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

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

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in 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 .

Example 2:
titleEntities: {'Subject': ['EIFS', 'STUCCO'], 'Date': ['2018']}
title: Most used EIFS and STUCCO brands in the U.S. 2018
X_Axis['Brand']: ['Dryvit', 'STO', 'Omega_Products', 'Senergy', 'Simplex_(Finestone)', 'Parex/La_Habra', 'TEC', 'None_of_these']
Y_Axis['Share', 'of', 'respondents']: ['26.9', '11.5', '6.7', '5.8', '5.8', '4.8', '3.8', '34.6']

gold: This statistic depicts the most used EIFS and STUCCO siding brands by U.S. construction firms in 2018 . The survey revealed that 26.9 percent of the respondents used Dryvit brand the most .
gold_template: This statistic depicts the templateTitle[0] templateTitle[1] templateTitleSubject[0] and templateTitleSubject[1] siding templateTitle[4] by templateTitle[5] construction firms in templateTitleDate[0] . The survey revealed that templateYValue[0] percent of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] the templateTitle[0] .

generated_template: This statistic presents the templateXLabel[0] of templateXLabel[1] templateTitle[2] per day in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] . Approximately 69 percent of templateYLabel[1] templateTitle[2] between templateXValue[0] and templateXValue[2] templateXLabel[1] templateTitle[3] in that year .
generated: This statistic presents the Brand of Brand EIFS per day in the EIFS ( STUCCO ) in 2018 . Approximately 69 percent of respondents EIFS between Dryvit and Omega Products Brand STUCCO in that year .

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[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

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

Example 4:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2018', '2018']}
title: Leading video gaming brands on Twitter 2018 , by followers
X_Axis['Brand']: ['PlayStation', 'Xbox', 'Nintendo_of_America', 'Rockstar_Games', 'Ubisoft', 'EA_SPORTS_FIFA', 'Sonic_the_Hedgehog', 'Fortnite', 'Electronic_Arts', 'League_of_Legends']
Y_Axis['Brand', 'fans', 'in', 'millions']: ['15.63', '12.87', '9.44', '9.31', '7.11', '6.4', '5.76', '5.64', '5.19', '4.36']

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

generated_template: This statistic gives information on the most popular templateXValue[4] templateTitle[1] templateTitle[3] templateXValue[6] as of 2019 . During the measured period , templateXValue[0] had approximately templateYValue[max] templateYLabel[3] global templateYLabel[1] on the social network , followed templateTitle[6] templateXValue[1] with about templateYValue[1] templateYLabel[3] followers .
generated: This statistic gives information on the most popular Ubisoft video brands Sonic the Hedgehog as of 2019 . During the measured period , PlayStation had approximately 15.63 millions global fans on the social network , followed by Xbox with about 12.87 millions followers .

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

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

generated_template: This statistic shows 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] templateYLabel[1] templateYLabel[2] ( or about 27 templateYLabel[1] U.S. dollars ) in templateYLabel[0] .
generated: This statistic shows UK people Employees from the fiscal Year of 2010 to the fiscal Year of 2018 . In the 2018 fiscal Year , UK generated around 160 thousands ( or about 27 thousands U.S. dollars ) in Employees .

Example 6:
titleEntities: {'Subject': ['Dublin'], 'Date': ['2019']}
title: Prime office rental prices in Dublin Q1 2015-Q3 2019
X_Axis['Quarter']: ["Q1_'15", "Q2_'15", "Q3_'15", "Q4_'15", "Q1_'16", "Q2_'16", "Q3_'16", "Q4_'16", "Q1_'17", "Q2_'17", "Q3_'17", "Q4_'17", "Q1_'18_", "Q2_'18", "Q3_'18", "Q4_'18", "Q1_'19", "Q2_'19", "Q3_'19"]
Y_Axis['Cost', 'per', 'square', 'meter', 'in', 'euros']: ['538', '552', '592', '619', '619', '619', '646', '646', '646', '646', '673', '673', '-', '673', '673', '-', '673', '673', '673']

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

generated_template: This statistic gives information on templateTitleSubject[0] 's templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] from the second templateXLabel[0] of templateTitleDate[0] to the fourth templateXLabel[0] of templateTitleDate[1] . As of the last reported templateXLabel[0] , the website 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic gives information on Dublin 's Cost rental prices Dublin from the second Quarter of 2019 to the fourth Quarter of 2019 . As of the last reported Quarter , the website 's Cost per amounted to 538 square 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[0] templateYLabel[1] templateYLabel[2] dollars.The templateTitle[0] templateTitleSubject[0] are owned by Peter Angelos , who bought the franchise for 173 templateYLabel[1] templateYLabel[2] templateYLabel[3] in 1993 .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Orioles , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the Revenue of the Orioles was 253 million U.S. dollars .

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

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

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the poverty rate in Wisconsin from 2000 to 2018 . In 2018 , 11 percent 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 million U.S. dollars were residing in templateXValue[0] in templateTitleDate[0] . That is about 50 percent of the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] worldwide .

generated_template: This statistic depicts the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] of templateTitle[4] in the templateTitle[5] in templateTitleDate[0] templateTitle[7] templateXLabel[0] and ethnicity . In templateTitleDate[0] , templateXValue[0] couples had a templateTitle[0] templateTitle[2] income of 70,852 templateYLabel[1] templateYLabel[2] .
generated: This statistic depicts the Ultra high net Number of individuals in the distribution in 2019 region and ethnicity . In 2019 , North America couples had a Ultra net income of 70,852 UHNW individuals .

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] 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[7] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] play their home games at the TD Garden .
generated: 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 52.22 U.S. dollars . The Chicago Blackhawks play their home games at the TD Garden .

Example 11:
titleEntities: {'Subject': ['Audi'], 'Date': ['2002', '2018']}
title: Audi - total assets 2002 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Total', 'assets', 'in', 'million', 'euros']: ['65598', '63680', '61090', '56763', '50769', '45156', '40401', '37019', '30772', '26550', '26056', '22578', '18910', '16112', '14904', '14063', '12650']

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

generated_template: This statistic shows the templateYLabel[1] of templateYLabel[0] of the templateTitle[1] and templateTitle[0] templateTitle[2] into the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] of such products came to a total of about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateYLabel[3] templateYLabel[0] of domestic templateTitle[2] – additional information Electrical devices used within a templateTitle[1] are part of a billion-dollar home appliance industry , which is projected to grow in the coming years .
generated: This statistic shows the assets of Total of the total and Audi assets into the 2018 from 2002 to 2018 . In 2018 , Total of such products came to a total of about 65598 million euros . euros Total of domestic assets – additional information Electrical devices used within a total are part of a billion-dollar home appliance industry , which is projected to grow in the coming years .

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

gold: In the three months leading up to November 5 , 2017 , the grocery market in Great Britain grew by 3.8 percent compared to the same period a year earlier . Compared to the growth during the last three years this constitutes a sizeable increase . The entire grocery market amounted to 185.2 billion British pounds in 2017 .
gold_template: In the templateYValue[0] months leading up to 5 , templateXValue[last] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] grew by templateYValue[7] percent compared to the same period a templateXLabel[0] earlier . Compared to the templateYLabel[1] during the last templateYValue[0] years this constitutes a sizeable increase . The entire templateTitle[0] templateTitle[1] amounted to 185.2 billion British pounds in templateXValue[last] .

generated_template: In templateXValue[max] , the templateTitle[1] templateYLabel[0] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] amounted to about templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] , all types included . The templateTitle[0] realized templateYLabel[0] of templateTitle[3] templateTitle[4] remained fairly steady throughout the years until templateXValue[3] , when it decreased to below templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitle[3] templateTitle[4] The templateTitle[0] templateYLabel[0] serves as an indicator for a variety of different selling prices on the templateTitle[4] market , gathering all templateYLabel[0] ranges of templateTitle[3] wines purchased in templateTitleSubject[0] .
generated: In 28 Nov 2010 , the market Percentage of year-on-year Great in Great Britain amounted to about 2.5 growth (year-on-year) , all types included . The Grocery realized Percentage of year-on-year Great remained fairly steady throughout the years until 10 Nov 2013 , when it decreased to below 2.5 growth (year-on-year) . year-on-year Great The Grocery Percentage serves as an indicator for a variety of different selling prices on the Great market , gathering all Percentage ranges of year-on-year wines purchased in Great Britain .

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

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

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Brooklyn Nets franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 304 million U.S. dollars .

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

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

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

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

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

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

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

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitleSubject[0] ' templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the poverty rate in Kansas from 2000 to 2018 . In 2018 , 12 percent of Kansas ' 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] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The best-selling templateXLabel[0] in templateTitleDate[0] was `` templateXValue[0] Hunter : templateXValue[0] '' for PS4 which templateYLabel[1] nearly templateYValue[max] templateYLabel[2] templateYLabel[0] in templateTitleSubject[0] . When it comes to hardware sales , it was found that templateTitleDate[0] was the year of the Nintendo Switch . That year , Nintendo templateYLabel[1] more than 3.48 templateYLabel[2] templateYLabel[0] of the templateTitle[1] in templateTitleSubject[0] .
generated: The best-selling Drought in 2016 was `` United States June 2012 Hunter : United States June 2012 '' for PS4 which loss nearly 20.0 billion Economic in Economic . When it comes to hardware sales , it was found that 2016 was the year of the Nintendo Switch . That year , Nintendo loss more than 3.48 billion Economic of the loss in Economic .

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

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

generated_template: In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] was of templateYValue[min] . templateTitleSubject[0] had a divorce rate of 51.2 per 100 marriages in templateXValue[10] . A templateYLabel[0] which was not one of the highest in Europe but that emphasizes the fact that in recent years , divorce is a phenomenon with a significant impact on Western countries .
generated: In 2013 , the Percentage of employees in Germany was of 18.13 . Germany had a divorce rate of 51.2 per 100 marriages in 2010 . A Percentage which was not one of the highest in Europe but that emphasizes the fact that in recent years , divorce is a phenomenon with a significant impact on Western countries .

Example 19:
titleEntities: {'Subject': ['Finland'], 'Date': ['2018']}
title: Urbanization in Finland 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['85.38', '85.33', '85.28', '85.23', '85.18', '85.13', '84.84', '84.31', '83.77', '83.43', '83.3']

gold: This statistic shows the degree of urbanization in Finland from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 85.38 percent of Finland 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in 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 .

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

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

generated_template: 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[1] . Excluding the country , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[4] templateTitleSubject[0] templateYLabel[3] amounted to templateYValue[0] templateYLabel[4] as of the most recent templateXLabel[0] . In total , templateTitleSubject[0] had 330 templateYLabel[4] global templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows a timeline with the amount of monthly active users Twitter users as of the first Quarter of 2019 . Excluding the country , the Number of monthly active users Twitter users 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] templateYLabel[0] of those between the ages of 25 and 64 . templateXValue[last] had the least amount of people templateYLabel[1] a templateYLabel[2] templateYLabel[3] , at templateYValue[min] templateYLabel[0] of people between the ages of 25 and 64 . templateYLabel[2] around the world Deciding which templateYLabel[2] to attend can be a difficult decision for some and in today 's world , people are not left wanting for choice .

generated_template: This statistic shows the templateYLabel[0] 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[max] years of templateYLabel[1] .

generated_template: The templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] aged templateTitle[4] templateYLabel[2] in the templateTitleSubject[0] has gradually increased 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 Median age for Projected aged 1950 age in the Projected has gradually increased since the 1960s . Now Projected in the 2100 aged 1950 can expect to live 24.0 more age on average . Women aged 1950 age can expect to live around 20.6 more age on average .

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[min] templateYLabel[0] cars and light vehicles produced worldwide were equipped with an templateTitle[2] templateTitle[3] . It is templateTitle[4] that this number will increase to about templateYValue[max] templateYLabel[0] automobiles produced worldwide in templateXValue[max] .

generated_template: This statistic 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 transmissions from 2010 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] templateYLabel[2] U.S. dollars , down from almost templateYValue[1] templateYLabel[2] U.S. dollars in templateXValue[1] . GM 's earnings were affected by falling vehicle sales , particularly in China .

generated_template: The templateTitleSubject[0] is the leading country in Europe in terms of online shopping penetration . By now a mature market , templateTitleSubject[1] templateTitle[0] templateYLabel[0] reached a value of templateYValue[max] templateYLabel[1] British pounds in templateXValue[max] . Between templateXValue[2] and templateXValue[1] alone , templateTitle[0] templateYLabel[0] went up by 80 templateYLabel[1] British pounds .
generated: The General Motors is the leading country in Europe in terms of online shopping penetration . By now a mature market , General Motors General EBIT reached a value of 12848 (adjusted; British pounds in 2019 . Between 2017 and 2018 alone , General EBIT went up by 80 (adjusted; British pounds .

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

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

generated_template: templateTitle[0] and templateTitle[1] have become one of the leading engines of growth for the Spanish economy , featuring an ongoing increase in the templateTitle[5] templateTitle[4] over the last years and projected to reach approximately templateYValue[1] templateYLabel[1] templateYLabel[2] in templateXValue[1] . templateTitleSubject[0] ranked second on the World templateTitle[1] Organization templateTitle[2] list of most visited countries in the world , with its number of international visitors amounting to nearly 82 templateYLabel[1] in templateXValue[2] . A popular holiday destination for Europeans The Mediterranean country is also one of Europe templateTitle[2] favorite holiday destinations in templateXValue[1] – the United Kingdom , Germany and France appeared in the leading positions of the largest number of international visitors to templateTitleSubject[0] .
generated: Travel and tourism have become one of the leading engines of growth for the Spanish economy , featuring an ongoing increase in the GDP contribution over the last years and projected to reach approximately 358.3 billion euros in 2018 . Germany ranked second on the World tourism Organization 's list of most visited countries in the world , with its number of international visitors amounting to nearly 82 billion in 2017 . A popular holiday destination for Europeans The Mediterranean country is also one of Europe 's favorite holiday destinations in 2018 – the United Kingdom , Germany and France appeared in the leading positions of the largest number of international visitors to Germany .

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

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

generated_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of the Chilean population accessed the templateYLabel[3] , up from templateYValue[16] percent in templateXValue[16] . In templateXValue[1] , templateYValue[max] percent 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 percent of the Chilean population accessed the internet , up from 9.78 percent in 2001 . In 2016 , 74.29 percent 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 templateYLabel[1] templateTitle[2] . The leading low-cost carrier had a capacity of 157.2 templateYLabel[1] available seat templateTitle[2] in that same templateXLabel[0] , and as such was efficient in using its fleet to transport paying customers . Flying with templateTitleSubject[0] ' main hub , Las Vegas McCarran International Airport , saw a traffic of 17.5 templateYLabel[1] templateTitleSubject[0] passengers in templateXValue[1] .

generated_template: The templateYLabel[0] templateTitle[1] of the templateTitle[2] market in templateTitleSubject[0] amounted to around templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[max] financial templateXLabel[0] . This is an increase of around 0.55 templateYLabel[1] templateYLabel[2] templateYLabel[3] since templateXValue[min] . templateYLabel[2] templateTitle[2] laws Canada has complex templateTitle[2] laws which have developed since Prohibition in the 1920s .
generated: The RPMs passenger of the miles market in Southwest Airlines amounted to around 131.35 billions in the 2019 financial Year . This is an increase of around 0.55 billions since 2010 . billions miles laws Canada has complex miles laws which have developed since Prohibition in the 1920s .

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

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

generated_template: There is no use denying that the way people consume newspapers and magazines has changed . As of templateXValue[max] , more than two thirds of individuals in templateTitleSubject[0] 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: The templateTitleSubject[0] is growing in every aspect . Over the last decade , the total templateYLabel[0] templateTitle[7] of templateTitle[3] in the templateTitleSubject[0] have more than quadrupled . In templateXValue[max] they amounted to approximately templateYValue[max] templateYLabel[3] templateYLabel[4] .
generated: The United Kingdom is growing in every aspect . Over the last decade , the total Number 2018 of Kingdom in the United Kingdom have more than quadrupled . In 2018 they amounted to approximately 674890 applicants .

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

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

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

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

gold: The statistic shows the player expenses ( payroll ) of the San Jose Earthquakes club of Major League Soccer by player in 2019 . Valeri `` Vako '' Qazaishvili received a salary of 1.6 million U.S. dollars .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] Earthquakes club of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[0] `` Vako '' templateXValue[0] received a salary of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] Red templateTitle[6] club of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[0] received a salary of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: 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[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . PepsiCoPepsiCo is an American food and beverage corporation , based in Purchase , NY .

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

Example 33:
titleEntities: {'Subject': ['Oakland Athletics'], 'Date': ['2019']}
title: Oakland Athletics all-time home run leaders 2019
X_Axis['Month']: ['Mark_McGwire', 'Jimmie_Foxx', 'Reggie_Jackson', 'Jose_Canseco', 'Bob_Johnson', 'Eric_Chavez', 'Al_Simmons', 'Jason_Giambi', 'Sal_Bando', 'Gus_Zernial']
Y_Axis['Number', 'of', 'home', 'runs']: ['363', '302', '269', '254', '252', '230', '209', '198', '192', '191']

gold: This statistic shows the Oakland Athletics all-time home run leaders as of October 15 , 2019 . Mark McGwire has hit the most home runs in Oakland Athletics franchise history with 363 home runs .
gold_template: This statistic shows the templateTitleSubject[0] all-time templateYLabel[1] templateTitle[4] templateTitle[5] as of October 15 , templateTitleDate[0] . templateXValue[0] has hit the most templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] franchise history with templateYValue[max] templateYLabel[1] templateYLabel[2] .

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

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

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

generated_template: The statistic presents the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateTitle[3] on templateTitleSubject[0] worldwide from templateXValue[last] to 2019 . Between templateXValue[0] and templateXValue[0] there were templateYValue[max] templateTitle[1] templateYLabel[1] templateTitle[3] on templateTitleSubject[0] , up from templateYValue[1] templateYLabel[1] templateTitle[3] in the corresponding period in templateXValue[1] .
generated: The statistic presents the Number of monthly views on BuzzFeed worldwide from May '16 to 2019 . Between Apr '15 and Apr '15 there were 7000 monthly views on BuzzFeed , up from 5000 monthly views in the corresponding period in Oct '15 .

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

gold: This statistic shows the number of robberies in Canada from 2000 to 2018 . There were 22,450 robberies in Canada in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . There were templateYValue[18] templateYLabel[1] in templateTitleSubject[0] in templateXValue[max] .

generated_template: This statistic shows the total templateTitleSubject[0] ( UK ) templateTitleSubject[0] templateYLabel[0] and templateYLabel[1] templateYLabel[2] templateYLabel[3] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] templateXValue[0] . Over the period there were a number of fluctuations but overall there was an increase . In the most recent recorded period , templateYLabel[0] and templateYLabel[1] templateYLabel[2] templateYLabel[3] totaled almost templateYValue[max] templateYLabel[4] British pounds , which was also the peak .
generated: This statistic shows the total Canada ( UK ) Canada Number and robberies from fiscal Year 2018 to fiscal Year 2000 . Over the period there were a number of fluctuations but overall there was an increase . In the most recent recorded period , Number and robberies totaled almost 34641 robberies British pounds , which was also the peak .

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

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

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

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

gold: This statistic represents the U.S. rental car industry 's total revenue between 2002 and 2017 . While the industry 's revenue grew to around 28.6 billion U.S. dollars in 2017 , about 1.59 million vehicles were purchased in 2017 by rental companies in the United States .
gold_template: This statistic represents the templateYLabel[2] templateTitle[1] templateTitle[0] templateTitle[2] 's total templateYLabel[0] between templateXValue[min] and templateXValue[max] . While the templateTitle[2] 's templateYLabel[0] grew to around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] , about 1.59 templateYLabel[1] vehicles were purchased in templateXValue[max] by templateTitle[1] companies in the templateTitle[3] .

generated_template: 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[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[3] .
generated: The statistic shows global Revenue from U.S. and U.S. 2002 from 2002 to 2017 . In 2017 , earphones and headphones were estimated to have a market value of 28.63 billion U.S. dollars U.S. .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] in the templateTitle[2] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] in templateXValue[1] was templateYValue[17] people .
generated: This statistic shows the Net income of income in the net in 2003 , 2003 Financial . In 2003 , the Net income of income in Q2 '15 was 2505.0 people .

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

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

generated_template: The statistic shows the annual templateYLabel[1] of the templateTitle[2] templateTitle[3] of the participating templateTitle[1] teams at the templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in templateTitleSubject[0] . The manager of the German templateTitle[1] templateTitle[2] , templateXValue[0] , is the highest paid manager with templateYLabel[1] at around templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the annual value of the Case Shiller of the participating housing teams at the National Home Price Index in Case Shiller National Home Price Index . The manager of the German housing Case , Aug 19 , is the highest paid manager with value at around 212.06 value .

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 . 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 increase in employment between templateXValue[3] and templateXValue[1] before dropping in templateXValue[max] . There were approximately templateYValue[7] 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 dropping 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[max] templateYLabel[2] templateTitle[1] templateTitle[2] templateYLabel[1] in templateTitleSubject[0] , up from around templateYValue[5] templateYLabel[2] in templateXValue[5] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] is expected to reach templateYValue[max] templateYLabel[2] , up from templateYValue[5] templateYLabel[2] in templateXValue[5] .
generated: This statistic shows the Number of Australia users in Australia from 2015 to 2022 . In 2017 , the Number of Australia users in Australia is expected to reach 19.27 millions , up from 17.19 millions in 2017 .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Nicaragua from 1999 to 2019 . In 2019 , the Unemployment rate in Nicaragua was at approximately 4.87 percent .

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

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

generated_template: This statistic provides information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] of templateYLabel[1] templateYLabel[2] amounted to templateYValue[max] templateYLabel[3] .
generated: This statistic provides information on the Number of internet users in the France from 2002 to 2016 . In 2016 , France 's Number of internet users amounted to 55.86 millions .

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

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

generated_template: The statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] teams with the most templateTitleSubject[0] Division I templateTitle[5] from templateTitleDate[0] to templateTitleDate[1] . templateXValue[0] has won the championship a record templateYValue[max] times . templateTitleSubject[0] templateTitle[3] templateTitle[4] championship - additional information templateTitleSubject[0] templateTitle[1] templateTitle[2] Division I templateTitle[4] tournament , also known as `` Madness '' , is the most important competition for collegiate templateTitle[4] , which is the second most followed templateTitle[3] sport in the U.S .
generated: The statistic shows the highest spending parks recreation teams with the most U.S. Division I U.S. from 2018 to 2018 . Minneapolis has won the championship a record 346.97 times . U.S. parks recreation championship - additional information U.S. highest spending Division I recreation tournament , also known as `` Madness '' , is the most important competition for collegiate recreation , which is the second most followed parks sport in the U.S .

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

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

generated_template: Since templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] in the templateTitle[3] has stayed more or less steady . In templateTitleDate[0] , the female templateYLabel[0] templateYLabel[1] was templateYValue[29] percent , and in 2018 , the templateYLabel[0] templateYLabel[1] was at templateYValue[0] percent . However , it reached a peak in 2000 at templateYValue[max] percent .
generated: Since 1990 , the Employment rate of men in the U.S. has stayed more or less steady . In 1990 , the female Employment rate was 72 percent , and in 2018 , the Employment rate was at 66.6 percent . However , it reached a peak in 2000 at 72 percent .

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

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

generated_template: This statistic shows the ten templateTitle[0] templateTitle[1] in templateTitleSubject[0] as of templateTitleDate[0] . In templateTitleDate[0] , around templateYValue[max] templateYLabel[1] 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 fatalities 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] templateYLabel[2] British pounds . This was an increase of over 1.4 templateYLabel[2] British pounds as compared to 2017 . When broken down by denomination , the twenty-pound note accounted for the highest share of templateTitle[2] in templateTitle[4] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[0] to templateTitleDate[1] . The templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] was valued at templateYValue[max] British pounds templateYLabel[2] templateYLabel[3] in 2019 templateYValue[3] an increase compared to the templateYLabel[0] a year earlier .
generated: This statistic shows the Amounts of notes coin in the UK ( UK ) from 2017 to 2019 . The Amounts of notes coin was valued at 82980 British pounds million GBP in 2019 82933 an increase compared to the Amounts a year earlier .

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

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

generated_template: 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] percent 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 percent 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] - additional information The templateTitleSubject[0] are a franchise of the National Basketball Association ( NBA ) that became part of the newly formed NBA in 1949 .
generated: The statistic shows the Revenue of the 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 - additional information The Utah Jazz are a franchise of the National Basketball Association ( NBA ) that became part of the newly formed NBA in 1949 .

Example 50:
titleEntities: {'Subject': ['MTV', 'United Kingdom', 'UK'], 'Date': ['2019']}
title: MTV viewers reached quarterly in the United Kingdom ( UK ) Q1 2012-Q3 2019
X_Axis['Quarter']: ['Q1_2012', 'Q2_2012', 'Q3_2012', 'Q4_2012', 'Q1_2013', 'Q2_2013', 'Q3_2013', 'Q4_2013', 'Q1_2014', 'Q2_2014', 'Q3_2014', 'Q4_2014', 'Q1_2015', 'Q2_2015', 'Q3_2015', 'Q4_2015', 'Q1_2016', 'Q2_2016', 'Q3_2016', 'Q4_2016', 'Q1_2017', 'Q2_2017', 'Q3_2017', 'Q4_2017', 'Q1_2018', 'Q2_2018', 'Q3_2018', 'Q4_2018', 'Q1_2019', 'Q2_2019', 'Q3_2019']
Y_Axis['Viewers', 'in', 'thousands']: ['10610', '9638', '10353', '9702', '9677', '10047', '10609', '11487', '9967', '10947', '9674', '9917', '10647', '9856', '9509', '8351', '10631', '10279', '10201', '9717', '8838', '8761', '8738', '8188', '8011', '7629', '8344', '6833', '6482', '5960', '6890']

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

generated_template: The templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitle[3] and templateTitle[4] templateTitle[5] amounted to approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the third templateXLabel[0] of templateXValue[0] . templateTitle[0] templateTitle[1] sector in the templateTitle[6] templateTitle[0] templateTitle[1] sector templateYLabel[0] in the templateTitle[6] has been steadily falling in recent years and is beginning to come out of a period of great difficulty and problems presented to it by the economic crisis of 2008 . For the previous generations in the templateTitle[6] the real estate market was quite stable .
generated: The MTV viewers of quarterly and United Kingdom amounted to approximately 11487 thousands in the third Quarter of Q1 2012 . MTV viewers sector in the UK MTV viewers sector Viewers in the UK has been steadily falling in recent years and is beginning to come out of a period of great difficulty and problems presented to it by the economic crisis of 2008 . For the previous generations in the UK the real estate market was quite stable .

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

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

generated_template: 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] percent of Americans consider themselves experts at templateTitle[1] templateTitle[2] . templateYValue[max] percent 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 percent of Americans consider themselves experts at attendance Americans . 29 percent 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[max] templateYLabel[1] in templateTitleSubject[0] in the templateXLabel[0] templateXValue[min] and by templateXValue[max] this figure had fallen to templateYValue[min] . This is a drop of over 23 percent in the provided time period .

generated_template: There were templateYValue[0] templateYLabel[1] of templateTitle[1] templateTitle[2] banks in the templateTitle[5] in templateXValue[max] . After a period of growth from templateXValue[min] to templateXValue[10] , this templateYLabel[0] has been slowly diminishing in the past few years . In templateXValue[max] , Wells Fargo led the ranking of banks with the highest templateYLabel[0] of templateYLabel[1] .
generated: There were 174 hospitals of Belgium 2000 banks in the 2018 in 2018 . After a period of growth from 2000 to 2008 , this Number has been slowly diminishing in the past few years . In 2018 , Wells Fargo led the ranking of banks with the highest Number of hospitals .

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

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

generated_template: This statistic gives information on templateTitleSubject[0] 's templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] from the second templateXLabel[0] of templateTitleDate[0] to the second templateXLabel[0] of templateTitleDate[1] . As of the last reported templateXLabel[0] , the website 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] belongs to Alibaba , one of the leading e-commerce companies in China and worldwide .
generated: This statistic gives information on Facebook 's Number monthly active Facebook from the second Quarter of 2011 to the second Quarter of 2016 . As of the last reported Quarter , the website 's Number mobile-only amounted to 1149 users millions . Facebook belongs to Alibaba , one of the leading e-commerce companies in China and worldwide .

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

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

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateXValue[0] templateYLabel[1] from templateTitleSubject[0] in templateTitleDate[0] . During the Deloitte survey , templateYValue[max] percent of templateYLabel[1] stated that templateXValue[0] the templateXValue[0] social or templateXValue[0] was their favorite source of templateTitle[3] .
generated: The statistic shows the Most popular games of casino Slot machines respondents from U.S. in 2014 . During the Deloitte survey , 48 percent of respondents stated that Slot machines the Slot machines social or Slot machines was their favorite source of casino .

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

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

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

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

gold: This statistic shows the number of live births in Norway from 2008 to 2018 . The number of births has declined during the period , from the peak of roughly 61.8 thousand in 2009 to around 55.1 thousand in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[2] has declined during the period , from the peak of roughly templateYValue[max] thousand in templateXValue[9] to around templateYValue[min] thousand in templateXValue[max] .

generated_template: The statistic shows the templateTitle[0] of templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[max] templateYLabel[2] people were living in templateTitleSubject[0] . The numbers peaked in templateTitleSubject[0] is one of the Mexican templateYLabel[2] , templateTitleSubject[0] is one of the largest population in the world .
generated: The statistic shows the Number of Number live in Norway from 2008 to 2018 . In 2018 , about 61807 births people were living in Norway . The numbers peaked in Norway is one of the Mexican births , Norway is one of the largest population in the world .

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[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's R & D templateYLabel[0] came to templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . This represented a small portion of templateTitleSubject[0] 's net revenue , which reached 29.1 templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The templateYLabel[0] of templateTitleSubject[0] Inc. , the Montreal-based dairy company , reached approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . Their templateYLabel[0] has gradually increased year-on-year from templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[min] . templateTitleSubject[0] Inc. templateTitleSubject[0] Inc. produces , markets and distributes dairy products .
generated: The Expenditure of HPE research development Inc. , the Montreal-based dairy company , reached approximately 2338 million U.S. dollars in 2019 . Their Expenditure has gradually increased year-on-year from 1486 million U.S. dollars in 2013 . HPE research development Inc. HPE research development Inc. produces , markets and distributes dairy products .

Example 58:
titleEntities: {'Subject': ['Colombia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Colombia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['8096.64', '7729.02', '7381.73', '7053.53', '6744.01', '6508.13', '6641.51', '6325.49', '5799.62', '6088.68', '7998.84', '8103.49', '7950.38', '7264.11', '6285.2', '5170.53', '5444.82', '4684.01', '3717.55', '3384.61', '2737.58', '2240.21', '2347.51', '2383.31', '2455.11', '2588.92', '2992.93', '3291.58', '3037.63', '2939.1', '2640.39', '2139.18', '1923.58', '1658.07', '1637.5', '1640.39', '1660.55', '1572.58', '1542.36', '1572.19', '1711.86']

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

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

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

gold: The statistic shows the growth in real GDP in Morocco from 2014 to 2018 , with projections up until 2024 . In 2018 , Morocco 's real gross domestic product increased by around 2.96 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from between templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . templateTitleSubject[0] 's economy on the rise templateTitleSubject[0] is a nation with a growing economy and a steadily increasing population .
generated: The statistic shows the growth in real GDP in Morocco from between 2014 to 2018 , with projections up until 2024 . In 2018 , Morocco 's real Gross domestic product grew by around 2.96 percent compared to the previous Year . Morocco 's economy on the rise Morocco is a nation with a growing economy and a steadily increasing population .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] is estimated to increase by templateYValue[2] percent on the templateXLabel[0] before . By templateXValue[max] , templateTitle[2] is projected to grow with another templateYValue[0] percent .
generated: This statistic shows the Net profit billion of the airlines worldwide 2006 from 2006 to 2020 . In 2018 , Net 's Net profit billion is estimated to increase by 27.3 percent on the Year before . By 2020 , airlines is projected to grow with another 29.3 percent .

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: This statistic shows the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were a templateTitle[0] of templateYValue[max] directory listings for templateYLabel[1] templateYLabel[2] throughout the templateTitleSubject[0] .
generated: This statistic shows the U.S. Capacity of megawatts in the PV from 2005 to 2018 . In 2018 , there were a U.S. of 2583 directory listings for megawatts throughout the PV .

Example 62:
titleEntities: {'Subject': ['American'], 'Date': ['2001', '2019']}
title: American imports of corn 2001 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Imports', 'in', 'million', 'bushels']: ['28', '36', '57', '57', '68', '32', '36', '160', '29', '28', '8', '14', '20', '12', '9', '11', '14', '14', '10']

gold: This statistic shows imports of corn in the United States from 2001 to 2019 . According to the report , U.S. corn imports amounted to approximately 57 million bushels in 2016 , down from 68 million bushels the previous year .
gold_template: This statistic shows templateYLabel[0] of templateTitle[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , U.S. templateTitle[2] templateYLabel[0] amounted to approximately templateYValue[2] templateYLabel[1] templateYLabel[2] in templateXValue[3] , down from templateYValue[4] templateYLabel[1] templateYLabel[2] the previous templateXLabel[0] .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] in templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[4] templateYLabel[5] templateYLabel[2] templateYLabel[3] . The templateYLabel[0] gate templateYLabel[1] of one templateYLabel[3] of templateTitle[4] templateTitle[5] in templateTitleSubject[0] has fluctuated over the past eighteen years , having hit a high of templateYValue[max] templateYLabel[4] templateYLabel[5] in templateXValue[3] . The volume of templateTitle[4] templateTitle[5] produced in templateTitleSubject[0] has also varied quite dramatically in the last few years .
generated: In 2019 , the Imports million of 2019 in American amounted to 28 bushels . The Imports gate million of one bushels of 2019 in American has fluctuated over the past eighteen years , having hit a high of 160 bushels in 2016 . The volume of 2019 produced in American has also varied quite dramatically in the last few years .

