateTitle[4] in the next templateXLabel[0] .
generated: This statistic shows the percentage of airstrikes that hired Yemen 2019 2002 to 2018 and then those airstrikes that were planning to hire Yemen 2019 for .  . In 2019 , around 8 % of airstrikes in this survey said that they intended to hire Yemen 2019 in the next Year .

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 templateScale of the templateTitle[4] templateTitle[5] templateTitle[6] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] 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] amounted to about 6.4 templateScale of 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 , the state deficit of Mexico amounted to about 6.4 % of gross domestic product .  . See GDP of Mexico for additional information .

Example 635:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000']}
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[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateYLabel[0] templateYLabel[1] on templateTitle[2] and home furnishings in templateYLabel[3] templateTitle[4] was about templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[4] of the templateYLabel[1] templateYLabel[2] templateYLabel[3] ( templateTitle[0] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] of templateTitleSubject[0] had templatePositiveTrend by templateYValue[idxmax(X)] templateScale over the previous templateXLabel[0] 's templateTitle[0] .
generated: This statistic shows the Gross dollars of the margin billion U.S. ( Gross ) of U.S. from 0 to 17 .  . In 17 , the Gross of U.S. had increased by 27.92 billion over the previous Year 's Gross .

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 templateTitleDate[0] . In 1991 , total 1,036,035 were templateTitle[3] due to templateXValue[0] in templateXValue[0] .

generated_template: This statistic represents the templateTitle[0] templateTitleSubject[0] templateTitle[1] templateTitle[2] on templateYLabel[0] templateYLabel[1] in templateTitleDate[0] . With some 842,000 templateYLabel[0] templateYLabel[1] , templateXValue[0] was ranked first that year . The total number of templateYLabel[0] templateYLabel[1] was more than 8.7 templateScale units in templateTitleDate[0] .
generated: This statistic represents the Volcanic eruptions people on Number victims in 2016 .  . With some 842,000 Number victims , Volcanic eruption in the Philippines (June 9 1991) was ranked first that year .  . The total number of victims was more than 8.7 million units in 2016 .

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] templateScale of templateYLabel[1] said templateXValue[2] templateTitleSubject[0] is a templateXValue[0] to templateXValue[0] for the templateXValue[0] .

generated_template: This statistic shows , according to survey templateYLabel[1] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[4] that should be addressed in the run up to the templateTitleDate[0] United Kingdom ( templateTitleSubject[1] ) General templateTitle[2] ( as of 2014 ) . With templateYValue[max] templateScale of templateYLabel[1] , templateXValue[0] was considered to be the templateTitle[0] templateTitle[1] topic , followed by the National Health Service ( templateXValue[1] ) and the templateXValue[2] ( EU ) .
generated: This statistic shows , according to survey respondents , the U.S. consumer sentiments Black that should be addressed in the run up to the 2017 United Kingdom ( Black Friday ) General sentiments ( as of 2014 ) .  . With 42 % of respondents , It is a great opportunity to buy gifts for the holidays was considered to be the U.S. consumer topic , followed by the National Health Service ( It's a tradition ) and the I like it even more now that I can shop online ( EU ) .

Example 638:
titleEntities: {'Subject': ['YouTube'], 'Date': []}
title: All-time most viewed YouTube channel owners 2020
X_Axis['Month']: ["Ryan's_World", 'PewDiePie', 'Like_Nastya_Vlog', '✿_Kids_Diana_Show', 'DanTDM_(TheDiamondMinecart)', 'Fun_Toys_Collector_Disney', 'Vlad_and_Nikita', 'FGTeeV', 'Family_Fun_Pack', 'CookieSwirlC', 'Markiplier']
Y_Axis['All-time', 'channel', 'views', 'in', 'billions']: ['35.18', '24.44', '22.68', '17.01', '16.01', '14.86', '14.07', '13.11', '12.66', '12.42', '12.29']

gold: As of January 2020 , Ryan from Ryan 's World ( formerly known as Ryan ToysReview ) had reached almost 35.2 billion lifetime video views , making the elementary schooler the most viewed YouTube channel owner of all time . This achievement unseated former first-ranked video game commentator Felix Kjellberg , who produces videos under the name PewDiePie . Ryan has been uploading YouTube videos since March 2015 , and mainly features in videos where he is playing with and reviews toys `` for kids , by a kid '' .
gold_template: As of 2020 , Ryan from Ryan 's templateXValue[0] ( formerly known as Ryan ToysReview ) had reached almost templateYValue[max] templateScale lifetime video templateYLabel[2] , making the elementary schooler the templateTitle[1] templateTitle[2] templateTitleSubject[0] templateYLabel[1] owner of all time . This achievement unseated former first-ranked video game commentator Felix Kjellberg , who produces videos under the name templateXValue[1] . Ryan has been uploading templateTitleSubject[0] videos since 2015 , and mainly features in videos where he is playing with and reviews templateXValue[5] `` for templateXValue[3] , by a kid '' .

generated_template: In templateTitleDate[0] , templateTitleSubject[0] was the templateTitle[0] templateTitle[1] templateXLabel[0] templateXLabel[1] in terms of templateTitle[1] . As of that templateXLabel[0] , the total templateYLabel[0] of templateTitle[1] on templateTitleSubject[0] amounted to approximately templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] . This was an templatePositiveTrend from which when it had more than for more than 11 templateScale templateTitleSubject[0] dollars .
generated: In , YouTube was the All-time most Month in terms of most .  . As of that Month , the total All-time of most on YouTube amounted to approximately 35.18 billions channel views .  . This was an increase from which when it had more than for more than 11 billions YouTube dollars .

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] templateScale of templateXValue[1] templateYLabel[1] claimed to listen to templateXValue[0] templateXValue[5] , whereas templateYValue[1] templateScale said they listened to templateXValue[1] templateXValue[0] .

generated_template: In this templateTitleDate[0] , templateXValue[0] and templateXValue[1] were the templateTitleSubject[0] commonly used used templateTitle[3] templateTitle[4] templateTitle[5] software templateTitle[6] around the world , with nearly templateYValue[max] templateScale of templateYLabel[1] stating that they used templateXValue[0] and templateYValue[1] templateScale using templateXValue[1] . templateXValue[2] . templateXValue[3] , and templateXValue[4] rounded out the top templateYValue[18] templateTitleSubject[0] templateTitle[1] used templateTitle[3] templateTitle[4] around the world .
generated: In this 2018 , Pop and Brazilian pop were the Brazil commonly used music genres 2018 software around the world , with nearly 55.5 % of respondents stating that they used Pop and 54 % using Brazilian pop .  . Sertanejo .  . Rock , and Samba/pagode rounded out the top 17.3 Brazil most used music genres around the world .

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[idxmax(X)] templateYLabel[3] in templateXValue[idxmax(Y)] .

generated_template: The timeline presents the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[max] .
generated: The timeline presents 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 2018 . During the survey , it was found that templateYValue[7] templateScale of U.S. internet users watched templateTitle[1] templateTitle[2] content on a templateTitleSubject[0] basis . Additionally , more than half of the internet users in templateXValue[5] watched templateTitle[1] videos every day .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] worldwide who have every experienced abuse or templateTitleSubject[0] on templateTitle[2] templateTitle[3] and templateTitle[4] templateTitle[5] templateTitle[6] . During the 2017 survey period , templateYValue[0] templateScale of survey templateYLabel[1] who have experienced templateXValue[13] abuse stated that they had experienced templateTitleSubject[0] via templateXValue[0] .
generated: This statistic shows the Share of online worldwide who have every experienced abuse or Daily on video usage and selected countries 2018 .  . During the 2017 survey period , 64 % of survey respondents who have experienced Germany abuse stated that they had experienced Daily via Saudi Arabia .

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[idxmax(X)] templateScale 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] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Youth unemployment rate of Singapore from 2005 to 2018 .  . In 2018 , the Youth unemployment rate in Singapore amounted to 4.2 rate .

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[idxmax(X)] 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: This statistic displays the total templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] per annum from templateXValue[min] to templateXValue[max] . The largest templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] was in templateXValue[7] with a total production of templateYValue[max] templateScale templateYLabel[2] of templateTitle[2] . Since then the total templateYLabel[0] of templateTitle[2] templateTitle[3] has declined .
generated: This statistic displays the total Commercial of Worldwide space launches per annum from 1990 to 2017 .  . The largest Commercial of Worldwide space launches was in 2010 with a total production of 35 million launches of space .  . Since then the total Commercial of space launches has declined .

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 templateScale 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] templateNegativeTrend until templateXValue[max] , when it again templatePositiveTrend and amounted to almost templateYValue[0] thousand templateYLabel[1] . A similar trend can be seen in the neighbor country Norway , where the templateYLabel[0] of templateYLabel[1] templateNegativeTrend for several years but templatePositiveTrend again in templateXValue[1] .
generated: 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] templateScale 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: The statistic shows the total templateYLabel[0] of templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] in Indonesian territorial waters from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[idxmax(X)] templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] . templateTitle[4] templateTitle[5] in templateTitleSubject[0] templateTitle[4] templateTitle[5] in Indonesian waters spiked in templateXValue[4] , when templateYValue[max] templateYLabel[1] were reported .
generated: The statistic shows the total GDP of 2018 and in Indonesian territorial waters from 2007 to 2018 .  . In 2018 , there were 3386.0 2018 and in Germany .  . 2018 2018 in Germany 2018 in Indonesian waters spiked in 2014 , when 3386.0 billion were reported .

Example 646:
titleEntities: {'Subject': ['Latin American'], 'Date': ['2018', '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 templateTitleDate[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: This statistic shows the templateYLabel[0] in the templateYLabel[2] of automotive templateYLabel[1] per liter in templateTitle[3] templateTitle[4] for the period between 2018 and 2019 . The templateYLabel[2] changes exclude value added taxes from prices for automotive templateYLabel[1] . In 2019 , the templateYLabel[1] templateYLabel[2] per liter in the templateXValue[5] templateXValue[6] was 2.2 seven templateScale lower than it was in 2019 .
generated: This statistic shows the Average in the years of automotive age per liter in Cup average for the period between 2018 and 2019 .  . The years changes exclude value added taxes from prices for automotive age .  . In 2019 , the age years per liter in the Colombia Uruguay was 2.2 seven percent lower than it was in 2019 .

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] templateTitle[4] in selected countries worldwide as of 2014 . templateXValue[0] ranked first with a templateTitle[0] templateTitle[1] templateYLabel[0] of more than templateYValue[max] templateYLabel[1] templateYLabel[2] , almost templateTitle[4] 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] templateTitle[4] was the highest selling platform worldwide in those years .

generated_template: In templateTitleDate[0] , templateXValue[0] was the city with the highest templateYLabel[0] of tourists staying at least templateYValue[8] night templateYLabel[2] capita . That same year , international overnight visitor spending in templateXValue[0] reached 28.5 templateScale US dollars . The city , which is the largest and most populous city in the United Arab Emirates , was also templateYValue[8] of the most expensive holiday destinations in the world in 2018 .
generated: In 2014 , Brazil was the city with the highest Price of tourists staying at least 557.07 night dollars capita .  . That same year , international overnight visitor spending in Brazil reached 28.5 billion US dollars .  . The city , which is the largest and most populous city in the United Arab Emirates , was also 557.07 of the most expensive holiday destinations in the world in 2018 .

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 templateScale less than in the templateXValue[4] templateXValue[last] .

generated_template: This statistic represents major countries at the end of year templateTitleDate[0] , templateTitle[5] templateYLabel[0] templateTitle[2] templateTitleSubject[0] templateYLabel[1] . templateXValue[0] 's templateYLabel[0] templateTitle[2] templateTitleSubject[0] ( photovoltaic ) templateYLabel[1] reached templateYValue[max] templateYLabel[2] at the end of templateTitleDate[0] . templateTitle[2] photovoltaic - additional information With the assistance of politically-driven subsidies , the templateTitle[2] photovoltaic ( templateTitleSubject[0] ) sector continues to attract investors , with investment volumes that eclipse the deals made in other markets , including the wind power and biomass energy sectors .
generated: This statistic represents major countries at the end of year 2016 , country Manufacturing pharmaceutical costs .  . Mexico 's Manufacturing pharmaceutical ( photovoltaic ) costs reached 100 index at the end of 2016 .  . pharmaceutical photovoltaic - additional information With the assistance of politically-driven subsidies , the pharmaceutical photovoltaic ( Manufacturing ) sector continues to attract investors , with investment volumes that eclipse the deals made in other markets , including the wind power and biomass energy sectors .

Example 649:
titleEntities: {'Subject': ['European'], 'Date': ['2015', '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 templatePositiveTrend to templateYValue[10] templateScale 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] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] templateTitle[1] templateXValue[min] to templateXValue[10] and forecasts up to templateXValue[max] . In templateXValue[9] , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] templateTitle[1] templatePositiveTrend by approximately templateYValue[9] templateScale compared to the previous templateXLabel[0] .
generated: This statistic shows the Volume of thousand metric in European Union-27 2015 to 2018 and forecasts up to 2028 .  . In 2019 , thousand metric in European Union-27 increased by approximately 11664 thousand compared to the previous Year .

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 templateScale 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] templateScale of templateYLabel[1] reported accessing the templateTitle[7] via templateTitle[5] , whereas in templateXValue[max] the templateYLabel[0] of templateYLabel[1] templatePositiveTrend to templateYValue[idxmax(X)] templateScale .

generated_template: Body modification , especially tattooing , has proven to be very popular over the last few years . In the templateTitleSubject[0] alone , templateXValue[last] a quarter of the population is adorned with at least templateXValue[0] tattoo , and less templateXValue[last] 40 templateScale of Americans would rule out getting templateXValue[0] completely . Most get their first in their twenties , and the vast majority never regrets that first prick of the needle .
generated: Body modification , especially tattooing , has proven to be very popular over the last few years .  . In the Great Britain alone , 2003 a quarter of the population is adorned with at least 2013 tattoo , and less 2003 40 % of Americans would rule out getting 2013 completely .  . Most get their first in their twenties , and the vast majority never regrets that first prick of the needle .

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 templateScale of templateYLabel[1] in the templateXValue[26] of America as of templateTitleDate[0] , in the last 30 days templateTitle[5] templateXLabel[0] . As of that year , templateYValue[22] templateScale of templateTitle[4] in templateXValue[22] consumed more than 4 ( women ) or 5 ( men ) alcoholic beverages on a single occasion within the preceding 30 days .

generated_template: templateXValue[0] saw the highest templateTitle[1] templateYLabel[0] in the templateXValue[28] in templateTitleDate[0] , with templateYValue[max] rapes templateYLabel[1] 100,000 templateYLabel[3] . The lowest templateYLabel[0] was found in templateXValue[6] templateXValue[last] , with templateYValue[min] rapes templateYLabel[1] 100,000 templateYLabel[3] . Sexual assault in templateXValue[0] Fighting sexual assault in templateXValue[0] is particularly difficult due to small , isolated , close-knit communities who can be wary of airing their dirty laundry to outsiders , as well as a low number of law enforcement employees in the templateXLabel[0] .
generated: District of Columbia saw the highest binge Percentage in the Kansas in 2018 , with 25.9 rapes binge 100,000 drinkers .  . The lowest Percentage was found in Minnesota West Virginia , with 11.5 rapes binge 100,000 drinkers .  . Sexual assault in District of Columbia Fighting sexual assault in District of Columbia is particularly difficult due to small , isolated close-knit communities who can be wary of airing their dirty laundry to outsiders , as well a low number of law enforcement employees in the State .

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[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Austria from 2008 to 2018 .  . Urbanization means the Share of urban population in the total population of a country .  . In 2018 , 58.3 % of Austria 's total population lived in urban areas and cities .

Example 653:
titleEntities: {'Subject': ['first Bundesliga', 'Germany'], 'Date': ['2020']}
title: Market value of first Bundesliga football clubs in Germany in 2020
X_Axis['Club', 'Name']: ['FC_Bayern_München', 'Borussia_Dortmund', 'RasenBallsport_Leipzig', 'Bayer_04_Leverkusen', 'Borussia_Mönchengladbach', 'FC_Schalke_04', 'TSG_1899_Hoffenheim', 'Hertha_BSC', 'VfL_Wolfsburg', 'Eintracht_Frankfurt', 'SV_Werder_Bremen', '1._FSV_Mainz_05', 'SC_Freiburg', 'FC_Augsburg', '1._FC_Köln', 'Fortuna_Düsseldorf', '1._FC_Union_Berlin', 'SC_Paderborn']
Y_Axis['Market', 'value', 'in', 'million', 'euros']: ['933.15', '637.4', '594.4', '445.75', '312.0', '242.75', '238.23', '233.2', '230.95', '215.8', '189.75', '147.4', '145.4', '131.15', '102.2', '93.15', '43.05', '31.25']

gold: This statistic shows the market value of the first Bundesliga football clubs in Germany as of February 11 , 2020 . The market value of FC Bayern Munich was highest at 933.15 million euros , followed by 637.4 million euros for Borussia Dortmund and 594.4 million euros for RB Leipzig .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] football templateTitle[5] in templateTitleSubject[1] as of 11 , templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] of templateXValue[0] Munich was highest at templateYValue[max] templateScale templateYLabel[3] , followed by templateYValue[1] templateScale templateYLabel[3] for templateXValue[1] and templateYValue[2] templateScale templateYLabel[3] for RB templateXValue[2] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[7] in templateTitleDate[0] . templateXValue[0] 's templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] .
generated: The statistic shows the Market value million of Bundesliga football clubs Germany in the 2020 in .  . FC Bayern München 's value million of Bundesliga football clubs Germany in 2020 .

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 templateScale of templateYLabel[1] aged templateXValue[3] to templateXValue[3] had already practiced templateTitle[3] at the templateTitle[4] or in a naturist camp . Discover also the level of interest of the French for naturism .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[1] a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateYValue[2] templateTitle[7] in the templateTitle[8] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] templateYLabel[1] had attended a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateXLabel[0] .
generated: This statistic shows the Share of women a practicing nudism beach at least once in the France 9 by in the age from 2017 to .  . In 2017 , 13 % of France women had attended a practicing nudism beach at least once in the France Year .

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[max] , 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] templateScale of templateTitleSubject[0] 's full-time templateYLabel[1] are located in the company 's home market , the country .

generated_template: This statistic shows the total templateYLabel[0] of templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , there were a total of templateYValue[min] people templateTitle[4] templateTitleSubject[0] .
generated: This statistic shows the total Number of employees in the Dell from 1996 to 2019 .  . In 2019 , there were a total of 8.4 people 2019 Dell .

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[idxmax(X)] templateScale .

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

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 2017 . During the measured period , templateXValue[0] was the most popular templateTitleSubject[0] reaction on templateTitle[2] templateTitle[3] posts on the social network .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] as of templateTitleSubject[0] templateTitleDate[0] . During the survey , templateYValue[3] templateScale of templateYLabel[1] said that they ate templateTitle[3] templateTitle[4] because they lacked templateXValue[3] .
generated: This statistic shows the Facebook reactions top shared content in the 2017 as of Facebook 2017 .  . During the survey , 12 % of reactions said that they ate shared content because they lacked Sad .

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[idxmax(X)] templateYLabel[5] templateYLabel[6] for templateTitle[2] households .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateTitle[2] in the templateTitle[0] was templateYValue[min] templateYValue[idxmax(X)] of every 1,000 templateYLabel[6] . This is a significant templateNegativeTrend from templateXValue[min] , when templateYLabel[0] templateYLabel[1] was at templateYValue[idxmin(X)] deaths out of every 1,000s templateYLabel[6] . What is templateYLabel[0] templateYLabel[1] ? The templateYLabel[0] templateYLabel[1] templateYLabel[2] is the number of deaths of babies under the age of one templateYLabel[3] 1,000 templateYLabel[5] templateYLabel[6] .
generated: In 2018 , the Median income black in the Household was 32210 41361 of every 1,000 dollars .  . This is a significant decrease from 1990 , when Median income was at 34068 deaths out of every 1,000s dollars .  . What is Median income ? The Median income 2018 is the number of deaths babies under the age of one CPI-U-RS 1,000 U.S. dollars .

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] templateScale 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] templateScale templateYLabel[2] templateYLabel[3] .
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. dollars .

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] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by the Pat Bowlen Trust , who bought the templateYLabel[0] for 78 templateScale templateYLabel[3] templateYLabel[4] in 1984 .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Saints from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] amounted to about templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Rays are owned by Charles Johnson , who bought the templateYLabel[0] for 100 templateScale templateYLabel[3] templateYLabel[4] in 1993 .
generated: This graph depicts the Franchise value of the Denver Broncos Saints from the National Football League from 2002 to 2019 .  . In 2019 , the Franchise value amounted to about 3000 million U.S. dollars .  . The Denver Broncos Rays are owned by Charles Johnson , who bought the Franchise for 100 million U.S. dollars in 1993 .

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] templateScale templateYLabel[2] templateYLabel[3] . The templateTitleSubject[0] Corporation is an American foodservice , templateTitle[0] , and clothing provider headquartered in Philadelphia , Pennsylvania .

generated_template: This statistic shows templateTitleSubject[0] 's templateTitle[1] annual templateYLabel[0] from templateXValue[min] to templateXValue[max] . As of templateXValue[max] , the daily deal website 's annual templateYLabel[0] amounted to 2.2 templateScale templateYLabel[2] templateYLabel[3] . The coupon portal had close to 43.6 templateScale active customers as the fourth quarter of templateXValue[max] .
generated: This statistic shows Aramark 's management annual Revenue from 2008 to 2019 .  . As of 2019 , the daily deal website 's annual Revenue amounted to 2.2 million U.S. dollars .  . The coupon portal had close to 43.6 million active customers as the fourth quarter of 2019 .

Example 662:
titleEntities: {'Subject': ['RIM/Blackberry'], 'Date': ['2004', '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] templateScale 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: This statistic represents templateTitleSubject[0] 's , formerly known as GSI Commerce , templateTitle[2] templateTitle[3] templateYLabel[0] from templateXValue[min] to templateXValue[max] , in templateScale templateYLabel[2] templateYLabel[3] . In templateXValue[11] , GSI Commerce reported a templateTitle[3] templateYLabel[0] of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] before being acquired by templateTitleSubject[0] in 2011 .
generated: This statistic represents RIM/Blackberry 's , formerly known as GSI Commerce , worldwide 2004 Revenue from 2004 to 2019 , in million U.S. dollars .  . In 2008 , GSI Commerce reported a 2004 Revenue of 19907 million U.S. dollars before being acquired by RIM/Blackberry in 2011 .

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] templateScale of the templateYLabel[1] felt that templateXValue[0] templateXLabel[0] templateXValue[4] for templateTitle[3] generated the most templateTitle[0] .

generated_template: This statistic shows , according to survey templateYLabel[1] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[4] that should be addressed in the run up to the templateTitleDate[0] United Kingdom ( templateTitleSubject[1] ) General templateTitle[2] ( as of 2014 ) . With templateYValue[max] templateScale of templateYLabel[1] , templateXValue[0] was considered to be the templateTitle[0] templateTitle[1] topic , followed by the National Health Service ( templateXValue[1] ) and the templateXValue[2] ( EU ) .
generated: This statistic shows , according to survey respondents , the Revenue share various decorating that should be addressed in the run up to the 2014 United Kingdom ( U.S. ) General various ( as of 2014 ) .  . With 45 % of respondents , Embroidery was considered to be the Revenue share topic , followed by the National Health Service ( Screen printing ) and the Heat transfers ( EU ) .

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[idxmax(X)] templateScale .

generated_template: templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] have been steadily templatePositiveTrend each templateXLabel[0] since the start of the decade . In templateXValue[max] , a record templateYValue[idxmax(X)] templateScale tourists ( including residents and non-residents ) arrived at templateTitle[3] in templateTitleSubject[0] . Tourism in templateTitleSubject[0] In line with trends across Europe , tourism in templateTitleSubject[0] has experienced a boost in the last ten years .
generated: tourist arrivals at accommodation establishments in Bulgaria have been steadily increasing each Year since the start of the decade .  . In 2018 , a record 7.8 millions tourists ( including residents and non-residents ) arrived at accommodation in Bulgaria .  . Tourism in Bulgaria line with trends across Europe , tourism in Bulgaria has experienced a boost in the last ten years .

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[idxmax(X)] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[idxmax(X)] 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 43423 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 represents the projected templateTitle[0] templateTitle[1] templateTitleSubject[0] templateYLabel[0] templateYLabel[1] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , it is estimated that the templateTitle[0] templateTitle[1] templateTitleSubject[0] industry will have a templateYLabel[0] templateYLabel[1] of around templateYValue[3] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic represents the projected Scotland forecasted Age in the age from 2014 to 2039 .  . In 2019 , it is estimated that the Scotland forecasted industry will have a Age of around 42.9 % Age .

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] templateScale of templateTitleSubject[0] companies had implemented templateXValue[2] templateXValue[6] templateXValue[2] .

generated_template: This statistic shows , according to survey templateYLabel[1] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[4] that should be addressed in the run up to the templateTitleDate[0] United Kingdom ( templateTitleSubject[1] ) General templateTitle[2] ( as of 2014 ) . With templateYValue[max] templateScale of templateYLabel[1] , templateXValue[0] was considered to be the templateTitle[0] templateTitle[1] topic , followed by the National Health Service ( templateXValue[1] ) and the templateXValue[2] ( EU ) .
generated: This statistic shows , according to survey respondents , the U.S. company data prevention that should be addressed in the run up to the 2017 United Kingdom ( U.S. ) General data ( as of 2014 ) .  . With 60 % of respondents , Training and awareness programs was considered to be the U.S. company topic , followed by the National Health Service ( Expanded use of encryption ) and the Endpoint security solutions ( EU ) .

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] templateScale of surveyed bank templateYLabel[1] in the templateXValue[0] indicated high levels of templateTitle[8] , ranking the templateXLabel[0] highest among templateTitleSubject[0] templateTitle[6] locations in templateTitleDate[0] . This was followed templateTitle[4] the templateXValue[1] , templateYLabel[2] templateYValue[1] templateScale of bank templateYLabel[1] templateYLabel[2] a templateYLabel[3] templateYLabel[4] throughout the year .

generated_template: This statistic shows the largest templateTitle[3] templateTitle[4] of the templateXValue[6] States regarding trade goods in templateTitleDate[0] , by templateYLabel[0] templateYLabel[1] . In templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] of trade goods from templateXValue[2] amounted to templateYValue[2] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the largest ranked by of the Poland States regarding trade goods in 2016 , by Share customers .  . In 2016 , the Share customers of trade goods from Austria amounted to 66.8 % experience .

Example 669:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '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] templateScale templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the total templateTitleSubject[0] templateTitle[0] templateTitle[1] templateYLabel[0] from templateXValue[min] to templateXValue[max] . According to the report , approximately templateYValue[0] templateScale templateYLabel[2] of templateTitle[0] templateTitle[1] were produced in the templateTitle[3] in templateXValue[max] . Around 31.86 templateScale metric tons of templateTitle[0] were exported to China that templateXLabel[0] .
generated: This statistic shows the total U.S. value Trillion from 2000 to 2019 .  . According to the report , approximately 3.12 trillion dollars of Total value were produced in the U.S. in 2019 .  . Around 31.86 trillion metric tons of Total were exported to China that Year .

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: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] 100,000 templateYLabel[6] in the templateXValue[26] in templateTitleDate[0] , distinguished templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , templateXValue[7] of templateXValue[7] was ranked first with templateYValue[max] cases templateYLabel[4] 100,000 templateYLabel[6] in the templateTitle[4] .
generated: This statistic shows the Number murder victims 100,000 in the Wisconsin in 2018 , distinguished 2018 State .  . In 2018 , Missouri of was ranked first with 1739 cases victims 100,000 in the U.S. .

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: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] templateTitle[9] worldwide as of templateTitleDate[0] . As of that year , templateXValue[0] had the templateTitle[2] templateTitle[3] of templateTitle[4] templateTitle[5] templateTitle[6] men worldwide . Only templateYValue[min] templateScale of templateTitle[7] templateTitle[8] templateTitle[9] templateYLabel[2] 15 - 49 years used condoms .
generated: This statistic shows the National Basketball Association Basketball Association all-time of triple double leaders 1946 2020 worldwide as of 1946 .  . As of that year , Oscar Robertson had the Association all-time of triple double leaders men worldwide .  . Only 31 % of 1946 2020 doubles 15 - 49 years used condoms .

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 templateScale people .

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

Example 673:
titleEntities: {'Subject': ['Global'], 'Date': ['2013', '2030']}
title: Global energy commodity price index 2013 to 2030
X_Axis['Year']: ['2030', '2025', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Price', 'index', 'in', 'real', '2010', 'U.S.', 'dollars']: ['87.2', '79.1', '74.7', '73.3', '72.0', '74.3', '87.0', '68.1', '55.1', '65.0', '111.7', '120.1']

gold: This statistic shows a commodity price index of energy from 2013 through 2018 , and gives projections for 2030 . In 2018 , the index stood at around 87 real US dollars . Real 2010 US dollars reflect buying power in 2010 .
gold_template: This statistic shows a templateTitle[2] templateYLabel[0] templateYLabel[1] of templateTitle[1] from templateXValue[min] through templateXValue[6] , and gives projections for templateXValue[max] . In templateXValue[6] , the templateYLabel[1] stood at around templateYValue[0] templateYLabel[2] US templateYLabel[5] . templateYLabel[2] templateYLabel[3] US templateYLabel[5] reflect buying power in templateYLabel[3] .

generated_template: 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[7] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] of templateYLabel[1] templateYLabel[2] ( in relation to PPP dollars ) amounted to about templateYValue[7] templateScale .
generated: The statistic shows Global energy Price of the index 2013 2030 ( real ) adjusted for Purchasing Power Parity ( PPP ) from 2013 to 2030 .  . In 2017 , Global energy Price of index real ( in relation to PPP dollars ) amounted to about 68.1 % .

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] templateScale of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] templateXValue[5] the templateTitle[0] .

generated_template: This statistic depicts templateTitle[2] templateTitle[3] templateTitle[1] the templateTitle[0] by templateTitleSubject[0] construction firms in templateTitleDate[0] . The survey revealed that templateYValue[max] templateScale of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] templateTitle[2] the templateTitle[0] . Residential construction involves the building and selling of both individual and multi-family dwellings .
generated: This statistic depicts paint brands used the Most by U.S. construction firms in 2018 .  . The survey revealed that 49.5 % of the respondents used Sherwin-Williams Brand paint the Most .  . Residential construction 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] templateScale templateYLabel[1] templatePositiveTrend while templateXValue[4] 's templateXValue[4] Rocket templateXValue[1] experienced a templateYValue[max] templateScale templatePositiveTrend compared to templateXValue[1] year .

generated_template: This statistic shows the templateTitleSubject[0] Education templateTitle[0] templateXValue[0] ranking for templateTitleDate[0] , sorted templateTitle[3] templateYLabel[0] templateYLabel[1] . templateXValue[0] was placed first as the templateXValue[0] with the highest templateYLabel[0] templateYLabel[1] worldwide , at templateYValue[max] .
generated: This statistic shows the U.S. Education Sales Neutrogena/ makeup remover implements ranking for 2014 , sorted U.S. Percent sales .  . Neutrogena/ makeup remover implements was placed first as the Neutrogena/ makeup remover implements with the highest Percent sales worldwide , at 144 .

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[idxmin(X)] templateScale 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] templateScale templateYLabel[3] templateYLabel[4] . The predicted compound annual growth rate between templateXValue[min] and templateXValue[idxmax(Y)] should be 26.3 templateScale .
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 billion .

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] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Estonia from 2014 to 2018 , with projections up until 2024 .  . In 2018 , Estonia 's real Gross domestic product increased by around 4.76 % 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] templateScale , but by templateXValue[max] this had templateNegativeTrend to templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was around templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Northern Ireland from 2013 to 2019 .  . In 2019 , the Unemployment rate in Northern Ireland was around 7.6 % .

Example 679:
titleEntities: {'Subject': ['Viki'], 'Date': ['2012', '2015']}
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 2015 . As of that templateXLabel[0] , the video portal had templateYValue[last] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide . The VoD service had been acquired by Japanese e-commerce company Rakuten in 2013 .

generated_template: This statistic shows the templateTitle[0] number of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from 2018 to 2019 . The data are seasonally adjusted . There were about templateYValue[max] templateScale templateYLabel[0] templateYLabel[1] on the last business day of 2019 .
generated: This statistic shows the Viki number of monthly active in the Viki from 2018 to 2019 .  . The data are seasonally adjusted .  . There were about 40.1 millions Number monthly on the last business day of 2019 .