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

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

generated_template: This statistic shows the results of a templateTitle[0] among adult Americans who have one or more templateTitle[2] . The templateTitle[0] was conducted in templateTitleDate[0] , asking the templateYLabel[1] whether they ever regret getting any of their templateTitle[2] , or not . templateYValue[max] percent of templateYLabel[1] stated they do not regret getting any of their templateTitle[2] .
generated: This statistic shows the results of a Share among adult Americans who have one or more who . The Share was conducted in 2012 , asking the respondents whether they ever regret getting any of their who , or not . 58.1 percent of respondents stated they do not regret getting any of their who .

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

gold: This statistic gives information on the most popular discount and promo code websites according to users in the United States . During the August 2016 survey , 75 percent of respondents stated that they were aware of groupon.com . Coupons.com was ranked second with 64 percent awareness rate and retailmenot.com was in third place with 62 percent of respondents stating that they knew the platform .
gold_template: This statistic gives information on the most popular discount and promo code websites templateTitle[4] to templateTitle[6] in the templateTitle[5] . During the 2016 survey , templateYValue[max] percent of templateYLabel[1] stated that they were aware of templateXValue[0] . templateXValue[1] was ranked second with templateYValue[1] percent templateTitle[3] rate and templateXValue[2] was in third place with templateYValue[2] percent of templateYLabel[1] stating that they knew the platform .

generated_template: 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] percent of templateYLabel[1] stating that they used templateXValue[0] and templateYValue[1] percent 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 percent of respondents stating that they used groupon.com and 64 percent 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 templateYLabel[2] templateTitle[1] templateTitle[2] templateYLabel[1] in the country , up from templateYValue[min] templateYLabel[2] in templateXValue[min] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] is expected to reach templateYValue[max] templateYLabel[2] , up from templateYValue[min] templateYLabel[2] in templateXValue[min] . Further information With more than 2 templateYLabel[2] monthly active templateYLabel[1] , templateTitleSubject[1] is the most popular social network worldwide .
generated: This statistic shows the Number of Thailand users in Thailand from 2017 to 2023 . In 2023 , the Number of Thailand users in Thailand is expected to reach 37.2 millions , up from 32.1 millions in 2017 . Further information With more than 2 millions monthly active users , Thailand is the most popular social network worldwide .

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

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

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

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

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

generated_template: The statistic displays the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] , in templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic displays the Unit shipments of Worldwide from 2013 to 2019 , in millions . In 2019 , Worldwide 's Unit shipments amounted to around 400.0 millions .

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

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

generated_template: 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 percent lower than it was in 2019 .
generated: This statistic shows 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 templateTitle[4] . templateXValue[0] ranked highest with a dog population of approximately templateYValue[max] templateYLabel[2] in templateTitle[4] , followed templateTitle[5] the templateXValue[1] ( UK ) with templateYValue[max] templateYLabel[2] . The templateYLabel[0] of templateYLabel[1] in Europe has seen a notable increase since 2010 , with the templateYLabel[0] of templateYLabel[1] significantly increasing templateTitle[5] more than eleven templateYLabel[2] from 2010 to templateTitle[4] .

generated_template: 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: dogs 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 , Germany and United Kingdom are the two countries leading the list of cat owners in the EU , with the former 's narrow win . While Germany also ranks as the top EU Country with the highest Number of pet dogs , dogs still win in European Union households .

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

gold: This statistic displays the percentage of people in the United States diagnosed with Hemophilia A , sorted by age group , as of 2018 . In that year , nine percent of all Americans diagnosed with hemophilia A were between 0 and 4 years of age .
gold_template: This statistic displays the templateYLabel[0] of templateYLabel[1] in the templateTitle[3] diagnosed templateTitle[1] templateTitle[2] A , sorted templateTitle[5] templateXLabel[0] templateXLabel[1] , as of templateTitleDate[0] . In that year , templateYValue[min] percent of all Americans diagnosed templateTitle[1] templateTitle[2] A were between 0 and 4 templateXValue[0] of templateXLabel[0] .

generated_template: The total templateYLabel[0] templateYLabel[1] at stadiums during templateTitle[1] matches in the templateTitleSubject[0] ( templateTitleSubject[1] ) was highest in the templateXValue[0] in templateTitleDate[0] . The templateXValue[0] is the most successful templateTitle[1] templateXValue[0] worldwide and in total there were templateYValue[0] templateYLabel[2] spectators in templateTitleDate[0] . The templateTitleSubject[1] is a prominent templateTitle[1] nation and templateYLabel[0] templateYLabel[1] in the templateXValue[1] had a slight increase in recent years from 9.19 templateYLabel[2] in the 2013/14 season to 11.31 templateYLabel[2] in the 2016/17 season .
generated: The total Percentage people at stadiums during hemophilia matches in the A U.S. ( A U.S. ) was highest in the 0-4 years in 2018 . The 0-4 years is the most successful hemophilia 0-4 years worldwide and in total there were 9 people spectators in 2018 . The A U.S. is a prominent hemophilia nation and Percentage people in the 5-13 years had a slight increase in recent years from 9.19 people in the 2013/14 season to 11.31 people in the 2016/17 season .

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

gold: This statistic shows the electricity prices for household end users in Estonia semi-annually from 2010 to 2017 . In the first half of 2017 , the average electricity price for households was 12.07 euro cents per kWh .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitleSubject[0] templateTitle[7] from templateXValue[0] to templateXValue[last] . In the first half of templateXValue[last] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[14] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .

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

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

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

generated_template: The statistic illustrates 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] was at about templateYValue[0] templateYLabel[3] templateYLabel[4] templateYLabel[5] . templateTitleSubject[0] is one of the top pharmaceutical companies worldwide .
generated: The statistic illustrates the Death rate per of Sweden from 2008 to 2018 . In 2018 , the Death rate per in Sweden was at about 9.1 thousand mid-year population . Sweden is one of the top pharmaceutical companies worldwide .

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

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

generated_template: The statistic shows the total templateTitleSubject[0] templateYLabel[0] in the global templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[4] and shows a forecast through to templateXValue[max] . In templateXValue[4] , global templateTitle[2] templateTitleSubject[0] templateYLabel[0] amounted to templateYValue[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the total Tesco Sales in the global sales forecast from 2010 to 2016 and shows a forecast through to 2020 . In 2016 , global sales Tesco Sales amounted to 46631.26 million US dollars .

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[0] templateYLabel[2] people visited the templateTitleSubject[0] art museum in templateTitleSubject[1] in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of recreational templateYLabel[1] to the templateTitleSubject[0] II templateTitleSubject[0] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] to the templateTitleSubject[0] II templateTitleSubject[0] amounted to approximately templateYValue[max] templateYLabel[2] in templateXValue[max] .
generated: This statistic shows the Number of recreational visitors to the Tate Modern II Tate Modern in the 2018 from 2007 to 2018 . The Number of visitors to the Tate Modern II Tate Modern amounted to approximately 5.84 millions 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[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitle[4] templateTitle[5] gate receipts in templateXValue[max] .

generated_template: The templateTitleSubject[0] is growing in every aspect . Over the last decade , the total templateYLabel[0] templateTitle[7] of templateTitle[3] in the templateTitleSubject[0] have more than quadrupled . In templateXValue[max] they amounted to approximately templateYValue[max] templateYLabel[3] templateYLabel[4] .
generated: The NFL is growing in every aspect . Over the last decade , the total Ticket sales of Patriots in the NFL have more than quadrupled . In 2018 they amounted to approximately 104 U.S. dollars .

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 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 Finland from 2010/11 to 2017/18 . In the 2017/18 season , there were a total of 76387 registered Ice hockey players in the Finland according to the International Ice hockey Federation .

Example 77:
titleEntities: {'Subject': ['Phoenix Suns', 'NBA'], 'Date': ['2003', '2020']}
title: Franchise value of the Phoenix Suns ( NBA ) 2003 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1625', '1500', '1280', '1100', '1000', '910', '565', '474', '395', '411', '429', '452', '449', '410', '395', '356', '282', '272']

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

generated_template: This graph depicts the templateYLabel[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] templateYLabel[2] 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[max] templateYLabel[4] templateYLabel[5] templateYLabel[6] templateYLabel[7] in templateXValue[max] . A ranking of the largest electric utilities companies in the templateTitle[0] can be found here as well as a ranking of global templateYLabel[1] templateTitle[4] in select countries .

generated_template: This statistic shows the templateYLabel[1] of templateYLabel[0] of the templateTitle[1] and templateTitle[0] templateTitle[2] into the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] of such products came to a total of about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateYLabel[3] templateYLabel[0] of domestic templateTitle[2] – additional information Electrical devices used within a templateTitle[1] are part of a billion-dollar home appliance industry , which is projected to grow in the coming years .
generated: This statistic shows the electricity of Average of the average and U.S. retail into the prices from 1990 to 2018 . In 2018 , Average of such products came to a total of about 10.58 price U.S. cents . U.S. Average of domestic retail – additional information Electrical devices used within a average are part of a billion-dollar home appliance industry , which is projected to grow in the coming years .

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] templateYLabel[1] in templateXValue[9] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] per capita is projected to reach templateYValue[max] templateYLabel[1] .

generated_template: Does it always templateYLabel[1] 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[0] 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 percent of all templateYLabel[2] .
generated: Does it always percent 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[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The spirits company is based in Wolfenbüttel , Germany .

generated_template: This statistic shows templateTitle[3] templateTitle[4] templateTitle[5] ' responses to a survey question asked from templateXValue[min] to templateXValue[max] , about increases in templateTitle[4] templateYLabel[3] . In templateXValue[max] , templateYValue[max] percent of templateTitle[3] templateTitle[4] templateTitle[5] surveyed said templateYLabel[3] had increased in the last templateXLabel[0] .
generated: This statistic shows profit 2009 2016 ' responses to a survey question asked from 2009 to 2016 , about increases in 2009 U.S. . In 2016 , 135.87 percent of profit 2009 2016 surveyed said U.S. had increased in the last Year .

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

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

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

Example 82:
titleEntities: {'Subject': ['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: First launched in 2002 , templateTitleSubject[0] is an online multiplayer service associated with Microsoft 's templateTitleSubject[0] line of consoles . In the fourth fiscal templateXLabel[0] of templateXValue[0] , Microsoft 's online gaming service had templateYValue[max] templateYLabel[2] monthly active templateYLabel[1] , up from just under 40 templateYLabel[2] at the beginning of templateXValue[12] . templateTitleSubject[0] perks templateTitleSubject[0] is available as a free service , but it also has paid tiers known as templateTitleSubject[0] Silver and templateTitleSubject[0] Gold .
generated: First launched in 2002 , Number M A is an online multiplayer service associated with Microsoft 's Number M A line of consoles . In the fourth fiscal Month of Jun 15 , Microsoft 's online gaming service had 1253 deals monthly active deals , up from just under 40 deals at the beginning of Jun 14 . Number M A perks Number M A is available as a free 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] increased almost twofold from templateXValue[min] to templateXValue[max] . The peak was reached in templateXValue[1] , when templateTitleSubject[0] 's annual templateYLabel[0] amounted to approximately templateYValue[max] templateYLabel[1] templateYLabel[2] . In templateXValue[max] the templateYLabel[0] decreased to templateYValue[0] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] for enterprise resource planning ( templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] templateTitle[4] templateYLabel[0] templateYLabel[1] is expected to reach templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the turnover for enterprise resource planning ( Bulgari ) from 2011 to 2017 . In 2017 , the Bulgari 2017 Turnover million is expected to reach 194.9 euros .

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

gold: This graph depicts the average ticket price for Washington Wizards games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 46.83 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[9] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] 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[8] 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 30.89 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 templateTitle[2] templateYLabel[2] in templateTitle[4] templateTitleSubject[0] templateTitle[6] in templateTitleDate[0] . The survey was conducted among 15 to 20 year-olds and is part of the Face of Global sex Report by Durex . According to the source , the respondents in templateXValue[0] had templateYValue[max] sex templateYLabel[2] on average .
generated: This statistic shows the Average working of average hours in hours Employees week in 2011 . The survey was conducted among 15 to 20 year-olds and is part of the Face of Global sex Report by Durex . According to the source , the respondents in Singapore had 44 sex hours on average .

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] templateYLabel[1] templateTitle[1] consoles , down from templateYValue[1] templateYLabel[1] templateYLabel[2] sold in the templateXLabel[0] prior . In terms of templateTitle[1] software , fiscal templateXValue[1] was the templateXLabel[0] of Pokemon , when more than 16 templateYLabel[1] copies of Pokemon X/Y for templateTitle[1] were sold .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] employees templateTitle[4] from templateXValue[min] to templateXValue[max] . According to the report , templateTitleSubject[0] had employed approximately templateYValue[max] people in 171 countries at the end of templateXValue[max] .
generated: This statistic shows the Nintendo Sales of Nintendo employees 2011 from 2011 to 2018 . According to the report , Nintendo had employed approximately 13.95 people in 171 countries at the end of 2018 .

Example 87:
titleEntities: {'Subject': ['Japan'], 'Date': ['2017', '2023']}
title: Japan : mobile internet users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'mobile', 'phone', 'internet', 'users', 'in', 'millions']: ['71.9', '71.5', '70.8', '69.9', '68.4', '66.6', '64.0']

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

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , templateYValue[5] templateYLabel[5] people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[max] templateYLabel[5] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Number of mobile internet users in Japan from 2017 to 2023 . In 2018 , 66.6 millions people accessed the internet through their mobile phone . In 2023 , this figure is projected to amount to 71.9 millions mobile phone internet users .

Example 88:
titleEntities: {'Subject': ['Penguin Random House'], 'Date': ['2005', '2018']}
title: Number of employees of Penguin Random House 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Number', 'of', 'employees']: ['10351', '10615', '10594', '10582', '12812', '11838', '5712', '5343', '5264', '5432', '5779', '5764', '5804', '5395']

gold: This statistic shows the number of employees of Penguin Random House from 2005 to 2018 . In 2018 , 10,351 people were employed by the company worldwide , down from 10,615 a year earlier .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] House from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] people were employed by the company worldwide , down from templateYValue[1] a templateXLabel[0] earlier .

generated_template: This statistic shows the approximate templateYLabel[0] of templateYLabel[1] at templateTitleSubject[0] each templateXLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] employed around templateYValue[min] people worldwide . Total templateYLabel[0] of GE templateYLabel[1] templateTitleSubject[0] conducts business in virtually every part of the world , with over 180 countries served .
generated: This statistic shows the approximate Number of employees at Penguin Random House each Year from 2005 to 2018 . In 2018 , Penguin Random House employed around 5264 people worldwide . Total Number of GE employees Penguin Random House conducts business in virtually every part of the world , with over 180 countries served .

Example 89:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2019']}
title: Cinema box office revenue in the United Kingdom ( UK ) 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Revenue', 'in', 'million', 'GBP']: ['1251.84', '1277.12', '1277.88', '1246.56', '1240.38', '1057.68', '1082.1', '1099.1', '1038.0', '988.0', '944.0', '854.4', '821.0', '762.1', '776.3', '769.6', '742.0', '755.0', '645.0', '627.5']

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

generated_template: British streaming giant templateTitleSubject[0] had a total templateYLabel[0] templateYLabel[1] of over 1.86 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] , whilst the company 's annual revenue reached 20.15 templateYLabel[2] templateYLabel[3] templateYLabel[4] . The number of templateTitleSubject[0] 's streaming subscribers worldwide has continued to grow in recent years , reaching 167 templateYLabel[2] in the fourth quarter of templateXValue[max] . Three years earlier , in the fourth quarter of templateXValue[4] , the figure stood at 74.76 templateYLabel[2] subscribers .
generated: British streaming giant United Kingdom had a total Revenue million of over 1.86 GBP in 2019 , whilst the company 's annual revenue reached 20.15 GBP . The number of United Kingdom 's streaming subscribers worldwide has continued to grow in recent years , reaching 167 GBP in the fourth quarter of 2019 . Three years earlier , in the fourth quarter of 2015 , the figure stood at 74.76 GBP subscribers .

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[0] , implying a 7.83 increase in the templateYLabel[1] level .

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] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the projected Average CPI CPI UAE Consumer price in the 2012 from 2012 to 2017 . In 2013 , it is estimated that the Average CPI CPI UAE industry will have a Consumer price of around 120.84 index .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[0] to the fourth templateXLabel[0] of templateTitleDate[1] . In the fourth templateXLabel[0] of templateTitleDate[1] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Net income of Amazon from the first Quarter of 2019 to the fourth Quarter of 2019 . In the fourth Quarter of 2019 , Amazon 's Net income amounted to 3268 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] percent of templateYLabel[1] visited templateXValue[last] restaurants templateXValue[0] once templateXValue[0] .

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

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

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

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

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

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

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

Example 95:
titleEntities: {'Subject': ['Houston Rockets', 'NBA'], 'Date': ['2003', '2020']}
title: Franchise value of the Houston Rockets ( NBA ) 2003 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['2475', '2300', '2200', '1650', '1500', '1250', '775', '568', '453', '443', '470', '469', '462', '439', '422', '369', '278', '255']

gold: This graph depicts the franchise value of the Houston Rockets of the National Basketball Association from 2003 to 2020 . In 2020 , the Houston Rockets franchise had an estimated value of 2.475 billion U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] franchise had an estimated templateYLabel[1] of 2.475 templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[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] templateYLabel[2] 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] templateYLabel[2] templateTitle[4] . templateXValue[1] ranked in second place templateTitle[1] a templateTitleSubject[0] templateYLabel[0] base of templateYValue[1] templateYLabel[2] templateTitle[4] . The photo-sharing platform is projected to surpass 365 templateYLabel[2] global templateTitle[4] in 2023 .

generated_template: This statistic gives information on the templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] templateYLabel[2] as of 2020 . As of the measured period , the templateXLabel[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateYLabel[3] was the templateXValue[0] , templateTitle[1] an templateTitle[3] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[max] templateYLabel[3] .
generated: This statistic gives information on the Snapchat most the Snapchat users Audience size millions as of 2020 . As of the measured period , the Country most the Snapchat users Audience size millions in millions was the United States , most an users Audience size millions of 101.25 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] - additional information The templateTitleSubject[0] are a professional basketball team of the National Basketball Association ( NBA ) , joining the league as an expansion team in 1989 .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2017/18 season . In 2017/18 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] - additional information The templateTitleSubject[0] are a professional basketball team that is part of the National Basketball Association ( NBA ) .
generated: The statistic shows the Revenue of the Minnesota Timberwolves franchise from the 2001/02 season to the 2017/18 season . In 2017/18 , the estimated Revenue of the National Basketball Association franchise amounted to 223 million U.S. dollars . Minnesota Timberwolves - additional information The Minnesota Timberwolves are a professional basketball team that is part of the National Basketball Association ( NBA ) .

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

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

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[2] a templateTitleSubject[0] subscription in the templateTitle[4] in templateXValue[min] and templateXValue[max] . In the presented time period , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[2] templateTitleSubject[0] rose from templateYValue[min] percent to templateYValue[max] percent .
generated: The statistic shows the Market of cap billion NASDAQ a NASDAQ subscription in the 1999 in 1999 and 2013 . In the presented time period , the Market of cap billion NASDAQ rose from 1.16 percent to 2.54 percent .

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

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

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

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] percent of templateYLabel[1] stated that templateXValue[16] and templateXValue[16] is essential for the templateTitleSubject[0] templateTitle[3] .

generated_template: The statistic shows templateTitle[1] brands ownership templateYLabel[0] in the templateTitleSubject[0] in templateTitleDate[0] according to a Statista survey . templateYValue[max] percent of templateYLabel[1] said that they own templateXValue[0] headphones .
generated: The statistic shows concept brands ownership Share in the Americans in 2017 according to a Statista survey . 66 percent of respondents said that they own Personal freedom headphones .

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[min] templateYLabel[1] people attended a templateTitle[1] templateTitle[2] event at least once . templateTitle[1] templateTitle[2] – additional information templateTitle[1] templateTitle[2] are any type of art form in which a performer physically conveys an artistic piece ; this includes dance , opera , symphony and theatre performances .

generated_template: 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[max] templateYLabel[1] people attended a templateTitle[0] templateTitle[1] concert at least once . The templateTitle[1] concert industry – additional information In 2016 , Beyonce and Guns ' N ' Rose were among the most successful templateTitle[1] tours in North America , generating 169.4 templateYLabel[1] templateTitleSubject[0] dollars and 130.8 templateYLabel[1] templateTitleSubject[0] dollars , respectively in gross revenue .
generated: This graph depicts the events Attendance at Attendance performing arts in the 2003 from 2003 to 2013 . In 2013 , 86.38 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] , California-based web company templateTitleSubject[0] had an templateYLabel[0] templateYLabel[1] of almost templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] , up from 10.89 templateYLabel[2] templateYLabel[3] templateYLabel[4] in the preceding fiscal templateXLabel[0] . templateTitleSubject[0] operates under the parent company Alphabet Inc .
generated: In the fourth Quarter of Q1 2019 , California-based web company British Telecommunications BT had an Average revenue of almost 39.6 per user GBP , up from 10.89 per user GBP in the preceding fiscal Quarter . British Telecommunications BT operates under the parent company Alphabet Inc .

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

gold: This statistic contains income data for the Regal Entertainment Group from 2006 to 2017 . In 2017 , REG reported a net profit of 112.3 million U.S dollars .
gold_template: This statistic contains templateYLabel[1] data for the templateTitleSubject[0] Group from templateXValue[min] to templateXValue[max] . In templateXValue[max] , REG reported a templateYLabel[0] profit of templateYValue[0] templateYLabel[2] U.S templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] Cheese templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . Family-style restaurant chain templateTitleSubject[0] Cheese templateTitle[5] made a templateYLabel[0] templateYLabel[1] ( loss ) of approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] .
generated: This statistic shows the Net income of Regal Entertainment Group Cheese income 2006 from 2006 to 2017 . Family-style restaurant chain Regal Entertainment Group Cheese income made a Net income ( loss ) of approximately 112.3 million U.S. dollars in 2017 .

Example 105:
titleEntities: {'Subject': ['Peru'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Peru 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['3.76', '3.9', '3.99', '4', '3.62', '2.6', '3.99', '2.47', '4.05', '3.27', '2.39']

gold: The statistic shows the growth in real GDP in Peru from 2014 to 2018 , with projections up until 2024 . In 2018 , Peru 's real gross domestic product grew by around 3.99 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[2] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) has been experiencing positive templateYLabel[1] for the past five years since templateXValue[min] , and is projected to continue to do so through templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateYLabel[0] increased by around templateYValue[0] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . Learning from real templateYLabel[0] Real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) is a measure that reflects the value of all goods and services an economy produces within a given templateXLabel[0] .
generated: Peru 's real Gross domestic product ( GDP ) has been experiencing positive growth for the past five years since 2014 , and is projected to continue to do so through 2024 . In 2018 , Peru 's real GDP increased by around 3.76 percent compared to the previous Year . Learning from real GDP Real Gross domestic product ( GDP ) is a measure that reflects the value of all goods and services an economy produces within a given Year .

Example 106:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Most used garage door openers in the U.S. 2018
X_Axis['Brand']: ['Liftmaster', 'Chamberlain', 'Overhead_Door', 'Genie', 'Craftsmen', 'Wayne-Dalton', 'Raynor', 'Linear', 'Access_Master', 'Marantec', 'None_of_these']
Y_Axis['Share', 'of', 'respondents']: ['50', '14.9', '9.6', '7.9', '5.3', '3.5', '3.5', '0.9', '0.9', '0.9', '2.6']

gold: This statistic depicts garage door openers brands used the most by U.S. construction firms in 2018 . The survey revealed that 50 percent of the respondents used LiftMaster garage door openers brand the most .
gold_template: This statistic depicts templateTitle[2] templateXValue[2] templateTitle[4] brands templateTitle[1] the templateTitle[0] by templateTitleSubject[0] construction firms in templateTitleDate[0] . The survey revealed that templateYValue[max] percent of the templateYLabel[1] templateTitle[1] templateXValue[0] templateTitle[2] templateXValue[2] templateTitle[4] templateXLabel[0] the templateTitle[0] .

generated_template: This statistic gives information on the percentage of templateTitle[4] templateTitleSubject[0] templateTitle[6] templateTitle[7] who access selected templateTitle[0] templateTitle[1] as of the third quarter templateTitleDate[0] . During the survey period , it was found that templateYValue[max] percent of templateTitle[4] templateTitle[6] templateTitle[7] in the templateTitle[5] used templateXValue[0] .
generated: This statistic gives information on the percentage of openers U.S. 2018 who access selected Most used as of the third quarter 2018 . During the survey period , it was found that 50 percent of openers 2018 in the U.S. used Liftmaster .

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

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

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Argentina from 2004 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Argentina amounted to about 34.28 percent 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] percent believed that the templateTitleSubject[0] templateTitle[1] will templateXValue[3] templateXValue[1] in templateTitleDate[0] .

generated_template: In templateTitleDate[0] , some templateYValue[max] percent of Canadians stated that , out of any templateTitle[3] category , they are templateTitle[0] likely to purchase templateXValue[0] from templateTitleSubject[0] producers or brands as opposed to imported products . templateXValue[1] and templateXValue[2] were the next two templateTitle[0] templateTitle[1] templateTitle[3] segments , with templateYValue[1] and templateYValue[2] percent of templateYLabel[1] stating they would likely buy templateTitleSubject[0] goods respectively . In the same survey , some 25 percent of templateTitle[7] said that they always buy templateTitleSubject[0] products .
generated: In 2018 , some 56 percent of Canadians stated that , out of any expectations category , they are Tourism likely to purchase Significantly decline from Tourism producers or brands as opposed to imported products . Slightly decline and No change were the next two Tourism industry expectations segments , with 11 and 14 percent of respondents stating they would likely buy Tourism goods respectively . In the same survey , some 25 percent of 2018 said that they always buy Tourism products .

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

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

generated_template: This statistic shows the results of a survey about the templateTitle[2] templateTitle[3] of several individual templateTitle[0] templateXValue[last] in templateTitleSubject[0] in 2015 , phrased templateXValue[1] the question : `` except reading books , which of the following templateTitle[0] templateXValue[last] do templateXValue[2] do in your free time ? '' . It was found that listening to templateXValue[0] was the most practiced templateTitle[0] activity templateXValue[1] a templateTitle[2] templateTitle[3] of templateYValue[max] percent of templateYLabel[1] , followed very closely by templateYValue[1] percent of templateYLabel[1] who stated that they templateXValue[1] with templateXValue[1] in their free time templateXValue[1] templateXValue[1] . Even though templateXValue[0] is a very activity , it seems that French templateXValue[2] would probably not choose to templateXValue[0] to templateXValue[0] if they had more free time .
generated: This statistic shows the results of a survey about the perception country of several individual Chile Drug consumption in Chile in 2015 , phrased Unemployment the question : `` except reading books , which of the following Chile Drug consumption do Corruption do in your free time ? '' . It was found that listening to Crime was the most practiced Chile activity Unemployment a perception country of 38.2 percent of respondents , followed very closely by 8.8 percent of respondents who stated that they Unemployment with Unemployment in their free time Unemployment . Even though Crime is a very activity , it seems that French Corruption would probably not choose to Crime to Crime if they had more free time .

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

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

generated_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Yankees from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Yankees was at templateYValue[0] templateYLabel[2] . • Major League Baseball average per game templateYLabel[1] • Major League Baseball total templateYLabel[1]
generated: This graph depicts the total Regular season Home attendance of the Los Angeles Chargers Yankees from 2006 to 2019 . In 2019 , the Regular season Home attendance of the Los Angeles Chargers Yankees was at 177755 attendance . • Major League Baseball average per game attendance • Major League Baseball total attendance

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

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

generated_template: The templateTitle[0] engine templateTitle[1] of templateTitle[2] registered in templateTitleSubject[0] increased between templateXValue[min] and templateXValue[max] . By templateXValue[max] , templateTitle[3] with a volume of templateYValue[max] templateYLabel[0] centimeters had become the norm , an increase of nearly 40 percent since the beginning of the reporting period . Most common engine templateTitle[1] As of templateXValue[max] , most registered templateTitle[2] had an engine templateTitle[1] either between 51 and 125 templateYLabel[0] centimeters or greater than 1,000 templateYLabel[0] centimeters .
generated: The Annual engine CAC of 40 registered in CAC increased between 1995 and 2019 . By 2019 , performance with a volume of 5978.06 Index centimeters had become the norm , an increase of nearly 40 percent since the beginning of the reporting period . Most common engine CAC As of 2019 , most registered 40 had an engine CAC either between 51 and 125 Index centimeters or greater than 1,000 Index centimeters .

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

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

generated_template: The templateXLabel[0] templateXLabel[1] with the largest templateYLabel[0] of individuals on the transplant templateTitle[3] templateTitle[4] in the templateTitleSubject[0] in 2019 was those aged 50 - 64 templateXValue[1] . This templateXLabel[0] templateXLabel[1] had templateYValue[max] patients templateTitle[3] to receive transplants at that time . There is an extensive need for templateTitle[2] donations in the templateTitle[5] .
generated: The Console with the largest Installed of individuals on the transplant U.S. installed in the U.S. in 2019 was those aged 50 - 64 Xbox One . This Console had 21 patients U.S. to receive transplants at that time . There is an extensive need for systems donations in the base .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] a templateTitle[3] templateTitle[4] in templateTitle[5] templateTitle[6] in the templateTitle[7] from templateXValue[min] to templateXValue[max] . Nails templateTitle[6] in the templateYLabel[2] charged an templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateTitle[2] a templateTitle[3] templateTitle[4] in templateXValue[max] .
generated: This statistic shows the Capital expenditure U.S. a chemical industry in 2004 2018 in the 2018 from 2004 to 2018 . Nails 2018 in the million charged an Capital expenditure of 33200 million U.S. a chemical industry in 2018 .

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

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

generated_template: The total templateYLabel[0] templateYLabel[1] at stadiums during templateTitle[1] matches in the templateTitleSubject[0] ( templateTitleSubject[1] ) was highest in the templateXValue[0] in templateTitleDate[0] . The templateXValue[0] is the most successful templateTitle[1] templateXValue[0] worldwide and in total there were templateYValue[0] templateYLabel[2] spectators in templateTitleDate[0] . The templateTitleSubject[1] is a prominent templateTitle[1] nation and templateYLabel[0] templateYLabel[1] in the templateXValue[1] had a slight increase in recent years from 9.19 templateYLabel[2] in the 2013/14 season to 11.31 templateYLabel[2] in the 2016/17 season .
generated: The total Number players at stadiums during poker matches in the Number ( Number ) was highest in the September 2006 in 2006 . The September 2006 is the most successful poker September 2006 worldwide and in total there were 17.8 (in spectators in 2006 . The Number is a prominent poker nation and Number players in the January 2007 had a slight increase in recent years from 9.19 (in in the 2013/14 season to 11.31 (in in the 2016/17 season .

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

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

generated_template: This statistic 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] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the projected Average price U.S. Average price in the full from 2009 to 2014 . In 2013 , it is estimated that the Average price U.S. industry will have a Average price of around 39.16 U.S. dollars .

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

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

generated_template: 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[max] percent 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 1600 percent 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] percent , which together with the previous templateXLabel[0] , was the lowest templateYLabel[1] recorded in this templateYValue[min] year period . Between 2017 and 2019 the templateTitle[2] templateYLabel[1] was at it 's highest in November of templateTitleDate[0] , when an templateYLabel[0] templateYLabel[1] of templateYValue[max] percent was recorded .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] and templateTitleDate[1] . The templateTitle[0] templateYLabel[0] templateYLabel[1] was valued at templateYValue[0] percent in the 's templateYLabel[0] templateYLabel[1] .
generated: This statistic shows the Inflation rate of United in the United Kingdom ( UK ) in 2017 and 2019 . The Inflation rate was valued at 1.5 percent in the 's Inflation rate .

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

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

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . During the marketing templateXLabel[0] templateXValue[max] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] amounted to about templateYValue[6] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the spending worldwide from 2011 to 2017 . During the marketing Year 2017 , Global spending amounted to about 4.83 billion U.S. dollars .

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

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

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] 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[0] templateYLabel[2] . templateTitle[1] is a popular recreational activity with more than templateYValue[min] templateYLabel[2] people partaking in templateTitle[1] activities in the templateTitle[2] each templateXLabel[0] .
generated: This statistic shows the Number of participants in indoor in the soccer U.S. 2006 to 2018 . In 2018 , the Number of participants ( aged six years and older ) in indoor amounted to approximately 5.23 millions . indoor is a popular recreational activity with more than 4.24 millions people partaking in indoor activities in the soccer each Year .