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: This statistic shows the templateYLabel[0] templateYLabel[1] ( templateTitle[2] ) of templateTitleSubject[0] Shell Plc from templateXValue[min] to templateXValue[max] , in templateScale templateYLabel[3] templateYLabel[4] . In templateXValue[max] , templateTitleSubject[0] 's templateTitle[2] amounted to some templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Number direct ( staff ) of Citigroup Shell Plc from 2011 to 2018 , in thousands .  . In 2018 , Citigroup 's staff amounted to some 204 thousands .

Example 681:
titleEntities: {'Subject': ['Mattel'], 'Date': ['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] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the total amount of templateTitle[2] mined in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , some templateYValue[idxmin(X)] thousand templateYLabel[2] templateYLabel[3] of templateTitle[2] were mined across the country . Since then , this figure templatePositiveTrend to some templateYValue[idxmax(X)] thousand in templateXValue[idxmax(Y)] .
generated: This statistic shows the total amount of toy mined in Mattel from 2006 to 2019 .  . In 2006 , some 2611.79 thousand million U.S. of toy were mined across the country .  . Since then , this figure rose to some 1980.78 thousand in 2013 .

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] , templateTitleDate[0] , the outbreak of the coronavirus disease ( templateTitle[0] ) had been confirmed in 65 countries , with the overwhelming majority of templateXValue[5] reported in templateXValue[1] . The virus had infected templateYValue[max] people templateTitle[2] , and the templateYLabel[0] of deaths had totaled 3,043 . The most severely affected countries outside of templateXValue[1] were the templateXValue[2] of templateXValue[2] and templateXValue[3] .

generated_template: 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 Number of unique 2 of COVID-19 cases in the worldwide in 2 , 2020 Country .  . In 2 , about 42 unique 2 of COVID-19 cases were March in Thailand .

Example 683:
titleEntities: {'Subject': ['Active Duty 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[idxmax(X)] 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] , the templateTitleSubject[0] 's global templateTitle[0] amounted to templateYValue[0] templateYValue[idxmax(X)] templateYLabel[2] . The templateTitleSubject[0] Ltd. is a Swiss company which primarily manufactures watches . templateTitleSubject[0] The templateTitleSubject[0] was formed in 1983 through the merging of two Swiss watch manufacturers : ASUAG and SSIH .
generated: In 2018 , the Active Duty Navy 's global Active amounted to 325395 personnel .  . The Active Duty Navy Ltd. is a Swiss company which primarily manufactures watches .  . Active Duty Navy The Active Duty Navy was formed in 1983 through the merging of two Swiss watch manufacturers : ASUAG and SSIH .

Example 684:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2002']}
title: Commodity prices of wheat in the United Kingdom ( UK ) 2002 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Price', 'per', 'tonne', 'in', 'GBP']: ['127.15', '143.06', '175.95', '179.26', '169.17', '123.76', '107.05', '137.87', '120.97', '78.88', '67.43', '80.3', '79.32', '65.02']

gold: This statistic shows the average price per tonne of wheat in the United Kingdom ( UK ) between 2002 and 2015 by year , according to published agricultural and commodity price figures . In 2012 , the price of wheat was 179.26 British Pound Sterling ( GBP ) per tonne .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) between templateXValue[min] and templateXValue[max] by templateXLabel[0] , according to published agricultural and templateTitle[0] templateYLabel[0] figures . In templateXValue[3] , the templateYLabel[0] of templateTitle[2] was templateYValue[max] British Pound Sterling ( templateYLabel[3] ) templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , there were a total of templateYValue[idxmax(X)] directory listings for templateYLabel[1] templateYLabel[2] throughout the templateTitle[5] , an templatePositiveTrend of 10 templateScale compared to the previous templateXLabel[0] .
generated: This statistic shows the Price of prices wheat United Kingdom in United Kingdom from 2002 to 2015 .  . In 2014 , there were a total of 127.15 directory listings for per tonne throughout the UK , an increase of 10 % compared to the previous Year .

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] templateScale , templateXValue[0] to templateXValue[0] templateXLabel[0] olds had the smallest proportion of charitable givers .

generated_template: Body modification , especially tattooing , has proven to be very popular over the last few years . In the templateTitleSubject[0] alone , templateXValue[last] a quarter of the population is adorned with at least templateXValue[0] tattoo , and less templateXValue[last] 40 templateScale of Americans would rule out getting templateXValue[0] completely . Most get their first in their twenties , and the vast majority never regrets that first prick of the needle .
generated: Body modification , especially tattooing , has proven to be very popular over the last few years .  . In the England alone , 75 and over a quarter of the population is adorned with at least 16 to 24 tattoo , and less 75 and over 40 % of Americans would rule out getting 16 to 24 completely .  . Most get their first in their twenties , and the vast majority never regrets that first prick of the needle .

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: In templateXValue[max] , templateYValue[idxmax(X)] individuals were killed in motor vehicle accidents in templateTitleSubject[0] . An overall templateNegativeTrend in the templateYLabel[0] of templateTitle[1] templateTitle[2] was observed within the time period shown . The highest templateYLabel[0] of templateTitle[1] templateYLabel[1] in this timeframe occurred in templateXValue[11] , with figures nearly twice as high as those reported in templateXValue[max] .
generated: In 2018 , 1867 individuals were killed in motor vehicle accidents in Romania .  . An overall decrease in the Number of road deaths was observed within the time period shown .  . The highest Number of road fatalities in this timeframe occurred in 2007 , with figures nearly twice as high those reported in 2018 .

Example 687:
titleEntities: {'Subject': ['USB', 'Germany'], 'Date': ['2004', '2018']}
title: Sales volume of USB flash drives in Germany 2004 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Sales', 'volume', 'in', 'millions']: ['12.88', '13.51', '16.17', '15.94', '15.46', '13.5', '15.85', '13.82', '11.78', '12.99', '13.0', '8.18', '5.0', '3.2', '2.03']

gold: USB flash drives experienced fluctuating sales numbers in recent years , with almost 12.9 million units sold in 2018 . Meanwhile , revenue generated amounted to 155 million euros in the same year , a decrease on the one before . Storage media USB flash drives revolutionized data storage for computer users everywhere when they emerged on the market , joining such well-known storage media as floppy discs , hard drives and optical storage units like CD-R and CD-RW discs .
gold_template: templateTitleSubject[0] templateTitle[3] templateTitle[4] experienced fluctuating templateYLabel[0] numbers in recent years , with almost templateYValue[0] templateScale units sold in templateXValue[max] . Meanwhile , revenue generated amounted to 155 templateScale euros in the same templateXLabel[0] , a templateNegativeTrend on the one before . Storage media templateTitleSubject[0] templateTitle[3] templateTitle[4] revolutionized data storage for computer users everywhere when they emerged on the market , joining such well-known storage media as floppy discs , hard templateTitle[4] and optical storage units like CD-R and CD-RW discs .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . According to the source , the templateYLabel[0] of people traveling over the templateTitle[0] templateTitle[1] period is forecast to amount to templateYValue[idxmax(X)] templateScale in templateXValue[max] .
generated: This statistic shows the Sales of volume in the flash drives 2004 to 2018 .  . According to the source , the Sales of people traveling over the Sales volume period is forecast to amount 12.88 millions in 2018 .

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[idxmax(X)] templateScale people . In that templateXLabel[0] , the labor participation rate was at 76.7 templateScale while the employment rate was at 75.2 templateScale .

generated_template: The statistic depicts the manufacturers ( templateTitle[0] ) templateYLabel[1] of sporting goods equipment , fitness equipment , templateTitle[3] apparel , athletic footwear , and templateTitle[3] licensed merchandise in the templateYLabel[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateYLabel[1] amounted to about templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . Sporting goods manufacturers templateTitle[0] templateYLabel[1] - additional information templateTitle[0] templateYLabel[1] of sporting goods in the templateYLabel[3] reached templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] , the highest figure to-date and a eight templateScale templateYLabel[3] dollar templatePositiveTrend on the figure from templateXValue[4] .
generated: The statistic depicts the manufacturers ( Working ) age of sporting goods equipment , fitness equipment , Vietnam apparel , athletic footwear , and Vietnam licensed merchandise in the millions from 2009 to 2018 .  . In 2018 , Working age amounted to about 72.59 millions .  . Sporting goods manufacturers Working age - additional information Working age of sporting goods in the millions reached 72.59 millions in 2018 , the highest figure to-date and a eight millions dollar increase on the figure from 2014 .

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] templateScale of templateYLabel[1] stated that the National Football League , templateXValue[0] , was their favorite men 's templateTitleSubject[0] professional templateTitle[2] league to templateXValue[last] .

generated_template: This statistic reveals the templateTitle[3] templateTitle[4] templateTitle[5] according to adults in the templateTitle[0] in templateTitleDate[0] . During a survey , templateYValue[max] templateScale of adults found the templateXValue[0] to be the templateTitle[3] templateTitle[4] templateTitle[5] to play .
generated: This statistic reveals the leagues U.S. 2019 according to adults in the Most in 2019 .  . During a survey , 33 % of adults found the NFL to be the leagues U.S. 2019 to play .

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 2019 , sorted templateTitle[6] templateXLabel[2] . During that period of time , templateYValue[min] templateScale of respondents earning 30,000 templateTitle[4] dollars or less used the social networking site .

generated_template: This statistic shows the share of adults in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateYValue[max] templateScale of respondents between 18 and 29 years used the social networking site .
generated: This statistic shows the share of adults in the United States who were using Pinterest as of 2019 , sorted by household income .  . During that period of time , 41 % 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] templateScale 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: 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] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] amounted to templateYValue[7] templateScale templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] Saudi templateTitleSubject[0] , the second largest Arab state , is a nation in development .
generated: This statistic shows the Total population of South Korea from 2014 to 2018 , with projections up until 2024 .  . In 2018 , South Korea 's Total population amounted to 51.43 millions Inhabitants .  . population of South Korea Saudi , the second largest Arab state , is a nation in development .

Example 692:
titleEntities: {'Subject': ['Market'], 'Date': ['2016', '2019']}
title: Market capitalization of leading 100 banks worldwide 2016 to 2019
X_Axis['Quarter']: ["Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16"]
Y_Axis['Market', 'capitalization', 'in', 'trillion', 'Euros']: ['5.3', '5.2', '5.2', '4.8', '5.3', '5.2', '5.4', '5.6', '5.4', '5.3', '5.4', '5.2', '4.4', '4.1', '4.2']

gold: This statistic presents the market capitalization of leading 100 banks worldwide from the first quarter 2016 to the third quarter 2019 . The market cap of top 100 global banks amounted to 5.3 trillion Euros in the third quarter of 2019 .
gold_template: This statistic presents the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from the first templateXLabel[0] templateTitleDate[min] to the third templateXLabel[0] templateTitleDate[max] . The templateYLabel[0] cap of top templateTitle[3] global templateTitle[4] amounted to templateYValue[0] templateScale templateYLabel[3] in the third templateXLabel[0] of templateTitleDate[max] .

generated_template: This statistic shows templateTitleSubject[0] 's templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] from the second templateXLabel[0] of templateTitleDate[min] to the fourth templateXLabel[0] of templateTitleDate[max] . In the fourth templateXLabel[0] of templateTitleDate[max] , the photo sharing app is projected to generate templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in global revenues , up from an templateYLabel[0] templateYValue[6] templateScale templateYLabel[3] templateYLabel[4] in the second templateXLabel[0] of 2019 .
generated: This statistic shows Market 's capitalization leading Market capitalization from the second Quarter of 2016 to the fourth Quarter of 2019 .  . In the fourth Quarter of 2019 , the photo sharing app is projected to generate 5.6 trillion Euros in global revenues , up from an Market 5.4 trillion Euros in the second Quarter of 2019 .

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 2013 , templateYValue[max] templateScale of the templateYLabel[1] answered that their family eat templateTitle[4] templateTitle[5] at templateTitle[6] on templateXValue[last] to templateXValue[last] templateXValue[0] a week .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among adult Cubans living in templateTitleSubject[0] on their opinion of templateTitle[1] templateTitle[2] , their former President . templateYValue[max] templateScale of templateYLabel[1] stated they have a very or somewhat templateXValue[1] opinion of templateTitle[1] templateTitle[2] .
generated: This statistic shows the results of a 2013 survey among adult Cubans living in American on their opinion of American families , their former President .  . 53 % of respondents stated they have a very or somewhat 4 to 5 nights opinion of American families .

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] templateScale of templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] owning templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . As of templateXValue[max] , there were templateYValue[idxmax(X)] templateScale templateYLabel[0] . The templateTitle[0] of templateYLabel[0] templatePositiveTrend during the period in consideration .
generated: This statistic shows the Iran of 's owning Share in Iran from 2014 to 2024 .  . As of 2024 , there were 30.26 % Share .  . The Iran of Share increasing during the period in consideration .

Example 695:
titleEntities: {'Subject': ['Dubai'], '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[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the change in the templateYLabel[1] of templateTitle[0] templateYLabel[0] of people in the templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . templateTitle[0] templateYLabel[0] do not include groups with high templateYLabel[0] ( 12 templateScale of the population , starting at 50,000 British pounds in templateYLabel[0] and over ) . In templateXValue[min] , the templateTitle[0] templateYLabel[1] people held in templateYLabel[0] was templateYValue[idxmin(X)] British pounds .
generated: This statistic shows the change in the contribution of Direct GDP of people in the Dubai between 2006 and 2026 .  . Direct GDP do not include groups with high GDP ( 12 billion of the population , starting at 50,000 British pounds in GDP and over ) .  . In 2006 , the Direct contribution people held in GDP was 4.0 British pounds .

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[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] in the templateTitle[2] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] amounted to approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Number of participants in hunting the U.S. from 2006 to 2018 .  . In 2018 , the Number of participants ( aged six years and older ) in hunting amounted to approximately 15.69 millions .

Example 697:
titleEntities: {'Subject': ['England'], 'Date': ['2010', '2017']}
title: Total household waste in England 2010 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Kilograms', 'per', 'person', 'per', 'year']: ['403', '412', '406', '413', '402', '412', '421', '425']

gold: Household waste volumes per person in England remained at a similar level between 2010 and 2017 . Although there was an overall decrease during this period , the household volumes were still over 400 kilograms per person in 2017 . The region which generated the largest volume of residual waste per household was the North East of England , where an average of 601 kilograms of waste was generated per person in 2017/2018 .
gold_template: templateTitle[1] templateTitle[2] volumes templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] remained at a similar level between templateXValue[min] and templateXValue[max] . Although there was an overall templateNegativeTrend during this period , the templateTitle[1] volumes were still over 400 templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[max] . The region which generated the largest volume of residual templateTitle[2] templateYLabel[1] templateTitle[1] was the North East of templateTitleSubject[0] , where an average of 601 templateYLabel[0] of templateTitle[2] was generated templateYLabel[1] templateYLabel[2] in 2017/2018 .

generated_template: This statistic presents a timeline of the templateTitle[0] of templateTitle[1] templateTitle[2] templateYLabel[1] templateTitle[5] individual templateTitle[5] day in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , individuals saw on average templateYValue[max] television commercials or templateYLabel[0] templateTitle[5] day .
generated: This statistic presents a timeline of the Total of household waste per 2017 individual day in the England from 2010 to 2017 .  . In 2014 , individuals saw on average 425 television commercials or Kilograms 2017 day .

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] templateScale of templateYLabel[1] prefer to templateXLabel[2] by templateXValue[0] .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among Americans aged 16 and older regarding the templateTitle[0] they are looking for in a close templateTitle[2] . This statistic only shows the top five answers to that question . templateYValue[max] templateScale of the templateYLabel[1] stated a close templateTitle[2] has to be loyal .
generated: This statistic shows the results of a 2015 survey among Americans aged 16 and older regarding the Preferred they are looking for in a close transportation .  . This statistic only shows the top five answers to that question .  . 63 % of the respondents stated a close transportation has to be loyal .