Example 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] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of fresh cucumbers in the U.S. from 2000 to 2018 . According to the report , the U.S. Per capita consumption of fresh cucumbers amounted to approximately 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[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] on advertising and promotion .

generated_template: The templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] templateTitle[4] templateYLabel[3] of templateTitleSubject[0] was to templateYValue[max] percent in templateXValue[max] . This was up from templateYValue[1] percent the templateXLabel[0] earlier , the highest templateYLabel[3] in the past six years . What is the templateYLabel[0] templateYLabel[1] templateTitle[4] templateYLabel[3] ? templateYLabel[0] templateYLabel[1] templateTitle[4] is essentially the amount of money a bank has on hand in case it needs to cover unexpected expenses from risky transactions .
generated: The Global ad expenditure from U.S. of Mattel was to 750.2 percent in 2019 . This was up from 524.3 percent the Year earlier , the highest U.S. in the past six years . What is the Ad expenditure from U.S. ? Ad expenditure from is essentially the amount of money a bank has on hand in case it needs to cover unexpected expenses from risky transactions .

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

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

generated_template: The statistic shows the templateYLabel[1] of the health templateTitle[5] fitness templateTitle[1] templateYLabel[0] in templateXValue[0] templateTitle[5] templateTitleSubject[0] countries in templateTitle[8] . The templateYLabel[1] of the fitness templateTitle[5] health templateTitle[1] templateYLabel[0] in templateXValue[3] was at around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitle[8] .
generated: The statistic shows the immigrants of the health 2015 fitness illegal in Mexico 2015 U.S. countries in 2015 . The immigrants of the fitness 2015 health illegal in India was at around 6580 thousands in 2015 .

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

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

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

Example 124:
titleEntities: {'Subject': ['Texas'], 'Date': ['1992', '2018']}
title: Texas - Unemployment rate 1992 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92"]
Y_Axis['Unemployment', 'rate']: ['3.9', '4.3', '4.6', '4.4', '5.1', '6.3', '6.7', '7.8', '8.1', '7.6', '4.8', '4.3', '4.9', '5.4', '5.9', '6.7', '6.4', '5', '4.3', '4.7', '4.9', '5.3', '5.7', '6.1', '6.5', '7.2', '7.6']

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

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

Example 125:
titleEntities: {'Subject': ['USA'], 'Date': ['1990', '2018']}
title: USA - number of arrests for all offenses 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Number', 'of', 'arrests']: ['10310960', '10554985', '10662252', '10797088', '11205833', '11302102', '12196959', '12408899', '13120947', '13687241', '14005615', '14209365', '14380370', '14094186', '13938071', '13639500', '13741400', '13699300', '13980300', '14031100', '14528300', '15284300', '15168100', '15119800', '14648700', '14036300', '14075100', '14211900', '14195100']

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

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[0] was templateYValue[min] out of every 1,000 templateYLabel[6] . This is a significant decrease from templateXValue[min] , when templateYLabel[0] templateYLabel[1] was at templateYValue[max] 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 out of every 1,000 arrests . This is a significant decrease from 1990 , when Number arrests was at 15284300 deaths out of every 1,000s arrests . What is Number arrests ? The Number arrests is the number of deaths of babies under the age of one arrests 1,000 arrests .

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: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] in the templateTitle[2] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] in templateXValue[1] was templateYValue[17] people .
generated: This statistic shows the Number homicide of by in the murdered in 2017 , men U.S. . In 2017 , the Number homicide of by in Texas was 40 people .

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

gold: This statistic shows the responses on a study about social network usage in Canada as of January 2015 . During the reported period , Facebook reached 75 percent of Canadians aged 18 to 34 , while among Canadians aged 65 and older Facebook had a 32 percent penetration rate . In general the platform reached 59 percent of people in Canada .
gold_template: This statistic shows the responses on a study about social network usage in templateTitleSubject[0] as of 2015 . During the reported period , templateTitleSubject[1] reached templateYValue[max] percent of Canadians aged 18 to 34 , while among Canadians aged 65 and older templateTitleSubject[1] had a templateYValue[min] percent templateTitle[3] rate . In general the platform reached templateYValue[5] percent 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] percent 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 age . We Are Flint found that 75 percent of internet users with age earnings of 18-34 30,000 US dollars per Year used the social networking site .

Example 128:
titleEntities: {'Subject': ['Current'], 'Date': []}
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 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 world calendars is an annual historical prize presented by France historical . The award , voted for by historical journalists , is given to the male player who was deemed to have played the best historical over the previous 12 months . Unsurprisingly , 228 of the greatest footballers of all time , Assyrian and Hebrew , top the list of all-time year .

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

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

generated_template: This statistic shows the estimated templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] to templateTitle[4] templateTitle[5] in templateTitleSubject[1] from templateXValue[last] to templateXValue[0] . In templateXValue[2] , the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] to templateTitle[4] templateTitle[5] amounted to approximately templateYValue[min] templateYLabel[2] templateYLabel[1] templateTitle[2] , the lowest amount in the provided time interval .
generated: This statistic shows the estimated Number of Coast U.S Coast to Guard personnel in Coast Guard from 1995 to 2010 . In 2008 , the Number of Coast U.S Coast to Guard personnel amounted to approximately 34804 Guard Coast U.S , the lowest amount in the provided time interval .

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

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] templateTitle[4] templateTitle[5] in the middle of templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] in templateXValue[0] was templateYValue[max] percent in the middle of 2014.The templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] arises from the birth templateYLabel[1] minus the death templateYLabel[1] and without including the effects of migration.Population growthAs shown in the statistic above , the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] continues to increase on almost every templateTitle[5] in 2013.Due to medical advances , better living conditions and the increase of agricultural productivity the world templateTitle[2] is continuously rising . The development of the world templateTitle[2] from 1950 to 2030 is estimated to be tripled according to United Nations ' data.The majority of the world templateTitle[2] lives in templateXValue[4] , but the templateTitle[2] in templateXValue[0] is forecasted to rise from 1,031 in year 2010 up to 4,185 in year 2100 .
generated: The statistic shows the Physicians per of worldwide 10,000 region 2013 in the middle of 2013 . The Physicians per of worldwide 10,000 in Europe was 32.1 percent in the middle of 2014.The Physicians per of worldwide 10,000 arises from the birth per minus the death per and without including the effects of migration.Population growthAs shown in the statistic above , the Physicians per of worldwide 10,000 continues to increase on almost every 2013 in 2013.Due to medical advances , better living conditions and the increase of agricultural productivity the world worldwide is continuously rising . The development of the world worldwide from 1950 to 2030 is estimated to be tripled according to United Nations ' data.The majority of the world worldwide lives in Eastern Mediterranean , but the worldwide in Europe is forecasted to rise from 1,031 in year 2010 up to 4,185 in year 2100 .

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

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

generated_template: In templateXValue[max] , an estimated templateYValue[max] templateYLabel[1] smartwatches were sold in the templateTitle[4] . Between templateXValue[min] and templateXValue[max] annual templateTitleSubject[0] templateYLabel[0] grew from just templateYValue[min] thousand templateYLabel[2] to over templateYValue[max] templateYLabel[1] as these devices skyrocketed in popularity . Smartwatches While digital wristwatches have been around for decades , true smartwatches are a relatively new phenomena in the consumer electronics industry .
generated: In 2018 , an estimated 15.16 active smartwatches were sold in the 2018 . Between 2013 and 2018 annual Wayfair Number grew from just 2.09 thousand customers to over 15.16 active as these devices skyrocketed in popularity . Smartwatches While digital wristwatches have been around for decades , true smartwatches are a relatively new phenomena in the consumer electronics industry .

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

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

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateXValue[0] templateYLabel[1] from templateTitleSubject[0] in templateTitleDate[0] . During the Deloitte survey , templateYValue[max] percent of templateYLabel[1] stated that templateXValue[0] the templateXValue[0] social or templateXValue[0] was their favorite source of templateTitle[3] .
generated: The statistic shows the Persons whom Italians of cheat Friend respondents from Italians in 2017 . During the Deloitte survey , 25.4 percent of respondents stated that Friend the Friend social or Friend was their favorite source of cheat .

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

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

generated_template: The statistic shows the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[6] with a forecast up to templateXValue[max] . The templateTitleSubject[0] templateYLabel[0] templateYLabel[1] is around templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] of a country in templateXValue[max] .
generated: The statistic shows the U.S. Cars service from 2002 to 2008 with a forecast up to 2012 . The U.S. Cars service is around 1617 thousands of a country in 2012 .

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

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

generated_template: This statistic shows the ten templateTitle[0] templateTitle[1] in templateTitleSubject[0] as of templateTitleDate[0] . In templateTitleDate[0] , around templateYValue[max] templateYLabel[1] 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] templateYLabel[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] in templateXValue[max] , a rise since the previous templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Over this period templateYLabel[1] of both domestic and international tourists in templateTitle[3] establishments in templateTitleSubject[0] has increased , reaching around templateYValue[max] templateYLabel[2] in templateXValue[max] .
generated: This statistic shows the Number of tourist arrivals at accommodation establishments in Spain from 2006 to 2017 . Over this period arrivals of both domestic and international tourists in accommodation establishments in Spain has increased , reaching around 129.4 millions in 2017 .

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

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

generated_template: The statistic depicts 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[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Number of Europe ( magnesium compounds ) users as of 2014 , Europe major by . At this point , Europe Number in Norway amounted to approximately 7800 users .

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

gold: This statistic shows the share of the global farming machinery market in 2015 , by region . The European Union and NAFTA accounted for 48 percent of the agricultural machinery market in this year , though China in third place is one of the fastest growing markets .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] farming templateTitle[3] templateYLabel[1] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . The templateXValue[0] and templateXValue[1] accounted for 48 percent of the templateTitle[2] templateTitle[3] templateYLabel[1] in this year , though templateXValue[2] in third place is one of the fastest growing markets .

generated_template: The templateTitleSubject[0] templateTitle[6] templateTitle[7] first began in 1956 templateTitle[3] templateYValue[max] templateTitle[2] contesting . Since its beginning , a total of 52 templateTitle[2] have participated in the hopes of becoming a champion . Of all the templateTitle[2] that have sung their lungs out for the coveted prize , templateXValue[0] have templateYLabel[2] the competition more than any .
generated: The Share region 2015 first began in 1956 machinery 26 agricultural contesting . Since its beginning , a total of 52 agricultural have participated in the hopes of becoming a champion . Of all the agricultural that have sung their lungs out for the coveted prize , European Union have market the competition more than any .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Tonga from 1999 to 2019 . In 2019 , the Unemployment rate in Tonga was at approximately 1.02 percent .

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

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

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] of nurse templateTitle[2] in templateTitle[3] templateTitle[4] in Canada , sorted templateTitle[5] templateTitle[6] , in templateTitleDate[0] . In templateXValue[0] , around templateYValue[max] templateYLabel[1] were part of the templateTitle[3] templateTitle[4] templateTitle[2] , while in templateXValue[1] there were almost 72,000 templateYLabel[1] .
generated: This statistic shows the species Production of nurse groups in global aquaculture in Canada , sorted production worldwide , in 2017 . In Carps barbels and other cyprinids , around 28345 thousand were part of the global aquaculture groups , while in Miscellaneous freshwater fishes there were almost 72,000 thousand .

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

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

generated_template: The statistic shows the templateYLabel[0] of 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[min] templateYLabel[1] 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: How many police templateYLabel[3] are there in the templateTitleSubject[0] ? In templateXValue[max] , there were templateYValue[0] full-time templateYLabel[1] templateYLabel[2] templateYLabel[3] employed in the templateTitle[4] . The templateYLabel[0] of full-time templateYLabel[1] templateYLabel[2] templateYLabel[3] reached a peak in templateXValue[10] with templateYValue[max] templateYLabel[3] , and hit a low in templateXValue[5] with templateYValue[min] templateYLabel[3] . Employment in templateYLabel[1] templateYLabel[2] According to the source , templateYLabel[1] templateYLabel[2] templateYLabel[3] are defined as those individuals who regularly carry a firearm and an official badge on their person , have full powers of arrest , and whose salaries are paid from federal funds set aside specifically for sworn templateYLabel[1] templateYLabel[2] .
generated: How many police shutdowns are there in the Nuclear ? In 2019 , there were 3 full-time shutdowns employed in the shutdowns . The Number of full-time shutdowns reached a peak in 2009 with 13 shutdowns , and hit a low in 2014 with 1 shutdowns . Employment in shutdowns According to the source , shutdowns are defined as those individuals who regularly carry a firearm and an official badge on their person , have full powers of arrest , and whose salaries are paid from federal funds set aside specifically for sworn shutdowns .

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[4] percent of survey templateYLabel[1] stated that they planned to do most of their holiday templateTitle[3] on templateTitleSubject[0] in the templateTitle[1] in templateXValue[max] . This is a decrease of 23 percent since templateXValue[min] , when some templateYValue[max] percent of templateYLabel[1] were planning to do the majority of their templateTitle[3] on templateTitleSubject[0] .

generated_template: 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 decreased from templateYValue[max] percent of templateYLabel[1] in templateXValue[min] to templateYValue[min] percent in templateXValue[max] .
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 percent of respondents in 2015 to 35 percent in 2019 .

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

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

generated_template: The templateTitleSubject[0] is growing in every aspect . Over the last decade , the total templateYLabel[0] templateTitle[7] of templateTitle[3] in the templateTitleSubject[0] have more than quadrupled . In templateXValue[max] they amounted to approximately templateYValue[max] templateYLabel[3] templateYLabel[4] .
generated: The BMW Group is growing in every aspect . Over the last decade , the total Production 2018 of production in the BMW Group have more than quadrupled . In 2018 they amounted to approximately 185682 units .

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

gold: This statistic presents the number of monthly active Facebook Messenger chat app users from April 2014 to September 2017 . As of the last reported period , the mobile messenger had 1.3 billion monthly active users worldwide , ranking second among mobile chat apps worldwide . Facebook messenger users – additional information Mobile messenger apps are on the rise , with optimistic projections for this market in the coming years .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[0] templateTitleSubject[0] chat app templateYLabel[3] from 2014 to 2017 . As of the last reported period , the mobile templateTitleSubject[0] had templateYValue[max] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide , ranking second among mobile chat apps worldwide . templateTitle[0] templateTitleSubject[0] templateYLabel[3] – additional information Mobile templateTitleSubject[0] apps are on the rise , with optimistic projections for this market in the coming years .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitleSubject[0] templateYLabel[0] from templateTitleSubject[0] templateTitle[5] to 2019 . As of the last reported period , the note-taking and productivity app had an estimated templateYValue[max] templateYLabel[2] templateYLabel[1] worldwide , up from templateYValue[1] templateYLabel[2] in 2015 .
generated: This statistic shows the Facebook Messenger Number from Messenger users to 2019 . As of the last reported period , the note-taking and productivity app had an estimated 1300 active monthly worldwide , up from 1200 active 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[min] percent of the templateYLabel[1] in the country accessed templateTitle[1] templateTitle[2] . In templateXValue[4] , this templateYLabel[0] is projected to reach templateYValue[4] percent .

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

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

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

generated_template: This statistic 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] templateYLabel[2] templateYLabel[1] templateTitle[2] , the lowest amount in the provided time .
generated: This statistic shows the estimated Revenue of (in industry Revenue to Easton Bell in Easton Bell Sports from 2006 to 2013 . In 2013 , the Revenue of (in industry Revenue to Easton Bell amounted to approximately 639.0 million (in industry , the lowest amount in the provided time .

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: This statistic shows the templateTitle[3] of cumulative templateTitleSubject[0] templateYLabel[0] from templateTitleSubject[0] 's templateTitleSubject[0] from 2008 to templateTitle[5] templateTitle[6] . As of the last reported period , templateTitleSubject[0] announced that templateYValue[max] templateYLabel[1] apps had been downloaded from its templateTitleSubject[0] . templateTitleSubject[0] download – additional information templateTitleSubject[0] Store was created in 2008 and , since then , the templateTitle[3] of available apps has been consistently increasing over the years .
generated: This statistic shows the sales of cumulative Mexico Number from Mexico 's Mexico from 2008 to manufacturer 2019 . As of the last reported period , Mexico announced that 174706 units apps had been downloaded from its Mexico . Mexico download – additional information Mexico Store was created in 2008 and , since then , the sales of available apps has been consistently increasing over the years .

Example 148:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2017', '2023']}
title: Mexico : number of social network users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['73.0', '71.1', '69.1', '66.7', '64.1', '61.7', '58.8']

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

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that there will be around templateYValue[max] templateYLabel[2] templateTitle[2] templateTitle[3] templateYLabel[1] in the country , up from templateYValue[min] templateYLabel[2] in templateXValue[min] .
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[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic represents Bombardier 's Revenue between the 2007 and 2018 fiscal years . In the 2018 fiscal Year , the manufacturer of regional aircraft , business jets , mass transportation equipment and recreational equipment had revenues of 8.91 billion U.S. dollars .

Example 150:
titleEntities: {'Subject': ['Lauder'], 'Date': ['2012', '2024']}
title: Estée Lauder 's share of the makeup products market worldwide 2012 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Market', 'share']: ['24.4', '23.4', '22.4', '21.4', '20.4', '18.9', '17.4', '16.4', '13.6', '12.9', '14.2', '13.8', '12']

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

generated_template: 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] percent . The company is a manufacturer and marketer of prestige templateTitle[4] templateTitle[5] , makeup , fragrance and hair templateTitle[5] templateTitle[6] , with global net sales of over 11.2 billion U.S. dollars .
generated: This statistic shows Estée Lauder 's share of the makeup products market 2012 from 2012 to 2024 . In 2017 , Estée Lauder 's share of the global makeup products market is estimated to be 16.4 percent . The company is a manufacturer and marketer of prestige makeup products , makeup , fragrance and hair products market , with global net sales of over 11.2 billion U.S. dollars .

Example 151:
titleEntities: {'Subject': ['South Korea'], 'Date': ['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[max] templateYLabel[2] templateTitle[3] templateTitle[4] templateYLabel[1] in templateTitleSubject[0] , up from templateYValue[5] templateYLabel[2] in templateXValue[5] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] is expected to reach templateYValue[max] templateYLabel[2] , up from templateYValue[5] templateYLabel[2] 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[max] thousand templateYLabel[1] are templateTitle[0] to have templateTitle[4] by templateXValue[max] 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 Malaysia in 2015 , 2030 and 2050 . According to the source , approximately 240 thousand people are Projected to have Malaysia by 2050 in Malaysia .

Example 153:
titleEntities: {'Subject': ['Qatar'], 'Date': ['2019']}
title: Unemployment rate in Qatar 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['0.15', '0.14', '0.14', '0.14', '0.16', '0.19', '0.27', '0.48', '0.56', '0.45', '0.31', '0.31', '0.52', '0.87', '1.21', '1.48', '1.53', '1.51', '1.46', '1.51', '1.57']

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

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

Example 154:
titleEntities: {'Subject': ['Twitter'], 'Date': ['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[0] to templateTitleDate[1] . At the end of the most recently reported year , the microblogging company employed templateYValue[max] people , up from templateYValue[1] people in the previous year . templateTitleSubject[0] 's corporate demography In 2018 , the majority of templateTitleSubject[0] 's templateYLabel[1] were male with a share of 59.8 percent and of a white ethnicity with 42.3 percent .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] around the world from 2018 to 2019 . In the end of templateTitle[5] , there were about templateYValue[max] templateTitleSubject[0] templateYLabel[1] templateTitle[5] in the templateTitleSubject[1] .
generated: This statistic shows the Number of Twitter employees around the world from 2018 to 2019 . In the end of 2019 , there were about 4900 Twitter employees 2019 in the Twitter .

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

gold: This statistic shows the results of a 2018 survey regarding patriotism in the United States . The respondents were asked how proud they are to be an American . In 2018 , some 47 percent of survey respondents stated they were extremely proud to be an American .
gold_template: This statistic shows the results of a templateTitleDate[0] survey regarding patriotism in the templateTitle[4] . The templateYLabel[1] were asked how proud they are to be an templateTitleSubject[0] . In templateTitleDate[0] , some templateYValue[max] percent of survey templateYLabel[1] stated they were templateXValue[0] proud to be an templateTitleSubject[0] .

generated_template: 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] percent of Americans consider themselves experts at templateTitle[1] templateTitle[2] . templateYValue[max] percent of Americans stated that they have templateXValue[3] templateXValue[last] to use templateTitle[2] .
generated: This statistic shows the results of a 2018 survey among Americans on the Americans of ' level . The survey shows that 1 percent of Americans consider themselves experts at ' level . 47 percent of Americans stated that they have Only a little No opinion to use level .

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

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

generated_template: The templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] reached templateYValue[0] percent in templateXValue[max] , a small increase from the previous templateXLabel[0] . Most of templateTitleSubject[0] 's population is of working age and employed . About 46 percent of templateTitleSubject[0] 's inhabitants work in the service sector , and another large portion of the population works in agriculture .
generated: The Unemployment rate in Bulgaria reached 4.82 percent in 2019 , a small increase from the previous Year . Most of Bulgaria 's population is of working age and employed . About 46 percent of Bulgaria 's inhabitants work in the service sector , and another large portion of the population works in agriculture .

Example 157:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2009', '2018']}
title: Retail sales value of yogurt & sour milk products in the United Kingdom 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Retail', 'value', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['2984.2', '2997.6', '3011.1', '3033.8', '3063.4', '3030.9', '2941.6', '2856.4', '2773.4', '2633.5']

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

generated_template: The templateTitleSubject[0] is growing in every aspect . Over the last decade , the total templateYLabel[0] templateTitle[7] of templateTitle[3] in the templateTitleSubject[0] have more than quadrupled . In templateXValue[max] they amounted to approximately templateYValue[max] templateYLabel[3] templateYLabel[4] .
generated: The United Kingdom is growing in every aspect . Over the last decade , the total Retail products of yogurt in the United Kingdom have more than quadrupled . In 2018 they amounted to approximately 3063.4 million U.S. .

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] million templateTitle[1] in templateXValue[1] . templateTitle[5] templateTitle[6] allows templateTitleSubject[0] citizens to enroll , switch plans , and get subsidies on various plans under the Affordable Care Act .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] and templateTitle[4] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In that year , most templateTitle[2] templateYLabel[1] and templateTitle[4] occurred in templateXValue[0] - a total of templateYValue[max] . In templateXValue[last] , no templateTitle[2] templateYLabel[1] and templateTitle[4] occurred in templateTitleDate[0] .
generated: The statistic shows the Number of during signups and Obamacare in the Obamacare U.S. in 2019 , enrollment State . In that year , most during signups and Obamacare occurred in Florida - a total of 1783304 . In Alaska , no during signups and Obamacare occurred in 2019 .

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

gold: This graph shows the real value added to the Gross Domestic Product ( GDP ) of Michigan in 2018 , by industry . In 2018 , the construction industry added 16.71 billion chained 2012 U.S. dollars of value to the total state GDP .
gold_template: This graph shows the templateXValue[1] templateYLabel[1] templateYLabel[2] to the Gross Domestic Product ( templateTitle[1] ) of templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[7] templateXLabel[0] templateYLabel[2] templateYValue[7] templateYLabel[3] templateYLabel[4] 2012 templateYLabel[6] templateYLabel[7] of templateYLabel[1] to the total state templateTitle[1] .

generated_template: This graph shows the templateXValue[0] templateYLabel[1] templateYLabel[2] to the Gross Domestic Product ( templateTitle[1] ) of templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[9] templateXLabel[0] templateYLabel[2] around templateYValue[9] templateYLabel[3] templateYLabel[4] 2012 templateYLabel[6] templateYLabel[7] of templateYLabel[1] to the state templateTitle[1] .
generated: This graph shows the Manufacturing value added to the Gross Domestic Product ( GDP ) of Michigan in 2018 , by Industry . In 2018 , the 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] percent of surveyed templateTitle[4] stated that templateXValue[0] most influenced their templateTitle[6] about templateTitle[8] .

generated_template: The statistic shows 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] percent 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 shows the Influence of friends people in the American would be willing to spend on their teenagers ' in 2012 according to a Statista survey . 38 percent of respondents said that they would be willing to spend Parents to 25 regarding dollars on teenagers ' .

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

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

generated_template: The statistic shows data on the templateTitle[0] of templateXValue[last] genres in the templateTitle[4] as of 2016 . During the survey , templateYValue[7] percent of templateYLabel[1] stated they watched templateXValue[7] templateXValue[last] shows .
generated: The statistic shows data on the functions of KPO genres in the 2017 as of 2016 . During the survey , 12 percent of respondents stated they watched HR BPO KPO shows .

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

gold: The statistic shows the total sales of sleeping bags in the United States from 2010 to 2011 . In 2010 , sales of sleeping bags amounted to 191.76 million U.S. dollars .
gold_template: The statistic shows the total templateYLabel[1] of templateTitle[4] templateTitle[5] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYLabel[1] of templateTitle[4] templateTitle[5] amounted to templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitleSubject[0] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[1] templateTitle[2] between templateXValue[min] and templateXValue[max] . It is expected that around templateYValue[max] templateYLabel[2] templateTitle[4] templateTitle[5] templateYLabel[1] will be in use by templateXValue[max] .
generated: The statistic shows the U.S. sleeping bags 2011 equipment sales between 2010 and 2011 . It is expected that around 210.38 million sleeping bags sales will be in use by 2011 .

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

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

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . As one of the biggest economies in Southeast Asia , templateTitleSubject[0] is an attractive destination for workers from neighboring countries seeking better wages and employment . Indonesians make up the majority of templateYLabel[1] With their shared culture , religion and a very similar language , Indonesia was the single biggest contributor of templateTitleSubject[0] .
generated: In 2018 , the Average sales in Outback Steakhouse amounted to approximately 3580 thousand U.S. dollars . As one of the biggest economies in Southeast Asia , Outback Steakhouse is an attractive destination for workers from neighboring countries seeking better wages and employment . Indonesians make up the majority of sales With their shared culture , religion and a very similar language , Indonesia was the single biggest contributor of Outback Steakhouse .

Example 166:
titleEntities: {'Subject': ['Chicago Blackhawks'], 'Date': ['2019']}
title: Franchise value of the Chicago Blackhawks 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1085', '1050', '1000', '925', '925', '825', '625', '350', '306', '300', '258', '205', '179', '168']

gold: This graph depicts the value of the Chicago Blackhawks franchise of the National Hockey League from 2006 to 2019 . In 2019 , the franchise had an estimated value of 1.09 billion U.S. dollars .
gold_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] franchise of the National Hockey League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the total 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 was at templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the total Franchise value of the Chicago Blackhawks Yankees from 2006 to 2019 . In 2019 , the Franchise value of the Chicago Blackhawks Yankees was at 1085 million 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 templateTitle[4] . In that year , templateXValue[0] was perceived as the least corrupt templateXLabel[0] with an templateYLabel[0] templateYLabel[1] of templateYValue[max] out of 100 .

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

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

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

generated_template: This statistic 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] percent of templateTitleSubject[0] 's templateYLabel[0] coming from Germany.IKEAIKEA is an internationally known home furnishing retailer . It has grown rapidly since it was founded in 1943 .
generated: This statistic shows the Metro Group 's sales of Metro Group 2018/2019 in . In that year , Western Europe (excluding Germany) was the Metro 's Country of Metro Group 2018/2019 , with 8885 percent of Metro Group 's Sales coming from Germany.IKEAIKEA is an internationally known home furnishing retailer . It has grown rapidly since it was founded in 1943 .

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 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 Nigeria from 1994 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a Year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

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

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

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

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

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] templateTitle[4] templateTitle[5] in the middle of templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] in templateXValue[0] was templateYValue[max] percent in the middle of 2014.The templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] arises from the birth templateYLabel[1] minus the death templateYLabel[1] and without including the effects of migration.Population growthAs shown in the statistic above , the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] continues to increase on almost every templateTitle[5] in 2013.Due to medical advances , better living conditions and the increase of agricultural productivity the world templateTitle[2] is continuously rising . The development of the world templateTitle[2] from 1950 to 2030 is estimated to be tripled according to United Nations ' data.The majority of the world templateTitle[2] lives in templateXValue[4] , but the templateTitle[2] in templateXValue[0] is forecasted to rise from 1,031 in year 2010 up to 4,185 in year 2100 .
generated: The statistic shows the Number hostages of hostages taken by region in the middle of . The Number hostages of hostages taken in Africa was 2651 percent in the middle of 2014.The Number hostages of hostages taken arises from the birth hostages minus the death hostages and without including the effects of migration.Population growthAs shown in the statistic above , the Number hostages of hostages taken continues to increase on almost every region in 2013.Due to medical advances , better living conditions and the increase of agricultural productivity the world hostages is continuously rising . The development of the world hostages from 1950 to 2030 is estimated to be tripled according to United Nations ' data.The majority of the world hostages lives in Western Hemisphere , but the hostages in Africa is forecasted to rise from 1,031 in year 2010 up to 4,185 in year 2100 .

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

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

generated_template: This statistic shows the results of the Football templateTitle[5] templateTitle[6] from 1961 to templateTitle[8] . templateXValue[0] have won the templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[1] , having lifted the trophy a total of templateYValue[max] times .
generated: This statistic shows the results of the Football generated by from 1961 to influencers . Valentino (Demi Lovato) have won the engagement generated by social , having lifted the trophy a total of 1385467 times .

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

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

generated_template: The statistic 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 percent templateYLabel[2] to the templateYLabel[3] templateYLabel[4] . templateYLabel[0] rates and the financial crisis Due to relatively stagnant worker wages as well as a hesitation from banks to so easily distribute loans to the ordinary citizen , templateYLabel[0] has remained considerably low .
generated: The statistic lists the 20 Euro inflation the rate Inflation rate in 2019 . In 2019 , Latvia ranked 1st inflation a negative Inflation rate of about 27.6 percent compared to the previous year . Inflation rates and the financial crisis Due to relatively stagnant worker wages as well as a hesitation from banks to so easily distribute loans to the ordinary citizen , Inflation has remained considerably low .

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

gold: The price of one pound of seedless grapes ( Thompson ) in the United States was 2.39 U.S. dollars in 2019 . U.S. seedless grape prices peaked in 2009 at 3.14 U.S. dollars per pound . Grape Production in the United States Grapes have the highest production volume of any fruit in the United States .
gold_template: The templateYLabel[0] of one templateYLabel[4] of seedless templateTitle[3] ( Thompson ) in the templateTitle[0] was templateYValue[22] templateYLabel[1] templateYLabel[2] in templateXValue[max] . templateYLabel[1] seedless grape prices peaked in templateXValue[12] at templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Grape Production in the templateTitle[0] templateTitle[3] have the highest production volume of any fruit in the templateTitle[0] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] in templateTitleSubject[0] in selected years from templateXValue[min] to templateXValue[max] . In templateXValue[min] , German templateTitle[1] templateTitle[2] had a templateTitle[1] templateTitle[0] of roughly templateYValue[max] templateYLabel[2] templateYLabel[1] . templateYValue[10] years later , templateTitle[0] figures amounted to less than templateYValue[8] templateYLabel[2] templateYLabel[1] .
generated: This statistic shows the U.S. of retail price in U.S. in selected years from 1995 to 2019 . In 1995 , German retail price had a retail U.S. of roughly 3.14 dollars U.S. . 2.72 years later , U.S. figures amounted to less than 2.76 dollars U.S. .

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[max] percent of the Bolivian population accessed the templateYLabel[3] , up from templateYValue[11] percent in templateXValue[11] .

generated_template: templateYValue[max] percent of the population of the templateTitleSubject[0] ( templateTitleSubject[1] ) used the templateYLabel[3] in templateXValue[max] , according to figures from Eurostat and the International Telecommunications Union . The figure is unchanged from templateXValue[1] , marking the first templateXLabel[0] without growth since templateXValue[10] . templateYLabel[3] users in Great Britain44.1 million people used the templateYLabel[3] daily in Great Britain during 2018 according to the templateTitleSubject[0] 's ( templateTitleSubject[1] ) Office for National Statistics ( ONS ) , breaking the record set in templateXValue[1] .
generated: 43.83 percent of the population of the Bolivia ( Bolivia ) used the internet in 2017 , according to figures from Eurostat and the International Telecommunications Union . The figure is unchanged from 2016 , marking the first Year without growth since 2007 . internet users in Great Britain44.1 million people used the internet daily in Great Britain during 2018 according to the Bolivia 's ( Bolivia ) Office for National Statistics ( ONS ) , breaking the record set in 2016 .

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

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

generated_template: The 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 templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Life expectancy in the China from 1960 to 2017 . In 2017 , the Life expectancy in the China was at 78.83 birth years .

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

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

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

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: As of the third quarter of templateTitleDate[0] , it was found that templateYValue[max] percent of templateTitleSubject[0] users in the templateTitleSubject[1] accessed the photo sharing app templateXValue[0] a templateXValue[0] . A further templateYValue[1] percent of templateYLabel[1] claimed that they used templateTitleSubject[0] on a templateXValue[1] basis .
generated: As of the third quarter of 16 , it was found that 51 percent of Great Britain users in the Great Britain accessed the photo sharing app Family a Family . A further 14 percent of respondents claimed that they used Great Britain on a Friends basis .

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

gold: Sportswear company Puma has increased its research and development expenditure since 2016 , with 61.7 million euros spent by the department in 2019 – an increase of 7.7 million euros on the previous year . What is the role of research and development ? As of December 2019 , Puma had 14,332 employees worldwide , of which 986 were employed in the field of research and development/product management . In a sportswear market that is constantly changing , Puma 's research and development team is responsible for providing insights into product design and new technologies .
gold_template: Sportswear company templateTitleSubject[0] has increased its research and development templateYLabel[1] since templateXValue[3] , with templateYValue[max] templateYLabel[2] templateYLabel[3] spent by the department in templateXValue[max] – an increase of 7.7 templateYLabel[2] templateYLabel[3] on the previous templateXLabel[0] . What is the role of research and development ? As of 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: The statistic shows the templateYLabel[0] templateYLabel[1] of the sports company templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] had a templateYLabel[0] templateYLabel[1] of templateYValue[last] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] .
generated: The statistic shows the R&D expenditure of the sports company Puma from 2013 to 2019 . Puma had a R&D expenditure of 47.9 million euros in 2019 .

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

gold: The statistic shows the growth in real GDP in Azerbaijan from 2014 to 2018 , with projections up until 2024 . In 2018 , Azerbaijan 's real gross domestic product increased by around 0.97 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] increased by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from between templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . templateTitleSubject[0] 's economy on the rise templateTitleSubject[0] is a nation with a growing economy and a steadily increasing population .
generated: The statistic shows the growth in real GDP in Azerbaijan from between 2014 to 2018 , with projections up until 2024 . In 2018 , Azerbaijan 's real Gross domestic product grew by around 0.97 percent compared to the previous Year . Azerbaijan 's economy on the rise Azerbaijan is a nation with a growing economy and a steadily increasing population .

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

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

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] 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] templateYLabel[0] surplus amounted to around templateYValue[max] percent 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 deficit . In 2018 , Argentina 's Budget surplus amounted to around -2.68 percent 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] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitleDate[0] . That year , the templateXValue[1] and templateXValue[2] were the next two largest templateTitle[4] of textiles , with imports of templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] and templateYValue[2] templateYLabel[2] templateYLabel[3] templateYLabel[4] respectively . What are textiles ? Textiles are the materials which are made from natural or synthetic fibers , thin threads or filaments .

generated_template: templateTitle[4] templateYLabel[1] are popular among people that want to enhance select features of their body through surgical and nonsurgical methods . As of templateTitleDate[0] , the templateXValue[0] had the templateTitle[2] templateYLabel[0] of templateTitle[4] templateYLabel[1] during that year templateTitle[1] over 4.3 million templateYLabel[1] . templateXValue[1] had the second templateTitle[2] templateYLabel[0] of templateTitle[4] templateYLabel[1] templateTitle[1] almost templateYValue[1] million templateYLabel[1] during that year .
generated: importers value are popular among people that want to enhance select features of their body through surgical and nonsurgical methods . As of 2018 , the European Union (28) had the 10 Import of importers value during that year leading over 4.3 million value . United States had the second 10 Import of importers value leading almost 30 million value during that year .

Example 183:
titleEntities: {'Subject': ['Los Angeles Angels'], 'Date': ['2002', '2019']}
title: Franchise value of the Los Angeles Angels 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1900', '1800', '1750', '1340', '1300', '775', '718', '656', '554', '521', '509', '500', '431', '368', '294', '241', '225', '195']

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

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] Royals templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Jays are owned by Rogers Communications , who bought the templateYLabel[0] for 137 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 2000 .
generated: This graph depicts the value of the Los Angeles Angels Royals Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 1900 million U.S. dollars . The Los Angeles Angels Jays are owned by Rogers Communications , who bought the Franchise for 137 million U.S. dollars in 2000 .

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

gold: This graph depicts the average ticket price for Detroit Pistons games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the average ticket price was 46.23 U.S. dollars .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[9] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] - additional information The templateTitleSubject[0] are an American professional basketball team playing in the National Basketball Association 's ( templateTitleSubject[1] ) Southeast Division of the Eastern Conference .
generated: This graph depicts the Average ticket price for Detroit Pistons games of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the Average ticket price was 31.42 U.S. dollars . Detroit Pistons - additional information The Detroit Pistons are an American professional basketball team playing in the National Basketball Association 's ( NBA ) Southeast Division of the Eastern Conference .

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

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

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] is the share of dwellings that were owner-occupied . The templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] is expected to be templateYValue[0] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[max] .
generated: The statistic presents the Average age years in U.S. from 2013–2015 to 2013–2015 . The Average age years is the share of dwellings that were owner-occupied . The Average age years in U.S. is expected to be 14.64 years in 2013–2015 .

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

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

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] railroad accidents from templateXValue[min] to templateXValue[max] . In templateXValue[max] , some templateYValue[0] people died in templateTitle[2] accidents in the templateTitle[1] , an increase from templateYValue[1] templateYLabel[1] in templateXValue[min] .
generated: This statistic shows the Number of companies in United Kingdom railroad accidents from 2004 to 2017 . In 2017 , some 436 people died in market accidents in the insurance , an increase from 464 companies in 2004 .

Example 187:
titleEntities: {'Subject': ['Los Angeles Rams', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Los Angeles Rams ( NFL ) 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['3800', '3200', '3000', '2900', '1450', '930', '875', '780', '775', '779', '913', '929', '908', '841', '757', '708', '602', '544']

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

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] Rays templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Rays are owned by Stuart Sternberg , who bought the templateYLabel[0] for 100 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[15] .
generated: This graph depicts the value of the Los Angeles Rams Rays Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 3800 million U.S. dollars . The Los Angeles Rams Rays are owned by Stuart Sternberg , who bought the Franchise for 100 million U.S. dollars in 2004 .

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

gold: The statistic displays the value added of the U.S. manufacturing industry as a percentage of GDP between 2007 and 2018 . In 2018 , this sector accounted for 11.4 % of the nation 's Gross Domestic Product , making it one of the largest sectors of the U.S. economy .
gold_template: The statistic displays the templateYLabel[0] templateYLabel[1] of the U.S. templateTitle[2] industry as a templateYLabel[2] of templateYLabel[3] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , this sector accounted for templateYValue[0] % 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] in templateTitleSubject[0] reached the lowest point in the last thirteen years when it was down to templateYValue[min] . The templateYLabel[0] templateYLabel[1] templateYLabel[2] is defined as the number of live births in a given geographical area in a given time period , at templateYLabel[3] .
generated: In 2018 , the Value added percentage in GDP reached the lowest point in the last thirteen years when it was down to 11.1 . The Value added percentage is defined as the number of live births in a given geographical area in a given time period , at GDP .

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

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

generated_template: 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] percent . 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 percent . 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] percent of responding parents stated that they had rules about templateXValue[0] templateXValue[1] their children were allowed to templateXValue[1] .

generated_template: This statistic presents the templateTitle[2] of users in the templateTitleSubject[0] accessing templateTitle[0] . As of the third quarter of templateTitleDate[0] , it was found that templateYValue[max] percent 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 digital of users in the U.S. accessing U.S. . As of the third quarter of 2015 , it was found that 79 percent of teen U.S. users accessed the social platform What sites he/she can access a What sites he/she can access . U.S. is the most popular social media site in the teen .

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[min] to templateXValue[max] , at year-end . In templateXLabel[0] templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] stood at templateYValue[max] , reflecting a 44.73 percent increase from the base year of 1999 . The average wages garnered in select countries around the world based on purchasing power can be accessed here .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from the fiscal templateXLabel[0] of templateTitle[5] to the fourth templateXLabel[0] templateXLabel[1] . In the templateXLabel[0] templateXLabel[1] of templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Chained Consumer of Chained from the fiscal December of urban to the fourth December value . In the December value of 2019 , Chained 's Chained Consumer amounted to approximately 144.73 Price Index (1999=100) .

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

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

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Median Household income in Texas from 1990 to 2018 . In 2018 , the Median Household income in Texas amounted to 59785 U.S. 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] templateYLabel[1] British pounds going toward templateTitle[2] and templateTitle[3] templateTitle[4] . In templateXValue[1] , templateYValue[min] templateYLabel[1] British pounds were spent on templateTitle[2] and templateTitle[3] templateTitle[4] in the templateTitleSubject[0] .

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

Example 194:
titleEntities: {'Subject': ['Czech Republic'], 'Date': ['2019']}
title: Unemployment rate in the Czech Republic 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['2.47', '2.4', '2.89', '3.95', '5.05', '6.11', '6.95', '6.98', '6.71', '7.28', '6.66', '4.39', '5.32', '7.15', '7.93', '8.21', '7.54', '7.02', '7.99', '8.76', '8.49']

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

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

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

gold: The statistic shows the budget balance in Italy from 2014 to 2018 , with projections up until 2024 . A positive value indicates a state surplus ; a negative value , a state deficit . In 2018 , the state deficit of Italy was around 37.51 billion euros .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a state surplus ; a negative value , a state deficit . In templateXValue[6] , the state deficit of templateTitleSubject[0] was around 37.51 templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] 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 templateYLabel[2] 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 increase in the price of templateTitle[0] templateTitle[2] templateTitle[3] in templateTitleSubject[0] . At the start of the decade templateTitle[3] paid less than templateYValue[17] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . However , this increased by more than five templateYLabel[0] templateYLabel[1] by the second half of templateXValue[11] .

generated_template: Since templateXValue[17] , the household price of templateTitle[0] in templateTitleSubject[0] has seen little change , increasing from templateYValue[18] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] to templateYValue[0] templateYLabel[0] templateYLabel[1] in the first semester of templateXValue[0] . Looking at the figures , it can be seen that on average templateTitle[1] were cheaper in the second half of each templateXLabel[0] . A noticeable exception was in the second half of templateXValue[13] , when templateTitle[1] increased by 1.11 templateYLabel[0] templateYLabel[1] compared the first half of the templateXLabel[0] .
generated: Since 2010 S2 , the household price of Electricity in Germany has seen little change , increasing from 23.75 Euro cents per kilowatt-hour to 30.88 Euro cents in the first semester of 2019 S1 . Looking at the figures , it can be seen that on average prices were cheaper in the second half of each Year . A noticeable exception was in the second half of 2012 S2 , when prices increased by 1.11 Euro cents compared the first half of the Year .

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

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

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Cyprus from 2014 to 2018 , with projections up until 2024 . In 2018 , Cyprus 's real Gross domestic product grew by around 3.88 percent compared to the previous Year .

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

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

generated_template: The statistic presents 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] percent from templateXValue[3] to templateXValue[2] .
generated: The statistic presents the Football average millions rate of the NFL Viewers millions from 2015 to 2019 . The Viewers millions is expected to grow by 10.94 percent from 2016 to 2017 .

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

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

generated_template: In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was approximately templateYValue[max] 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 2018 , the domestic Passenger enplanements (in in U.S. was approximately 777.91 millions) . In 2018 , this was forecasted to reach around 777.91 millions) enplanements (in . U.S. 's domestic meat Passenger in 2017 was above the OECD average of that Year .

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

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

generated_template: In templateXValue[max] , California-based full-service restaurant ( FSR ) chain The templateTitleSubject[0] generated approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] worldwide , up from templateYValue[1] templateYLabel[1] the previous templateXLabel[0] . Leading FSR chains With a templateYLabel[0] of over templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , The templateTitleSubject[0] stands firmly alongside other top FSR chains in the country such as Applebee 's , Olive Garden , Denny 's and more . Fellow American casual dining restaurant chain Applebee 's accounted for 4.12 templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateYLabel[2] , making it the leading full-service restaurant chain in the country in terms of templateYLabel[0] .
generated: In 2018 , California-based full-service restaurant ( FSR ) chain The Bloomin Brands generated approximately 4.44 billion U.S. dollars in Revenue worldwide , up from 4.21 billion the previous Year . Leading FSR chains With a Revenue of over 4.44 billion U.S. dollars , The Bloomin Brands stands firmly alongside other top FSR chains in the country such as Applebee 's , Olive Garden , Denny 's and more . Fellow American casual dining restaurant chain Applebee 's accounted for 4.12 billion U.S. dollars in the U.S. , making it the leading full-service restaurant chain in the country in terms of Revenue .

Example 201:
titleEntities: {'Subject': ['Autonomous'], 'Date': ['2015', '2030']}
title: Autonomous driving sensor components - global market size 2015 to 2030
X_Axis['Year']: ['2030', '2025', '2020', '2015']
Y_Axis['Market', 'size', 'in', 'billion', 'U.S.', 'dollars']: ['25.0', '15.0', '4.0', '0.4']

gold: This statistic represents the projected size of the global market for autonomous driving sensor components from 2015 to 2030 . In 2025 , the market for autonomous driving sensor components is projected to be sized at 15 billion U.S. dollars in an optimistic scenario .
gold_template: This statistic represents the projected templateYLabel[1] of the templateTitle[4] templateYLabel[0] for templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateYLabel[0] for templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] is projected to be sized at templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in an optimistic scenario .

generated_template: The statistic provides information on brands ' templateYLabel[0] on templateTitleSubject[0] templateTitle[3] and templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . According to the estimates , brands will invest templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in marketing and sponsoring templateTitleSubject[0] related events in templateXValue[1] .
generated: The statistic provides information on brands ' Market on Autonomous components and global market from 2015 to 2030 . According to the estimates , brands will invest 15.0 size billion U.S. in marketing and sponsoring Autonomous related events in 2025 .

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[max] templateYLabel[3] in templateXValue[max] .

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

Example 203:
titleEntities: {'Subject': ['U.S. UHNW'], 'Date': ['2014']}
title: Wealth in the U.S. - UHNW ( super rich ) population in 2014 , by city
X_Axis['State']: ['New_York', 'San_Francisco', 'Los_Angeles', 'Chicago', 'Washington', 'Houston', 'Dallas', 'Toronto', 'Atlanta', 'Seattle']
Y_Axis['Number', 'of', 'Ultra-High-Net-Worth', 'people']: ['8655', '5460', '5135', '2885', '2730', '2545', '2330', '1840', '1230', '1095']

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

generated_template: The templateTitleSubject[0] templateXLabel[0] with the highest templateYLabel[0] of templateTitle[5] templateTitle[6] in templateTitleDate[0] was templateXValue[0] with templateYValue[max] templateYLabel[1] head of chicken . The templateTitle[1] is also the second leading producer of eggs worldwide . Egg industry The global production volume of eggs reached 80 templateYLabel[1] metric tons in 2017 , up from 55.1 templateYLabel[1] metric tons in 2000 .
generated: The U.S. UHNW State with the highest Number of population 2014 in 2014 was New York with 8655 Ultra-High-Net-Worth head of chicken . The U.S. is also the second leading producer of eggs worldwide . Egg industry The global production volume of eggs reached 80 Ultra-High-Net-Worth metric tons in 2017 , up from 55.1 Ultra-High-Net-Worth metric tons in 2000 .

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

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] in templateTitle[4] templateTitleSubject[0] templateTitle[6] in templateTitleDate[0] . The survey was conducted among 15 to 20 year-olds and is part of the Face of Global sex Report by Durex . According to the source , the respondents in templateXValue[0] had templateYValue[max] sex templateYLabel[2] on average .
generated: The statistic shows the Gross revenue of music million in North North America 2019 in 2019 . The survey was conducted among 15 to 20 year-olds and is part of the Face of Global sex Report by Durex . According to the source , the respondents in The Rolling Stones had 177.8 sex million on average .

Example 205:
titleEntities: {'Subject': ['Samoa'], 'Date': ['2019']}
title: Unemployment rate in Samoa 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['8.46', '8.33', '8.32', '8.61', '8.62', '8.72', '8.8', '8.75', '5.68', '5.52', '5.35', '4.77', '4.75', '4.96', '5.14', '5.14', '5.15', '5.11', '4.96', '4.72', '4.43']

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

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

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

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

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

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

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

generated_template: The graph shows the templateYLabel[0] templateYLabel[1] for templateTitle[2] templateTitle[3] in the templateYLabel[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] templateTitle[3] sold for an templateYLabel[0] templateYLabel[1] of templateYValue[3] templateYLabel[2] templateYLabel[3] , a templateYLabel[1] increase of 10 percent compared to templateXValue[4] . More statistics and facts on recreational boating
generated: The graph shows the Production million for production U.S. in the cubic from 2003 to 2016 . In 2016 , production U.S. sold for an Production million of 703 cubic feet , a million increase of 10 percent compared to 2012 . More statistics and facts on recreational boating

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

gold: This statistic displays a forecast of the number of mobile internet users in the UK from third quarter 2013 to second quarter 2016 . It is forecast that there will be 43 million mobile internet users as of quarter two 2016 .
gold_template: This statistic displays a forecast of the templateTitle[0] of templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitleSubject[1] from third templateXLabel[0] templateXValue[10] to second templateXLabel[0] templateXValue[0] . It is forecast that there will be templateYValue[max] templateYLabel[1] templateTitle[1] templateTitle[2] templateYLabel[0] as of templateXLabel[0] two templateXValue[0] .

generated_template: This statistic illustrates the templateTitle[1] templateTitle[2] templateTitle[3] sales of templateTitle[0] Inc. as of the fourth templateXLabel[0] of templateTitle[6] . During that period , the C2C e-commerce company generated templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the corresponding templateXLabel[0] of the previous year . This represents a 32.8 percent year-on-year growth .
generated: This statistic illustrates the mobile internet users sales of Number Inc. as of the fourth Quarter of UK . During that period , the C2C e-commerce company generated Users of 42.77 millions , up from 42.03 millions in the corresponding Quarter of the previous year . This represents a 32.8 percent year-on-year growth .

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

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

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

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

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

generated_template: templateTitle[0] templateTitle[1] is a templateTitle[4] empire , and templateTitleSubject[1] templateTitle[0] templateTitle[4] is its heartland . In templateXValue[max] , the British templateTitle[0] fashion retailer 's store portfolio included templateYValue[max] locations in the templateTitleSubject[1] . In addition to its own-brand templateYLabel[1] , templateTitle[0] templateTitle[1] 's templateTitle[0] templateTitle[4] business also includes templateYLabel[1] operated by the branded clothing company USC .
generated: Domestic travel is a 2012 empire , and France Domestic 2012 is its heartland . In 2028 , the British Domestic fashion retailer 's store portfolio included 131.4 locations in the France . In addition to its own-brand billion , Domestic travel 's Domestic 2012 business also includes billion operated by the branded clothing company USC .

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[max] templateTitle[0] templateTitle[1] templateTitle[2] poisoning templateYLabel[2] 100,000 people in the templateTitle[5] .

generated_template: This statistic shows the templateYLabel[0] of goods and services from the templateTitle[0] between templateXValue[min] and templateXValue[max] , as a templateYLabel[1] of templateYLabel[2] . In templateXValue[max] , the templateYLabel[1] of templateTitle[0] templateYLabel[0] of the templateYLabel[2] amounted to templateYValue[0] percent .
generated: This statistic shows the Death of goods and services from the Deaths between 1950 and 2017 , as a rate of per . In 2017 , the rate of Deaths Death of the per amounted to 20.1 percent .

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

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

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

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

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

generated_template: This statistic gives information on templateTitleSubject[0] 's templateYLabel[0] templateTitle[2] templateTitle[3] templateTitle[4] from the second templateXLabel[0] of 2012 to the second templateXLabel[0] of templateTitleDate[0] . As of the last reported templateXLabel[0] , the website 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] with 75 percent of templateTitle[2] templateTitle[3] sales being generated via mobile .
generated: This statistic gives information on Alibaba 's Percentage share gross merchandise from the second Quarter of 2012 to the second Quarter of 2016 . As of the last reported Quarter , the website 's Percentage mobile amounted to 75 GMV with 75 percent of share gross sales being generated via mobile .

Example 214:
titleEntities: {'Subject': ['Latvia'], 'Date': ['2010', '2019']}
title: Electricity prices for households in Latvia 2010 - 2019 , semi-annually
X_Axis['Year']: ['2019_S1', '2018_S2', '2018_S1', '2017_S2', '2017_S1', '2016_S2', '2016_S1', '2015_S2', '2015_S1', '2014_S2', '2014_S1', '2013_S2', '2013_S1', '2012_S2', '2012_S1', '2011_S2', '2011_S1', '2010_S2', '2010_S1']
Y_Axis['Euro', 'cents', 'per', 'kilowatt-hour']: ['16.29', '15.11', '15.31', '15.82', '15.86', '16.24', '16.28', '16.5', '16.35', '13.01', '13.65', '13.58', '13.78', '13.69', '13.82', '13.42', '11.68', '10.48', '10.49']

gold: This statistic shows the electricity prices for household end users in Latvia semi-annually from 2010 to 2019 . In the second half of 2018 , the average electricity price for households was 15.11 euro cents per kWh .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitleSubject[0] templateTitle[7] from templateXValue[17] to templateXValue[0] . In the second half of templateXValue[1] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .

generated_template: Since templateXValue[17] , the household price of templateTitle[0] in templateTitleSubject[0] has seen little change , increasing from templateYValue[18] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] to templateYValue[0] templateYLabel[0] templateYLabel[1] in the first semester of templateXValue[0] . Looking at the figures , it can be seen that on average templateTitle[1] were cheaper in the second half of each templateXLabel[0] . A noticeable exception was in the second half of templateXValue[13] , when templateTitle[1] increased by 1.11 templateYLabel[0] templateYLabel[1] compared the first half of the templateXLabel[0] .
generated: Since 2010 S2 , the household price of Electricity in Latvia has seen little change , increasing from 10.49 Euro cents per kilowatt-hour to 16.29 Euro cents in the first semester of 2019 S1 . Looking at the figures , it can be seen that on average prices were cheaper in the second half of each Year . A noticeable exception was in the second half of 2012 S2 , when prices increased by 1.11 Euro cents compared the first half of the Year .

Example 215:
titleEntities: {'Subject': ['Luxembourg'], 'Date': ['2010', '2019']}
title: Electricity prices for households in Luxembourg 2010 - 2019 , semi-annually
X_Axis['Year']: ['2019_S1', '2018_S2', '2018_S1', '2017_S2', '2017_S1', '2016_S2', '2016_S1', '2015_S2', '2015_S1', '2014_S2', '2014_S1', '2013_S2', '2013_S1', '2012_S2', '2012_S1', '2011_S2', '2011_S1', '2010_S2', '2010_S1']
Y_Axis['Euro', 'cents', 'per', 'kilowatt-hour']: ['17.98', '16.91', '16.71', '16.18', '16.15', '16.98', '16.98', '17.67', '17.67', '17.38', '17.38', '16.46', '16.65', '17.06', '16.96', '16.62', '16.78', '17.47', '17.26']

gold: This statistic shows the electricity prices for household end users in Luxembourg semi-annually from 2010 to 2019 . In the second half of 2018 , the average electricity price for households was 16.91 euro cents per kWh .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitleSubject[0] templateTitle[7] from templateXValue[17] to templateXValue[0] . In the second half of templateXValue[1] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .

generated_template: Since templateXValue[17] , the household price of templateTitle[0] in templateTitleSubject[0] has seen little change , increasing from templateYValue[18] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] to templateYValue[0] templateYLabel[0] templateYLabel[1] in the first semester of templateXValue[0] . Looking at the figures , it can be seen that on average templateTitle[1] were cheaper in the second half of each templateXLabel[0] . A noticeable exception was in the second half of templateXValue[13] , when templateTitle[1] increased by 1.11 templateYLabel[0] templateYLabel[1] compared the first half of the templateXLabel[0] .
generated: Since 2010 S2 , the household price of Electricity in Luxembourg has seen little change , increasing from 17.26 Euro cents per kilowatt-hour to 17.98 Euro cents in the first semester of 2019 S1 . Looking at the figures , it can be seen that on average prices were cheaper in the second half of each Year . A noticeable exception was in the second half of 2012 S2 , when prices increased by 1.11 Euro cents compared the first half of the Year .

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

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

generated_template: There were templateYValue[min] templateTitle[1] templateTitle[2] recorded in templateTitleSubject[0] in templateXValue[max] . templateTitleSubject[0] had been able to decrease the templateYLabel[0] of people fatally injured on its roads by nearly half since templateXValue[min] . templateXValue[7] and templateXValue[3] were the only years in which the templateYLabel[0] of fatal accidents increased .
generated: There were 216 road deaths recorded in Switzerland in 2018 . Switzerland had been able to decrease the Number of people fatally injured on its roads by nearly half since 2006 . 2011 and 2015 were the only years in which the Number of fatal accidents increased .

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

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In its templateXValue[max] fiscal templateXLabel[0] , templateTitleSubject[0] paid out a templateYLabel[0] of approximately templateYValue[0] templateYLabel[3] templateYLabel[1] templateYLabel[2] .
generated: The statistic shows the Operating income billion of Walmart from the fiscal of 2006 to the fiscal of 2019 . In its 2019 fiscal , Walmart paid out a Operating of approximately 21.96 U.S. income billion .

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

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

generated_template: In templateXValue[max] , the templateTitleSubject[0] 's global templateTitle[0] amounted to templateYValue[0] templateYLabel[1] 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] increased about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

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

Example 220:
titleEntities: {'Subject': ['Ethiopia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Ethiopia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['1411.77', '1328.18', '1248.69', '1161.57', '1066.2', '953.18', '852.88', '817.49', '777.29', '702.77', '613.1', '535.26', '493.32', '379.38', '360.83', '397.81', '334.15', '249.21', '198.34', '165.22', '138.55', '120.87', '116.79', '125.72', '129.68', '128.21', '134.23', '152.17', '155.12', '152.66', '151.97', '176.47', '293.35', '287.65', '269.08', '262.23', '257.56', '256.7', '247.98', '246.51', '217.42']

gold: The statistic shows gross domestic product ( GDP ) per capita in Ethiopia from 1984 to 2024 . All figures are estimates . GDP is the total value of all goods and services produced in a country in a year .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . All figures are estimates . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] .

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

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

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

generated_template: This statistic presents 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] percent of reporting templateTitle[3] were templateXValue[0] .
generated: This statistic presents the distribution of Canada cannabis past in 2019 , months Canada . According to cases handled months Canada harassment support group WHOA , 18.4 percent of reporting past were Male .

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

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

generated_template: 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[0] 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] templateYLabel[1] templateYLabel[2] in templateXValue[max] . This figure represents a decrease compared to the peak reached by the templateTitle[3] in templateXValue[3] , when the templateYLabel[0] reported amounted to templateYValue[2] templateYLabel[1] templateYLabel[2] . The reduction in templateYLabel[0] coincided with lower profits for the templateTitle[3] during the same period .

generated_template: In templateXValue[max] , the templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] Group reported a templateYLabel[0] of almost templateYValue[min] templateYLabel[1] templateYLabel[2] . Despite the impressive figure , the templateYLabel[0] of templateXValue[max] represented a decrease for templateTitle[4] compared to the previous years . Indeed , in templateXValue[min] , the first templateXLabel[0] considered in this graph , the templateYLabel[0] of the templateTitle[3] amounted to approximately templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: In 2018 , the Italian fashion company Giorgio Group reported a Turnover of almost 1275 million euros . Despite the impressive figure , the Turnover of 2018 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 templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in Russia . The Brazilian squad was the templateTitleSubject[0] team with the highest market templateYLabel[0] , amounting to templateYValue[max] templateYLabel[1] euros . templateXValue[1] ranked second , with a market templateYLabel[0] of templateYValue[1] templateYLabel[1] euros .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitle[3] in leading countries in templateTitleSubject[0] in templateTitleDate[0] . templateXValue[0] has the highest levels of templateTitle[3] templateYLabel[0] templateTitle[0] templateTitle[1] at templateYValue[max] templateYLabel[1] , followed templateTitle[6] Gemany at templateYValue[1] templateYLabel[1] .
generated: This statistic shows the 2018 FIFA Value of Cup in leading countries in Latin American in 2018 . Brazil has the highest levels of Cup Value 2018 FIFA at 981.0 million , followed Latin Gemany at 699.0 million .

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

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

generated_template: templateTitleSubject[0] generated around templateYValue[0] templateYLabel[1] templateYLabel[2] in templateYLabel[0] in templateXValue[max] . This is a large increase from their templateYLabel[0] in templateXValue[min] , which totaled templateYValue[min] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] Inc. is a leading company in cleaning , sanitizing , pest elimination services , food safety , and infection prevention products and services worldwide .
generated: Vinci Group revenue generated around 43.52 billion euros in Revenue in 2018 . This is a large increase from their Revenue in 2009 , which totaled 30.74 billion euros . Vinci Group revenue Inc. is a leading company in cleaning , sanitizing , pest elimination services , food safety , and infection prevention products and services worldwide .

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

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

generated_template: Data online fashion retailer templateTitleSubject[0] plc is amongst the leading apparel brands with the highest brand value templateTitle[2] , ranking alongside retailers who sell both online and in-store . In the six-year period between templateXValue[min] and templateXValue[max] , templateTitleSubject[0] quadrupled its templateTitle[2] templateYLabel[0] and reached templateYValue[max] templateYLabel[1] British pounds as can be seen in this statistic . templateTitleSubject[0] wins over the EU market templateTitleSubject[0] 's templateYLabel[0] growth is paralleled with its expansion of its shopper base .
generated: Data online fashion retailer Portugal plc is amongst the leading apparel brands with the highest brand value spending , ranking alongside retailers who sell both online and in-store . In the six-year period between 2012 and 2028 , Portugal quadrupled its spending and reached 26.4 billion British pounds as can be seen in this statistic . Portugal wins over the EU market Portugal 's Spending growth is paralleled with its expansion of its shopper base .

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

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

generated_template: 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[6] , 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] percent .
generated: This statistic shows Johnson Johnson & Johnson Johnson 's Market share of the over-the-counter and market worldwide from 2013 to 2021 . In 2019 , Johnson Johnson & Johnson Johnson 's Market share of the care over-the-counter and market is estimated to be 3.85 percent .

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

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

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

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

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

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in 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 .

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

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

generated_template: 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] percent 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 percent 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] Padres from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] dollars.The templateTitleSubject[0] Padres are owned by Ron Fowler and Peter Seidler , who bought the franchise for 600 templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[6] .
generated: The statistic depicts the Revenue of the Green Bay Packers Padres 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 Padres are owned by Ron Fowler and Peter Seidler , who bought the franchise for 600 million U.S. dollars in 2012 .

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

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

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

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

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

generated_template: In templateXValue[max] , the templateTitleSubject[0] produced approximately templateYValue[max] templateYLabel[2] templateYLabel[3] of templateYLabel[0] worldwide , an increase of 53 templateYLabel[2] templateYLabel[3] on the previous templateXLabel[0] . The company templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] has increased by about 75 percent since templateXValue[6] . Asia at the center of templateTitle[3] operations network The templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] has risen year-on-year since templateXValue[6] .
generated: In 2018 , the Denmark produced approximately 2245954 Danish kroner of GDP worldwide , an increase of 53 Danish kroner on the previous Year . The company product GDP million has increased by about 75 percent since 2014 . Asia at the center of GDP operations network The Denmark 's GDP million has risen year-on-year since 2014 .

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[max] percent of the population accessed the templateYLabel[3] , up from templateYValue[min] percent in templateXValue[min] . In templateXValue[2] , templateTitleSubject[0] 's population amounted to 18.11 million inhabitants .

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

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

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

generated_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] was at about templateYValue[min] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Infant mortality in South Sudan from 2008 to 2018 . In 2018 , the Infant mortality in South Sudan was at about 63.7 Deaths per 1,000 live births .

Example 236:
titleEntities: {'Subject': ['Global'], 'Date': ['2007', '2019']}
title: Global mine production of asbestos 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Production', 'in', 'thousand', 'metric', 'tons']: ['1100', '1150', '1170', '1280', '2000', '2020', '2020', '1970', '2030', '2010', '2070', '2090', '2200']

gold: This statistic depicts the total world mine production of asbestos from 2007 to 2019 . The world mine production of asbestos amounted to approximately 2.2 million metric tons in 2007 . In 2019 , total production dropped just above 1.1 million metric tons .
gold_template: This statistic depicts the total world templateTitle[1] templateYLabel[0] of templateTitle[3] from templateXValue[min] to templateXValue[max] . The world templateTitle[1] templateYLabel[0] of templateTitle[3] amounted to approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[min] . In templateXValue[max] , total templateYLabel[0] dropped just above templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic reveals the templateYLabel[0] of pharmaceutical company templateTitleSubject[0] and Company on templateTitle[3] and templateTitle[4] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] is one of the top global pharmaceutical companies and the world templateTitle[2] largest manufacturer and distributor of psychiatric medications . In templateXValue[max] , spending on templateTitle[3] and templateTitle[4] came to around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic reveals the Production of pharmaceutical company Global and Company on asbestos and 2007 from 2007 to 2019 . Global is one of the top global pharmaceutical companies and the world production largest manufacturer and distributor of psychiatric medications . In 2019 , spending on asbestos and 2007 came to around 2200 thousand metric tons .

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

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

generated_template: This statistic gives information on the primary annual of templateTitleSubject[0] for templateTitle[1] templateTitle[2] of targeted templateTitle[3] templateTitle[4] on companies in global markets in templateTitleDate[0] . During the survey period it was found that templateXValue[0] accumulated an average templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in annual templateXValue[2] from a targeted templateTitle[3] attack on a templateXValue[0] .
generated: This statistic gives information on the primary annual of Netflix for content watching of targeted worldwide by on companies in global markets in 2017 . During the survey period it was found that Television accumulated an average 70 time spent in annual Mobile from a targeted worldwide attack on a Television .

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

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

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

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

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

generated_template: This statistic gives information on the templateTitleSubject[0] templateTitle[1] online and tech templateTitle[3] in templateTitleDate[0] , based on templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Food delivery templateXLabel[0] templateXValue[0] went public in 2014 and was ranked first with a templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateYValue[max] percent .
generated: This statistic gives information on the U.S. engagement online and tech U.S. in 2019 , based on Share daily active users . Food delivery Platform Facebook went public in 2014 and was ranked first with a Share daily active users of 63.7 percent .