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] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . After a severe drop below templateYValue[9] templateScale in templateXValue[9] , templateTitleSubject[0] 's templateYLabel[0] seems to have stabilized again and is expected to level off at around templateYValue[0] templateScale 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] templateScale , and gross domestic product slumping to a dramatic low .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . The templateYLabel[0] templateYLabel[1] is calculated using the price templatePositiveTrend of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the templateXLabel[0] .
generated: The statistic shows the Inflation rate in Vietnam from 1984 to 2017 , 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 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] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmin(Y)] and is forecast to amount to around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] by templateXValue[idxmax(Y)] .

generated_template: This statistic represents templateYLabel[2] templateYLabel[0] generated with online apparel and templateTitle[2] retail sales between templateXValue[min] and templateXValue[max] . In templateXValue[5] , retail e-commerce revenues from apparel and templateTitle[2] sales amounted to templateYValue[5] templateScale templateYLabel[2] templateYLabel[3] and are projected to templatePositiveTrend to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] . E-commerce templateYLabel[0] from apparel in the templateYLabel[2] – additional information With retail e-commerce sales worldwide expected to reach just over four templateScale templateYLabel[2] templateYLabel[3] in templateXValue[3] , the global e-commerce industry appears to show no signs of slowing down .
generated: This statistic represents U.S. Revenue generated with online apparel and augmented retail sales between 2016 and 2022 .  . In 2017 , retail e-commerce revenues from apparel and augmented sales amounted to 409.67 million U.S. dollars and are projected to increase 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] templateYValue[idxmax(X)] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYValue[idxmax(X)] templateYLabel[4] .
generated: This statistic shows the Median Household income in West Virginia from 1990 to 2018 .  . In 2018 , the Median Household income in West Virginia amounted to 50573 dollars .

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 templateTitleDate[min] to the second templateXLabel[0] of the fiscal year templateTitleDate[max] . In the second fiscal templateXLabel[0] of templateTitleDate[max] , which ended on 30 , 2019 , templateTitleSubject[0] generated a net templateYLabel[0] of templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] . Here you can find information about EA 's templateTitle[0] net income .

generated_template: In the fourth templateXLabel[0] of templateTitleDate[max] , The templateTitleSubject[0] Times templateTitleSubject[0] generated a total templateYLabel[0] of templateYValue[last] templateScale templateYLabel[2] templateYLabel[3] , marking a small templatePositiveTrend 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 templateScale for the first time in years .
generated: In the fourth Quarter of 2020 , The Electronic Arts Times 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 2014 . templateXValue[0] had the most templateYLabel[2] in 2014 , with an estimated templateYLabel[0] of templateYLabel[2] of templateYValue[max] templateScale .

generated_template: In templateTitleDate[0] , the Italian among the 20 administrative regions of templateTitleSubject[0] was templateXValue[0] , which at same time was also the biggest island in the country . On the other hand , the smallest templateTitle[5] of Belpaese was templateXValue[last] , located in the north on the border with Switzerland and France . The island of volcanoes The symbol of templateXValue[0] , Mount Etna , is one of the most active volcanos on the planet .
generated: In 2014 , the Italian among the 20 administrative regions of Leading was Booking.com , which at same time was also the biggest island in the country .  . On the other hand , the smallest by of Belpaese was HomeAway.com , located in the north on the border with Switzerland and France .  . The island of volcanoes The symbol of Booking.com , Mount Etna , is one of the most active volcanos on the planet .

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] templateScale . 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] templateScale respectively .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] templatePositiveTrend to templateYValue[max] children per woman in templateXValue[idxmax(Y)] but started to decline since then . In templateXValue[max] , the templateYLabel[1] was templateYValue[idxmax(X)] children .
generated: This statistic shows the Share individuals in Netherlands from 2009 to 2019 .  . The Share individuals increased to 9 children per woman in 2014 but started to decline since then .  . In 2019 , the individuals was 4.3 children .

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 2018 and 2019 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] templateScale of templateYLabel[1] answered templateXValue[0] .

generated_template: The statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] of the average templateTitleSubject[0] templateTitle[5] templateTitle[6] user as of templateTitleSubject[0] templateTitleDate[0] . During the survey , templateYValue[1] templateScale of templateYLabel[1] stated they would choose templateXValue[1] templateTitle[1] if they could only listen to templateYValue[11] genre of templateTitle[1] for the rest of their lives . The most popular answer was templateXValue[0] , which templateYValue[max] templateScale of templateYLabel[1] selected as the templateYValue[11] genre of templateTitle[1] they would listen to for the rest of their lives .
generated: The statistic presents the Mexico most important of the average Mexico women girls user as of Mexico 2019 .  . During the survey , 37 % 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 % 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] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . templateTitleSubject[0] 's economy templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] has been on the decline since templateXValue[13] , but it is expected to rise again in templateXValue[8] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . The templateYLabel[0] templateYLabel[1] is calculated using the price templatePositiveTrend of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the templateXLabel[0] .
generated: The statistic shows the Inflation rate in Nicaragua 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 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[idxmax(X)] points , which shows a templateTitle[1] templateTitle[2] of approximately 28 templateScale ( women are 28 templateScale less likely than men to have equal opportunities ) . In templateXValue[idxmax(Y)] , the templateTitle[1] templateTitle[2] in the area of political empowerment in templateTitleSubject[0] amounted to 69 templateScale .

generated_template: The graph presents the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] scored templateYValue[idxmax(X)] , which shows a templateTitle[1] templateTitle[2] of approximately 28 templateScale ( women are 28 templateScale less likely than men to have equal opportunities ) . That same templateXLabel[0] , the templateTitle[1] templateTitle[2] in the area of political empowerment in templateTitleSubject[0] amounted to 72 templateScale .
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 % ( women are 28 % 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 % .

Example 708:
titleEntities: {'Subject': ['Instagram', '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 2019 , templateYValue[max] templateScale of templateTitle[4] adults aged between 18 and 29 years used the photo sharing app templateTitleSubject[0] . Furthermore , it was found that 43 templateScale of female adults in the templateTitleSubject[1] used templateTitleSubject[0] compared to only 31 templateScale 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 templateScale templateTitle[1] templateTitle[2] among the adult population .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] in the templateTitle[0] that currently smoke templateTitle[4] as of 2017 , templateTitle[6] templateTitle[7] templateTitle[8] . During the survey , templateYValue[max] templateScale of templateYLabel[1] earning 30 thousand templateTitleSubject[0] dollars per year or less said that they smoke templateTitle[4] .
generated: This statistic shows the Share of usage in the Instagram that currently smoke States as of 2017 , by age group .  . During the survey , 67 % of respondents earning 30 thousand Instagram dollars per year or less said that they smoke States .

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[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYValue[idxmax(X)] templateYLabel[4] .
generated: This statistic shows the median Household income in Colombia from 1990 to 2018 .  . In 2018 , the median Household income in Colombia amounted to 85750 dollars .

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] templateScale templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[7] templateScale templateYLabel[0] .
generated: This statistic shows the Total population of Iraq from 2014 to 2018 , with projections up until 2024 .  . In 2018 , the Total population of Iraq amounted to approximately 37.14 millions Inhabitants .

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: This graph depicts the templateYLabel[0] templateTitle[0] templateTitle[1] templateYLabel[1] of all National Hockey League teams in the templateTitle[6] during templateTitleDate[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[max] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average European football attendance of all National Hockey League teams in the 2013/14 during .  . In the Rangers FC season , the Average attendance was at 80295 attendance .

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] templateScale of the population unemployed , compared with just templateYValue[min] templateYValue[idxmax(X)] in the most recent reporting templateXLabel[0] of templateXValue[idxmin(Y)] .

generated_template: The templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] in templateXValue[max] was templateYValue[idxmax(X)] templateScale , the lowest it has been since the mid-1970s . From templateXValue[min] until the templateXValue[11] financial crash the templateYLabel[0] templateYLabel[1] in the UK fluctuated between templateYValue[14] and templateYValue[11] templateScale , before it templatePositiveTrend suddenly in templateXValue[10] to templateYValue[6] templateScale . After peaking at templateYValue[max] templateScale in templateXValue[idxmax(Y)] , the templateYLabel[0] templateYLabel[1] gradually declined before returning to the levels seen in the early 2000s by templateXValue[4] .
generated: The Unemployment rate of the Northern Ireland in 2019 was 2.7 % , the lowest it has been since the mid-1970s .  . From 2000 until the 2008 financial crash the Unemployment rate in the UK fluctuated between 4.6 and 4.4 percent , before it increased suddenly in 2009 to 7.5 % .  . After peaking at 7.5 % in 2013 , the Unemployment rate gradually declined before returning to the levels seen in the early 2000s by 2015 .

Example 713:
titleEntities: {'Subject': ['Video'], 'Date': ['2015', '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] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic displays a forecast of the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the Russian Federation from templateXValue[min] to templateXValue[max] . For templateXValue[4] , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] is estimated to reach templateYValue[4] templateScale , with the templateYLabel[0] of templateYLabel[1] templateYLabel[2] worldwide forecast to exceed 2.5 templateScale templateYLabel[2] by that time . By templateXValue[max] another ten templateScale individuals are expected to own a templateYLabel[1] taking the predicted templateYLabel[0] of templateYLabel[1] templateYLabel[2] to templateYValue[idxmax(X)] templateScale .
generated: This statistic displays a forecast of the Market of million U.S. in the Russian Federation from 2015 to 2022 .  . For 2018 , the Market of million U.S. in Video is estimated to reach 1665.5 million , with the Market of million U.S. worldwide forecast to exceed 2.5 million U.S. by that time .  . By 2022 another ten million individuals are expected to own a million taking the predicted Market of million U.S. to 2997.8 million .

Example 714:
titleEntities: {'Subject': ['American Customer Satisfaction'], '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[idxmax(X)] .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] scores for templateTitleSubject[1] restaurants in the templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] for templateTitleSubject[1] restaurants in the templateTitle[7] was templateYValue[min] templateYValue[idxmax(X)] down from templateYValue[last] templateYValue[idxmin(X)] previous templateXLabel[0] .
generated: This statistic shows the American Customer Satisfaction Customer Satisfaction Index scores for American Customer Satisfaction restaurants in the 2007 from to 2019 .  . In 2019 , the ACSI for American Customer Satisfaction restaurants in the 2007 was 78 81 down from 81 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] templateScale templateYLabel[2] templateYLabel[3] of sales revenues .

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] between templateXValue[min] and templateXValue[max] . templateTitleSubject[0] Corporation is a U.S.-based technology and specialty materials company . In templateXValue[max] , the corporation made some templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] of templateYLabel[0] .
generated: The statistic shows Eastman Chemical Chemical Revenue between 2008 and 2018 .  . Eastman Chemical Corporation is a U.S.-based technology and specialty materials company .  . In 2018 , the corporation made some 10151 million U.S. dollars of Revenue .

Example 716:
titleEntities: {'Subject': ['Sears Holdings'], 'Date': ['2009']}
title: Number of stores of Sears Holdings worldwide 2009 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'stores']: ['1002', '1430', '1672', '1725', '2429', '2548', '4010', '3949', '3862']

gold: This statistic depicts the total number of stores of Sears Holdings from 2009 to 2017 . In 2017 , Sears Holdings had a total of 1,002 stores worldwide . The company was founded in 2005 in Hoffman Estates , Illinois and is the tenth largest retailer by annual revenue in the United States .
gold_template: This statistic depicts the total templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] had a total of templateYValue[min] templateYValue[idxmax(X)] templateTitle[4] . The company was founded in 2005 in Hoffman Estates , Illinois and is the tenth largest retailer by annual revenue in the country .

generated_template: This statistic depicts the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] employed 91,254 people worldwide , of which 35,284 were located in South Korea .
generated: This statistic depicts the Number of stores Sears Holdings from 2009 to 2017 .  . In 2017 , Sears Holdings employed 91,254 people worldwide , of which 35,284 were located in South Korea .

Example 717:
titleEntities: {'Subject': ['Colombia'], 'Date': ['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 templateNegativeTrend throughout recent years . In templateXValue[max] , approximately templateYValue[idxmax(X)] templateScale of Colombians were living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] templateScale 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 templateTitle[2] rate in templateTitleSubject[0] has been templateNegativeTrend in the past few years . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was templateYValue[idxmax(X)] templateScale templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] . This figure is projected to be templateYValue[idxmax(X)] templateScale in templateXValue[idxmax(Y)] .
generated: The headcount rate in Colombia has been decreasing in the past few years .  . In 2017 , the Colombia poverty headcount in Colombia was 10.8 percentage population 1,000 .  . This figure is projected to be 10.8 percentage in 2005 .

Example 718:
titleEntities: {'Subject': ['Iran'], 'Date': ['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 and 2011 . 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 the total templateTitleSubject[0] templateYLabel[0] templateTitle[3] in 2018/2019 , broken down templateTitle[4] templateXLabel[0] . According to the report , the templateXValue[0] was responsible for over a third of the world 's templateTitleSubject[0] templateYLabel[0] that year . Most of the U.S. templateYLabel[0] is attributable to the templateTitleSubject[0] Belt , which is located in the Midwest of the templateXValue[0] .
generated: This statistic shows the distribution of the total Iran Oil exports in 2018/2019 , broken down 2011 Country .  . According to the report , the China was responsible for over a third of the world 's Iran Oil that year .  . Most of the U.S. Oil is attributable to the Iran Belt , which is located in the Midwest of the China .

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 templateTitleDate[max] , templateYLabel[4] consumer templateTitleSubject[0] company templateTitleSubject[0] reported an templateYLabel[0] templateYLabel[1] of nearly templateYValue[0] templateScale templateYLabel[4] templateYLabel[5] or around templateYValue[30] templateScale U.S. dollars . Many of the company 's segments experienced increases in their overall sales numbers in the third templateXLabel[0] of templateTitleDate[max] , but templatePositiveTrend competition throughout the consumer templateTitleSubject[0] industry meant that profitability templateNegativeTrend . 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: In the fourth templateXLabel[0] of templateTitleDate[max] , adjusted templateYLabel[3] templateTitle[3] templateYLabel[0] templateYLabel[1] amounted to over templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . templateTitle[3] templateYLabel[0] templateYLabel[1] in the templateTitle[1] are estimated from samples used for the Monthly templateTitle[3] Trade Survey and exclude online travel services , ticket templateYLabel[1] agencies and financial brokers . Online templateTitle[3] templateYLabel[1] currently account for one tenth of total templateTitle[3] and approximately 5 templateScale of annual templateYLabel[0] revenue in the templateTitle[1] .
generated: In the fourth Quarter of 2019 , adjusted South operating profit amounted to over 17.57 trillion South Korean .  . operating 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 trillion 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] templateScale templateYLabel[3] templateYLabel[4] , up from templateYValue[3] templateScale templateYLabel[3] templateYLabel[4] the previous templateXLabel[0] .

generated_template: This statistic presents the templateTitle[0] of templateTitle[1] peer to peer templateYLabel[0] in the templateTitle[4] from templateXValue[min] to templateXValue[3] and a forecast thereof until templateXValue[max] . In templateXValue[3] , the templateTitle[0] of templateTitle[1] templateTitleSubject[0] templateYLabel[0] amounted to templateYValue[3] templateScale templateYLabel[2] templateYLabel[3] and it was predicted to templatePositiveTrend to templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] .
generated: This statistic presents the Retail of sales peer to Retail in the market from 2013 to 2015 and a forecast thereof until 2018 .  . In 2015 , the Retail of sales U.S. Retail amounted to 39767.0 million U.S. and it was predicted to increase 35725.0 million U.S. in 2016 .

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[idxmax(X)] 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: There were just under templateYValue[max] templateScale templateTitleSubject[0] templateTitle[1] templateYLabel[1] in templateTitle[3] globally in templateXValue[idxmax(Y)] . Almost 1.5 templateScale templateTitleSubject[0] templateTitle[1] templateYLabel[1] were sold templateTitle[4] in templateXValue[max] . templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitle[3] In light of tightening environmental regulations and templatePositiveTrend templateTitle[4] acceptance of templateTitle[1] transmission templateYLabel[1] , a templatePositiveTrend templateYLabel[0] of automakers are intending to tap into the market for templateTitle[1] templateYLabel[1] .
generated: There were just under 46 million UFC number in 2012 globally in 2014 .  . Almost 1.5 million UFC number were sold 2018 in .  . Number of number UFC in 2012 light of tightening environmental regulations and growing 2018 acceptance of number transmission UFC , a growing Number of automakers are intending to tap into the market for number UFC .

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] templateScale 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[max] templateScale 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 36.77 % 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: In templateXValue[max] , templateYValue[idxmax(X)] individuals were killed in motor vehicle accidents in templateTitleSubject[0] . An overall templateNegativeTrend in the templateYLabel[0] of templateTitle[1] templateTitle[2] was observed within the time period shown . The highest templateYLabel[0] of templateTitle[1] templateYLabel[1] in this timeframe occurred in templateXValue[11] , with figures nearly twice as high as those reported in templateXValue[max] .
generated: In 2018 , 317 individuals were killed in motor vehicle accidents in Croatia .  . An overall decrease in the Number of road was observed within the time period shown .  . The highest Number of fatalities in this timeframe occurred in 2007 , with figures nearly twice as high those reported in 2018 .

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] , about templateYValue[idxmax(X)] babies were born in templateTitleSubject[0] , a slight templateNegativeTrend 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 templateScale inhabitants , making it the most populous country in Europe .
generated: In 2017 , about 16.48 babies were born in U.S. , a slight decrease from the Year before .  . U.S. is the country with the highest Fatalities of 100,000 in the European Union , with over 20 thousand more 100,000 than France , the country with the next highest Fatalities of per 100,000 in Europe .  . Population growth in U.S. has about 83 million inhabitants , making it the most populous country in Europe .

Example 725:
titleEntities: {'Subject': ['Chicago White Sox'], 'Date': ['2002', '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] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Sox are owned by Jerry Reinsdorf , who bought the templateYLabel[0] for 20 templateScale templateYLabel[3] templateYLabel[4] in 1981 .

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] Mets templateYLabel[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the value of the Chicago White Sox Mets Franchise of the National Basketball Association from 2002 to 2019 .  . In 2019 , the Franchise had an estimated value of 1600 million U.S. dollars .

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[max] , there were templateYValue[max] templateScale templateYLabel[1] in the world . Bitcoin is a virtual currency . It can be bought and sold through online exchanges or ATMs .

generated_template: Messaging app templateTitle[0] is templateTitleSubject[0] 's most popular online communication tool , peaking at approximately templateYValue[max] templateScale templateYLabel[1] actives templateYLabel[2] in templateTitleSubject[0] during the fourth templateXLabel[0] of templateTitleDate[max] . templateTitle[0] 's main target group are young adults aged 15 to 34 years , representing more than 50 templateScale of the company 's Japanese user base in 2018 . The rise of templateTitle[0] in templateTitleSubject[0] The success story of messaging service templateTitle[0] , a Japanese subsidiary of South Korean internet powerhouse Naver , started back in 2011 during the Great East templateTitleSubject[0] Earthquake .
generated: Messaging app Number is Bitcoins 's most popular online communication tool , peaking at approximately 18.13 millions Bitcoins actives millions in Bitcoins during the fourth Quarter of 2019 .  . Number 's main target group are young adults aged 15 to 34 years , representing more than 50 millions of the company 's Japanese user base in 2018 .  . The rise of Number in Bitcoins The success story of messaging service Number , a Japanese subsidiary of South Korean internet powerhouse Naver , started back in 2011 during the Great East Bitcoins Earthquake .

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[idxmax(X)] templateScale 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[idxmax(Y)] .

generated_template: The statistic shows the templateYLabel[0] of the templateYLabel[2] templateTitle[2] templateTitle[3] templateTitle[4] in templateXValue[min] provides a forecast for templateXValue[max] . templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] in total templateYLabel[0] was generated by the templateYLabel[2] templateTitle[2] templateTitle[3] templateTitle[4] in templateXValue[idxmin(Y)] .
generated: The statistic shows the Revenue of the U.S. network revenue 2018 in provides a forecast for 2018 .  . 10.58 billion U.S. dollars in total Revenue was generated by the U.S. network revenue 2018 in .

Example 728:
titleEntities: {'Subject': ['Average'], 'Date': ['2009']}
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 templatePositiveTrend to templateYValue[2] templateYLabel[3] templateYLabel[4] in templateXValue[5] and again to templateYValue[idxmax(X)] templateYLabel[4] in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] of templateYLabel[1] enrolled in Canadian colleges in templateTitle[7] , templateTitle[4] templateTitle[5] templateTitle[6] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitleSubject[0] was templateYValue[max] templateYLabel[2] .
generated: This statistic shows the global Average of hotel enrolled in Canadian colleges in 2015 , from 2009 2015 .  . In 2015 , the Average of hotel ( aged six years and older ) in Average was 179 rate .

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[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale . templateYLabel[0] in templateTitleSubject[0] 's templateYLabel[0] is relatively low and stable at around templateYValue[0] templateScale which means the population is experiencing close to full employment .
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 % .  . Unemployment in Algeria 's Unemployment is relatively low and stable at around 12.35 % which means the population is experiencing close to full employment .

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] templateScale templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] amounted to templateYValue[7] templateScale templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] Saudi templateTitleSubject[0] , the second largest Arab state , is a nation in development .
generated: This statistic shows the Total population of Kenya from 2014 to 2017 , with projections up until 2024 .  . In 2018 , Kenya 's Total population amounted to 46.73 millions Inhabitants .  . population of Kenya Saudi , the second largest Arab state , is a nation in development .

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[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the templateScale of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
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 % of the total population in Qatar lived in cities .

Example 732:
titleEntities: {'Subject': ['Michigan'], 'Date': ['2000', '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[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of templateTitleSubject[0] was about templateYValue[idxmax(X)] templateScale 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[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the templateScale of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
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 % 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: This graph depicts the templateYLabel[0] regular season templateTitle[1] templateYLabel[1] of all National Hockey League teams in the templateTitle[6] season . The templateYLabel[0] regular season templateTitle[1] templateYLabel[1] of the templateXValue[17] franchise was templateYValue[17] , slightly lower than the overall templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] .
generated: This graph depicts the Average regular season division attendance of all National Hockey League teams in the attendance season .  . The Average regular season division attendance of the Maryland franchise was 14009 , slightly lower than the overall Average attendance in the NCAA .

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[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[2] line in templateXValue[max]

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] ' templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the Jersey poverty in New Jersey from 2000 to 2018 .  . In 2018 , 9.5 percentage of New Jersey ' 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 2018 , templateTitle[9] templateYLabel[0] templateYLabel[1] . The most valuable player was templateXValue[0] , with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] .

generated_template: The statistic shows the top-25 templateTitle[0] at the templateTitle[6] templateTitle[8] templateTitle[9] templateTitleDate[0] in templateTitleSubject[0] according to their current templateYLabel[0] / templateTitle[4] templateYLabel[1] . templateXValue[0] of Argentina is the templateTitle[2] valued player templateTitle[1] a templateTitle[4] templateYLabel[0] templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] . templateYLabel[0] values of templateTitle[0] at the templateTitle[8] templateTitle[9] templateTitleDate[0] - 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 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 - 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 2018 . When asked to think about they templateTitle[6] methods for their ten most recent templateTitle[2] , it was found that templateYValue[4] templateScale were made via templateXValue[4] . templateXValue[0] still accounted for the single largest templateYLabel[0] of everyday templateTitle[2] with templateYValue[max] templateScale .

generated_template: This statistic shows , according to survey templateYLabel[1] , the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[4] that should be addressed in the run up to the templateTitleDate[0] United Kingdom ( templateTitleSubject[1] ) General templateTitle[2] ( as of 2014 ) . With templateYValue[max] templateScale of templateYLabel[1] , templateXValue[0] was considered to be the templateTitle[0] templateTitle[1] topic , followed by the National Health Service ( templateXValue[1] ) and the templateXValue[2] ( EU ) .
generated: This statistic shows , according to survey respondents , the Distribution consumer transactions 2018 that should be addressed in the run up to the 2018 United Kingdom ( Distribution ) General transactions ( as of 2014 ) .  . With 41 % of respondents , In-store was considered to be the Distribution consumer topic , followed by the National Health Service ( Other online ) and the Buy buttons ( EU ) .

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 templateScale of templateYLabel[2] that were templateTitle[1] templateYLabel[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , some templateYValue[last] templateScale of templateYLabel[1] templateYLabel[2] in the templateTitle[0] were templateTitle[1] templateYLabel[2] . A templateTitle[1] templateTitle[2] means that a child was delivered after less than 37 weeks of gestation .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[7] templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[idxmax(X)] templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] members , as compared to templateYValue[14] in templateXValue[14] .
generated: This statistic shows the Percentage of U.S. preterm birth all births 2018 1990 to 2018 .  . In 2018 , there were 10.02 U.S. preterm birth all births members , as compared to 11.64 in 2000 .

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] templateScale of the world 's total templateTitle[3] templateYLabel[2] grid-connected templateTitleSubject[0] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[1] of disposable income spent on templateTitle[1] consumed in templateTitleDate[0] , broken down by templateTitle[5] templateTitle[6] . U.S. consumers spent templateYValue[min] templateScale of their disposable income on templateTitle[1] at templateTitle[2] , compared to a templateTitle[1] templateYLabel[0] templateYLabel[1] of templateYValue[1] templateScale in templateXValue[1] .
generated: This statistic shows the newly of disposable income spent on PV consumed in 2018 , broken down by worldwide .  . U.S. consumers spent 1 percentage of their disposable income on PV at capacity , compared to a PV Percentage newly of 11 percentage in India .

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 templateScale templateYLabel[4] , an decline from the previous templateXLabel[0] 's templateYLabel[0] templateYLabel[1] of templateYValue[1] templateScale US templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of The templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . Fast food chain templateTitleSubject[0] templateTitle[3] had a templateYLabel[0] templateYLabel[1] of approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[max] . This shows a 70 templateScale templateNegativeTrend over previous templateXLabel[0] templateTitle[3] total amounting to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Net income of The Groupon income worldwide from 2009 to 2019 .  . Fast food chain Groupon income had a Net income of approximately -11.7 million U.S. dollars in 2019 .  . This shows a 70 million decrease over previous Year income total amounting to 33.68 million U.S. dollars .

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] templateScale smartphones were shipped in templateTitleSubject[0] .

generated_template: The statistic illustrates the templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from the fourth templateXLabel[0] of templateXValue[0] to the third templateXLabel[0] of templateXValue[0] . In the templateXLabel[0] of templateXValue[0] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to around templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic illustrates the Shipments million of smartphone unit shipments in the China ( ) from the fourth Quarter of Q1 2018 to the third Quarter of Q1 2018 .  . In the Quarter of Q1 2018 , China 's Shipments million amounted to around 454.4 million units .

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[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Kazakhstan from 2008 to 2018 .  . Urbanization means the Share of urban population in the total population of a country .  . In 2018 , 57.43 % of Kazakhstan 's total population lived in urban areas and cities .

Example 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[max] amounted to templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] . This represents a three templateScale year-on-year change . The templateTitle[2] templateYLabel[0] is counted towards the company 's marketing services and other revenues segment .

generated_template: As of the fourth templateXLabel[0] of templateTitleDate[max] , the chat app company templateTitleSubject[0] had generated templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] , up from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] . In templateTitleDate[max] , templateTitleSubject[0] 's annual templateYLabel[0] amounted to close to 1.7 templateScale templateYLabel[2] templateYLabel[3] , up from 1.18 templateScale in the preceding year . templateTitleSubject[0] is the parent company of social photo sharing app Snapchat .
generated: As of the fourth Quarter of 2019 , the chat app company eBay had generated 271 million U.S. dollars in Revenue , up from 265 million U.S. dollars in the previous Quarter .  . In 2019 , eBay 's annual Revenue amounted to close 1.7 million U.S. dollars , up from 1.18 million in the preceding year .  . eBay is the parent company of social photo sharing app Snapchat .

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] templateNegativeTrend by about templateYValue[max] templateScale templateYLabel[2] to the templateYLabel[3] templateYLabel[4] , making it the templateXLabel[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitleDate[0] . The templateYLabel[0] 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 shows the templateTitle[0] templateTitle[1] templateTitle[2] the highest templateTitle[4] of the templateTitle[5] templateTitle[6] templateTitle[7] ( GDP ) in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[5] ranked 1st among the templateTitle[1] templateTitle[2] the highest GDP templateTitle[4] templateTitle[2] a templateTitle[4] of approximately 35 templateScale compared to the previous year .
generated: This statistic shows the Countries highest population the highest rate of the 2017 ( GDP ) in 2017 .  . In 2017 , Latvia ranked 1st among the highest population the highest GDP rate population a rate of approximately 35 % compared to the previous year .

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 templateScale of templateTitleSubject[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateXLabel[1] in templateTitleDate[0] , by the templateXLabel[0] of templateXLabel[1] . templateYValue[max] templateScale of templateYLabel[1] with templateXValue[last] and templateXValue[last] templateXLabel[1] used templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] .

generated_template: This statistic shows the templateScale of online consumers in the templateTitle[0] who are templateTitleSubject[0] templateTitle[2] members . During the most recent survey period in 2016 , it was found that templateYValue[min] templateScale of templateXValue[last] templateYLabel[1] had an templateTitleSubject[0] templateTitle[2] templateTitle[3] .
generated: This statistic shows the percentage of online consumers in the Percentage who are U.S. companies members .  . During the most recent survey period in 2016 , it was found that 8 % of 1000 and more companies had an U.S. companies using .

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] templateScale 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: The timeline presents data on templateTitle[0] templateTitle[1] templateYLabel[0] generated in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: The timeline presents data on BP 's Revenue generated in the Upstream from 2010 to 2018 .  . In 2018 , the Revenue amounted to 56399 million U.S. dollars .

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 2015 , sorted templateTitle[7] templateTitle[8] and templateTitle[9] group . During that period of time , templateYValue[max] templateScale of female templateTitleSubject[0] teens aged 15 to 17 years used the social networking app .

generated_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] templateScale 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 % 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] templateScale 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 templateTitle[1] in the templateTitle[0] that currently smoke templateTitle[4] as of 2017 , templateTitle[6] templateTitle[7] templateTitle[8] . During the survey , templateYValue[max] templateScale of templateYLabel[1] earning 30 thousand templateTitleSubject[0] dollars per year or less said that they smoke templateTitle[4] .
generated: This statistic shows the Share of individuals in the Proportion that currently smoke tried as of 2017 , shisha hooka EU-28 .  . During the survey , 87 % of respondents earning 30 thousand EU-28 dollars per year or less said that they smoke tried .

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 templateTitle[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 10 , templateTitleDate[0] , was carried out templateTitle[4] the Taliban in templateXValue[0] , templateXValue[0] , and caused templateYValue[max] templateYLabel[1] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] as of 2017 , templateTitle[6] templateTitle[7] . In templateTitleDate[0] , about templateYValue[max] templateScale templateTitle[1] templateTitle[2] templateYLabel[1] were living in the templateTitle[2] .
generated: The statistic shows the Number of worst terrorist fatalities as of 2017 , fatalities 2018 .  . In 2018 , about 466 million worst terrorist fatalities were living in the terrorist .

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[idxmax(X)] templateYLabel[1] . At the beginning of the observed period , in templateXValue[min] , the templateYLabel[0] of templateYLabel[1] amounted to templateYValue[idxmin(X)] .

generated_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] at 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] stood at templateYValue[max] templateYLabel[1] which was relatively high considering the whole 10-year period .
generated: This statistic shows the total Number of hospitals at Finland from 2007 to 2017 .  . The Number of hospitals has fluctuated in this period .  . In 2017 , the Number stood at 325 hospitals which was relatively high considering the whole 10-year period .

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] templateScale templateYLabel[2] templateYLabel[3] . Ranked second , the templateXValue[0] Men templateXValue[1] templateXLabel[0] generated templateYLabel[0] of around templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] that year .

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] men 's templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[0] men 's templateTitle[1] templateTitle[2] templateXLabel[0] in the templateTitle[4] with templateYLabel[0] of about templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . Additional information on the templateTitle[1] templateTitle[2] market templateTitle[1] templateTitle[2] is used by men in order to facilitate templateTitle[1] , creating a more comfortable process and a better finish .
generated: The statistic shows the Sales of the Leading men 's hair in the coloring in 2019 .  . In that year , Just For Men was the Leading men 's Brand in the coloring with Sales of about 138.0 million U.S. dollars .  . Additional information on the men 's market men 's is used by men in order to facilitate men , creating a more comfortable process and a better finish .