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

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

generated_template: This statistic shows the results of an opinion poll conducted in templateTitleDate[0] asking Americans to describe templateTitle[0] templateTitleSubject[0] in one templateTitle[3] . In templateTitleDate[0] , templateYValue[max] percent of Americans chose the words `` templateXValue[0] '' to describe templateTitle[0] templateTitleSubject[0] , templateYValue[1] percent chose `` templateXValue[1] '' and templateYValue[2] percent chose `` templateXValue[2] '' . templateTitleSubject[0] 's perception as a presidential candidate In templateTitleDate[0] , when Americans who were asked to describe templateTitle[0] templateTitleSubject[0] in one templateTitle[3] , close to templateYValue[max] percent of Americans chose the words `` templateXValue[0] '' .
generated: This statistic shows the results of an opinion poll conducted in 2019 asking Americans to describe Most in one among . In 2019 , 49.9 percent of Americans chose the words `` Node.js '' to describe Most , 37.4 percent chose `` .NET '' and 23.7 percent chose `` .NET Core '' . Most 's perception as a presidential candidate In 2019 , when Americans who were asked to describe Most in one among , close to 49.9 percent of Americans chose the words `` Node.js '' .

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

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

generated_template: The median templateYLabel[2] of templateYLabel[0] templateYLabel[1] , including fuel , reached a peak templateYLabel[2] of over 423 templateYLabel[3] British pounds in templateXValue[max] . This marked an increase of about 18 templateYLabel[3] British pounds on the previous templateXLabel[0] . For the templateYLabel[0] industry excluding fuel templateYLabel[1] , this figure similarly indicated a record templateYLabel[2] in templateXValue[max] with a significant rise on the templateXLabel[0] prior .
generated: The median million of Net profit , including fuel , reached a peak million of over 423 euros British pounds in 2019 . This marked an increase of about 18 euros British pounds on the previous Year . For the Net industry excluding fuel profit , this figure similarly indicated a record million in 2019 with a significant rise on the Year prior .

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

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

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

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

generated_template: 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 gaming is an annual games prize presented by France games . The award , voted for by games journalists , is given to the male player who was deemed to have played the best games over the previous 12 months . Unsurprisingly , 301.4 of the greatest footballers of all time , FIFA 19 and Red Dead Redemption 2 , top the list of all-time selling .

Example 244:
titleEntities: {'Subject': ['Taiwan'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Taiwan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['31483.8', '29870.22', '28324.43', '26861.07', '25525.81', '24827.9', '25007.75', '24389.68', '22572.7', '22373.56', '22638.92', '21887.99', '21269.61', '20911.64', '19261.67', '16959.78', '18102.95', '17780.93', '16984.54', '16503.31', '15360.72', '14094.37', '13715.53', '13408.38', '14876.88', '13768.27', '12787.26', '13968.1', '13597.25', '13706.01', '12108.75', '11200.81', '10725.7', '9092.11', '8178.15', '7577.05', '6337.5', '5325.22', '4010.11', '3295.11', '3203.47']

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

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

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

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

generated_template: This timeline shows templateTitle[0] and templateTitle[0] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , templateYLabel[1] templateTitle[0] and templateTitle[0] templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[0] templateYLabel[0] 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 templateYLabel[0] templateYLabel[1] templateYLabel[2] and was estimated to employ approximately 75 templateYLabel[0] people , making it templateXValue[16] of the largest industries in the world .
generated: This timeline shows Luggage and Luggage goods store sales in the U.S. from 1992 to 2017 . In 2017 , U.S. Luggage and Luggage 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 Luggage goods from fixed point-of-sale locations.The Luggage Industry in the U.S. StatesIn 2012 , the global apparel market was valued at 1.7 Billion U.S. dollars and was estimated to employ approximately 75 Billion people , making it 1 of the largest industries in the world .

Example 246:
titleEntities: {'Subject': ['Italy'], 'Date': ['2011', '2018']}
title: Italy : number of banks 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Number', 'of', 'banks']: ['505', '538', '604', '643', '664', '684', '706', '740']

gold: The total number of banks and credit institutions in Italy steadily decreased from 2011 to 2018 , sinking from 740 to 505 units . Such a decrease in the number of banks in the country might be due to the financial crisis of the years 2007 and 2008 , which hit Italy quite harshly also in the following years . During the same period , the number of bank branches also decreased steadily , and , as a consequence , the population size per bank branch increased from 1,734 in 2008 to 2,067 in 2016 .
gold_template: The total templateYLabel[0] of templateYLabel[1] and credit institutions in templateTitleSubject[0] steadily decreased from templateXValue[min] to templateXValue[max] , sinking from templateYValue[max] to templateYValue[min] units . Such a decrease in the templateYLabel[0] of templateYLabel[1] in the country might be due to the financial crisis of the years 2007 and 2008 , which hit templateTitleSubject[0] quite harshly also in the following years . During the same period , the templateYLabel[0] of bank branches also decreased steadily , and , as a consequence , the population size per bank branch increased from 1,734 in 2008 to 2,067 in templateXValue[2] .

generated_template: The population in templateTitleSubject[0] has been growing annually and reached 10.23 million in templateXValue[max] , and these individuals were living in almost templateYValue[max] million templateYLabel[1] . As with the growing population , the templateYLabel[0] of templateYLabel[1] were growing annually as well , from 4.3 templateYLabel[1] in templateXValue[min] , up to the 4.7 in templateXValue[max] . Single household without children most common Most of the templateYLabel[1] in templateTitleSubject[0] in templateXValue[max] were single templateYLabel[1] without children and amounted to around 1.8 million .
generated: The population in Italy has been growing annually and reached 10.23 million in 2018 , and these individuals were living in almost 740 million banks . As with the growing population , the Number of banks were growing annually as well , from 4.3 banks in 2011 , up to the 4.7 in 2018 . Single household without children most common Most of the banks in Italy in 2018 were single banks without children and amounted to around 1.8 million .

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

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

generated_template: The templateTitle[0] engine templateTitle[1] of templateTitle[2] registered in templateTitleSubject[0] increased between templateXValue[min] and templateXValue[max] . By templateXValue[max] , templateTitle[3] with a volume of templateYValue[max] templateYLabel[0] centimeters had become the norm , an increase of nearly 40 percent since the beginning of the reporting period . Most common engine templateTitle[1] As of templateXValue[max] , most registered templateTitle[2] had an engine templateTitle[1] either between 51 and 125 templateYLabel[0] centimeters or greater than 1,000 templateYLabel[0] centimeters .
generated: The Trade engine export of volume registered in Trade increased between 1950 and 2018 . By 2018 , worldwide with a volume of 19453.36 Export centimeters had become the norm , an increase of nearly 40 percent since the beginning of the reporting period . Most common engine export As of 2018 , most registered volume had an engine export either between 51 and 125 Export centimeters or greater than 1,000 Export centimeters .

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

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

generated_template: The statistic shows the templateTitle[1] templateYLabel[1] of the templateTitleSubject[0] Group from templateXValue[min] to templateXValue[max] . In the fiscal templateXLabel[0] ending 31 , templateXValue[max] , Chinese e-commerce corporation templateTitleSubject[0] recorded consolidated revenues of templateYValue[max] templateYLabel[2] templateYLabel[3] . This translates to approximately 54.5 templateYLabel[2] U.S. dollars .
generated: The statistic shows the development million of the Research Group from 2010 to 2019 . In the fiscal Year ending 31 , 2019 , Chinese e-commerce corporation Research recorded consolidated revenues of 1911 U.S. dollars . This translates to approximately 54.5 U.S. dollars .

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

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

generated_template: 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 percent ( on average , women 's income in templateXValue[last] was estimated to be 54 percent 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 percent ( on average , women 's income in Guatemala was estimated to be 54 percent 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[min] 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[min] 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[0] to templateXValue[max] . It is expected that the templateYLabel[0] of templateTitleSubject[1] templateTitle[3] will reach some templateYValue[7] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[7] .

generated_template: This statistic 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 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] templateYLabel[2] templateTitleSubject[0] templateTitle[1] , a templateTitle[6] is templateXLabel[1] templateXValue[1] to templateXValue[2] templateXValue[1] templateXLabel[2] templateXLabel[3] .

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

Example 253:
titleEntities: {'Subject': ['Illinois'], 'Date': ['1990', '2018']}
title: Illinois - median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['70145', '64609', '61386', '60413', '54916', '57196', '51738', '50637', '50728', '52870', '53254', '52506', '48671', '48398', '46077', '45153', '42710', '46171', '46064', '46330', '43178', '41283', '39554', '38071', '35081', '32857', '31551', '31884', '32542']

gold: This statistic shows the median household income in Illinois from 1990 to 2018 . In 2018 , the median household income in Illinois amounted to 70,145 U.S. dollars .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the median Household income in Illinois from 1990 to 2018 . In 2018 , the median Household income in Illinois amounted to 70145 U.S. 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 depicts the 25 templateTitle[0] at the templateTitle[6] templateTitle[8] templateTitle[9] templateTitle[7] in templateTitleSubject[0] according to their current templateYLabel[0] / templateTitle[4] templateYLabel[1] . templateXValue[0] of Argentina is the templateTitle[2] valued player templateTitle[1] a templateTitle[4] templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] . templateYLabel[0] values of templateTitle[0] at the templateTitle[8] templateTitle[9] templateTitle[7] - additional information The market/transfer templateYLabel[1] of a football player is determined by a number of statistics , including the player 's age , salary , playing stats and recent performances .
generated: The statistic depicts the 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 professionals . Number values of Number at the 2019 - additional information The market/transfer professionals of a football player is determined by a number of statistics , including the player 's age , salary , playing stats and recent performances .

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

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

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in 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 .

Example 256:
titleEntities: {'Subject': ['Singapore'], 'Date': ['2017', '2023']}
title: Singapore : mobile phone internet users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'mobile', 'phone', 'internet', 'users', 'in', 'millions']: ['4.9', '4.9', '4.8', '4.7', '4.6', '4.5', '4.3']

gold: The statistic shows the number of mobile internet users in Singapore from 2017 to 2023 . In 2017 , 4.3 million people accessed the internet through their mobile phone . In 2023 , this figure is projected to amount to 4.9 million mobile phone internet users .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] templateYLabel[5] people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[max] templateYLabel[5] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

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

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

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to about templateYValue[6] percent 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 68.05 percent of the gross domestic product .

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

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

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] in templateXValue[0] between January and October templateTitleDate[0] , templateTitle[4] templateXLabel[0] of templateTitle[6] . The templateTitle[0] templateYLabel[1] refer to templateYLabel[1] in all accommodation establishments in the respective year . Finnish people made up for the majority of templateTitle[0] templateYLabel[1] , the templateYLabel[0] amounting to nearly templateYValue[max] million .
generated: This statistic shows the Number of Terrorism kidnappings in Somalia between January and October , country of country . The Terrorism kidnappings refer to kidnappings in all accommodation establishments in the respective year . Finnish people made up for the majority of Terrorism kidnappings , the Number amounting to nearly 2527 million .

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

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[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] percent of the templateYLabel[3] .
generated: The 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 as social security . In 2018 , Pakistan 's National debt amounted to around 71.69 percent of the GDP .

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

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

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

Example 261:
titleEntities: {'Subject': ['Azerbaijan'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in Azerbaijan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['57.13', '54.15', '51.52', '49.4', '48.04', '47.17', '46.94', '41.38', '37.83', '50.84', '75.24', '74.16', '69.69', '65.99', '52.91', '44.29', '48.98', '33.09', '21.03', '13.27', '8.68', '7.28', '6.23', '5.48', '5.27', '4.58', '4.28', '3.96', '3.18', '2.42', '2.26']

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

generated_template: The statistic shows 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 Azerbaijan from 1994 to 2018 , with projections up until 2024 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year . GDP is an important indicator of a country 's economic power .

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

gold: This statistic displays the volume of harvested tomato production in Europe in 2018 , by country . In this year , Turkey produced the largest volume of tomatoes at over 12.15 million tonnes , followed by Italy with 6.06 million tonnes produced .
gold_template: This statistic displays the templateYLabel[0] of harvested tomato templateTitle[0] in templateTitleSubject[0] in templateTitleDate[0] , by templateXLabel[0] . In this year , templateXValue[0] produced the largest templateYLabel[0] of templateTitle[2] at over templateYValue[max] templateYLabel[1] templateYLabel[2] , followed by templateXValue[1] with templateYValue[1] templateYLabel[1] templateYLabel[2] produced .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[2] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In that year , the majority of templateTitle[1] templateTitle[2] were based in the templateXValue[0] and Europe . In templateTitleDate[0] , there were templateYValue[max] templateTitle[1] templateTitle[2] in the templateXValue[0] .
generated: The statistic shows the Volume of volume tomatoes tonnes in 2018 , 2018 Country . In that year , the majority of volume tomatoes were based in the Turkey and Europe . In 2018 , there were 12150.0 volume tomatoes in the Turkey .

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

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

generated_template: This statistic 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 increase by 1.7 years , the largest increase predicted between templateXValue[2] and templateXValue[1] at 0.8 years .
generated: This statistic shows the worldwide of the number CFPs in the Growth ( Growth ) from 2008 to 2012 and visualises the predicted 'ageing 2012 ' _ . Over the 20 Year period , the number CFPs is expected to increase by 1.7 years , the largest increase predicted between 2010 and 2011 at 0.8 years .

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

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

generated_template: 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[19] templateYLabel[1] 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] increased by templateYValue[0] percent .

generated_template: Between 2014 and 2020 templateYValue[35] templateTitle[3] prices in the templateTitleSubject[0] increased by templateYValue[0] percent . A period of continuous deflation between 2015 and 2017 preceded a return to a sustained rise of the cost of templateTitle[3] from 2017 onwards . templateYLabel[0] templateYLabel[1] and consumer price index templateYLabel[0] is commonly measured via the consumer price index , which illustrates changes to prices paid by consumers templateTitle[2] a representative basket of goods and services .
generated: Between 2014 and 2020 12.9 value prices in the United Kingdom increased by 12.4 percent . A period of continuous deflation between 2015 and 2017 preceded a return to a sustained rise of the cost of value from 2017 onwards . Percentage change and consumer price index Percentage is commonly measured via the consumer price index , which illustrates changes to prices paid by consumers sales a representative basket of goods and services .

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

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] in templateXValue[max] was templateYValue[max] templateYLabel[3] . Standard of living in templateTitleSubject[0] is one of the so-called BRIC countries , an acronym which stands for Brazil , Russia , templateTitleSubject[0] and China , the four states considered the major emerging market countries .
generated: The statistic shows the Life expectancy at birth in Germany from 2007 to 2017 . The average Life expectancy at birth in Germany in 2017 was 81.09 years . Standard of living in Germany is one of the so-called BRIC countries , an acronym which stands for Brazil , Russia , Germany and China , the four states considered the major emerging market countries .

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

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

generated_template: This graph shows the templateTitle[0] templateTitle[1] in the federal state of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] stood at templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] of land area .
generated: 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 232.0 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[min] in templateTitle[6] countries , marking a loss of about 17,000 of them since templateXValue[min] . In contrast , the turnover figures of the industry demonstrated a consistently positive trend before peaking at 181 billion euros in templateXValue[1] .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] stood at approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] . Despite being the fourth largest country in the world in terms of templateYLabel[0] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] is mitigated by its abundance of land – templateTitleSubject[0] is an archipelago of more than 17 thousand islands sprawled across more than five thousand kilometers from east to west . Java as the heart of templateTitleSubject[0] Despite the many thousands of islands , templateTitleSubject[0] 's templateYLabel[0] , politics , and economy are mostly centered on the island of Java .
generated: In 2018 , the Number companies of European Union stood at approximately 191459 companies . Despite being the fourth largest country in the world in terms of Number , European Union 's Number companies is mitigated by its abundance of land – European Union is an archipelago of more than 17 thousand islands sprawled across more than five thousand kilometers from east to west . Java as the heart of European Union Despite the many thousands of islands , European Union 's Number , politics , and economy are mostly centered on the island of Java .

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

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

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

generated_template: 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[max] , 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] increased , reaching around templateYValue[2] templateYLabel[1] Turkish liras in templateXValue[max] .
generated: This statistic shows the rate 2010 of French and households to 2017 in French from 2010 to 2017 , with a forecast for 2017 . Over this period , the 2010 of the French and households industry to 2017 in French increased , reaching around 14.5 rate Turkish liras in 2017 .

Example 271:
titleEntities: {'Subject': ['Philadelphia', 'NBA'], 'Date': ['2003', '2020']}
title: Franchise value of the Philadelphia 76ers ( NBA ) 2003 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['2000', '1650', '1180', '800', '700', '700', '469', '418', '314', '330', '344', '360', '380', '373', '351', '342', '328', '298']

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

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

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

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

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

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

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

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

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

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

generated_template: The statistic represents the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , an estimated templateYValue[0] templateYLabel[1] templateYLabel[2] occurred nationwide . templateYLabel[1] templateTitle[4] in the templateTitle[0] The Federal Bureau of Investigation templateYLabel[0] that templateYLabel[1] templateTitle[4] fell nationwide in the period from templateXValue[min] to templateXValue[max] .
generated: The statistic represents the Percentage change in the Change from 1990 to 2019 . In 2019 , an estimated -1.3 change occurred nationwide . change goods in the Change The Federal Bureau of Investigation Percentage that change goods fell nationwide in the period from 1990 to 2019 .

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

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

generated_template: templateTitle[1] is one of the most popular grains in the templateTitle[0] , with Americans consuming around templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[1] in the templateXValue[0] fiscal templateXLabel[0] . The templateYLabel[0] volume of templateTitle[1] in the templateTitle[0] has remained more or less stable between templateXValue[min] and templateXValue[max] . Leading consumers of templateTitle[1] A high share of the world 's templateTitle[1] templateYLabel[0] is concentrated between a few countries , namely China , India , and Indonesia .
generated: cement is one of the most popular grains in the Consumption , with Americans consuming around 2395070 thousand metric tons of cement in the 2016 fiscal Year . The Consumption volume of cement in the Consumption has remained more or less stable between 2006 and 2016 . Leading consumers of cement A high share of the world 's cement Consumption is concentrated between a few countries , namely China , India , and Indonesia .

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

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

generated_template: The statistic depicts the total templateYLabel[2] of the templateYLabel[0] templateTitle[4] templateTitle[6] of the English templateTitleSubject[0] in templateXValue[4] templateTitle[10] templateXLabel[0] in the period from templateTitle[8] to templateTitle[9] . templateYLabel[0] templateTitle[6] of the templateTitleSubject[0] in templateXValue[1] have a total templateYLabel[2] of templateYValue[1] templateYLabel[3] templateYLabel[4] templateYLabel[5] in that timeframe .
generated: The statistic depicts the total billion of the Amount United 2019/20 of the English United Kingdom in Defense function Industry in the period from function to function . Amount 2019/20 of the United Kingdom in Health have a total billion of 166 GBP in that timeframe .

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] templateYLabel[2] templateYLabel[3] templateYLabel[4] in the measured period . Overall , Tencent reported 70.84 templateYLabel[2] Chinese yuan revenue from its online games in templateTitleDate[0] .

generated_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] templateTitle[5] templateTitle[6] average templateYLabel[0] templateYLabel[1] in templateTitle[9] . YouGov 's templateXLabel[0] templateYLabel[0] templateTitle[9] templateYLabel[0] results show that templateXValue[0] templateTitle[5] highest with a score of templateYValue[max] , followed templateTitle[6] templateXValue[1] at templateYValue[1] .
generated: This statistic shows Video game industry 's in the wealthiest entrepreneurs 2016 average Net worth in 2016 . YouGov 's Entrepreneur Net 2016 Net results show that Ma Huateng (Tencent) entrepreneurs highest with a score of 21.9 , followed 2016 William Ding (NetEase) at 11.5 .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[min] percent .
generated: This statistic shows the Unemployment rate in Senegal from 1999 to 2019 . In 2019 , the Unemployment rate in Senegal was at approximately 5.6 percent .

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

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

generated_template: The statistic shows 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] percent .
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 percent .

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

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

generated_template: In templateXValue[max] , the templateTitleSubject[0] produced approximately templateYValue[max] templateYLabel[2] templateYLabel[3] of templateYLabel[0] worldwide , an increase of 53 templateYLabel[2] templateYLabel[3] on the previous templateXLabel[0] . The company templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] has increased by about 75 percent since templateXValue[6] . Asia at the center of templateTitle[3] operations network The templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] has risen year-on-year since templateXValue[6] .
generated: In 2018 , the Belgium produced approximately 16.4 rate of At-risk-of-poverty worldwide , an increase of 53 rate on the previous Year . The company rate Belgium At-risk-of-poverty rate has increased by about 75 percent since 2012 . Asia at the center of Belgium operations network The Belgium 's At-risk-of-poverty rate has risen year-on-year since 2012 .

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

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

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] survey templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] with sensitive information templateTitle[8] or templateTitle[7] , while traveling . templateYValue[max] percent of templateYLabel[1] templateTitle[4] templateTitle[8] or templateTitle[5] a templateXValue[0] templateTitle[7] whilst traveling .
generated: The statistic shows the Share of Sweden survey respondents payment services Sweden 2017 with sensitive information 2017 or 2017 , while traveling . 75 percent of respondents services 2017 or Sweden a Swish 2017 whilst traveling .

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

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

generated_template: In the fourth templateXLabel[0] of templateTitleDate[0] , templateTitleSubject[0] announced that over templateYValue[max] templateYLabel[4] templateYLabel[2] templateYLabel[3] were using the social networking platform to promote their products and services , up from templateYValue[2] templateYLabel[4] 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 templateYLabel[4] U.S. dollars , compared to merely 825 templateYLabel[4] payments and other fees revenue .
generated: In the fourth Quarter of 2017 , Q1 announced that over -15.3 percent were using the social networking platform to promote their products and services , up from -20.1 percent in the first Quarter of the previous year . Q1 advertising revenue Q1 generates the vast majority of its revenues through advertising . In 2018 , the social network 's ad revenue amounted to over 55 percent U.S. dollars , compared to merely 825 percent payments and other fees revenue .

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] templateYLabel[1] templateYLabel[2] templateYLabel[3] , down from the all-time highest templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[2] .

generated_template: The amount of templateTitleSubject[0] templateTitle[1] templateYLabel[1] templateYLabel[2] in the country has increased each templateXLabel[0] since templateXValue[min] , from templateYValue[min] thousand templateYLabel[3] templateYLabel[1] templateXLabel[0] to around templateYValue[19] thousand templateYLabel[3] in templateXValue[19] . This figure is expected to increase to templateYValue[max] thousand templateYLabel[3] of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] by templateXValue[max] . templateTitleSubject[0] templateYLabel[0] Worldwide On a global scale , the templateYLabel[0] volume of templateYLabel[2] templateTitleSubject[0] reached over 500 million metric tons in templateXValue[19] and is expected to increase slightly in the next templateXLabel[0] .
generated: The amount of Capital spending billion U.S. in the country has increased each Year since 2000 , from 26.1 thousand dollars billion Year to around 38.7 thousand dollars in 2001 . This figure is expected to increase to 105.9 thousand dollars of Capital billion U.S. by 2020 . Capital Spending Worldwide On a global scale , the Spending volume of U.S. Capital reached over 500 million metric tons in 2001 and is expected to increase slightly in the next Year .

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

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

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

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

generated_template: This statistic shows the 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[6] , the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] to templateTitle[4] templateTitle[5] amounted to approximately templateYValue[min] templateYLabel[2] templateYLabel[1] templateTitle[2] , the lowest amount in the provided time .
generated: This statistic shows the estimated Emissions of metric emissions per to person U.S. in U.S. from 2019 to 2050 . In 2020 , the Emissions of metric emissions per to person U.S. amounted to approximately 12.5 tons metric emissions , the lowest amount in the provided time .

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 represents templateTitleSubject[0] 's projected light templateTitle[2] templateTitle[3] between templateXValue[min] and templateXValue[max] . It is templateTitle[4] that just under templateYValue[max] million templateTitleSubject[0] templateYLabel[1] will be produced in templateXValue[max] . templateTitleSubject[0] is a premium templateTitle[0] brand manufactured by Bayerische Motoren Werke ( templateTitleSubject[0] ) AG .
generated: This statistic represents North America 's projected light traffic North between 2011 and 2016 . It is America that just under 96 million North America volume will be produced in 2016 . North America is a premium Online brand manufactured by Bayerische Motoren Werke ( North America ) AG .

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

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

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

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

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

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

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

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

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

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

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

generated_template: This statistic shows the templateTitle[0] development of the templateTitleSubject[0] index from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] index reflects the templateTitle[4] of the largest stocks traded on the Hong Kong Stock Exchange . The templateXLabel[0] value of the templateTitleSubject[0] index amounted to templateYValue[0] by the end of templateTitleDate[1] .
generated: This statistic shows the NCAA development of the NCAA index from 1980 to 2013 . The NCAA index reflects the TV/television of the largest stocks traded on the Hong Kong Stock Exchange . The Year value of the NCAA index amounted to 684.3 by the end of 2013 .

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

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at templateYValue[0] percent . Trade of templateTitleSubject[0] A member of the Group templateYValue[min] and G20 , templateTitleSubject[0] is one of the worlds most powerful , advanced and emerging economies .
generated: The statistic shows the Unemployment rate in Fiji from 1999 to 2019 . In 2019 , the Unemployment rate in Fiji was at 4.15 percent . Trade of Fiji A member of the Group 3.62 and G20 , Fiji is one of the worlds most powerful , advanced and emerging economies .

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

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

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

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

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

generated_template: This statistic gives information on the consolidated templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from the second templateXLabel[0] of templateTitleDate[0] to the fourth templateXLabel[0] of templateTitleDate[1] . As of the last reported templateXLabel[0] , the Japanese company 's templateYLabel[1] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic gives information on the consolidated LinkedIn 's Number mobile from the second Quarter of 2013 to the fourth Quarter of 2016 . As of the last reported Quarter , the Japanese company 's mobile amounted to 63 visiting members millions .

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] templateYLabel[3] templateYLabel[1] templateYLabel[2] in templateXValue[0] alone , making it the leading templateXLabel[0] in terms of templateYLabel[1] audience size . To put this into context , if templateXValue[0] 's templateYLabel[1] audience were a templateXLabel[0] then it would be ranked fourth in terms of largest population worldwide . Apart from templateXValue[0] , there are several other markets templateTitle[1] more than 100 templateYLabel[3] templateYLabel[1] templateYLabel[2] each : The templateXValue[1] , templateXValue[2] , and templateXValue[3] templateTitle[1] templateYValue[1] templateYLabel[3] , templateYValue[2] templateYLabel[3] , and templateYValue[3] templateYLabel[3] templateYLabel[1] templateYLabel[2] respectively .

generated_template: 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 as 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[max] templateYLabel[1] templateYLabel[2] dollars.The templateTitleSubject[0] are owned by Ken Kendrick , who bought the franchise for 238 templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[14] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] dollars.The templateTitleSubject[0] are owned by John Stanton and Chris Larson , who bought the franchise for 1.2 templateYLabel[1] templateYLabel[2] templateYLabel[3] and 100 templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[2] and 1992 respectively .
generated: The statistic depicts the Revenue of the Arizona Diamondbacks from 2001 to 2018 . In 2018 , the Revenue of the Major League Baseball franchise amounted to 275 million U.S. dollars.The Arizona Diamondbacks are owned by 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 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] templateTitle[6] . During the survey , templateYValue[2] percent of templateYLabel[1] said that they ate templateTitle[2] templateTitle[3] templateXValue[2] per templateXValue[1] .

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

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

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

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] survey templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] with sensitive information templateTitle[8] or templateTitle[7] , while traveling . templateYValue[max] percent of templateYLabel[1] templateTitle[4] templateTitle[8] or templateTitle[5] a templateXValue[0] templateTitle[7] whilst traveling .
generated: The statistic shows the Share of U.S. survey respondents user account privacy 2018 with sensitive information 2018 or 2018 , while traveling . 45 percent of respondents account 2018 or privacy a Yes all of my social media accounts are private 2018 whilst traveling .

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

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

generated_template: This statistic shows 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows 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[1] , the templateTitle[3] templateYLabel[0] transaction value of the templateTitleSubject[0] 's templateTitle[4] sectors amounted to approximately templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] , up 8.1 percent compared to the fourth templateXLabel[0] of the previous year . The company is headquartered in Tokyo , where it was founded in 1997 .

generated_template: In the fourth templateXLabel[0] of templateTitleDate[1] , templateTitleSubject[0] 's net templateYLabel[1] templateYLabel[2] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] , representing a 22 percent year-on-year growth . This templateYLabel[1] templateYLabel[2] was generated through the over 3.46 templateYLabel[3] transactions which templateTitleSubject[0] processed during that period . In 2018 , the templateYLabel[1] provider 's annual templateYLabel[1] templateYLabel[2] came to 578 templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: In the fourth Quarter of 2019 , Rakuten Group 's net merchandise sales amounted to 1053.1 billion Japanese yen , representing a 22 percent year-on-year growth . This merchandise sales was generated through the over 3.46 billion transactions which Rakuten Group processed during that period . In 2018 , the merchandise provider 's annual merchandise sales came to 578 billion Japanese yen .

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

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

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

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

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

generated_template: The templateXLabel[0] templateTitle[4] the templateTitle[5] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitleSubject[0] is templateXValue[0] , templateTitle[4] templateYValue[max] individuals having won the award since templateTitle[7] , when French poet and essayist Sully Prudhomme became the first ever winner of the award . Jean-Paul Sartre was also given the templateYLabel[2] in 1964 but voluntarily declined it . templateTitleSubject[0] – additional information The templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] is one of the five templateYLabel[1] Prizes announced each year in early October .
generated: The Country market the 2009 annual growth in Annual is Rest of world , market 19 individuals having won the award since by , when French poet and essayist Sully Prudhomme became the first ever winner of the award . Jean-Paul Sartre was also given the growth in 1964 but voluntarily declined it . Annual – additional information The annual growth in Annual is one of the five annual Prizes announced each year in early October .

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

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

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

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] templateYLabel[2] units . templateTitleSubject[0] templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[2] - additional information Total templateYLabel[0] templateYLabel[1] of templateTitle[0] kitchen and laundry templateTitle[2] in the templateTitle[5] are expected to reach templateYValue[0] templateYLabel[2] units in templateXValue[max] .

generated_template: The statistic depicts the templateTitle[0] templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] templateYLabel[1] was around templateYValue[max] templateYLabel[2] . MLB templateYLabel[0] templateYLabel[1] – additional information The global templateTitle[0] templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] has consistently grown over the last few years .
generated: The statistic depicts the Major Unit shipments in the U.S. from 2005 to 2017 . In 2017 , the Major Unit shipments was around 69.13 millions . MLB Unit shipments – additional information The global Major Unit shipments in the U.S. has consistently grown over the last few years .

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

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

generated_template: The templateYLabel[0] of the templateTitleSubject[0] luxury brand templateTitle[4] templateTitle[5] has increased twofold over the period surveyed , growing from roughly templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[min] to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . Despite the steady increase in templateYLabel[0] during the period considered , the templateTitle[3] reported a net loss of approximately 25 templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . Worldwide recognition Founded in Milan , templateTitle[4] templateTitle[5] is one of the leading international international templateTitle[2] design houses .
generated: The Sales of the U.S. luxury brand wholesale value has increased twofold over the period surveyed , growing from roughly 1.84 billion U.S. dollars in 1990 to 12.66 billion U.S. dollars in 2018 . Despite the steady increase in Sales during the period considered , the total reported a net loss of approximately 25 billion U.S. dollars in 2018 . Worldwide recognition Founded in Milan , wholesale value is one of the leading international market design houses .

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

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

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to about templateYValue[5] percent of the country 's templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The statistic shows the Ratio of government expenditure to gross domestic product ( GDP ) in Argentina from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure in Argentina amounted to about 37.56 percent of the country 's gross domestic product .

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

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

generated_template: The statistic shows 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] percent 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 shows the Biggest of U.S. people in the U.S. U.S. would be willing to spend on their security problems in 2017 according to a Statista survey . 72 percent of respondents said that they would be willing to spend Hacking by foreign governments to 25 U.S. dollars on security problems .

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[min] templateTitle[3] templateYLabel[2] . US templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[2] America 's electricity needs are met by a selection of energy carriers including coal , oil and natural gas , as well as templateTitle[1] fuel and renewable energy sources .

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

Example 308:
titleEntities: {'Subject': ['Portland Trail Blazers'], 'Date': ['2001', '2019']}
title: Portland Trail Blazers ' revenue 2001 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['287', '246', '223', '178', '157', '153', '140', '117', '132', '127', '121', '114', '82', '77', '78', '88', '97', '96']

gold: The statistic shows the revenue of the Portland Trail Blazers franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise amounted to 287 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] Blazers franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Portland Trail Blazers franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 287 million U.S. dollars .

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

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

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] - additional information templateTitleSubject[0] is one of the four largest accounting and audit firms in the world .
generated: This statistic shows the Teva Expenditure million of Teva from 2006 to 2019 . In the fiscal Year of 2019 , Teva 's Expenditure million amounted to 2077 U.S. dollars . Teva - additional information Teva is one of the four largest accounting and audit firms in the world .

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 templateYLabel[1] 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 templateYValue[0] templateYLabel[1] 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 30446.6 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: 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 2014 , there were 52 by employees industry employment worldwide , with the majority being built in China . Comparatively , there were 186 by employees shut down permanently as of that year . The Germany Mexico had permanently shut down 37 by facilities as of 2020 .