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: The statistic shows templateTitleSubject[0] templateTitle[4] players with the most templateYLabel[0] templateYLabel[1] in franchise history . templateXValue[0] is the templateTitle[0] templateTitle[1] leader of the templateTitleSubject[0] templateTitle[4] with templateYValue[max] templateYLabel[0] .
generated: The statistic shows Italy by players with the most Birth rate in franchise history .  . Trentino-South Tyrol is the Birth rate leader of the Italy by with 9.0 Birth .

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] templateScale of the templateYLabel[1] used an templateXValue[0] , while templateYValue[max] templateScale did not . templateYValue[min] templateScale of templateYLabel[1] said they did n't templateXValue[last] if they used templateXValue[0] blocking software .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among adult Cubans living in templateTitleSubject[0] on their opinion of templateTitle[1] templateTitle[2] , their former President . templateYValue[max] templateScale of templateYLabel[1] stated they have a very or somewhat templateXValue[1] opinion of templateTitle[1] templateTitle[2] .
generated: This statistic shows the results of a 2018 survey among adult Cubans living in United Kingdom on their opinion of blocker usage , their former President .  . 53 % of respondents stated they have a very or somewhat Don't use ad blocker opinion of blocker usage .

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] templateXValue[idxmin(Y)] This represented a templateNegativeTrend of approximately 7,200 templateYLabel[1] in the last ten years , and over 13,200 templateYLabel[1] since templateXValue[last] . 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] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitleSubject[0] were valued at approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . templateYLabel[3] templateYLabel[0] templateTitle[1] abroad is defined as ownership by a templateYLabel[3] investor of at least templateYValue[3] templateScale of a foreign business .
generated: This statistic shows the Number pubs United of the Kingdom in United Kingdom from 48 to 2018 , on a historical-cost basis .  . In 2018 , the thousands pubs made in United Kingdom were valued at approximately 47.6 thousands .  . thousands Number pubs abroad is defined as ownership by a thousands investor of at least 50.8 thousands of a foreign business .

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[idxmax(X)] templateYLabel[5] templateYLabel[6] for templateTitle[2] , non-Hispanic templateTitle[3] . The templateYLabel[0] templateTitle[0] templateYLabel[1] of the templateTitle[4] can be accessed here .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateTitle[2] in the templateTitle[0] was templateYValue[min] templateYValue[idxmax(X)] of every 1,000 templateYLabel[6] . This is a significant templateNegativeTrend from templateXValue[min] , when templateYLabel[0] templateYLabel[1] was at templateYValue[idxmin(X)] deaths out of every 1,000s templateYLabel[6] . What is templateYLabel[0] templateYLabel[1] ? The templateYLabel[0] templateYLabel[1] templateYLabel[2] is the number of deaths of babies under the age of one templateYLabel[3] 1,000 templateYLabel[5] templateYLabel[6] .
generated: In 2018 , the Median income white in the Household was 55568 70642 of every 1,000 dollars .  . This is a significant decrease from 1990 , when Median income was at 56917 deaths out of every 1,000s dollars .  . What is Median income ? The Median income 2018 is the number of deaths babies under the age of one CPI-U-RS 1,000 U.S. dollars .

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[idxmax(X)] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic depicts the templateYLabel[0] templateYLabel[1] templateTitle[1] templateYLabel[2] per consumer unit in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] expenditures of American households amounted to about templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] per templateXLabel[0] .
generated: This statistic depicts the Expenditure U.S. annual dollars per consumer unit in the Average from 2010 to 2018 .  . In 2018 , the Expenditure U.S. expenditures of American households amounted to about 3459 dollars per Year .

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] templateScale .

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] templateScale of the country 's templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The statistic shows the Budgetary of balance relation to gross domestic ( gross ) in Turkey from 2014 to 2018 , with projections up until 2024 .  . In 2018 , balance relation in Turkey amounted to about 35.68 % of the country 's relation gross domestic .

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 2014 , templateTitle[9] templateTitle[10] . During the survey , templateYValue[max] templateScale of templateXValue[0] said that they found templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: This statistic shows the distribution of templateTitleSubject[0] templateTitle[1] templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . According to cases handled templateTitle[5] templateTitleSubject[0] harassment support group WHOA , templateYValue[max] templateScale of reporting templateTitle[3] were templateXValue[0] .
generated: This statistic shows the distribution of U.S. find in 2014 , vacation planning .  . According to cases handled vacation U.S. harassment support group WHOA , 74 % of reporting find were Women .

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] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . The templateYLabel[0] templateYLabel[1] is calculated using the price templatePositiveTrend of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the templateXLabel[0] .
generated: The statistic shows the Inflation rate in 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] templateScale templateYLabel[3] templateYLabel[4] in that year .

generated_template: The statistic illustrates the templateYLabel[0] of templateTitleSubject[0] ( magnesium compounds ) templateTitle[2] as of templateTitleDate[0] , templateTitle[3] major templateTitle[5] . At this point , templateTitleSubject[0] templateYLabel[0] in templateXValue[10] amounted to approximately templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic illustrates the Export of Global ( magnesium compounds ) broiler as of 2019 , meat major by .  . At this point , Global Export in Canada amounted to approximately 3248 thousand metric .

Example 761:
titleEntities: {'Subject': ['France'], 'Date': ['2013', '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 templateTitleDate[0] , it appears that the majority of French teenagers were in middle school when they had templateTitle[5] templateTitle[6] templateTitle[7] . Love appears to be an important area of life at a templateTitle[1] templateTitle[4] , with more than 50 templateScale of templateTitle[1] French templateTitle[2] stating that love relationships were important for them . templateTitle[6] love experiences Even though new technologies and smartphones may have changed the way teenagers live templateTitle[5] love life , it seems that the templateTitle[4] for templateTitle[6] love and sex experiences has templateXValue[last] really changed templateXValue[5] the templateXValue[0] .

generated_template: This statistic gives information on the templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] as of 2019 . During this period of time , it was found that templateYValue[max] templateScale of templateTitle[3] templateTitle[4] in the Latin American country were aged between 25 and 34 templateXValue[0] .
generated: This statistic gives information on the young people of according age in France as of 2019 .  . During this period of time , it was found that 31 % of according age in the Latin American country were aged between 25 and 34 Under 11 years old .

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: This statistic gives a ranking of major templateTitle[1] of templateTitle[2] templateTitle[3] templateTitleSubject[0] templateTitle[5] templateYLabel[0] in 2018 . According to the report , the templateXValue[5] States exported approximately templateYValue[1] templateYLabel[1] templateYLabel[2] of templateTitle[5] to templateXValue[1] that year .
generated: This statistic gives a ranking of major rice of exports top U.S. country Exports in 2018 .  . According to the report , the Costa Rica States exported approximately 508527 metric tons of country to Haiti that year .

Example 763:
titleEntities: {'Subject': ['National Football League'], '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] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Yankees from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Yankees franchise amounted to templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic depicts the Operating income of the National Football League Yankees from 2001 to 2018 .  . In 2018 , the Operating income of the National Football League Yankees franchise amounted to 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] templatePositiveTrend templateTitle[4] almost 50 templateScale between templateXValue[4] and templateXValue[0] . Year-to-date templateYLabel[1] templatePositiveTrend 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: Over the past decade the profits templateYLabel[1] templateYLabel[2] have templatePositiveTrend drastically for templateTitleSubject[0] Munich , templateYValue[8] of the most prominent football club of the first templateXLabel[0] of templateXValue[0] . During the last reported period , templateTitle[0] ' templateTitleSubject[0] amounted to templateYValue[0] templateScale templateYLabel[4] templateYLabel[5] .
generated: Over the past decade the profits deliveries units have increased drastically for Tesla Munich , 29870 of the most prominent football club of the first Quarter of Q4 2019 .  . During the last reported period , Tesla ' amounted to 112000 million units .

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] templateScale in templateXValue[7] – and a real , significant templateNegativeTrend 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 templatePositiveTrend . 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] templateScale 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 % compared to the previous Year .

Example 766:
titleEntities: {'Subject': ['Online Great Britain'], 'Date': ['2019', '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 templateTitleDate[0] , 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] templateScale of templateYLabel[1] .

generated_template: In templateTitleDate[0] , templateYValue[1] templateScale 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] . templateTitle[3] templateTitle[2] was most common among the 25 to 34-year-old templateTitle[8] , with templateYValue[max] templateScale of individuals within this group having made purchases in templateTitleDate[0] . European templateTitle[2] templateTitle[3] compared Overall , 29 templateScale of British adults bought templateTitle[1] templateTitle[2] in templateTitleDate[0] .
generated: In 2019 , 18 % of Women and around a quarter of Men had purchased Medicine or Online purchasing in the previous 12 months in Online Great Britain .  . Great purchasing was most common among the 25 to 34-year-old demographic , with 26 % of individuals within this group having made purchases in 2019 .  . European purchasing Great compared Overall , 29 % 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[idxmax(X)] people were killed on roads in the templateTitleSubject[0] . Between templateXValue[min] and templateXValue[max] , templateTitle[1] traffic templateYLabel[1] had seen a net decline of 16 templateScale , with the peak recorded at the beginning of the reporting period in templateXValue[min] . The Dutch Government is dedicated to making its roads even safer in the future and aims to bring the templateYLabel[0] of templateTitle[1] templateTitle[2] down to below 500 by 2020 .

generated_template: There were templateYValue[0] reported templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateXValue[max] . This was a slight templatePositiveTrend compared to the previous templateXLabel[0] , but an templatePositiveTrend 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 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 templateScale since a high of templateYValue[max] was reported in templateXValue[idxmax(Y)] .

generated_template: In templateXValue[max] , the templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] Group reported a templateYLabel[0] of almost templateYValue[min] templateScale templateYLabel[2] . Despite the impressive figure , the templateYLabel[0] of templateXValue[idxmin(Y)] represented a templateNegativeTrend 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] templateScale templateYLabel[2] .
generated: In 2018 , the Balfour Beatty Group Group 's average Group reported a Average of almost 22450 % employees .  . Despite the impressive figure , the Average of 2016 represented a decrease for average compared to the previous years .  . Indeed , in 2011 , the first Year considered in this graph , the Average of the 's amounted to approximately 50304 % employees .

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] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of International Workplace Group ( templateTitleSubject[0] ) , formerly Regus , templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] generated a templateYLabel[0] of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] templateTitle[2] . In templateXValue[2] , Regus changed its holding company to templateTitleSubject[0] but hold the Regus name as their brand .
generated: This statistic shows the Revenue of International Workplace Group ( Cintas ) , formerly Regus , revenue from 2012 to 2019 .  . In 2019 , Cintas generated a Revenue of 6.89 billion U.S. dollars revenue .  . In 2017 , Regus changed its holding company to Cintas but hold the Regus name as their brand .

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[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[3] , templateTitle[4] , and distilled alcoholic beverages templateYLabel[0] of templateYLabel[2] merchant wholesalers amounted to about templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . Alcohol in the templateTitle[0] During the recession , alcohol-related industries were some of the few seeing continued growth , proving alcohol to be a frequent purchase for many Americans .

generated_template: This timeline shows the templateTitle[0] templateYLabel[0] of templateTitle[2] in the templateTitle[1] from templateTitleDate[min] to templateTitleDate[max] . In templateXValue[min] , templateTitle[0] templateTitle[2] templateYLabel[0] came to templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: This timeline shows the U.S. Sales of in the wholesale from 2002 to 2017 .  . In 02 , U.S. sales came to 87.56 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 displays the projected templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] hospitals in the templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . Over the six templateXLabel[0] period the average templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templatePositiveTrend to almost templateYValue[max] templateYLabel[1] in templateXValue[idxmax(Y)] .
generated: This statistic displays the projected Average spending Christmas gifts U.S in Christmas U.S hospitals in the Christmas U.S from 2006 to 2011 .  . Over the six Year period the average spending Christmas gifts U.S increased to almost 909 estimated in 2007 .

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[idxmin(X)] templateYValue[idxmin(X)] US templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of International Workplace Group ( templateTitleSubject[0] ) , formerly Regus , templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] generated a templateYLabel[0] of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] templateTitle[2] . In templateXValue[2] , Regus changed its holding company to templateTitleSubject[0] but hold the Regus name as their brand .
generated: This statistic shows the Revenue of International Workplace Group ( Advance Publications ) , formerly Regus , ' from 2006 to 2014 .  . In 2014 , Advance Publications generated a Revenue of 8.0 billion U.S. dollars ' .  . In 2008 , Regus changed its holding company to Advance Publications but hold the Regus name as their brand .

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] templateScale templateYLabel[3] templateYLabel[4] , which was an templatePositiveTrend of over two templateScale 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 templateScale 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[last] templateScale templateYLabel[2] templateYLabel[3] in R & D costs in templateXValue[max] . This figure puts the car manufacturer templateTitle[1] R & D intensity at five templateScale . That said , the company declared interest to enter into cost sharing arrangements with third parties .
generated: General Motors incurred around 28041 million U.S. in R & D costs in 2020 .  . This figure puts the car manufacturer brand R & D intensity at five million .  . 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] templateScale British pounds which was the highest point of templateYLabel[0] over the nine templateXLabel[0] period .

generated_template: The statistic illustrates the annual templateYLabel[0] of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[1] templateTitle[2] were generated in templateTitleSubject[0] .
generated: The statistic illustrates the annual Turnover of United Kingdom retail sales turnover from 2008 to 2017 .  . In 2017 , approximately 4784 million GBP of retail sales were generated in United Kingdom .

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[idxmin(X)] templateScale . In the same templateXLabel[0] , templateYLabel[0] penetration rate was at 86.95 templateScale .

generated_template: This statistic shows the total annual spend of templateTitle[0] tourists ( 'visitor exports ' _ ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with a forecast for templateXValue[max] . Inbound templateTitle[1] expenditure in templateTitleSubject[0] reached around templateYValue[5] templateScale templateYLabel[2] in templateXValue[5] .
generated: This statistic shows the total annual spend of Forecast tourists ( 'visitor exports ' _ ) in Norway from 2018 to , with a forecast for 2024 .  . Inbound smartphone expenditure in Norway reached around 4.77 millions in 2019 .

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[idxmax(X)] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Infant mortality rate in 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 2018 . During a survey , templateYValue[max] templateScale of templateYLabel[1] stated that the templateXValue[0] of templateXValue[0] was the most templateTitle[0] feature of templateXValue[0] templateXValue[5] templateTitle[4] .

generated_template: templateXValue[2] than half of templateTitleSubject[1] wanted to start templateTitleDate[0] by saving templateXValue[0] and by getting in templateXValue[1] . The most popular templateXValue[5] templateTitleSubject[0] templateTitle[2] templateTitle[3] ever – `` templateXValue[7] '' – was not as high up on the list as it used to be , but at least templateYValue[7] templateScale decided to finally give up templateXValue[7] once and templateTitle[5] all . templateTitle[3] makers , templateTitle[3] keepers ? While some might say that they do not need templateXValue[5] templateTitleSubject[0] templateTitle[2] Eve to finally turn their life around , making resolutions on 31 is a common , well-liked tradition , especially in the Western world .
generated: 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 % 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 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[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The graph presents data on the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] sales templateYLabel[0] from templateXValue[min] to templateXValue[max] . The source predicts that the templateTitleSubject[0] VR templateTitle[4] market size in templateXValue[max] will be worth templateYValue[4] templateScale templateYLabel[2] templateYLabel[3] . This number is expected to grow to templateYValue[idxmax(X)] templateScale by the end of templateXValue[idxmax(Y)] .
generated: The graph presents data on the 's revenue 2013 2018 sales Revenue from 2013 to 2018 .  . The source predicts that the Gannett VR 2018 market size in 2018 will be worth 3.17 billion U.S. dollars .  . This number is expected to grow 2.92 billion by the end of 2013 .

Example 779:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2020', '2024']}
title: Forecast on U.S. petroleum refinery end-use market output 2020 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020']
Y_Axis['Change', 'from', 'year', 'to', 'year']: ['2', '0.5', '0.5', '0.8', '2.2']

gold: This statistic displays a forecast of the petroleum and refinery end-use market output in the United States from 2020 to 2024 . Through 2020 , the petroleum and refinery end-use market output is expected to increase by 2.2 percent . U.S. petroleum refinery market It is projected that the growth of output from the U.S. petroleum refinery end-use market will slow , from a rate of 2.2 percent in 2020 to 0.5 percent in 2023 , and grow again to 2.2 percent in 2024 .
gold_template: This statistic displays a templateTitle[0] of the templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[1] templateYLabel[1] templateXValue[min] to templateXValue[max] . Through templateXValue[min] , the templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] is expected to templatePositiveTrend by templateYValue[idxmin(X)] templateScale . templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[5] It is projected that the growth of templateTitle[6] templateYLabel[1] the templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] will slow , templateYLabel[1] a rate of templateYValue[max] templateScale in templateXValue[idxmax(Y)] to templateYValue[min] templateScale in templateXValue[idxmin(Y)] , and grow again to templateYValue[idxmin(X)] templateScale in templateXValue[max] .

generated_template: This statistic shows the templateTitle[3] of the templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] and visualises the predicted 'ageing templateTitle[6] ' _ . Over the 20 templateXLabel[0] period , the templateYLabel[1] templateYLabel[2] is expected to templatePositiveTrend by 1.7 years , the largest templatePositiveTrend predicted between templateXValue[2] and templateXValue[1] at templateDelta[2,1] years .
generated: This statistic shows the refinery of the from year in the U.S. ( ) from 2020 to 2024 and visualises the predicted 'ageing output ' _ .  . Over the 20 Year period , the from year is expected to increase by 1.7 years , the largest increase predicted between 2022 and 2023 at 0 years .

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] templateScale templateYLabel[3] in templateXValue[5] .

generated_template: In templateXValue[max] , the total templateYLabel[0] of companies producing templateTitle[0] and templateTitle[2] was approximately templateYValue[0] templateScale 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] templateScale templateYLabel[3] in templateXValue[min] .
generated: In 2018 , the total of companies producing Chocolate and products was approximately 164.9 million kilograms .  . Between 2011 and 2018 the Total fluctuated .  . However , prior to 2011 Total was much higher , reaching over 150.54 million kilograms in 2008 .

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] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . The templateYLabel[0] templateYLabel[1] is calculated using the price templatePositiveTrend of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the templateXLabel[0] .
generated: The statistic shows the average 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] templateTitleDate[0] . During the survey , templateYValue[2] templateScale of templateYLabel[1] said they ordered templateTitle[2] templateTitle[3] templateTitle[4] or templateTitle[5] templateXValue[2] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[7] in the templateTitle[6] in templateTitleDate[0] . During the survey , templateYValue[max] templateScale of the templateYLabel[1] stated that they planned to visit a templateXValue[2] or templateXValue[2] during their templateTitle[2] templateTitle[3] in templateTitleDate[0] .
generated: This statistic shows the Methods ordering food for takeout delivery April in the U.S. in 2014 .  . During the survey , 50.5 % of the respondents stated that they planned to visit a Online or during their food for in 2014 .

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] templateScale of templateYLabel[2] .

generated_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] . The overall trend was one of templatePositiveTrend , seeing a peak in templateXValue[0] with 27.99 templateScale British pounds and a total rise of over 5 templateScale British pounds throughout this period .
generated: This statistic shows the total United Kingdom ( UK ) GDP Expenditure share GDP from fiscal Year 00/01 to fiscal Year 18/19 .  . The overall trend was one of increasing , seeing a peak in 18/19 with 27.99 million British pounds and a total rise of over 5 billion British pounds throughout this period .

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] templateScale templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] in templateXValue[0] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , around templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] were recorded in templateTitleSubject[0] . In templateXValue[idxmin(Y)] templateTitleSubject[0] was the country with the third-most templateYLabel[1] templateYLabel[2] and the sixth-most fatalities due to templateTitle[0] worldwide .
generated: The statistic shows the Number of arrivals thousands in Luxembourg between 2006 and 2018 .  . In 2018 , around 967.88 arrivals thousands were recorded in Luxembourg .  . In 2010 Luxembourg was the country with the third-most arrivals thousands and the sixth-most fatalities due to Number worldwide .

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] templateScale templateYLabel[2] in templateYLabel[0] in the fiscal templateXLabel[0] of templateXValue[idxmax(Y)] .

generated_template: The statistic presents the annual templateYLabel[0] templateTitle[1] templateTitle[2] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the developer of a popular mobile game - Angry Birds - templateTitle[1] templateYValue[0] templateScale templateYLabel[2] in templateYLabel[0] .
generated: The statistic presents the annual Revenue 2009 Iberdrola worldwide from 2009 to 2018 .  . In 2018 , the developer of a popular mobile game - Angry Birds - revenue 35075.9 million euros in Revenue .

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] templateScale 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: templateTitle[3] templateXValue[last] templateXValue[0] but templateXValue[0] templateXValue[1] or templateXValue[3] was the templateTitle[0] common templateTitle[2] of templateTitle[3] templateXValue[last] among British templateYLabel[1] ( templateYValue[max] templateScale ) , followed by templateXValue[0] sugar ( templateYValue[last] templateScale ) . templateYValue[2] templateScale of Brits liked to templateXValue[last] their templateXValue[last] without either templateXValue[0] or templateXValue[1] . In 2017 , templateTitle[0] people in the UK would templateXValue[last] templateYValue[5] to templateYValue[4] cups a day .
generated: same-sex No opinion Religion/Bible says it is wrong but Marriage should be between a man and a woman or Civil unions are sufficient was the Reasons common opposing of same-sex No opinion among British respondents ( 47 % ) , followed by Religion/Bible says it is wrong sugar ( 4 % ) .  . 16 % of Brits liked to No opinion their without either Religion/Bible says it is wrong or Marriage should be between a man and a woman .  . In 2017 , Reasons people in the UK would No opinion 5 to cups a day .

Example 787:
titleEntities: {'Subject': ['Nissan', 'Europe'], 'Date': ['2003', '2018']}
title: Nissan car sales in Europe 2003 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Number', 'of', 'units', 'sold']: ['487017', '560415', '547343', '554046', '477703', '421134', '436169', '470004', '411084', '372096', '338169', '313437', '332150', '387325', '409717', '409511']

gold: This statistic shows the number of cars sold by Nissan in Europe between 2003 and 2018 . European sales of the Nissan cars rose from 400 thousand units sold in 2003 to over 560 thousand units sold by 2017 . In 2018 , there were 487 thousand units of Nissan cars sold in Europe .
gold_template: This statistic shows the templateYLabel[0] of cars templateYLabel[2] by templateTitleSubject[0] in templateTitleSubject[1] between templateXValue[min] and templateXValue[max] . European templateTitle[2] of the templateTitleSubject[0] cars templatePositiveTrend from 400 thousand templateYLabel[1] templateYLabel[2] in templateXValue[min] to over templateYValue[max] thousand templateYLabel[1] templateYLabel[2] by templateXValue[idxmax(Y)] . In templateXValue[max] , there were templateYValue[0] thousand templateYLabel[1] of templateTitleSubject[0] cars templateYLabel[2] in templateTitleSubject[1] .

generated_template: 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 2018 , the Number units in Nissan reached its lowest level since 2003 .  . Europe 2003 to 2018 , the Number units in Nissan has been fluctuating between more than 313437 births and almost 436169 births for 1,000 sold .  . The highest Number units in Nissan during this period was recorded in 2006 .

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 templatePositiveTrend by templateYValue[idxmax(X)] templateScale 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: In templateXValue[max] , there were templateYValue[idxmax(X)] templateScale people templateYLabel[1] to vote in the templateTitleSubject[0] . Although this was an templatePositiveTrend from templateXValue[min] , when templateYValue[idxmin(X)] templateScale people were templateYLabel[1] to vote , it 's lower than the peak of templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[idxmax(Y)] . Voting requirements While voting laws differ from state to state , the basic requirements are the same across the entire country .
generated: In 2018 , there were 3.7 million people growth to vote in the Annual .  . Although this was an increase from 2010 , when 4.7 million people were growth to vote , it 's lower than the peak of 7.4 % growth average in 2011 .  . Voting requirements While voting laws differ from state to , the basic requirements are the same across the entire country .

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 templateScale 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: templateXValue[0] was ranked first in the number of templateTitleSubject[0] templateTitle[1] among templateTitleSubject[1] countries in 2017 . While this is mainly due to the population size , it also reflects the templatePositiveTrend popularity and widespread use of social media in templateXValue[0] . Hence , it is not surprising that , as of 2018 , the penetration rate of the other major social media platforms in templateXValue[0] was on a high level too .
generated: Spain was ranked first in the number of Muslims Spain among countries in 2017 .  . While this is mainly due to the population size , it also reflects the growing popularity and widespread use of social media in Spain .  . Hence , it is not surprising that , as of 2018 , the penetration rate of the other major social media platforms in Spain was on a high level too .

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] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the 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 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] templatePositiveTrend by an estimated templateYValue[idxmax(X)] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . See templateTitleSubject[0] 's templateYLabel[0] figures for comparison .

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] templateNegativeTrend until templateXValue[max] , when it again templatePositiveTrend and amounted to almost templateYValue[0] thousand templateYLabel[1] . A similar trend can be seen in the neighbor country Norway , where the templateYLabel[0] of templateYLabel[1] templateNegativeTrend for several years but templatePositiveTrend again in templateXValue[1] .
generated: The Population of growth in Afghanistan peaked in 2013 , when almost 3.49 thousand couples got divorced .  . Since then the divorce Population decreased until 2018 , when it again increased and amounted to almost 2.38 thousand growth .  . A similar trend can be seen in the neighbor country Norway , where the Population of growth fell for several years but rose again in 2017 .

Example 792:
titleEntities: {'Subject': ['Philippines'], 'Date': ['2017']}
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] templateScale in templateXValue[idxmin(Y)] , amounting to about templateYValue[1] templateScale 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 templateScale in that templateXLabel[0] , the templateTitle[1] templateTitle[2] templateTitle[4] was projected to templatePositiveTrend to templateYValue[max] templateScale of the templateYLabel[1] by templateXValue[idxmax(Y)] . 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 gives information on the templateTitle[1] templateTitle[3] rate in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the Indonesian templateYLabel[1] were using the templateTitle[1] . In templateXValue[max] , this figure is projected to grow to templateYValue[idxmax(X)] templateScale .
generated: This statistic gives information on the social user rate in Philippines from 2017 to 2023 .  . In 2017 , 49 % of the Indonesian population were using the social .  . In 2023 , this figure is projected to grow 55 % .

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[min] to templateTitleDate[max] . In templateTitleDate[max] , the nationwide templateYLabel[0] was templateYValue[min] cases templateYLabel[1] 100,000 of the templateYLabel[3] .

generated_template: This graph shows the templateTitle[1] templateTitle[2] and non-negligent templateTitle[4] templateYLabel[0] in the country from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the nationwide templateYLabel[0] was templateYValue[0] cases templateYLabel[1] 100,000 of the templateYLabel[3] .
generated: This graph shows the reported burglary and non-negligent 1990 Rate in the country from 1990 to 2018 .  . In 2018 , the nationwide Rate was 376.0 cases per 100,000 of the population .

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[idxmin(X)] templateYLabel[4] templateYLabel[1] one templateYLabel[2] of upland templateTitle[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In calendar templateXLabel[0] templateXValue[1] , the average templateTitle[1] templateYLabel[0] templateYLabel[1] one templateYLabel[2] of templateTitle[0] templateTitle[2] was about templateYValue[0] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Cotton price received per pound in the U.S. from 1990 to 2018 .  . In calendar Year 2017 , the average price per one pound of Cotton received was about 86.85 U.S. cents .

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 templateScale . The lowest figure of such templateYLabel[1] was recorded in templateXValue[1] at templateYValue[min] . These figures stand in contrast to an templatePositiveTrend in the volume of passenger templateTitle[1] transport .
generated: Between 2006 and 2018 , deaths in Spain had seen a net decline of 27 % .  . 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', '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[last] athletes participated . This figure templatePositiveTrend to templateYValue[1] participating athletes from 82 nations during the templateXValue[1] Winter Olympics .

generated_template: In templateTitleDate[max] , there were approximately templateYValue[0] templateScale people templateYLabel[1] in the templateTitle[2] sector in the templateTitle[4] . Employment in the templateTitle[2] templateTitle[3] – additional information Employment within the templateTitle[2] templateTitle[3] has shown significant growth since templateXValue[min] . Each decade , between templateXValue[min] and templateXValue[18] , there was an templatePositiveTrend in the templateYLabel[0] of templateYLabel[1] which were employed in the templateTitle[3] – templatePositiveTrend by around 300,000 templateYLabel[1] each decade .
generated: In 2014 , there were approximately 2800 thousand 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 1924 Chamonix .  . Each decade , between 1924 Chamonix 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': ['2000', '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 templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of templateTitleSubject[0] was about templateYValue[idxmax(X)] templateScale 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] templateScale templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows Gross domestic product ( GDP ) in South Korea 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 799:
titleEntities: {'Subject': ['Iraq'], 'Date': ['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: In the templateTitleSubject[0] , the definition of templateTitle[3] is a death with an underlying cause of intentional self-harm or an injury or poisoning with undetermined intent . In templateXValue[max] , the templateTitle[3] templateTitle[4] in the templateTitleSubject[1] was templateYValue[idxmax(X)] deaths templateYLabel[1] 100,000 templateYLabel[3] , the highest templateTitle[4] of templateTitle[3] in the templateTitleSubject[1] since templateXValue[min] . Overall , the highest templateTitle[3] templateTitle[4] was in the templateXLabel[0] templateXValue[min] , when the templateTitle[4] was almost templateYValue[17] deaths templateYLabel[1] 100,000 .
generated: In the Iraq , the definition of from is a death with an underlying cause of intentional self-harm or an injury or poisoning with undetermined intent .  . In 2018 , the from Iraq in the Iraq was 521 deaths thousand 100,000 per , the highest Iraq of from in the Iraq since 2000 .  . Overall , the highest from Iraq was in the Year 2000 , when the Iraq was almost 795 deaths thousand 100,000 .

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] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[4] has undergone a period of decline in recent years . As of templateXValue[max] estimates suggest that there were just templateYValue[0] templateYLabel[2] templateYLabel[3] of or produced in the templateTitleSubject[0] templateTitle[0] templateTitle[1] is an important mineral substance that is used in the templateTitle[0] and steel industry . templateTitle[0] templateTitle[1] templateTitle[0] templateTitle[1] is mined in about templateYValue[max] countries globally and is used almost exclusively to create steel .
generated: Cree 's Net in the 2019 has undergone a period of decline in recent years .  . As of 2019 estimates suggest that there were just 1080.0 million U.S. of or produced in the Cree 's is an important mineral substance that is used in the Cree and steel industry .  . Cree 's Cree is mined in about 1080.0 countries globally and is used almost exclusively to create steel .

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] templatePositiveTrend by approximately templateYValue[idxmax(X)] templateScale 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] templateScale 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] templateScale templateYLabel[3] in templateXValue[min] .
generated: In 2018 , the total Population of companies producing Population and Malaysia was approximately 1.35 million compared previous .  . Between 2011 and 2018 the Population fluctuated .  . However , prior to 2011 Population was much higher , reaching over 1.69 million previous in 2008 .

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] templateScale people .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . All figures have been estimated . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[7] templateScale templateYLabel[0] .
generated: This statistic shows the Total population of Nepal from 2014 to 2024 .  . All figures have been estimated .  . In 2018 , the Total population of Nepal amounted to approximately 27.63 millions Inhabitants .

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] templateScale of templateYLabel[1] stated that they used Gmail as their primary templateTitle[3] provider . templateXValue[1] was ranked second with templateYValue[1] templateScale .

generated_template: The statistic presents the templateTitle[0] of templateTitle[1] people in the templateTitleSubject[0] would be willing to spend on their templateTitle[4] templateTitle[5] in templateTitleDate[0] according to a Statista survey . templateYValue[max] templateScale of templateYLabel[1] said that they would be willing to spend templateXValue[0] to 25 templateTitle[7] dollars on templateTitle[4] templateTitle[5] .
generated: The statistic presents the Leading of U.S. people in the U.S. would be willing to spend on their providers 2016 in according to a Statista survey .  . 53 % of respondents said that they would be willing to spend Google (Gmail) to 25 2016 dollars on providers 2016 .

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: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] at least one templateTitle[0] in the templateTitleSubject[0] by templateXLabel[0] in templateTitleDate[0] . templateXValue[0] and templateXValue[1] ranked highest with templateYValue[max] templateScale of templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitleDate[0] , followed templateTitle[5] the templateXValue[2] with templateYValue[2] templateScale .
generated: This statistic shows the Price of U.S. dollars at least one Global in the Global by Country in 2019 .  . China and United States ranked highest with 17400 billion of U.S. dollars per in 2019 , followed 2019 the Russia with 7100 billion .