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

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

generated_template: The templateTitleSubject[0] is growing in every aspect . Over the last decade , the total templateYLabel[0] templateTitle[7] of templateTitle[3] in the templateTitleSubject[0] have more than quadrupled . In templateXValue[max] they amounted to approximately templateYValue[max] templateYLabel[3] templateYLabel[4] .
generated: The Southwest Airlines is growing in every aspect . Over the last decade , the total ASMs 2019 of seat in the Southwest Airlines have more than quadrupled . In 2019 they amounted to approximately 159.8 billions .

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

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

generated_template: In templateXValue[max] , the Ultimate Fighting Championship ( templateTitleSubject[0] ) staged a total of 39 events . However , after a considerable drop in templateTitle[3] buy rates in templateXValue[1] , the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateYLabel[3] recovered to approximately templateYValue[0] thousand templateYLabel[4] templateYLabel[5] in templateXValue[max] . One major contributor for this was templateTitleSubject[0] 229 on October 6th templateXValue[max] , featuring Khabib Nurmagomedov versus Conor McGregor , resulting in the top templateTitleSubject[0] templateYLabel[5] in terms of templateTitle[3] templateYLabel[3] with a total of 2.4 templateYLabel[6] templateYLabel[3] .
generated: In 2018 , the Ultimate Fighting Championship ( U.S. ) staged a total of 39 events . However , after a considerable drop in 's buy rates in 2017 , the Number recalls of 's recalls recovered to approximately 52 thousand recalls in 2018 . One major contributor for this was U.S. 229 on October 6th 2018 , featuring Khabib Nurmagomedov versus Conor McGregor , resulting in the top U.S. recalls in terms of 's recalls with a total of 2.4 recalls .

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

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

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

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

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

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

Example 317:
titleEntities: {'Subject': ['Tanzania'], 'Date': ['2019']}
title: Youth unemployment rate in Tanzania in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['3.43', '3.45', '3.43', '3.66', '3.7', '3.71', '5.69', '6.34', '6.76', '5.9', '4.88', '4.58', '5.04', '6.07', '6.29', '6.19', '6.04', '5.81', '5.36', '5.64', '5.88']

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

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

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] templateYLabel[1] templateYLabel[2] templateYLabel[3] . The internet templateXLabel[0] 's most expensive acquisition was templateXValue[0] in 2011 , tallying templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: Which templateXLabel[0] has appeared most often in the templateYLabel[1] templateYLabel[2] ? The templateXValue[0] Patriots have appeared in the National Football League 's annual championship game a record templateYValue[max] times , winning the templateYLabel[1] templateYLabel[2] six times . The templateXValue[0] and the templateXValue[1] share the honor of winning the templateYLabel[1] templateYLabel[2] the highest templateYLabel[0] of times ( six wins each ) . templateTitle[8] of the templateXValue[0] six templateYLabel[1] templateYLabel[2] wins have come with Bill Belichick as the head coach and Tom Brady under center at the quarterback position .
generated: Which Company has appeared most often in the million U.S. ? The Motorola Mobility (2012) Patriots have appeared in the National Football League 's annual championship game a record 12500.0 times , winning the million U.S. six times . The Motorola Mobility (2012) and the Nest Labs (2014) share the honor of winning the million U.S. the highest Price of times ( six wins each ) . 2017 of the Motorola Mobility (2012) six million U.S. wins have come with Bill Belichick as the head coach and Tom Brady under center at the quarterback position .

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] increased by 151 to reach a total of templateYValue[max] females holding a director position , as of 2019 .

generated_template: In templateXValue[max] , the templateTitle[1] templateYLabel[0] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] amounted to about templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] , all types included . The templateTitle[0] realized templateYLabel[0] of templateTitle[3] templateTitle[4] remained fairly steady throughout the years until templateXValue[3] , when it decreased to below templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . The templateTitle[0] templateYLabel[0] serves as an indicator for a variety of different selling prices on the templateTitle[4] market , gathering all templateYLabel[0] ranges of templateTitle[3] wines purchased in templateTitleSubject[0] .
generated: In 2019 , the female Number of FTSE 100 in UK amounted to about 264 female directors , all types included . The Number realized Number of FTSE 100 remained fairly steady throughout the years until 2016 , when it decreased to below 292 female directors in 2019 . The Number serves as an indicator for a variety of different selling prices on the 100 market , gathering all Number ranges of FTSE wines purchased in UK .

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] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitleSubject[0] are owned by William DeWitt Jr. , who bought the templateYLabel[0] for 150 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1996 .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Daniel Snyder who bought the templateYLabel[0] for 150 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1999 .
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 around 2100 million U.S. dollars . The Louis Cardinals are owned by Daniel Snyder who bought the Franchise for 150 million U.S. dollars in 1999 .

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

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

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

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

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

generated_template: 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] templateYLabel[2] 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 budgetary the highest Operating 2017 by budgetary in 2017 . In the Poland ( UK ) , approximately 8.57 balances budgetary were reported from the 2017 by segment , making the UK Europe 's leading member state in terms of 2017 by budgetary . The dawn of 2017 payments The practice of paying without cash has its roots in the Poland States and was first used in the 1940s as a means of paying for automobile fuel .

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 decreased by 1.9 percent in templateTitleSubject[0] over a 12-week templateXLabel[2] templateXLabel[3] 12 , templateTitleDate[1] compared to the same time templateXLabel[2] in 2018 . templateTitle[0] has seen its templateTitle[1] grow during the last templateXValue[19] and a half years . The second quarter of 2017 saw the highest templateYLabel[1] , with templateTitle[1] going up over five percent .

generated_template: This statistic shows the templateXLabel[0] ranking of the 2019 templateTitleSubject[0] Index . Leading the ranking was templateXValue[0] with a total templateYLabel[0] score of 90.95.The templateTitleSubject[0] index measures the extent of which countries provide for the templateTitleSubject[0] and environmental needs of their citizens . The templateYLabel[0] consists of 52 indicators in total .
generated: This statistic shows the 12 ranking of the 2019 Great Britain Index . Leading the ranking was 14 Jul 19 with a total Percentage score of 90.95.The Great Britain index measures the extent of which countries provide for the Great Britain and environmental needs of their citizens . The Percentage consists of 52 indicators in total .

Example 326:
titleEntities: {'Subject': ['Louisiana'], 'Date': ['2000', '2018']}
title: Louisiana - poverty rate 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'population']: ['18.6', '19.7', '20.2', '19.6', '19.8', '19.8', '19.9', '20.4', '18.7', '17.3', '17.3', '18.6', '19', '19.8', '19.4', '20.3', '18.8', '19.1', '20']

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

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitleSubject[0] line .
generated: This statistic shows the Louisiana rate in Louisiana from 2000 to 2018 . In 2018 , 18.6 percent of Louisiana 's population lived below the Louisiana 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 templateTitleSubject[0] templateTitle[0] templateTitleSubject[0] templateYLabel[0] from templateTitleSubject[0] templateTitle[5] to templateTitleSubject[0] templateTitleDate[0] . The templateYLabel[0] is normalized to have a value of templateYValue[1] in 1964 and based on a monthly survey of consumers , conducted in the continental templateTitle[3] . It consists of about 50 core questions which cover consumers ' assessments of their personal financial situation , their buying attitudes and overall economic conditions .
generated: This statistic shows the June Business June Index from June 2019 to June 2019 . The Index is normalized to have a value of 56.5 in 1964 and based on a monthly survey of consumers , conducted in the continental June . It consists of about 50 core questions which cover consumers ' assessments of their personal financial situation , their buying attitudes and overall economic conditions .

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

generated_template: 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] percent 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 2017 New American Bible in 2017 . During the survey , 13 percent of respondents cited New International Version as Bible U.S. U.S. 2017 a 2017 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 templateTitleSubject[0] templateTitle[0] templateTitleSubject[0] templateYLabel[0] from templateTitleSubject[0] templateTitle[5] to templateTitleSubject[0] templateTitleDate[0] . The templateYLabel[0] is normalized to have a value of templateYValue[1] in 1964 and based on a monthly survey of consumers , conducted in the continental templateTitle[3] . It consists of about 50 core questions which cover consumers ' assessments of their personal financial situation , their buying attitudes and overall economic conditions .
generated: This statistic shows the June Consumer June Index from June 2019 to June 2019 . The Index is normalized to have a value of 113.6 in 1964 and based on a monthly survey of consumers , conducted in the continental June . It consists of about 50 core questions which cover consumers ' assessments of their personal financial situation , their buying attitudes and overall economic conditions .

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 20 templateTitle[0] templateTitle[1] in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] , by templateXLabel[0] . In templateTitleDate[0] , about templateYValue[max] templateYLabel[1] templateYLabel[2] of templateXValue[0] were produced in the templateTitleSubject[0] .
generated: This statistic shows the 20 Canada average in the Canada ( Canada ) in 2019 , by Industry . In 2019 , about 45.0 usual weekly of Forestry fishing mining quarrying oil and gas were produced in the Canada .

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

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

generated_template: This statistic represents the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per day in the templateTitleSubject[0] between templateTitle[6] and templateTitle[7] , templateTitle[8] templateTitle[9] . In this templateYValue[min] templateXLabel[0] period , individuals aged between 11 and 18 templateXValue[0] old templateTitle[3] templateYValue[max] templateYLabel[1] of templateTitle[1] templateTitle[2] per day .
generated: This statistic represents the Global of market share U.S. per day in the U.S. between footwear and retail , market 2004 . In this 24.3 Year period , individuals aged between 11 and 18 2004 old U.S. 27.3 market of market share per day .

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

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

generated_template: This statistic shows the templateTitle[3] templateTitle[4] templateTitle[5] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] from templateTitleDate[0] to templateTitleDate[1] in leading countries . According to Statista calculations , templateXValue[0] will rank first in terms of B2C templateTitle[4] development with a templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateYValue[max] in the projected period . Online shopping is one of the most popular internet activities worldwide - with some product categories being more popular than others .
generated: This statistic shows the oil imported Q1 Import volume tons from 2018 to 2018 in leading countries . According to Statista calculations , Azarbaijan will rank first in terms of B2C imported development with a Import volume tons of 12298989 in the projected period . Online shopping is one of the most popular internet activities worldwide - with some product categories being more popular than others .

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

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

generated_template: This statistic shows the templateYLabel[0] 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[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] . This shows a 70 percent decrease over previous templateXLabel[0] templateTitle[3] total amounting to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Return equity of The HSBC shareholders HSBC worldwide from 2009 to 2019 . Fast food chain HSBC shareholders had a Return equity of approximately 3.6 equity in 2019 . This shows a 70 percent decrease over previous Year shareholders total amounting to 10.9 equity .

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: The statistic illustrates a templateYLabel[0] templateYLabel[1] templateTitle[6] of the worldwide portable templateTitle[3] device ( templateTitleSubject[0] ) and templateTitle[2] templateTitle[3] templateYLabel[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitleSubject[0] and templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] is projected to be templateYValue[0] templateYLabel[2] templateYLabel[3] in templateXValue[max] .
generated: The statistic illustrates a AIR thousands 2006 of the worldwide portable United device ( FourFourTwo ) and magazine United AIR from 2006 to 2016 . The Readership FourFourTwo and magazine United AIR thousands is projected to be 483 thousands in 2016 .

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] templateYLabel[2] templateYLabel[0] templateYLabel[1] in templateXValue[0] . 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 average templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] and templateTitle[7] for inmates on death row in the templateTitle[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , an average of templateYValue[max] templateYLabel[2] templateTitle[4] templateTitle[5] templateTitle[6] and templateTitle[7] for inmates on death row . This is an increase from templateTitleDate[0] , when an average of templateYValue[min] templateYLabel[2] passed templateTitle[5] templateTitle[6] and templateTitle[7] .
generated: This statistic shows the average Metrolink United Kingdom 1992 and 2019 for inmates on death row in the Passenger from 1992 to 2019 . In 2019 , an average of 43.7 millions United Kingdom 1992 and 2019 for inmates on death row . This is an increase from 1992 , when an average of 8.1 millions passed Kingdom 1992 and 2019 .

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

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

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

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

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

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

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

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

generated_template: The statistic shows the 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[0] homicides templateTitle[1] templateTitle[2] templateYLabel[1] 100,000 population were committed in the templateTitle[4] .
generated: The statistic shows the Volume wine produced Thousands hectoliters 100,000 population in the 2006 from 2006 to 2018 . In 2018 , about 6.1 homicides wine produced hectoliters 100,000 population were committed in the 2006 .

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: What were the top 20 countries of templateTitle[6] of templateYLabel[1] living in templateTitleSubject[0] as of templateTitleDate[0] ? The biggest group of templateYLabel[1] living in templateTitleSubject[0] were from templateXValue[0] , with more than 41 thousand people living in templateTitleSubject[0] on 1 , templateTitleDate[0] . The second and third biggest immigrant groups were from templateXValue[1] and templateXValue[2] , amounting to almost templateYValue[1] thousand and templateYValue[2] thousand people respectively . Only one Nordic templateXLabel[0] was represented within the ten biggest group of templateYLabel[1] in templateTitleSubject[0] this year , namely templateXValue[8] , with close to templateYValue[8] thousand Norwegians living in templateTitleSubject[0] .
generated: What were the top 20 countries of number of offerings living in U.S. as of 2017 ? The biggest group of offerings living in U.S. were from Wefunder , with more than 41 thousand people living in U.S. on 1 , 2017 . The second and third biggest immigrant groups were from Start Engine and Seed Invest , amounting to almost 52 thousand and 29 thousand people respectively . Only one Nordic Country was represented within the ten biggest group of offerings in U.S. this year , namely Microventures , with close to 11 thousand Norwegians living in U.S. .

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

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

generated_template: 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] grew by templateYValue[0] percent compared to the previous templateXLabel[0] . Wine industryWine is classified as alcoholic beverage which goes well with a large variety of occasions : some often choose to serve this classy drink as an aperitif , others see wine as a perfect accompaniment to a multi-course meal , and others again prefer drinking wine while spending time with friends or family .
generated: This statistic shows the U.S. Value per of value sheep from 2001 to 2017 . In 2017 , Value from value sheep in the Head grew by 203 percent compared to the previous Year . Wine industryWine is classified as alcoholic beverage which goes well with a large variety of occasions : some often choose to serve this classy drink as an aperitif , others see wine as a perfect accompaniment to a multi-course meal , and others again prefer drinking wine while spending time with friends or family .

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 templateYLabel[0] of gross domestic product ( templateYLabel[1] ) in the templateTitleSubject[0] ( templateTitleSubject[1] ) from fiscal templateXLabel[0] 2000/01 to templateXValue[0] . After templateXValue[16] , templateTitle[3] templateTitle[4] as a templateYLabel[0] of templateYLabel[1] rose continuously over the remainder of the period to a peak in templateXValue[2] .

generated_template: The total templateTitle[0] expenditure in templateTitleSubject[0] in templateTitleDate[0] accounted for approximately templateYValue[max] templateTitle[2] of templateTitleSubject[0] 's Gross Domestic Product ( templateYLabel[1] ) that templateXLabel[0] . This was the result of the steepest increase in the past ten years and was the first time templateTitle[0] templateTitle[1] as a share of the templateYLabel[1] exceeded templateYValue[max] templateTitle[2] . This share saw a continuous increase over the past decade , indicating that as the templateYLabel[1] grew , templateTitle[0] templateTitle[1] grew at an even faster rate .
generated: The total United expenditure in United Kingdom in 2000 accounted for approximately 86.5 UK 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 UK . 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 the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] in templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
generated: The statistic shows the Gross domestic product ( GDP ) per capita in Portugal in 1984 and 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a Year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

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

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

generated_template: templateTitleSubject[0] templateTitle[1] templateYLabel[0] grew to almost templateYValue[max] templateYLabel[1] templateYLabel[2] in the templateXValue[max] templateXLabel[0] of templateXValue[0] , up from templateYValue[1] templateYLabel[1] templateYLabel[2] in the preceding templateXLabel[0] . This is considerably lower than that of all vehicle sales , templateTitleSubject[0] accounted from its templateYLabel[0] templateTitleSubject[0] in 2011 . templateTitleSubject[0] is one of the most popular mobile manufacturers in the country .
generated: Information Technology Technology Revenue grew to almost 18.0 billion euros in the Q3 2019 Quarter of Q3 2019 , up from 12.83 billion euros in the preceding Quarter . This is considerably lower than that of all vehicle sales , Information Technology accounted from its Revenue Information Technology in 2011 . Information Technology is one of the most popular mobile manufacturers in the country .

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

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

generated_template: The statistic shows the templateYLabel[1] of templateTitle[1] templateYLabel[0] in templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[0] to the fourth templateXLabel[0] of templateTitleDate[1] . As of the second templateXLabel[0] of templateTitleDate[0] , the social network is projected to reach templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the growth of office Rent in U.S. from the first Quarter of 2015 to the fourth Quarter of 2016 . As of the second Quarter of 2015 , the social network is projected to reach 1 growth .

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

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

generated_template: This statistic gives information on the templateTitleSubject[0] templateYLabel[0] templateTitle[2] in templateTitleDate[0] . The source projected that templateXValue[3] would be the fourth templateTitleSubject[0] contributor to the global templateYLabel[0] market investments , with spending of templateYValue[3] templateYLabel[2] U.S templateYLabel[4] that year . In total , it is projected that global templateYLabel[0] spending will reach 19 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitleDate[0] .
generated: This statistic gives information on the Twitter Share in 2018 . The source projected that United Kingdom would be the fourth Twitter contributor to the global Share market investments , with spending of 5.5 users U.S users that year . In total , it is projected that global Share spending will reach 19 users in 2018 .

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: In templateTitleDate[0] , templateXValue[0] was the most popular templateTitle[1] templateXLabel[0] in the templateTitle[3] with templateYValue[max] percent of all year . The templateTitle[0] templateTitle[1] is also the second templateTitle[0] templateTitle[1] city of the templateTitle[3] templateTitle[4] . During the third quarter of templateTitle[7] , the percent of all templateTitleSubject[0] ( templateYLabel[1] ) was located in the UK .
generated: In 2018 , Indigo was the most popular share Airline in the carried with 39.7 percent of all year . The Market share is also the second Market share city of the carried India . During the third quarter of domestic , the percent of all India ( market ) was located in the UK .

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

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

generated_template: In templateTitleDate[0] , templateXValue[0] LLP was the largest templateTitle[1] templateXLabel[0] in the templateTitle[5] , at templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] that year . templateXValue[0] is a multinational corporation , headquartered in New York templateXValue[14] with a templateYLabel[0] templateYLabel[1] of templateYValue[max] . For behind different , in 2019 , where it was founded in five years .
generated: In 2018 , Tibet LLP was the largest ethnic Province in the minority , at 90.05 ethnic minorities in Share that year . Tibet is a multinational corporation , headquartered in New York Guangxi with a Share ethnic of 90.05 . For behind different , in 2019 , where it was founded in five years .

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 provides a forecast of the templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] in templateTitle[6] , templateTitle[4] templateXLabel[0] . It was forecasted , that the templateYLabel[0] templateYLabel[1] of templateXValue[1] would amount to some templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The global templateYLabel[1] of templateYLabel[0] oil is expected to reach nearly 28 templateYLabel[2] templateYLabel[3] templateYLabel[4] templateTitle[4] that year .
generated: This statistic provides a forecast of the Number refugees origin countries in Canada , refugees Country . It was forecasted , that the Number refugees of Eritrea would amount to some 33266 admitted . The global refugees of Number oil is expected to reach nearly 28 admitted refugees that year .

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: templateXValue[0] was the leading templateTitle[2] templateTitle[6] in templateTitleSubject[0] in templateTitleDate[0] , holding a templateYLabel[1] of around templateYValue[max] percent of templateXValue[last] templateTitle[2] sales in templateTitleSubject[0] in the 52 weeks ending 21 , templateTitleDate[0] . templateXValue[1] and templateXValue[2] were in second and third place , with templateYLabel[0] shares of templateYValue[1] and templateYValue[2] percent respectively . The growth of templateXValue[0] is a Swiss food and drink templateTitle[6] which was formed through a merger in 1905 .
generated: Tesco was the leading waste United in United Kingdom in 2016 , holding a tonnes of around 59400 percent of Iceland waste sales in United Kingdom in the 52 weeks ending 21 , 2016 . Sainsbury's and Asda were in second and third place , with Volume shares of 35832 and 32020 percent respectively . The growth of Tesco is a Swiss food and drink United which was formed through a merger in 1905 .

Example 351:
titleEntities: {'Subject': ['North Carolina'], 'Date': ['1990', '2018']}
title: North Carolina - Median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['53369', '50343', '53764', '50797', '46784', '41208', '41553', '45206', '43830', '41906', '42930', '43513', '39797', '42056', '40238', '37279', '36515', '38162', '38317', '37254', '35838', '35840', '35601', '31979', '30114', '28820', '27771', '26853', '26329']

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

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Carolina Household income in North Carolina from 1990 to 2018 . In 2018 , the Carolina Household income in North Carolina amounted to 53369 U.S. 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 percent 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] percent 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 percent 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[0] to templateTitleDate[1] , in templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . The templateTitle[4] of templateTitle[3] is expected to increase to templateYValue[0] templateYLabel[0] templateYLabel[1] in templateXValue[0] .

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

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

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

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

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

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

generated_template: In templateXValue[max] , an estimated templateYValue[max] templateYLabel[1] smartwatches were sold in the templateTitle[4] . Between templateXValue[min] and templateXValue[max] annual templateTitleSubject[0] templateYLabel[0] grew from just templateYValue[min] thousand templateYLabel[2] to over templateYValue[max] templateYLabel[1] as these devices skyrocketed in popularity . Smartwatches While digital wristwatches have been around for decades , true smartwatches are a relatively new phenomena in the consumer electronics industry .
generated: In 2018 , an estimated 2025 level smartwatches were sold in the Great . Between 2013 and 2018 annual Great Britain Production grew from just 1555 thousand million to over 2025 level as these devices skyrocketed in popularity . Smartwatches While digital wristwatches have been around for decades , true smartwatches are a relatively new phenomena in the consumer electronics industry .

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 decreasing lately . In templateXValue[max] , approximately templateYValue[0] percent of the Ecuadorian templateYLabel[1] was living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] percent in 2005.Still , social inequality remains a challenge in templateTitleSubject[0] and Latin America as a whole .

generated_template: In templateXValue[max] , the templateTitle[2] templateTitle[3] in templateTitleSubject[0] reached templateYValue[min] percent of the total . In templateXValue[max] , Europe was ranked third continent worldwide in terms of degree of urbanization . 80 percent of the European templateYLabel[2] was living in cities in templateXValue[max] , but this figure is expected to decrease by 2050 .
generated: In 2017 , the headcount ratio in Ecuador reached 8.6 percent of the total . In 2017 , Europe was ranked third continent worldwide in terms of degree of urbanization . 80 percent of the European population was living in cities in 2017 , but this figure is expected to decrease by 2050 .

Example 357:
titleEntities: {'Subject': ['U.S.'], 'Date': ['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] percent of the American templateYLabel[2] was 65 years old or over ; a figure which is expected to reach templateYValue[max] percent by templateXValue[max] . This is a significant increase from templateXValue[min] , when only templateYValue[min] percent of the templateYLabel[2] was 65 or over . A rapidly aging templateYLabel[2] In recent years , the aging templateYLabel[2] of the templateTitle[0] has come into focus as a cause for concern , as the nature of work and retirement is expected to change in order to keep up .

generated_template: The statistic depicts the templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the global templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] was expected to templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: The statistic depicts the U.S. seniors Percentage total from 1950 to 2050 . In 2040 , the global U.S. seniors Percentage total was expected to 8 population .

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] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Terry and Kim Pegula .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] 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] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Jets are owned by Robert Wood Johnson IV , who bought the templateYLabel[0] for 635 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 2000 .
generated: This graph depicts the Franchise value of the Buffalo Bills Jets of the National Football League from 2002 to 2019 . In 2019 , the Franchise value amounted to around 1900 million U.S. dollars . The Buffalo Bills Jets are owned by Robert Wood Johnson IV , who bought the Franchise for 635 million U.S. dollars in 2000 .

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 decreasing recently . In templateXValue[max] , approximately templateYValue[min] percent of Uruguayans was living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] percent of the country 's templateYLabel[1] in 2006.Still , social inequality remains a challenge in Latin America as a whole .

generated_template: The templateTitle[2] rate in the templateTitleSubject[0] has been decreasing in the past few years . In templateXValue[max] , approximately templateYValue[min] percent 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[max] percent 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 percent of the Dominicans were living on less than U.S. dollars day per 2006 , almost three-times lower than 2006 , when 3.7 percent 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 percentage of annual IT templateTitle[5] of templateTitle[1] businesses in the templateTitle[0] . During the 2017 survey period , templateYValue[0] percent of C-level templateYLabel[1] stated that templateTitle[3] templateTitle[4] accounted for templateXValue[last] 10 percent of their annual IT templateTitle[5] .

generated_template: As of templateTitleDate[0] , right-wing American news website templateTitle[0] was rated as templateXValue[2] at templateXValue[2] by templateYValue[2] percent of templateYLabel[1] from a survey of over two thousand templateTitleSubject[0] adults . Additionally , over half of templateYLabel[1] had either templateXValue[0] of the publication or had templateXValue[1] about templateTitle[0] 's templateTitle[1] . Conservative view of news media While right-wing websites and online publications have grown in number and popularity in recent years , Fox News remains the largest and perhaps most influential conservative news source in the templateTitle[2] .
generated: As of 2017 , right-wing American news website U.S. was rated as 5 to 6 at 5 to 6 by 21 percent of respondents from a survey of over two thousand U.S. adults . Additionally , over half of respondents had either 3 to 4 of the publication or had 4 to 5 about U.S. 's consumer . Conservative view of news media While right-wing websites and online publications have grown in number and popularity in recent years , Fox News remains the largest and perhaps most influential conservative news source in the 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[0] .

generated_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Yankees from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Yankees was at templateYValue[0] templateYLabel[2] . • Major League Baseball average per game templateYLabel[1] • Major League Baseball total templateYLabel[1]
generated: This graph depicts the total Regular season Home attendance of the Denver Broncos Yankees from 2006 to 2019 . In 2019 , the Regular season Home attendance of the Denver Broncos Yankees was at 607497 attendance . • Major League Baseball average per game attendance • Major League Baseball total attendance

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

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

generated_template: This statistic shows the 20 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[0] ranked 4th templateTitle[1] an estimated templateYLabel[0] templateYLabel[1] of approximately templateYValue[max] percent 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 , Malta ranked 4th gross an estimated GDP growth of approximately 4.44 percent 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] percent of the templateTitle[3] templateTitle[4] templateTitle[5] .

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

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

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

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

Example 365:
titleEntities: {'Subject': ['Netherlands', '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 two templateTitle[3] templateTitle[4] templateTitleSubject[0] templateTitle[6] in templateTitleDate[0] were the oil companies templateXValue[0] and templateXValue[1] , the former of which is in possession of a staggering templateYLabel[1] of templateYValue[max] templateYLabel[2] templateTitleSubject[0] pounds . This was not different in the previous year either , when templateXValue[0] ranked as the templateTitle[3] templateTitle[4] templateTitleSubject[0] templateXLabel[0] while the rest of the list had some small shifts and variations . Oil , banks and Telecom The templateTitle[3] templateTitle[4] templateTitleSubject[0] templateTitle[6] as of templateTitleDate[0] is a balanced mixture of oil and gas companies , banks and telecommunications .
generated: The two 2018 ranked Netherlands Buzz in 2018 were the oil companies Samsung and Albert Heijn , the former of which is in possession of a staggering score of 47.7 score Netherlands pounds . This was not different in the previous year either , when Samsung ranked as the 2018 ranked Netherlands Platform while the rest of the list had some small shifts and variations . Oil , banks and Telecom The 2018 ranked Netherlands Buzz as of 2018 is a balanced mixture of oil and gas companies , banks and telecommunications .

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

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

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] of templateTitle[4] in the templateTitle[5] in templateTitleDate[0] templateTitle[7] templateXLabel[0] and ethnicity . In templateTitleDate[0] , templateXValue[0] couples had a templateTitle[0] templateTitle[2] income of 70,852 templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the Total retail sales Total of shopping in the malls in 2005 by Gross and ethnicity . In 2005 , Less than 100001 couples had a Total sales income of 70,852 retail sales .

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

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

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was estimated at almost templateYValue[6] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] has a surprisingly low ( and decreasing ) templateTitle[1] growth rate ; despite it being home to the largest number of Catholics in the world , the majority of women in templateTitleSubject[0] use some form of contraception , which is often government-subsidized or free , even though the Catholic Church retains its stance that the use of contraceptives is inherently wrong .
generated: This statistic shows the Total population of Russia from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Russia was estimated at almost 146.8 millions Inhabitants . population of Russia has a surprisingly low ( and decreasing ) population growth rate ; despite it being home to the largest number of Catholics in the world , the majority of women in Russia use some form of contraception , which is often government-subsidized or free , even though the Catholic Church retains its stance that the use of contraceptives is inherently wrong .

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

Example 369:
titleEntities: {'Subject': ['Italy'], 'Date': ['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 increased by more than 15 percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Italy from 2004 to 2019 . In 2019 , the Unemployment rate in Italy was at approximately 23.5 percent .

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: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] templateTitle[4] templateTitle[5] in the middle of templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] in templateXValue[0] was templateYValue[max] percent in the middle of 2014.The templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] arises from the birth templateYLabel[1] minus the death templateYLabel[1] and without including the effects of migration.Population growthAs shown in the statistic above , the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] continues to increase on almost every templateTitle[5] in 2013.Due to medical advances , better living conditions and the increase of agricultural productivity the world templateTitle[2] is continuously rising . The development of the world templateTitle[2] from 1950 to 2030 is estimated to be tripled according to United Nations ' data.The majority of the world templateTitle[2] lives in templateXValue[4] , but the templateTitle[2] in templateXValue[0] is forecasted to rise from 1,031 in year 2010 up to 4,185 in year 2100 .
generated: The statistic shows the Surface area of Nordic square 2017 in the middle of 2017 . The Surface area of Nordic square in Sweden was 447420 percent in the middle of 2014.The Surface area of Nordic square arises from the birth area minus the death area and without including the effects of migration.Population growthAs shown in the statistic above , the Surface area of Nordic square continues to increase on almost every 2017 in 2013.Due to medical advances , better living conditions and the increase of agricultural productivity the world Nordic is continuously rising . The development of the world Nordic from 1950 to 2030 is estimated to be tripled according to United Nations ' data.The majority of the world Nordic lives in Iceland , but the Nordic in Sweden is forecasted to rise from 1,031 in year 2010 up to 4,185 in year 2100 .

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

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

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

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[min] templateYLabel[3] in templateXValue[max] .

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

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

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

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

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

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

generated_template: This statistic shows the templateYLabel[0] in the templateYLabel[2] of 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[2] templateXValue[6] was 2.2 seven percent lower than it was in 2019 .
generated: This statistic shows the Debt in the euros of automotive billion per liter in nations 2011 for the period between 2018 and 2019 . The euros changes exclude value added taxes from prices for automotive billion . In 2019 , the billion euros per liter in the Greece Turkey was 2.2 seven percent lower than it was in 2019 .

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

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

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

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

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

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

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

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

generated_template: 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] , an increase from 952.8 templateYLabel[1] templateYLabel[2] templateYLabel[3] compared the previous templateXLabel[0] .
generated: The timeline shows Brunswick Corporation revenue Corporation Revenue 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] templateYLabel[2] templateYLabel[3] 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[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] . This shows a 70 percent decrease over previous templateXLabel[0] templateTitle[3] total amounting to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Net income of the Southwest Airlines Airlines Southwest Airlines worldwide from 2010 to 2019 . Fast food chain Southwest Airlines Airlines had a Net income of approximately 2300 million U.S. dollars in 2019 . This shows a 70 percent decrease over previous Year Airlines total amounting to 3357 million U.S. dollars .

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] increased 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[16] .
generated: This statistic shows the FIFA World Ranking of the Faroe Islands Islands ' national from 1993 to 2019 . Until 2013 , the national did not reach a position better than 170 . Rank 194 was the lowest result of the national , which was reached in 2009 .

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

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

generated_template: The templateYLabel[0] of the men 's lifestyle and entertainment magazine templateTitleSubject[0] has dropped significantly in recent years , falling to just over 200 thousand in templateXValue[max] from templateYValue[4] templateYLabel[1] templateYValue[11] years earlier . The magazine reported its highest templateTitle[1] templateYLabel[0] in templateXValue[12] , when an average issue sold templateYValue[max] templateYLabel[1] copies . templateTitleSubject[0] templateYLabel[0] numbers – additional information Founded in 1953 by the late Hugh Hefner , templateTitleSubject[0] is a men 's lifestyle and entertainment magazine .
generated: The Consumption of the men 's lifestyle and entertainment magazine Germany has dropped significantly in recent years , falling to just over 200 thousand in 2018 from 20.2 million 20.8 years earlier . The magazine reported its highest wine Consumption in 2004 , when an average issue sold 20.8 million copies . Germany Consumption numbers – additional information Founded in 1953 by the late Hugh Hefner , Germany is a men 's lifestyle and entertainment magazine .

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

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

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

generated_template: This statistic 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 increase by 1.7 years , the largest increase predicted between templateXValue[2] and templateXValue[1] at 0.8 years .
generated: This statistic shows the Thailand of the population older in the Thailand ( Thailand ) from 2015 to 2035 and visualises the predicted 'ageing 2035 ' _ . Over the 20 Year period , the population older is expected to increase by 1.7 years , the largest increase predicted between 2025 and 2030 at 0.8 years .

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 templateTitleSubject[0] & Cie. from templateXValue[last] to templateXValue[0] . In its fiscal templateXLabel[0] templateXValue[1] , templateTitleSubject[0] made total templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] worldwide , a record high . templateTitleSubject[0] 's annual sales have witnessed continuous growth during the measured period .
generated: The statistic illustrates the Number of U.S. & Cie. from 2012 to 2002 . In its fiscal Year 2003 , U.S. made total Number of 53590 new books worldwide , a record high . U.S. 's annual sales have witnessed continuous growth during the measured period .

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 August 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] templateYLabel[2] lifetime templateYLabel[1] on the video platform . Music templateTitle[3] on templateTitleSubject[0] `` Descpacito '' might be the current record-holder in terms of total templateYLabel[1] , but Korean artist templateXValue[6] 's `` templateXValue[6] '' video remained on the top spot templateXValue[3] longest ( 1,689 days or 4.6 years ) before ceding its spot to its successor .

generated_template: 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] templateYLabel[2] templateYLabel[3] .
generated: The statistic presents the ranking of ten wealthiest viewed in YouTube as of 2014 . At that time , the richest woman in YouTube was Luis Fonsi - Despacito ft. Daddy Yankee , the heiress of the French cosmetics and beauty company L'Oreal , with time 2019 amounting to approximately 6.55 billions .

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

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

generated_template: This statistic shows the 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[0] ranked 1st templateTitle[1] an estimated templateYLabel[0] templateYLabel[1] of approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the 20 Canada Gross the Domestic million of the GDP by industry ( GDP ) in 2019 . In 2019 , Real estate and rental and leasing ranked 1st Gross an estimated GDP million of approximately 254294 chained 2012 Canadian .

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

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

generated_template: 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] templateYLabel[0] surplus amounted to around templateYValue[max] percent of templateYLabel[3] .
generated: The statistic shows Japan balance Budget balance in trillion to yen between 2014 and 2018 , with projections up until 2024 . A positive value indicates a Budget surplus , a negative value indicates a deficit . In 2018 , Japan balance Budget surplus amounted to around -10.24 percent of yen .

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

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

generated_template: This statistic shows the 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[max] templateYLabel[1] 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 .
generated: This statistic shows the spending for enterprise resource planning ( Global ) sponsorships from 2010 to 2012 , and a Global for 2016 . By 2016 , spending revenues for Global sponsorships are predicted to reach 1.82 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 .

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

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

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

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

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

generated_template: The templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] aged templateTitle[4] templateYLabel[2] in the templateTitleSubject[0] has gradually increased 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

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

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

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

generated_template: This statistic shows the templateTitle[5] templateYLabel[0] of templateTitleSubject[0] market mergers and acquisitions ( templateTitleSubject[0] templateTitle[3] A ) templateTitle[7] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , based on the values of disclosed templateTitle[4] , the templateTitleSubject[0] 's templateTitleSubject[0] templateTitle[3] A deals amounted to templateYValue[max] templateYLabel[1] 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 2016 France , based on the values of disclosed 1992 , the UEFA European Championships 's UEFA European Championships Championships A deals amounted to 1916.0 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[0] percent of school children aged eight to 18 years reported templateTitle[2] templateTitle[0] books , which was a considerable decline from templateXValue[min] . templateTitle[2] comics was less common than templateTitle[2] magazines , fiction and newspapers in templateXValue[1] .

generated_template: This statistic illustrates the proportion of templateTitle[3] templateTitle[4] who read magazines outside of class in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The percentage of school children aged 8 to 18 years who read magazines declined since templateXValue[min] , and was templateYValue[min] percent in templateXValue[max] . Magazines ranked highly among media read templateTitle[2] templateTitle[3] templateTitle[4] .
generated: This statistic illustrates the proportion of by young who read magazines outside of class in the United Kingdom from 2005 to 2015 . The percentage of school children aged 8 to 18 years who read magazines declined since 2005 , and was 25.1 percent in 2015 . Magazines ranked highly among media read reading by young .

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

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

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateTitleDate[1] , templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: The statistic shows the Expenditure of U.S. musical instruments U.S. from 1999 to 2013 . In 2013 , U.S. musical instruments U.S. amounted to about 5.52 billion U.S. .

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

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

generated_template: This statistic shows average ad templateTitle[2] templateTitle[3] templateTitle[4] businesses in the United Kingdom in templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitle[2] reached templateYValue[max] templateYLabel[0] templateYLabel[1] . templateTitle[0] templateTitle[2] grew templateTitle[3] more than half during the period in consideration .
generated: This statistic shows average ad from smart wearable businesses in the United Kingdom in 2014 and 2019 . In 2019 , from reached 53.2 Retail revenue . Retail from grew smart more than half during the period in consideration .

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

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

generated_template: This statistic shows the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] ( UK ) from templateTitle[4] to templateTitle[5] . The largest single year on year increase came from 1919 to 1920 , not long after the end of the First World War , when templateYLabel[2] increased by 300,647 . Despite the lower level of population , from templateTitle[4] to 1914 the templateYLabel[0] of templateYLabel[1] templateYLabel[2] was consistently above templateYValue[0] million .
generated: This statistic shows the total Population of million inhabitants in the China ( UK ) from region to region . The largest single year on year 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 region 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] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] of vice-championships at soccer templateTitleSubject[0] Cups since templateTitleDate[0] templateTitle[4] templateXLabel[0] . templateXValue[0] has been templateYLabel[1] templateYValue[max] times .
generated: The statistic shows the Production of vice-championships at soccer Global Cups since 2016 Country . China mainland has been value 3434.02 times .

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

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

generated_template: templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[3] grew by 21.8 percent in templateXValue[max] compared to templateXValue[1] , templateTitle[4] templateYValue[1] templateYLabel[1] to 107 templateYLabel[1] templateTitle[3] templateYLabel[3] . This is the first time the figure has surpassed 100 templateYLabel[1] templateYLabel[3] , owing to the emergence of new channels and formats including virtual and augmented reality , podcasts and OTT content as well as strong growth templateTitle[4] existing channels such as mobile . templateTitle[0] templateTitle[1] at a glance Search is dominating the internet templateTitle[1] scene among the remaining formats in the templateTitle[3] , with a 45 percent share .
generated: Google network Revenue in the advertising grew by 21.8 percent in 2019 compared to 2018 , revenue 20.01 billion to 107 billion advertising dollars . This is the first time the figure has surpassed 100 billion dollars , owing to the emergence of new channels and formats including virtual and augmented reality , podcasts and OTT content as well as strong growth revenue existing channels such as mobile . Google network at a glance Search is dominating the internet network scene among the remaining formats in the advertising , with a 45 percent share .

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] templateYLabel[3] 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] templateYLabel[3] templateYLabel[4] from its templateYLabel[0] sponsor SportPesa .
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 SportPesa .

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[11] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( or about 326 templateYLabel[1] euros ) .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Global hotel company templateTitleSubject[0] International generated approximately templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] in templateXValue[max] , up from templateYValue[1] templateYLabel[1] the previous templateXLabel[0] .
generated: This statistic shows the Mechanical Revenue of United States from 2006 to 2017 . Global hotel company United States International generated approximately 340.0 billion U.S. dollars in Revenue in 2017 , up from 331.37 billion the previous Year .

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

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

generated_template: This statistic shows the estimated percentage of templateYLabel[0] templateYLabel[1] a templateTitle[0] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . Since templateXValue[5] , the share of household templateYLabel[1] templateYLabel[2] in the templateTitleSubject[1] has increased , with an estimated templateYValue[0] percent templateYLabel[1] one in templateXValue[7] . However , the share of household templateYLabel[1] templateYLabel[2] decreased to templateYValue[min] percent in templateXValue[max] .
generated: This statistic shows the estimated percentage of Occupancy rate a Hotel in the Rome ( Rome ) from 2011 to 2019 . Since 2016 , the share of household rate in the Rome has increased , with an estimated 67 percent rate one in 2018 . However , the share of household rate decreased to 66 percent in 2019 .

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

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

generated_template: The statistic shows the female to male templateYLabel[0] templateYLabel[1] in the templateTitle[0] in the fourth quarter of templateTitle[8] , based on the median income in current templateTitle[0] dollars , templateTitle[4] templateXLabel[0] templateXLabel[1] . In the fourth quarter of templateTitle[8] , the templateYLabel[0] templateYLabel[1] of female to male workers aged between templateXValue[0] to templateXValue[0] years was at about templateYValue[max] templateYLabel[2] .
generated: The statistic shows the female to male Number workers in the Music in the fourth quarter of sector , based on the median income in current Music dollars , Kingdom Industry . In the fourth quarter of sector , the Number workers of female to male workers aged between Music creators to Music creators years was at about 139352 workers .

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] million mobile templateYLabel[2] were templateYLabel[1] , representing a 4.07 percent increase compared to the previous templateXLabel[0] .

generated_template: As of the fourth templateXLabel[0] of templateTitleDate[0] , templateYValue[max] mobile templateYLabel[2] were templateYLabel[1] in the templateTitle[0] templateTitleSubject[0] . This represents a 0.4 percent growth in templateYLabel[1] templateYLabel[2] compared to the previous templateXLabel[0] . The templateTitle[0] templateTitleSubject[0] does not only offer Android templateYLabel[2] but also templateYLabel[2] specifically optimized for its range of Kindle Fire tablets as well as the Fire TV and Fire Phone .
generated: As of the fourth Quarter of 2019 , 3849865 mobile apps were available in the Google Play Q4 . This represents a 0.4 percent growth in available apps compared to the previous Quarter . The Google Play Q4 does not only offer Android apps but also apps specifically optimized for its range of Kindle Fire tablets as well as the Fire TV and Fire Phone .

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

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

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

Example 406:
titleEntities: {'Subject': ['Wisconsin'], 'Date': ['1990', '2018']}
title: Wisconsin - Median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['62629', '63451', '59817', '55425', '58080', '55258', '53079', '52058', '50351', '51237', '51200', '51277', '51692', '44650', '45732', '46269', '45903', '45346', '45088', '45667', '41327', '39595', '40001', '40955', '35388', '31766', '33308', '31133', '30711']

gold: This statistic shows the median household income in Wisconsin from 1990 to 2018 . In 2018 , the median household income in Wisconsin amounted to 62,629 U.S. dollars .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Median Household income in Wisconsin from 1990 to 2018 . In 2018 , the Median Household income in Wisconsin amounted to 62629 U.S. 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[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] for such purposes . templateTitleSubject[0] Shell is one of the top oil and gas companies worldwide , operating in every segment of the oil and gas industry .

generated_template: This statistic 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[0] templateYLabel[3] 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 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] templateYLabel[1] templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was estimated at almost templateYValue[7] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] has a surprisingly low ( and decreasing ) templateTitle[1] growth rate ; despite it being home to the largest number of Catholics in the world , the majority of women in templateTitleSubject[0] use some form of contraception , which is often government-subsidized or free , even though the Catholic Church retains its stance that the use of contraceptives is inherently wrong .
generated: This statistic shows the Total population of Pakistan from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Pakistan was estimated at almost 197.26 millions Inhabitants . population of Pakistan has a surprisingly low ( and decreasing ) population growth rate ; despite it being home to the largest number of Catholics in the world , the majority of women in Pakistan use some form of contraception , which is often government-subsidized or free , even though the Catholic Church retains its stance that the use of contraceptives is inherently wrong .

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

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

generated_template: The statistic shows the templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] ( VPN ) templateYLabel[0] worldwide , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] VPN templateYLabel[0] is forecast to reach templateYValue[max] templateYLabel[2] 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 2023 , the Global VPN Market is forecast to reach 37.19 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: In 2020 , the templateYLabel[0] for one templateYLabel[4] of templateTitle[0] templateTitleSubject[0] ( templateTitleSubject[0] ) templateTitle[4] templateTitle[5] stood at some templateYValue[0] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] is also known as `` templateTitleSubject[0] light sweet '' , and is a grade of templateTitle[4] templateTitle[5] often used as a benchmark for templateTitle[5] pricing . templateYLabel[0] of templateTitle[0] templateTitleSubject[0] crude templateTitle[5] , templateTitle[7] templateTitleSubject[0] is also known as `` templateTitleSubject[0] light sweet '' and is a grade of templateTitle[4] templateTitle[5] .
generated: In 2020 , the Number for one licenses of Canada ( Canada ) licenses held stood at some 616489 firearms licenses . Canada is also known as `` Canada light sweet '' , and is a grade of licenses held often used as a benchmark for held pricing . Number of Canada crude held , province Canada is also known as `` Canada light sweet '' and is a grade of licenses held .

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] percent . This is a decrease in comparison to the previous templateXLabel[0] , and the lowest templateYLabel[0] templateYLabel[1] in the last ten years . templateYLabel[0] peaked in templateXValue[4] and templateXValue[3] , at templateYValue[max] percent .

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

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

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

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

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

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

generated_template: The statistic shows the share of templateTitle[1] that have air-conditioning templateTitle[4] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . templateXValue[0] ranked the highest in air-conditioning templateYLabel[0] templateYLabel[1] , templateTitle[2] around templateYValue[max] percent of Japanese templateTitle[1] having some form of air-conditioning .
generated: The statistic shows the share of global that have air-conditioning value in 2012 , country . United States ranked the highest in air-conditioning Market value , seeds around 26.71 percent of Japanese global having some form of air-conditioning .

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] templateYLabel[1] British pounds on templateTitle[1] in the templateTitleSubject[0] ( UK ) . This is the highest consumer spending recorded in the past thirteen years and the second time spending surpassed templateYValue[1] templateYLabel[1] pounds . Spending has generally grown since templateXValue[8] .

generated_template: This statistic shows the total annual templateYLabel[0] on templateTitle[0] and furnishings in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , based on volume . In templateXValue[max] , templateTitleSubject[1] households purchased approximately 19.1 templateYLabel[1] British pounds worth of templateTitle[0] and furnishings , an increase on the previous templateXLabel[0] of roughly one and a half templateYLabel[1] British pounds . According to results of the templateXValue[1] Statista Global Consumer survey , 24 percent of templateTitleSubject[1] consumers have bought templateTitle[0] and household goods online in the last 12 months , while 33 percent claim to mostly look online for information about these products .
generated: This statistic shows the total annual Expenditure on Expenditure and furnishings in the United Kingdom from 2005 to 2018 , based on volume . In 2018 , United Kingdom households purchased approximately 19.1 million British pounds worth of Expenditure and furnishings , an increase on the previous Year of roughly one and a half million British pounds . According to results of the 2017 Statista Global Consumer survey , 24 percent of United Kingdom consumers have bought Expenditure and household goods online in the last 12 months , while 33 percent claim to mostly look online for information about these products .

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

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

generated_template: The templateYLabel[0] of templateTitleSubject[0] increased from approximately templateYValue[8] templateYLabel[1] U.S. dollars in templateXValue[8] to approximately templateYValue[max] templateYLabel[1] templateYLabel[2] in templateXValue[max] . Their templateYLabel[0] has increased steadily since templateXValue[10] and reached its decade-long peak in templateXValue[max] Who are templateTitleSubject[0] ? templateTitleSubject[0] was founded in Berlin in 1890 , but the headquarters was moved to Munich , Germany after the Berlin headquarters were destroyed in the second World War . templateTitleSubject[0] offer property and casualty insurance , health and life insurance , asset management and business insurance .
generated: The Total of European increased from approximately 398040 number U.S. dollars in 2010 to approximately 420200 number ATMs in 2018 . Their Total has increased steadily since 2008 and reached its decade-long peak in 2018 Who are European ? European was founded in Berlin in 1890 , but the headquarters was moved to Munich , Germany after the Berlin headquarters were destroyed in the second World War . European offer property and casualty insurance , health and life insurance , asset management and business insurance .

Example 416:
titleEntities: {'Subject': ['Bosnia-Herzegovina'], 'Date': ['2019']}
title: Unemployment rate in Bosnia-Herzegovina 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['21.22', '20.84', '20.47', '25.41', '27.65', '27.52', '27.45', '28.01', '27.58', '27.31', '24.07', '23.41', '28.98', '31.11', '30.49', '29.87', '29.03', '28.22', '27.13', '26.19', '25.31']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent . templateYLabel[0] in templateTitleSubject[0] 's templateYLabel[0] is relatively low and stable at around templateYValue[0] percent 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 percent . Unemployment in Bosnia-Herzegovina 's Unemployment is relatively low and stable at around 21.22 percent 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] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

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

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

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

generated_template: The statistic shows the templateYLabel[0] 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] templateYLabel[2] 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 Share market/transfer search of the participating search market at the share 2017 by 2017 in Brazil . The Spanish squad has a combined market/transfer search of 33 traffic . Transfer search of 2017 by squads – additional information In international football , a team 's Share market/transfer search refers to the sum of all of the transfer fees that each individual player could command in the current market at their club level .

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[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: This statistic depicts the Average annual prices for zinc from 2014 through 2025 . In 2018 , the Average Price for zinc stood at 2922 nominal U.S. dollars per metric 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[0] to templateTitleDate[1] . In templateTitleDate[1] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at templateYValue[0] percent .

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

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

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

generated_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . According to Deloitte , in the templateXValue[0] season the templateYLabel[0] of the Spanish football club grew by 22 percent , to templateYValue[max] templateYLabel[1] templateYLabel[2] , with record matchday and commercial templateYLabel[0] for a football club . templateTitleSubject[0] - additional information templateTitleSubject[0] 's brand and team value increased significantly in 2014 , thanks in part to the high-profile signing of Luis Suarez from Liverpool templateTitleSubject[0] in 2014 .
generated: This statistic shows the Revenue of the European from the 2006/07 season to the 2019/20 season . According to Deloitte , in the 2019/20 season the Revenue of the Spanish football club grew by 22 percent , to 17.95 billion euros , with record matchday and commercial Revenue for a football club . European - additional information European 's brand and team value increased significantly in 2014 , thanks in part to the high-profile signing of Luis Suarez from Liverpool European in 2014 .

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] 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 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 .

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

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

generated_template: In templateXValue[max] , the templateTitle[1] templateYLabel[0] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] amounted to about templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] , all types included . The templateTitle[0] realized templateYLabel[0] of templateTitle[3] templateTitle[4] remained fairly steady throughout the years until templateXValue[3] , when it decreased to below templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitle[3] templateTitle[4] The templateTitle[0] templateYLabel[0] serves as an indicator for a variety of different selling prices on the templateTitle[4] market , gathering all templateYLabel[0] ranges of templateTitle[3] wines purchased in templateTitleSubject[0] .
generated: In 2028 , the contribution Value of tourism GDP in Saudi Arabia amounted to about 258.1 billion Saudi Riyal , all types included . The Total realized Value of tourism GDP remained fairly steady throughout the years until 2016 , when it decreased to below 573.1 billion Saudi Riyal . tourism GDP The Total Value serves as an indicator for a variety of different selling prices on the GDP market , gathering all Value ranges of tourism wines purchased in Saudi Arabia .

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

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

generated_template: The statistic shows the templateYLabel[0] of the templateTitle[0] templateTitleSubject[0] franchise from the 2004/05 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise is templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Revenue U.S. franchise from the 2004/05 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise is 72.3 million U.S. dollars .

Example 426:
titleEntities: {'Subject': ['Norway'], 'Date': ['2024']}
title: Norway 's budget balance in relation to GDP 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'balance', 'in', 'relation', 'to', 'GDP']: ['8.6', '8.21', '7.84', '7.75', '7.82', '7.57', '7.25', '4.92', '4.04', '6.07', '8.77']

gold: The statistic shows Norway 's budget balance in relation to GDP between 2014 and 2018 , with projections up until 2024 . A positive value indicates a budget surplus , a negative value indicates a deficit . In 2018 , Norway 's surplus amounted to around 7.25 percent of GDP .
gold_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a deficit . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] surplus amounted to around templateYValue[6] percent of templateYLabel[3] .

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a deficit . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] surplus amounted to around templateYValue[max] percent of templateYLabel[3] .
generated: The statistic shows Norway 's Budget balance in relation to GDP between 2014 and 2018 , with projections up until 2024 . A positive value indicates a Budget surplus , a negative value indicates a deficit . In 2018 , Norway 's Budget surplus amounted to around 8.77 percent of GDP .

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

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

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] amounted to approximately templateYValue[0] percent of the templateYLabel[3] templateYLabel[4] templateYLabel[5] . The economic situation in templateTitleSubject[0] Amid the political and economic crisis , templateTitleSubject[0] 's templateTitle[0] is rising .
generated: This statistic shows the U.S active of the U.S from 1990 to 2010 . In 2010 , the U.S active of the U.S amounted to approximately 289 percent of the suicides . The economic situation in U.S Amid the political and economic crisis , U.S 's U.S is rising .

Example 428:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2012', '2014']}
title: PC online games revenue in Malaysia 2012 to 2014
X_Axis['Year']: ['2014', '2013', '2012']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['66.5', '54.1', '44.1']

gold: The statistic presents a forecast of the PC online games revenue in Malaysia from 2012 to 2014 . It was estimated that the 2014 PC online games revenue for Malaysia would be 66.5 million U.S. dollars .
gold_template: The statistic presents a forecast of the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . It was estimated that the templateXValue[max] templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] for templateTitleSubject[0] would be templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic 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[max] templateYLabel[1] 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[1] . As of that period , the mobile messaging app announced more than templateYValue[0] templateYLabel[3] templateYLabel[1] templateTitle[3] templateYLabel[2] . In October templateTitleDate[0] , templateTitleSubject[0] had also reported 560 templateYLabel[3] registered templateYLabel[2] worldwide .

generated_template: This statistic illustrates the global templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] as of the second templateXLabel[0] of templateTitleDate[0] , measured templateTitle[7] total number of times an templateTitle[1] was used . During the survey period it was found that templateYValue[0] percent of apps were only used once . Overall , social media templateTitle[1] had the highest monthly launch templateYLabel[1] out of all templateTitle[1] verticals with an average of 20.28 templateTitle[1] launches per month .
generated: This statistic illustrates the global LINE number monthly as of the second Quarter of 2014 , measured 2016 total number of times an number was used . During the survey period it was found that 217.0 percent of apps were only used once . Overall , social media number had the highest monthly launch monthly out of all number verticals with an average of 20.28 number launches per month .

Example 430:
titleEntities: {'Subject': ['Liberia'], 'Date': ['2019']}
title: Unemployment rate in Liberia 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['2.03', '2.03', '2.03', '2.15', '2.18', '2.21', '2.29', '2.26', '2.28', '2.27', '2.25', '1.96', '2.03', '2.23', '2.42', '2.53', '2.61', '2.66', '2.61', '2.77', '2.78']

gold: This statistic shows the unemployment rate in Liberia from 1999 to 2019 . In 2019 , the unemployment rate in Liberia was at 2.03 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at templateYValue[0] percent .

generated_template: The templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] reached templateYValue[0] percent in templateXValue[max] , a small increase from the previous templateXLabel[0] . Most of templateTitleSubject[0] 's population is of working age and employed . About 46 percent of templateTitleSubject[0] 's inhabitants work in the service sector , and another large portion of the population works in agriculture .
generated: The Unemployment rate in Liberia reached 2.03 percent in 2019 , a small increase from the previous Year . Most of Liberia 's population is of working age and employed . About 46 percent of Liberia 's inhabitants work in the service sector , and another large portion of the population works in agriculture .

Example 431:
titleEntities: {'Subject': ['Zimbabwe'], 'Date': ['2019']}
title: Youth unemployment rate in Zimbabwe in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['8.13', '8.16', '8.18', '8.56', '8.79', '8.86', '8.96', '8.86', '8.47', '8.15', '8.06', '7.39', '6.96', '6.85', '7.21', '7.41', '8.54', '9.5', '10.11', '11.02', '11.95']

gold: The statistic shows the youth unemployment rate in Zimbabwe from 1999 and 2019 . The data are ILO estimates . In 2019 , the estimated youth unemployment rate in Zimbabwe was at 8.13 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . The data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[0] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[0] percent .
generated: The statistic shows the Youth unemployment rate in Zimbabwe from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated Youth unemployment rate in Zimbabwe was at 8.13 percent .

Example 432:
titleEntities: {'Subject': ['El Pais', 'Spain'], 'Date': ['1999', '2018']}
title: El Pais ( Spain ) : circulation 1999 to 2018
X_Axis['Month']: ['July_2017-June_2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Circulation']: ['163759', '194005', '221390', '259775', '292227', '324814', '365118', '370080', '391816', '431034', '435083', '432204', '453602', '469183', '440226', '435299', '433617', '436302', '435433']

gold: This timeline shows the average circulation of the Spanish daily El Pais from 1999 to 2018 . In the period between July 2017 and June 2018 , the Spanish newspaper sold on average 163.8 thousand copies daily .
gold_template: This timeline shows the average templateYLabel[0] of the Spanish daily templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In the period between templateXValue[0] 2017 and 2018 , the Spanish newspaper sold on average templateYValue[min] thousand copies daily .

generated_template: This statistic illustrates a ranking of the templateTitle[2] templateTitle[3] verticals with the highest install and user base templateYLabel[0] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] templateYLabel[2] with rating of templateYValue[max] . This , according to the source , is due to the templateYLabel[0] of battle royale and MOBA templateXValue[0] such as Fortnite .
generated: This statistic illustrates a ranking of the Spain circulation verticals with the highest install and user base Circulation in 1999 . In 1999 , July 2017-June 2018 had the highest Circulation with rating of 469183 . This , according to the source , is due to the Circulation of battle royale and MOBA July 2017-June 2018 such as Fortnite .

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: 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 Best cars the largest number of based Percentage 10,000 Percentage in the 2016 in 2016 . In Land Rover Discovery MkIV , California , there were 94.63 based Percentage 10,000 Percentage in 2016 .

Example 434:
titleEntities: {'Subject': ['EU'], 'Date': ['2009', '2018']}
title: Number of illegal entries between BCPs to the EU 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'illegal', 'entries', 'in', 'thousands']: ['150.1', '204.72', '511.05', '1822.18', '282.93', '107.37', '72.44', '141.05', '104.06', '104.6']

gold: This statistic shows the total number of individuals detected entering the European Union ( EU ) illegally between border-crossing points ( BCPs ) from 2009 to 2018 . In 2013 , there was a total of approximately 107 thousand illegal entries between BCPs , making it a 48 percent increase on the previous year . By 2015 the number of individuals had increased to almost two million illegal entries .
gold_template: This statistic shows the total templateYLabel[0] of individuals detected entering the European Union ( templateTitleSubject[0] ) illegally templateTitle[3] border-crossing points ( templateTitle[4] ) from templateXValue[min] to templateXValue[max] . In templateXValue[5] , there was a total of approximately templateYValue[5] thousand templateYLabel[1] templateYLabel[2] templateTitle[3] templateTitle[4] , making it a 48 percent increase on the previous templateXLabel[0] . By templateXValue[3] the templateYLabel[0] of individuals had increased to almost templateYValue[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of templateTitleSubject[0] amounted to approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Number illegal of the EU of EU from 2009 to 2018 . In 2018 , the Number illegal of the EU of EU amounted to approximately 1822.18 entries 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[max] templateYLabel[1] in revenues in templateXValue[min] . The figure is estimated to decrease to $ templateYValue[min] templateYLabel[1] by templateXValue[max] .

generated_template: The statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[4] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] amounted to templateYValue[3] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the U.S. e-learning industry revenue U.S. from 2016 to 2021 . In 2017 , the U.S. e-learning industry revenue amounted to 19.53 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 million attendees .

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 America 2018 is an annual North prize presented by France North . The award , voted for by North journalists , is given to the male player who was deemed to have played the best North over the previous 12 months . Unsurprisingly , 353 of the greatest footballers of all time , AMC Theatres and Regal Entertainment Group , top the list of all-time cinema .

Example 437:
titleEntities: {'Subject': ['North America'], 'Date': ['2017']}
title: Reasons for cutting the cord in North America 2017
X_Axis['Response']: ['Price_-_too_expensive', 'I_use_an_internet_streaming_service_such_as_Netflix_Hulu_Amazon_Video_etc.', 'I_use_an_antenna_to_get_the_basic_channels_on_my_TV', 'I_like_to_binge_watch_an_entire_season_of_a_TV_series_through_my_streaming_service', 'I_moved/relocated_and_I_do_not_plan_to_sign-up_for_cable/satellite_service_again', 'The_bulk_of_my_TV_viewing_was_the_original_series_on_streaming_services', "I_share_a_friend/family_member's_login_to_watch_shows_on_their_cable/satellite_provider's_app"]
Y_Axis['Share', 'of', 'respondents']: ['86.7', '39.7', '23', '15.9', '13', '7.7', '0.9']

gold: The graph shows reasons for cutting the cord named by respondents from North America in the fourth quarter of 2017 . During a a survey , it was found that 86.7 percent of respondents cut off their cable or satellite service because it was too expensive .
gold_template: The graph templateXValue[last] templateTitle[0] templateXValue[4] templateTitle[2] the templateTitle[3] named by templateYLabel[1] from templateTitleSubject[0] in the fourth quarter of templateTitleDate[0] . During a survey , it templateXValue[5] found that templateYValue[max] percent of templateYLabel[1] cut off templateXValue[last] cable or satellite templateXValue[1] because it templateXValue[5] templateXValue[0] .

generated_template: The statistic shows 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] percent 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 shows the Reasons of for people in the North America would be willing to spend on their North America in 2017 according to a Statista survey . 86.7 percent of respondents said that they would be willing to spend Price - too expensive to 25 2017 dollars on North America .

Example 438:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Dietary supplement usage in U.S. adults by gender 2018
X_Axis['Sex']: ['Female', 'Male']
Y_Axis['Percentage', 'of', 'U.S.', 'adults']: ['77', '73']

gold: This statistic indicates the percentage of U.S. adults that take dietary supplements , distributed by gender . The statistic is based on a survey conducted in August 2018 . Among U.S. adult males , some 73 percent reported taking dietary supplements .
gold_template: This statistic indicates the templateYLabel[0] of templateYLabel[1] templateYLabel[2] that take templateTitle[0] supplements , distributed templateTitle[5] templateTitle[6] . The statistic is based on a survey conducted in 2018 . Among templateYLabel[1] adult males , some templateYValue[min] percent reported taking templateTitle[0] supplements .

generated_template: The statistic shows the distribution of templateTitle[0] and templateTitle[1] templateTitle[2] templateYLabel[0] to templateYLabel[1] in Japan in templateTitleDate[0] , templateTitle[7] templateXLabel[1] of templateTitle[0] . That year , templateXValue[0] templateTitle[0] templateXValue[0] generated approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] of direct templateTitle[0] and templateTitle[1] templateYLabel[1] .
generated: The statistic shows the distribution of Dietary and supplement usage Percentage to U.S. in Japan in 2018 , 2018 Sex of Dietary . That year , Female Dietary Female generated approximately 77 adults of direct Dietary and supplement U.S. .

Example 439:
titleEntities: {'Subject': ['Boston Bruins'], 'Date': ['2005', '2019']}
title: Boston Bruins ' revenue 2005 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['228', '191', '176', '169', '158', '164', '114', '129', '125', '110', '108', '97', '87', '86']

gold: The statistic shows the revenue of the Boston Bruins from the 2005/06 season to the 2018/19 season . The revenue of the Boston Bruins amounted to 228 million U.S. dollars in the 2018/19 season .
gold_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .

generated_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .
generated: This graph depicts the annual National Hockey League Revenue of the Boston Bruins from the 2005/06 season to the 2018/19 season . The Revenue of the Boston Bruins amounted to 228 million U.S. dollars in the 2018/19 season .

Example 440:
titleEntities: {'Subject': ['Rwanda'], 'Date': ['2018']}
title: Urbanization in Rwanda 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['17.21', '17.13', '17.06', '17', '16.97', '16.95', '16.94', '16.94', '16.93', '16.93', '16.93']

gold: This statistic shows the degree of urbanization in Rwanda from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 17.21 percent of Rwanda 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

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

Example 441:
titleEntities: {'Subject': ['Production'], 'Date': ['2013', '2020']}
title: Production of pork worldwide 2013 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Thousand', 'tons', 'carcass', 'weight']: ['96.38', '106.0', '112.94', '112.07', '111.39', '112.01', '110.65', '108.85']

gold: This statistic depicts the production of pork worldwide from 2013 to 2020 . The net production of pork worldwide amounted to about 113 million tons carcass weight in 2018 , and forecasted to decrease to 96.4 million metric tones by 2020 .
gold_template: This statistic depicts the templateTitleSubject[0] of templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . The net templateTitleSubject[0] of templateTitle[1] templateTitle[2] amounted to about templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[2] , and forecasted to decrease to templateYValue[min] templateYLabel[0] metric tones by templateXValue[max] .

generated_template: This statistic 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 increased , reaching templateYValue[2] templateYLabel[1] templateYLabel[2] in templateXValue[max] .
generated: This statistic shows 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 has increased , reaching 112.94 tons carcass in 2020 .