Example 805:
titleEntities: {'Subject': ['Minnesota Wilds'], 'Date': ['2005', '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] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] Leafs from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] Leafs amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .
generated: The statistic shows the Revenue of the Minnesota Wilds Leafs from the 2005/06 season to the 2018/19 season .  . The Revenue of the Minnesota Wilds Leafs amounted to 149 million U.S. dollars in the 2018/19 season .

Example 806:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '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] templateYValue[idxmax(X)] 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 templateScale templateYLabel[2] templateYLabel[3] . This means that every day the market was open , the templateTitle[0] amount of templateYLabel[2] government templateTitle[6] bought and sold amounted to half a templateScale templateYLabel[2] templateYLabel[3] in that templateXLabel[0] . What are templateTitle[5] templateTitle[6] ? templateTitle[5] templateTitle[6] are templateYLabel[2] government debt , bonds sold to finance the templateTitle[4] government .
generated: 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] templateScale of templateTitleSubject[0] 's templateTitle[1] templateYLabel[1] templateYLabel[2] came from templateXValue[0] . Founded in 1961 in Agordo , Italy , the templateTitleSubject[0] Group S.p.A. is the templateXValue[last] 's largest eyewear company .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] the templateXLabel[0] templateXLabel[1] of the child 's templateTitle[5] in the templateTitle[6] in templateTitleDate[0] . In templateTitleDate[0] , templateYValue[3] children were born templateYLabel[2] 1,000 templateYLabel[4] who held a Bachelor 's templateXValue[2] and templateYValue[max] children were born templateYLabel[2] 1,000 templateYLabel[4] with a templateXValue[0] or templateXValue[last] templateXValue[2] .
generated: This statistic shows the Share global net the Geographical area of the child 's by in the geographical in 2018 .  . In 2018 , 6 children were born sales 1,000 who held a Bachelor 's Asia-Pacific and 58 children were born sales 1,000 with a North America or Rest of the world Asia-Pacific .

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] templateScale templateYLabel[3] templateYLabel[4] in templateYLabel[0] templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[1] templateTitle[2] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . According to the report , there were templateYValue[idxmax(X)] templateYLabel[1] templateTitle[1] templateTitle[2] in the templateTitle[3] in templateXValue[max] templateXValue[idxmin(Y)] a templateNegativeTrend of more almost 10 templateScale from templateXValue[min] .
generated: This statistic shows the Retail of sales frame in the market from 2011 to 2017 .  . According to the report , there were 9732.2 sales frame in the market in 2017 2011 a decrease of more almost 10 million from 2011 .

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 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] templateScale of templateTitleSubject[0] templateTitle[1] are templateXValue[0] that templateXValue[0] is a templateXValue[0] .

generated_template: This statistic shows the distribution of templateTitle[1] beliefs in templateXValue[1] in templateTitleDate[0] . Approximately 36 templateScale of people claimed that they were a templateXValue[3] denomination , with the majority of those belonging to the templateXValue[1] of templateXValue[1] . On the templateXValue[3] hand , over half of the respondent group said that they did n't identify themselves as having a templateXValue[last] .
generated: This statistic shows the distribution of teenagers beliefs in Somewhat certain that there is a God .  . Approximately 36 % of people claimed that they were a Absolutely certain that there is no God denomination , with the majority of those belonging to the Somewhat certain that there is a God of .  . On the Absolutely certain that there is no God hand , over half of the respondent group said that they did n't identify themselves as having a Not sure whether or not there is a God .

Example 810:
titleEntities: {'Subject': ['Nepal'], 'Date': ['2008', '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[idxmax(X)] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The templateTitle[0] templateTitle[1] in templateTitleSubject[0] was templateYValue[max] people templateYLabel[1] templateYLabel[2] templateYLabel[3] ( 47.24 templateYLabel[1] templateYLabel[2] mile ) in templateXValue[idxmax(Y)] . This number has been slowly templatePositiveTrend for the past ten years . Higher templateTitle[0] templateTitle[1] is associated with urbanization , but not necessarily economic growth .
generated: The Population density in Nepal was 195.94 people per square kilometer ( 47.24 per square mile ) in 2018 .  . This number has been slowly increasing for the past ten years .  . Higher Population density is associated with urbanization , but not necessarily economic growth .

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[idxmin(X)] templateScale templateYLabel[4] dollars .

generated_template: The statistic illustrates the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateTitleDate[max] . In templateTitleDate[max] , about templateYValue[max] templateYLabel[1] in the templateTitle[4] were committed templateTitle[2] templateTitle[3] .
generated: The statistic illustrates the Vended of volume sales in the vended from 1999 to 2010 .  . In 2010 , about 47.5 volume in the vended were committed sales volume .

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[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYValue[idxmax(X)] templateYLabel[4] .
generated: This statistic shows the household income in Ohio from 1990 to 2018 .  . In 2018 , the household income in Ohio amounted to 61633 dollars .

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[last] 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 main industries of the small and medium enterprises ( SMEs ) in templateTitleSubject[0] as of 2018 . Of templateYLabel[1] templateYLabel[2] , templateYValue[max] templateScale were in the templateXValue[0] or templateXValue[0] templateXLabel[0] .
generated: This statistic shows the main industries of the small and medium enterprises ( SMEs ) in U.S. as of 2018 .  . Of persons persons , 2914 % were in the Under 1 year or Age .

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 templateScale 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 2019 survey , templateYValue[7] templateScale 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: The statistic shows which templateTitle[1] templateTitle[2] are templateTitleSubject[0] to U.S. survey templateYLabel[1] templateTitle[3] templateTitle[4] a templateTitle[5] templateXValue[10] in templateTitleDate[0] . During the survey , templateYValue[1] templateScale of templateYLabel[1] cited templateXValue[1] as templateTitleSubject[0] templateTitle[3] templateTitle[4] a templateTitle[5] templateXValue[10] .
generated: The statistic shows which bullying common are Cyber to U.S. survey respondents types bullying a 2019 Posten mean names or comments online about my religion in 2019 .  . During the survey , 24.9 % of respondents cited Mean or hurtful comments online as Cyber types bullying a 2019 Posten mean names or comments online about my religion .

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] templateScale templateYLabel[3] .

generated_template: The statistic depicts the manufacturers ( templateTitle[0] ) templateYLabel[1] of sporting goods equipment , fitness equipment , templateTitle[3] apparel , athletic footwear , and templateTitle[3] licensed merchandise in the templateYLabel[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateYLabel[1] amounted to about templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . Sporting goods manufacturers templateTitle[0] templateYLabel[1] - additional information templateTitle[0] templateYLabel[1] of sporting goods in the templateYLabel[3] reached templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] , the highest figure to-date and a eight templateScale templateYLabel[3] dollar templatePositiveTrend on the figure from templateXValue[4] .
generated: The statistic depicts the manufacturers ( LEGO ) profit of sporting goods equipment , fitness equipment , profit apparel , athletic footwear , and profit licensed merchandise in the Euros from 2009 to 2018 .  . In 2018 , LEGO profit amounted to about 1440.6 million Euros .  . Sporting goods manufacturers LEGO profit - additional information LEGO profit of sporting goods in the Euros reached 1674.3 million Euros in 2016 , the highest figure to-date and a eight million Euros dollar increase on the figure from 2014 .

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[max] , it was found that templateTitle[0] devices accounted for templateYValue[0] templateScale of templateYLabel[1] templateYLabel[2] templateTitle[5] templateYLabel[3] .

generated_template: Messaging app templateTitle[0] is templateTitleSubject[0] 's most popular online communication tool , peaking at approximately templateYValue[max] templateScale templateYLabel[1] actives templateYLabel[2] in templateTitleSubject[0] during the fourth templateXLabel[0] of templateTitleDate[max] . templateTitle[0] 's main target group are young adults aged 15 to 34 years , representing more than 50 templateScale of the company 's Japanese user base in 2018 . The rise of templateTitle[0] in templateTitleSubject[0] The success story of messaging service templateTitle[0] , a Japanese subsidiary of South Korean internet powerhouse Naver , started back in 2011 during the Great East templateTitleSubject[0] Earthquake .
generated: Messaging app Mobile is U.S. 's most popular online communication tool , peaking at approximately 60 million organic actives search in U.S. during the fourth Quarter of 2019 .  . Mobile 's main target group are young adults aged 15 to 34 years , representing more than 50 % of the company 's Japanese user base in 2018 .  . The rise of Mobile in U.S. The success story of messaging service Mobile , a Japanese subsidiary of South Korean internet powerhouse Naver , started back in 2011 during the Great East U.S. Earthquake .

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] templateScale 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[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . templateYLabel[3] templateYLabel[0] templateTitle[1] abroad is defined as ownership by a templateYLabel[3] investor of at least templateYValue[12] templateScale 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 billion 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] templateScale templateYLabel[2] templateYLabel[3] in the period templateXValue[0] templateXValue[1] to templateXValue[0] .

generated_template: The statistic shows the templateYLabel[0] of unlinked templateYLabel[1] templateYLabel[2] provided templateTitle[1] templateTitleSubject[0] in the fiscal year of templateTitleDate[0] , broken down templateTitle[1] templateTitleSubject[0] templateTitle[3] . In that year , templateTitleSubject[0] reported some templateYValue[2] templateScale templateYLabel[1] templateYLabel[2] on its templateXValue[2] lines . templateTitleSubject[0] is the second largest commuter templateXValue[2] provider in the country .
generated: The statistic shows the Earnings of unlinked million U.S. provided R.R R.R Martin in the fiscal year of 2011 , broken down R.R R.R Martin earnings .  . In that year , R.R Martin reported some 12.0 million U.S. on its June 2013 to June 2014 lines .  . R.R Martin is the second largest commuter June 2013 to June 2014 provider in the country .

Example 819:
titleEntities: {'Subject': ['Lexus', 'United Kingdom', 'UK'], 'Date': ['2016', '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 2016 and 2019 . Peaks in registration numbers were recorded in and of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In 2019 , templateYValue[max] new templateTitleSubject[0] cars were templateYLabel[1] in the templateTitleSubject[2]

generated_template: This statistic shows the number of cars templateYLabel[1] by templateTitleSubject[0] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between 2016 and 2019 . templateTitleSubject[0] is a British company and subsidiary of the Malaysian automotive company Proton since the bankruptcy of its former owner Romano Artioli in 1996 . templateTitle[2] of the British brand peaked at templateYValue[max] templateYLabel[0] in 2016 .
generated: This statistic shows the number of cars sold by Lexus in the United Kingdom ( UK ) between 2016 and 2019 .  . Lexus is a British company and subsidiary of the Malaysian automotive company Proton since the bankruptcy of its former owner Romano Artioli in 1996 .  . sales of the British brand peaked at 3852 Units in 2016 .

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 templatePositiveTrend by one templateScale 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 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] templatePositiveTrend , reaching around templateYValue[2] templateScale Turkish liras in templateXValue[2] .
generated: This statistic presents the numbers globally of Banco and Santander to 2012 in Banco Santander from 2012 to 2018 , with a forecast for 2019 .  . Over this period , the globally of the Banco and Santander industry to 2012 in Banco Santander grew , reaching around 133.0 millions Turkish liras in 2017 .

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[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Guatemala from 1999 to 2019 .  . In 2019 , the Unemployment rate in Guatemala was at approximately 2.75 % .

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[idxmax(X)] templateScale of all private wage and salary workers were unemployed in templateXValue[idxmin(Y)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in U.S. from 2001 to 2018 .  . In 2018 , the Unemployment rate in U.S. was at approximately 5.5 % .

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[last] templateScale of the respondents stated that they templateTitle[3] between $ 1,000 and $ 1,999 on templateTitle[4] in templateTitleDate[0] .

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] in templateTitleDate[0] . In templateTitleDate[0] , templateYValue[3] children were born templateYLabel[2] 1,000 templateYLabel[4] who held a Bachelor 's templateXValue[2] and templateYValue[max] children were born templateYLabel[2] 1,000 templateYLabel[4] with a templateXValue[0] or templateXValue[last] templateXValue[2] .
generated: This statistic shows the Survey amount money the Yearly expenses of the child 's U.S. in the 2012 in .  . In 2012 , 17.4 children were born owners 1,000 who held a Bachelor 's $1000 to $1999 and 30.5 children were born owners 1,000 with a under 500$ or over $5000 $1000 to $1999 .

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 2013 . During the survey , templateYValue[max] templateScale 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] in the templateTitle[7] as of 2013 . During the survey , templateYValue[2] templateScale of templateTitleSubject[0] stated templateXValue[3] with templateXValue[3] and templateXValue[3] as a favorite templateTitle[2] activity .
generated: This statistic shows the Most popular leisure activities among U.S. in the 2013 as of 2013 .  . During the survey , 20 % of U.S. stated Playing video games and computer/internet games with 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: As of 2019 , templateXValue[last] was the Canadian templateXLabel[0] home to the most templateTitle[1] templateTitle[2] templateYLabel[1] in the whole of templateTitleSubject[0] , with templateYValue[max] . templateXValue[3] and templateXValue[2] ranked in second and third places , with templateYValue[3] and templateYValue[2] templateYLabel[1] respectively . At that time , there were templateTitle[1] templateTitle[2] templateYLabel[1] located in five of templateTitleSubject[0] 's ten provinces .
generated: As of 2019 , Other was the Canadian Month home to the most stores operated in the whole of Canadian Tire Corporation , with 503 .  . Canadian Tire gas bar locations and Mark's ranked in second and third places , with 297 and 386 stores respectively .  . At that time , there were stores operated located in five of Canadian Tire Corporation 's ten provinces .

Example 826:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['1995', '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[min] to templateTitleDate[max] , by templateXLabel[1] type . In this period , templateYValue[max] templateScale of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] were caused by templateXValue[0] during this period .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] a templateTitle[3] templateTitle[4] in the templateTitle[5] , templateTitle[7] templateXLabel[0] templateXLabel[1] . In templateTitleDate[0] , templateYValue[max] templateScale of templateTitleSubject[0] templateYLabel[1] aged between 18 and 24 years played a templateTitle[3] templateTitle[4] .
generated: This statistic shows the Share of deaths unintentional a carbon monoxide in the poisoning , Kingdom Fossil Fuel .  . In 1995 , 35 % of United Kingdom deaths aged between 18 and 24 years played a carbon monoxide .

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] templateYValue[idxmax(X)] .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale of the total labor force . The templateYLabel[0] templateYLabel[1] is the templateScale of a country 's labor force that are without jobs but are available to work and actively seeking employment . templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] is above the worldwide templateYLabel[0] templateYLabel[1] , and compared to other Sub-Saharan African countries and other regions , templateTitleSubject[0] has a relatively average templateYLabel[1] of templateYLabel[0] .
generated: In 2019 , the Unemployment rate in El Salvador was at approximately 4.35 % 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] templateScale 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[7] templateScale templateYLabel[0] .
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 36.49 millions Inhabitants .

Example 829:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2016', '2019']}
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 2016 to 2019 , as an templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateYLabel[4] . During this period of time , templateTitle[3] templateYLabel[2] templatePositiveTrend significantly , measuring at templateYValue[0] templateYLabel[0] points in 2019 . 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[min] 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 2019 , 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[min] , 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 2019 , 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 2016 , 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[idxmin(X)] templateScale people . In templateXValue[max] , the number of templateYLabel[0] templatePositiveTrend to around templateYValue[idxmax(X)] templateScale .

generated_template: The 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] 's templateTitle[0] amounted to templateYValue[idxmax(X)] templateScale 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: The statistic shows the Population Norway of from 2009 to 2019 .  . In 2019 , the Norway of 's Population amounted to 5.33 millions Inhabitants .  . The Syrian refugee crisis The war in Norway has been escalating since 2011 , which has made it difficult to estimate the current Norway of the country .

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] templateScale of templateYLabel[1] stated they templateTitle[3] in templateTitle[4] templateTitle[5] .

generated_template: This statistic shows the results of a templateTitle[0] among adult Americans who have one or more templateTitle[2] . The templateTitle[0] was conducted in templateTitleDate[0] , asking the templateYLabel[1] whether they ever regret getting any of their templateTitle[2] , or not . templateYValue[max] templateScale of templateYLabel[1] stated they do not regret getting any of their templateTitle[2] .
generated: This 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 % of respondents stated they do not regret getting any of their who .