Example 442:
titleEntities: {'Subject': ['UEFA Champions League'], 'Date': ['2005', '2018']}
title: UEFA Champions League total performance and bonus payments to clubs 2005 to 2018
X_Axis['Year']: ['2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Bonus', 'payments', 'in', 'million', 'euros']: ['1412.6', '1396.13', '1349.43', '1033.43', '904.6', '910.0', '754.1', '786.3', '757.5', '583.4', '585.6', '584.9', '437.1']

gold: The statistic shows the total amount of bonus payments to the participating clubs in the UEFA Champions League from the 2005/06 season to the 2017/18 season . In the 2017/18 season , the total bonus payments to the participating clubs amounted to 1,412.6 million euros .
gold_template: The statistic shows the templateTitle[3] amount of templateYLabel[0] templateYLabel[1] to the participating templateTitle[7] in the templateTitleSubject[0] League from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[0] season , the templateTitle[3] templateYLabel[0] templateYLabel[1] to the participating templateTitle[7] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: In 2020 , the templateTitle[0] templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] was roughly 22,70 templateYLabel[2] . templateYLabel[0] wages varied between some four templateYLabel[2] per hour ( for those templateXValue[0] 15 ) to just over 27 templateYLabel[2] templateYLabel[0] ( for those 50 to 60 templateXValue[0] old ) . Highest templateYLabel[0] wages in the mining and quarrying industry Wages were highest in the mining and quarrying sector , at nearly 35 templateYLabel[2] per hour .
generated: In 2020 , the UEFA Bonus payments in the UEFA Champions League was roughly 22,70 million . Bonus wages varied between some four million per hour ( for those 2017/18 15 ) to just over 27 million Bonus ( for those 50 to 60 2017/18 old ) . Highest Bonus wages in the mining and quarrying industry Wages were highest in the mining and quarrying sector , at nearly 35 million per hour .

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 shows 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] in research and development . templateTitleSubject[0] was an agricultural company specialized on genetically engineered seeds .
generated: This statistic shows 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] templateYLabel[1] , up from templateYValue[1] the previous templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[max] templateTitleSubject[0] templateYLabel[1] templateTitle[3] .
generated: This statistic shows the Number of The Cheesecake Factory in the number establishments 2009 to 2018 . In 2018 , there were 201 The Cheesecake Factory restaurants number .

Example 445:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2017']}
title: Life expectancy at birth in Vietnam 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Life', 'expectancy', 'at', 'birth', 'in', 'years']: ['75.24', '75.17', '75.11', '75.06', '75.01', '74.96', '74.9', '74.84', '74.75', '74.63', '74.47']

gold: This statistic shows the life expectancy at birth in Vietnam from 2007 to 2017 . In 2017 , the average life expectancy at birth in Vietnam was 75.24 years .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] was templateYValue[max] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] in templateXValue[max] was templateYValue[max] templateYLabel[3] . Standard of living in templateTitleSubject[0] is one of the so-called BRIC countries , an acronym which stands for Brazil , Russia , templateTitleSubject[0] and China , the four states considered the major emerging market countries .
generated: The statistic shows the Life expectancy at birth in Vietnam from 2007 to 2017 . The average Life expectancy at birth in Vietnam in 2017 was 75.24 years . Standard of living in Vietnam is one of the so-called BRIC countries , an acronym which stands for Brazil , Russia , Vietnam and China , the four states considered the major emerging market countries .

Example 446:
titleEntities: {'Subject': ['Major League Soccer'], 'Date': ['2019']}
title: Major League Soccer teams ranked by operating income 2019
X_Axis['Team', 'Name']: ['Atlanta_United', 'LA_Galaxy', 'Portland_Timbers', 'Real_Salt_Lake', 'Seattle_Sounders', 'D.C._United', 'Sporting_Kansas_City', 'Orlando_City_SC', 'New_England_Revolution', 'Philadelphia_Union', 'Los_Angeles_FC', 'Vancouver_Whitecaps', 'Colorado_Rapids', 'San_Jose_Earthquakes', 'New_York_Red_Bulls', 'Houston_Dynamo', 'FC_Dallas', 'Columbus_Crew', 'Minnesota_United', 'Montreal_Impact', 'Chicago_Fire', 'New_York_City_FC', 'Toronto_FC']
Y_Axis['Operating', 'income/loss', 'in', 'million', 'U.S.', 'dollars']: ['7', '5', '4', '2', '1', '1', '1', '-1', '-2', '-5', '-5', '-5', '-5', '-5', '-6', '-6', '-7', '-8', '-8', '-12', '-16', '-16', '-19']

gold: The statistic shows a ranking of Major League Soccer teams according to their operating income/loss . Atlanta United had an operating income of seven million U.S. dollars in the 2019 MLS season .
gold_template: The statistic shows a ranking of templateTitleSubject[0] Soccer templateTitle[3] according to their templateYLabel[0] templateYLabel[1] . templateXValue[0] had an templateYLabel[0] templateTitle[7] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in the templateTitleDate[0] MLS season .

generated_template: This statistic shows the winners of the Football templateTitle[5] templateTitle[6] from 1961 to templateTitle[8] . templateXValue[0] have won the templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[1] , having lifted the trophy a total of templateYValue[max] times .
generated: This statistic shows the winners of the Football by operating from 1961 to 2019 . Atlanta United have won the ranked by operating income/loss , having lifted the trophy a total of 7 times .

Example 447:
titleEntities: {'Subject': ['Eritrea'], 'Date': ['2024']}
title: Total population of Eritrea 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['6.68', '6.58', '6.48', '6.38', '6.27', '6.16', '6.05', '5.93', '5.82', '5.7', '5.58']

gold: This statistic shows the total population of Eritrea from 2014 to 2024 . All figures are estimates . In 2018 , the total population of Eritrea was estimated to amount to approximately 6.05 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . All figures are estimates . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was estimated to amount to approximately templateYValue[6] templateYLabel[1] templateYLabel[0] .

generated_template: This statistic shows templateTitleSubject[0] 's templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[7] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[7] templateYLabel[1] templateYLabel[0] . The templateTitle[1] of templateTitleSubject[0] is the ten largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .
generated: This statistic shows Eritrea 's Total population from 2014 to 2018 , with projections up until 2024 . In 2017 , the Total population of Eritrea amounted to approximately 5.93 millions Inhabitants . The population of Eritrea is the ten largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .

Example 448:
titleEntities: {'Subject': ['Europe'], 'Date': ['2020']}
title: Europe : forecasted distribution of golfers in 2020 , by age group
X_Axis['Year']: ['20_or_younger', '20_to_40_years', '40_to_50_years', '50_to_60_years', '60_or_older']
Y_Axis['Share', 'of', 'average', 'increase']: ['6', '11', '18', '24', '42']

gold: The statistic displays the forecast of a golf player distribution in Europe in 2020 , by age group . With data from five European countries it was forecasted that in 2020 approximately 24 percent of golf players will be between 50 and 60 years old .
gold_template: The statistic displays the forecast of a golf player templateTitle[2] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateTitle[6] templateTitle[7] . With data from five European countries it was templateTitle[1] that in templateTitleDate[0] approximately templateYValue[3] percent of golf players will be between templateXValue[2] and templateXValue[3] templateXValue[1] old .

generated_template: This statistic shows the templateTitle[3] of the templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[0] to templateTitleDate[1] and visualises the predicted 'ageing templateTitle[6] ' _ . Over the 20 templateXLabel[0] period , the templateYLabel[1] templateYLabel[2] is expected to increase by 1.7 years , the largest increase predicted between templateXValue[2] and templateXValue[1] at 0.8 years .
generated: This statistic shows the golfers of the average increase in the Europe ( Europe ) from 2020 to 2020 and visualises the predicted 'ageing age ' _ . Over the 20 Year period , the average increase is expected to increase by 1.7 years , the largest increase predicted between 40 to 50 years and 20 to 40 years at 0.8 years .

Example 449:
titleEntities: {'Subject': ['European'], 'Date': ['2015']}
title: Number of people with hearing loss global vs European countries 2015
X_Axis['Country']: ['Global', 'Europe', 'European_Union', 'United_Kingdom', 'France', 'Spain', 'Netherlands', 'Austria', 'Sweden', 'Belgium', 'Poland', 'Denmark', 'Ireland']
Y_Axis['Estimated', 'number', 'of', 'people', 'with', 'hearing', 'loss']: ['328.0', '119.0', '51.0', '10.0', '6.0', '3.5', '1.6', '1.6', '1.4', '1.3', '1.0', '0.8', '0.8']

gold: This statistic shows the estimated number of people with hearing loss worldwide and in Europe as of 2015 , by country , in millions . As of this time an estimated 119 million people in the whole of Europe were hard of hearing , with 3.5 million of these people located in Spain .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] worldwide and in templateXValue[1] as of templateTitleDate[0] , by templateXLabel[0] , in millions . As of this time an templateYLabel[0] templateYValue[1] million templateYLabel[2] in the whole of templateXValue[1] were hard of templateYLabel[4] , templateYLabel[3] templateYValue[5] million of these templateYLabel[2] located in templateXValue[5] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] in templateTitle[5] templateTitle[6] templateTitle[7] from templateTitleDate[0] to templateTitleDate[1] . The data are a templateYLabel[0] templateYLabel[1] of about templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateTitleDate[0] .
generated: This statistic shows the Estimated number of loss global in vs European countries from 2015 to 2015 . The data are a Estimated number of about 10.0 people hearing loss in 2015 .

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] percent in templateXValue[9] , reaching 42 billion 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 percent 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] templateYLabel[2] templateYLabel[1] , as opposed to its templateYValue[min] templateYLabel[2] follower base on templateXValue[last] .

generated_template: This statistic gives information on the templateYLabel[1] number of templateTitle[1] templateYLabel[3] templateYLabel[2] by templateTitleSubject[0] users . As of the fourth templateXLabel[0] of templateTitle[7] , users of the templateTitle[1] messaging app were sending templateYValue[max] templateYLabel[4] templateYLabel[3] each day .
generated: This statistic gives information on the followers number of followers millions by Michael Kors users . As of the fourth Platform of 2020 , users of the followers messaging app were sending 17.91 millions each day .

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[1] . As of that templateXLabel[0] , the social check-in app community had accumulated over templateYValue[max] templateYLabel[3] templateYLabel[2] worldwide .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[3] templateXLabel[1] . In templateTitleDate[0] , about templateYValue[max] templateYLabel[2] people were counted in templateTitleSubject[0] .
generated: This statistic shows the Number of Number registered in Foursquare in 2010 , Foursquare Month . In 2010 , about 55 members people were counted in Foursquare .

Example 453:
titleEntities: {'Subject': ['United States'], 'Date': ['1998', '2018']}
title: Natural gas production - United States 1998 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1998']
Y_Axis['Production', 'in', 'billion', 'cubic', 'meters']: ['831.8', '745.8', '727.4', '740.3', '704.7', '655.7', '649.1', '617.4', '575.2', '557.6', '546.1', '521.9', '524.0', '511.1', '526.4', '540.8', '536.0', '555.5', '543.2', '538.7']

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

generated_template: The templateYLabel[0] templateTitle[2] templateYLabel[1] of templateTitle[4] templateTitle[5] has risen steadily in the templateTitle[0] , reaching a peak of almost templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[max] . In the templateTitle[0] , templateTitle[5] prices tend to reflect costs related to construction , finance , maintenance , and operation of power plants and templateTitle[5] grids . How do templateTitle[5] rates differ ? The templateYLabel[1] of templateTitle[5] can vary widely across the states with Hawaii having one of the highest rates and Idaho one of the lowest .
generated: The Production billion of States 1998 has risen steadily in the Natural , reaching a peak of almost 831.8 cubic meters in 2018 . In the Natural , 1998 prices tend to reflect costs related to construction , finance , maintenance , and operation of power plants and 1998 grids . How do 1998 rates differ ? The billion of 1998 can vary widely across the states with Hawaii having one of the highest rates and Idaho one of the lowest .

Example 454:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Dating website or app usage among U.S. online users 2019
X_Axis['Response']: ["Yes_I'm_doing_so_currently", "Yes_I've_done_so_in_the_past", 'No_never', "Don't_know"]
Y_Axis['Share', 'of', 'respondents']: ['7', '24', '65', '3']

gold: This statistic presents the percentage of adult online users in the United States who have used a dating website or app as of January 2019 . According to the findings , only seven percent of respondents stated that they were currently using a dating website or app , while in comparison 65 percent of respondents reported to have never used a dating app or website before .
gold_template: This statistic presents the percentage of adult templateTitle[6] templateTitle[7] in the templateTitle[5] who have used a templateTitle[0] templateTitle[1] or templateTitle[2] as of 2019 . According to the findings , only templateYValue[0] percent of templateYLabel[1] stated that they were templateXValue[0] using a templateTitle[0] templateTitle[1] or templateTitle[2] , while in comparison templateYValue[max] percent of templateYLabel[1] reported to have templateXValue[2] used a templateTitle[0] templateTitle[2] or templateTitle[1] before .

generated_template: This statistic shows the results of a survey among female templateTitleSubject[0] high school students templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . According to the source , templateYValue[max] percent of female students in the templateTitle[1] were templateTitle[5] templateTitle[6] as of 2013 .
generated: This statistic shows the results of a survey among female U.S. high school students usage among U.S. online . According to the source , 65 percent of female students in the website were U.S. online as of 2013 .

Example 455:
titleEntities: {'Subject': ['Atlanta Falcons'], 'Date': ['2019']}
title: Regular season home attendance of the Atlanta Falcons 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Home', 'attendance']: ['572811', '583184', '575681', '559998', '562845', '493515', '561795', '560773', '551892', '542800', '545384', '512520', '547610', '563456']

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

generated_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Yankees from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Yankees was at templateYValue[0] templateYLabel[2] . • Major League Baseball average per game templateYLabel[1] • Major League Baseball total templateYLabel[1]
generated: This graph depicts the total Regular season Home attendance of the Atlanta Falcons Yankees from 2006 to 2019 . In 2019 , the Regular season Home attendance of the Atlanta Falcons Yankees was at 572811 attendance . • Major League Baseball average per game attendance • Major League Baseball total attendance

Example 456:
titleEntities: {'Subject': ['Inflation'], 'Date': ['2019']}
title: Inflation rate of the main industrialized and emerging countries 2019
X_Axis['Country']: ['Russia', 'Brazil', 'India', 'China', 'USA', 'United_Kingdom', 'Germany', 'France', 'Japan']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['4.68', '3.79', '3.44', '2.31', '1.82', '1.81', '1.49', '1.17', '0.99']

gold: This statistic shows the inflation rate of the main industrialized and emerging countries in 2019 . In 2019 , the inflation rate in China was estimated to amount to approximately 2.31 percent compared to the previous year .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] and templateTitle[4] templateTitle[5] in templateTitleDate[0] . In templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] in templateXValue[3] was estimated to amount to approximately templateYValue[3] percent templateYLabel[2] to the templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[3] templateXLabel[0] . In templateTitleDate[0] , the estimated templateYLabel[0] templateYLabel[1] in templateTitleDate[0] was at templateYValue[7] percent .
generated: The statistic shows the Inflation rate in the Inflation in 2019 , industrialized Country . In 2019 , the estimated Inflation rate in 2019 was at 1.17 percent .

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] percent as of 30 , templateTitleDate[1] . The smaller the templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[3] , the cheaper the templateTitle[2] is .

generated_template: The templateYLabel[1] of the DJIA templateYLabel[0] amounted to templateYValue[0] on 31 , templateTitleDate[1] . templateTitleSubject[0] Industrial templateTitleSubject[0] templateYLabel[0] – additional information The templateTitleSubject[0] Industrial templateTitleSubject[0] templateYLabel[0] is a price-weighted templateTitleSubject[0] of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ , including Goldman Sachs , IBM and Walt Disney . This templateYLabel[0] is considered to be a barometer of the state of the American economy .
generated: The rate of the DJIA Interest amounted to 4.56 on 31 , 2020 . U.S. Industrial U.S. Interest – additional information The U.S. Industrial U.S. Interest is a price-weighted U.S. of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ , including Goldman Sachs , IBM and Walt Disney . This Interest is considered to be a barometer of the state of the American economy .

Example 458:
titleEntities: {'Subject': ['Europe'], 'Date': ['2016']}
title: Number of natural mineral waters in Europe 2016 , by country
X_Axis['Country']: ['Germany', 'Italy', 'Hungary', 'Spain', 'Poland', 'France', 'Romania', 'United_Kingdom', 'Greece', 'Austria', 'Belgium', 'Bulgaria', 'Portugal', 'Slovakia', 'Lithuania', 'Netherlands', 'Czech_Republic', 'Denmark', 'Sweden', 'Slovenia', 'Latvia', 'Estonia', 'Croatia', 'Ireland', 'Finland']
Y_Axis['Litres', 'consumed', 'per', 'capita']: ['821', '322', '214', '165', '119', '90', '69', '67', '44', '33', '27', '22', '21', '20', '17', '13', '11', '11', '11', '9', '5', '4', '4', '2', '1']

gold: This statistic represents the number of natural mineral waters in Europe in 2016 . Germany had the highest number of natural mineral waters with 821 certified natural mineral water sources .
gold_template: This statistic represents the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateTitleDate[0] . templateXValue[0] had the highest templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] with templateYValue[max] certified templateTitle[1] templateTitle[2] water sources .

generated_template: This statistic shows the templateYLabel[2] templateYLabel[3] templateTitle[2] of templateTitle[0] templateTitle[1] in templateTitleSubject[0] in templateTitleDate[0] . In this year , templateXValue[0] was the leading market for the templateTitle[2] of templateTitle[0] templateTitle[1] with templateYValue[max] liters of templateTitle[0] templateTitle[1] templateYLabel[1] templateYLabel[2] person and templateXValue[1] was the second largest consumer of templateTitle[0] templateTitle[1] with templateYValue[1] liters templateYLabel[1] templateYLabel[2] person . templateTitle[0] templateTitle[1] in the European Union is predominantly made up of the natural mineral templateTitle[1] category.Germany is the market with the largest amount of different mineral templateTitle[1] brands .
generated: This statistic shows the per capita mineral of Number natural in Europe in 2016 . In this year , Germany was the leading market for the mineral of Number natural with 821 liters of Number natural consumed per person and Italy was the second largest consumer of Number natural with 322 liters consumed per person . Number natural in the European Union is predominantly made up of the natural mineral natural category.Germany is the market with the largest amount of different mineral natural brands .

Example 459:
titleEntities: {'Subject': ['Luxury'], 'Date': ['2016']}
title: Luxury destinations with the largest growth in travel worldwide 2016
X_Axis['Country']: ['Kenya', 'Iceland', 'Saint_Martin', 'China', 'Ecuador', 'Japan', 'South_Africa', 'Tanzania', 'Croatia', 'Jamaica']
Y_Axis['Year-over-year', 'travel', 'growth']: ['59', '56', '39', '35', '34', '32', '28', '27', '25', '23']

gold: This statistic shows the luxury travel destinations with the largest growth in travel worldwide as of August 2016 . Luxury travel to Kenya grew by 59 percent in 2016 compared with the previous year . In 2017 , the Caribbean was the leading international destination for a spring vacation according to U.S. respondents .
gold_template: This statistic shows the templateTitleSubject[0] templateYLabel[1] templateTitle[1] templateTitle[2] the templateTitle[3] templateYLabel[2] in templateYLabel[1] templateTitle[6] as of 2016 . templateTitleSubject[0] templateYLabel[1] to templateXValue[0] grew by templateYValue[max] percent in templateTitleDate[0] compared templateTitle[2] the previous year . In 2017 , the Caribbean was the leading international destination for a spring vacation according to U.S. respondents .

generated_template: This statistic shows the countries and regions that consumed the most templateTitle[2] in templateTitleDate[0] . The templateXValue[0] demanded templateYValue[max] percent of the templateTitle[1] 's total templateTitle[2] production . templateYLabel[1] of templateTitle[2] is one of the templateTitle[1] 's most valued metals and included within the so called transition metals group .
generated: This statistic shows the countries and regions that consumed the most largest in 2016 . The Kenya demanded 59 percent of the destinations 's total largest production . travel of largest is one of the destinations 's most valued metals and included within the so called transition metals group .

Example 460:
titleEntities: {'Subject': ['Europe'], 'Date': ['2012', '2016']}
title: Forecast for the number of new hotel rooms opening in Europe from 2012 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'hotel', 'rooms']: ['34060', '34451', '39178', '30982', '37818']

gold: This statistic shows a forecast for the number of new hotel rooms opening in Europe from 2012 to 2016 . In 2013 , 30,982 new hotel rooms opened in the European hotel market . It was forecasted that 34,060 new hotel rooms would open in 2016 .
gold_template: This statistic shows a templateTitle[0] templateTitle[1] the templateYLabel[0] of templateTitle[3] templateYLabel[1] templateYLabel[2] templateTitle[6] in templateTitleSubject[0] templateTitle[8] templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateYValue[min] templateTitle[3] templateYLabel[1] templateYLabel[2] opened in the European templateYLabel[1] market . It was forecasted that templateYValue[0] templateTitle[3] templateYLabel[1] templateYLabel[2] would open in templateXValue[max] .

generated_template: This statistic 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 increase by 1.7 years , the largest increase predicted between templateXValue[2] and templateXValue[1] at 0.8 years .
generated: This statistic shows the new of the hotel rooms in the Europe ( 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 0.8 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] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] was about templateYValue[6] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Reduction of the templateYLabel[0] templateYLabel[1] and recovery of the economy in the UK The templateYLabel[1] of the templateTitleSubject[0] , which amounted to around 1,600 templateYLabel[2] pounds in templateXValue[min] - more than it has ever been - is projected to keep rising .
generated: This statistic shows the National debt of the Switzerland from 2014 to 2018 , with projections up until 2024 . In 2018 , the National debt in the Switzerland was about 280.14 billion U.S. dollars . Reduction of the National debt and recovery of the economy in the UK The debt of the Switzerland , which amounted to around 1,600 billion pounds in 2014 - more than it has ever been - is projected to keep rising .

Example 463:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2017', '2023']}
title: Mexico : mobile phone internet user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['59', '58', '56', '53', '50', '47', '43']

gold: The statistic shows the mobile phone internet user penetration in Mexico from 2017 to 2023 . In 2017 , 43 percent of the population users accessed the internet through their mobile device . This figure is projected to grow to 59percent in 2023 .
gold_template: The statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the templateYLabel[1] users accessed the templateTitle[3] through their templateTitle[1] device . This figure is projected to grow to 59percent in templateXValue[max] .

generated_template: This statistic provides information on the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it was estimated that templateYValue[max] percent of the Indian templateYLabel[1] will be accessing templateTitle[1] networks , up from templateYValue[5] percent in templateXValue[5] .
generated: This statistic provides information on the mobile phone internet in Mexico from 2017 to 2023 . In 2023 , it was estimated that 59 percent of the Indian population will be accessing mobile networks , up from 47 percent in 2018 .

Example 464:
titleEntities: {'Subject': ['Stryker'], 'Date': ['2011', '2019']}
title: Stryker 's annual research , development and engineering expenses 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Expenses', 'in', 'million', 'U.S.', 'dollars']: ['971', '862', '787', '715', '625', '614', '536', '471', '462']

gold: The statistic shows the annual research , development and engineering expenses of Stryker from 2011 to 2019 . Stryker 's research , development and engineering expenses have gradually increased since 2011 , reaching 971 million U.S. dollars in 2019 . The Stryker Corporation is a U.S. medical technology company headquartered in Kalamazoo , Michigan .
gold_template: The statistic shows the templateTitle[2] templateTitle[3] , templateTitle[4] and templateTitle[5] templateYLabel[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] templateTitle[1] templateTitle[3] , templateTitle[4] and templateTitle[5] templateYLabel[0] have gradually increased since templateXValue[min] , reaching templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . The templateTitleSubject[0] Corporation is a templateYLabel[2] medical technology company headquartered in Kalamazoo , Michigan .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to 5 , templateXValue[max] . In templateXValue[1] , the templateTitle[0] carried out a total of templateYValue[1] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] .
generated: The statistic shows the Expenses of Stryker million U.S. in Stryker from 2011 to 5 , 2019 . In 2018 , the Stryker carried out a total of 862 million U.S. in Stryker .

Example 465:
titleEntities: {'Subject': ['European Union'], 'Date': []}
title: Fresh orange production volume in the European Union 2016/17 , by country
X_Axis['Country']: ['Spain', 'Italy', 'Greece', 'Portugal', 'Cyprus']
Y_Axis['Volume', 'in', '1,000', 'tons']: ['3731', '1500', '920', '344', '30']

gold: In 2018/2019 , Spain was the leading producer of fresh oranges in the European Union ( EU28 ) , with over 3.7 million tons of fresh oranges produced . The Spanish production was more than two times the production of Italy , the second largest producer of oranges . The other three producers in the EU produced less than one million tons during this year .
gold_template: In 2018/2019 , templateXValue[0] was the leading producer of templateTitle[0] oranges in the templateTitleSubject[0] ( EU28 ) , with over 3.7 million templateYLabel[2] of templateTitle[0] oranges produced . The Spanish templateTitle[2] was more than two times the templateTitle[2] of templateXValue[1] , the second largest producer of oranges . The other three producers in the EU produced less than one million templateYLabel[2] during this year .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleSubject[0] templateTitle[5] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[0] templateTitle[2] templateXLabel[0] of templateTitleSubject[0] templateTitle[5] , with templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[0] coming from Germany.IKEAIKEA is an internationally known home furnishing retailer . It has grown rapidly since it was founded in 1943 .
generated: This statistic shows the Fresh orange production volume of European Union Union in . In that year , Spain was the Fresh production Country of European Union Union , with 3731 percent of European Union 's Volume coming from Germany.IKEAIKEA is an internationally known home furnishing retailer . It has grown rapidly since it was founded in 1943 .

Example 466:
titleEntities: {'Subject': ['China'], 'Date': ['1990', '2018']}
title: Average size of households in China 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2000', '1995', '1990']
Y_Axis['Number', 'of', 'persons']: ['3.03', '3.17', '3.11', '3.1', '2.97', '2.98', '3.02', '2.87', '2.88', '2.89', '3.13', '3.23', '3.5']

gold: This graph shows the average size of households in China from 1990 to 2018 . That year , approximately three people were living in an average Chinese household.Average number of people per household in China – additional information A household is commonly defined as one person living alone or a group of people living together and sharing certain living accommodations . The average number of people living in one household in China dropped from 3.5 in 1990 to 2.87 in 2011 .
gold_template: This graph shows the templateTitle[0] templateTitle[1] of templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . That templateXLabel[0] , approximately templateYValue[0] people were living in an templateTitle[0] Chinese household.Average templateYLabel[0] of people per household in templateTitleSubject[0] – additional information A household is commonly defined as one person living alone or a group of people living together and sharing certain living accommodations . The templateTitle[0] templateYLabel[0] of people living in one household in templateTitleSubject[0] dropped from templateYValue[max] in templateXValue[min] to templateYValue[min] in templateXValue[7] .

generated_template: This statistic shows the global templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] between the fiscal templateXLabel[0] of templateXValue[min] and the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , the Germany-based multinational engineering and electronics company employed some templateYValue[max] people .
generated: This statistic shows the global Number of China persons between the fiscal Year of 1990 and the fiscal Year of 2018 . In the fiscal Year of 2018 , the Germany-based multinational engineering and electronics company employed some 3.5 people .

Example 467:
titleEntities: {'Subject': ['Phoenix Suns'], 'Date': ['2001', '2019']}
title: Phoenix Suns ' revenue 2001 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['246', '235', '218', '173', '154', '145', '137', '121', '136', '147', '148', '148', '145', '132', '132', '111', '109', '107']

gold: The statistic shows the revenue of the Phoenix Suns franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise amounted to 246 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] 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] templateYLabel[1] 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] percent in exports .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in templateYLabel[2] in templateTitleDate[0] , based on sales value . In that year , Coca Cola 's templateYLabel[0] templateYLabel[1] in templateYLabel[2] amounted to templateYValue[max] percent . The leading 3 templateTitle[2] templateTitle[3] templateTitle[4] in templateYLabel[2] had a templateYLabel[0] templateYLabel[1] of 45.3 percent .
generated: The statistic shows the Share total of export partner countries in export in 2017 , based on sales value . In that year , Coca Cola 's Share total in export amounted to 21.8 percent . The leading 3 export partner countries in export had a Share total of 45.3 percent .

Example 469:
titleEntities: {'Subject': ['Stuxnet'], 'Date': []}
title: Stuxnet - percentage of infected hosts by country
X_Axis['Country']: ['Iran', 'Indonesia', 'India', 'Azerbaijan', 'Pakistan', 'Malaysia', 'U.S.', 'Uzbekistan', 'Russia', 'Great_Britain', 'Other']
Y_Axis['Percentage', 'of', 'infected', 'hosts']: ['58.31', '17.83', '9.96', '3.4', '1.4', '1.16', '0.89', '0.71', '0.61', '0.57', '5.15']

gold: The statistic shows the percentage of Stuxnet infected hosts by country in 2010 . 58.31 percent of infected hosts were located in Iran .
gold_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] templateTitle[4] templateXLabel[0] in 2010 . templateYValue[max] percent of templateYLabel[1] templateYLabel[2] were located in templateXValue[0] .

generated_template: This statistic shows the estimated templateYLabel[0] of the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitle[4] templateTitle[5] in templateTitleDate[0] . In that year , The templateXValue[0] ' templateTitle[1] templateTitle[2] templateTitle[3] was projected to have generated templateYValue[max] templateYLabel[1] euros.Luxury templateTitle[2] industryGenerally speaking , garments , accessories , jewellery , watches , fragrances and cosmetics are considered to be constituent parts of the templateTitle[1] market . The U.S. templateTitle[1] templateTitle[2] market has continued to soar to post-crisis heights in 2014 , with its fifth year of growth .
generated: This statistic shows the estimated Percentage of the percentage infected hosts in by country in . In that year , The Iran ' percentage infected hosts was projected to have generated 58.31 infected euros.Luxury infected industryGenerally speaking , garments , accessories , jewellery , watches , fragrances and cosmetics are considered to be constituent parts of the percentage market . The U.S. percentage infected market has continued to soar to post-crisis heights in 2014 , with its fifth year of growth .

Example 470:
titleEntities: {'Subject': ['U.S. January TV'], 'Date': ['2020', '2020']}
title: Leading trailers in the U.S. January 2020 , by weekly TV ad spend
X_Axis['Year']: ['1917', 'Dolittle', 'Bad_Boys_for_Life', 'Like_a_Boss', 'Just_Mercy']
Y_Axis['Spending', 'in', 'million', 'U.S.', 'dollars']: ['10.41', '5.77', '4.18', '3.9', '3.72']

gold: The leading movie commercial in the United States based on weekly television advertising spending for the week ending January 5 , 2020 was for war drama film ' 1917 ' _ , with a 10.41 million U.S. dollar spend by studio Universal Pictures . Universal also spent 5.77 million U.S. dollars on TV promotion of 'Dolittle ' _ .
gold_template: The templateTitle[0] movie commercial in the templateTitle[2] based on templateTitle[6] television advertising templateYLabel[0] templateXValue[2] the week ending templateTitleSubject[0] 5 , templateTitle[4] was templateXValue[2] war drama film ' templateXValue[0] ' _ , with a templateYValue[max] templateYLabel[1] templateYLabel[2] dollar templateTitle[9] templateTitle[5] studio Universal Pictures . Universal also spent templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] on templateTitleSubject[0] promotion of 'Dolittle ' _ .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of templateTitle[2] from the templateTitle[1] from 2014 to templateTitleDate[1] . According to the report , approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[2] were exported from the templateTitleSubject[0] during the fiscal templateXLabel[0] templateXValue[2] .
generated: This statistic shows the Leading Spending of U.S. from the trailers from 2014 to 2020 . According to the report , approximately 10.41 million U.S. dollars of U.S. were exported from the U.S. January TV during the fiscal Year Bad Boys for Life .

Example 471:
titleEntities: {'Subject': ['LinkedIn'], 'Date': ['2020']}
title: LinkedIn : distribution of global audiences 2020 , by gender
X_Axis['Sex']: ['Female', 'Male']
Y_Axis['Percentage', 'of', 'users']: ['43', '57']

gold: This statistic gives information on the distribution of LinkedIn users worldwide as of January 2020 , sorted by gender . During the survey period , 43 percent of LinkedIn audiences were female and 57 percent were male .
gold_template: This statistic gives information on the templateTitle[1] of templateTitleSubject[0] templateYLabel[1] worldwide as of 2020 , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[min] percent of templateTitleSubject[0] templateTitle[3] were templateXValue[0] and templateYValue[max] percent were templateXValue[last] .

generated_template: This statistic gives information on the templateTitle[1] of templateTitleSubject[0] templateYLabel[1] worldwide as of 2020 , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[min] percent of templateTitleSubject[0] templateTitle[3] were templateXValue[0] and templateYValue[max] percent were templateXValue[last] .
generated: This statistic gives information on the distribution of LinkedIn users worldwide as of 2020 , sorted by gender . During the survey period , 43 percent of LinkedIn audiences were Female and 57 percent were Male .

Example 472:
titleEntities: {'Subject': ['Premier League'], 'Date': ['2010', '2019']}
title: Premier League total broadcasting payments to clubs 2010 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11']
Y_Axis['Broadcasting', 'payments', 'in', 'million', 'GBP']: ['2456.01', '2419.6', '2398.5', '1633.9', '1605.3', '1563.0', '1061.0', '1055.0', '953.0']

gold: The statistic depicts the broadcasting payments to Premier League clubs from 2010/11 to 2018/19 . In the 2018/19 season , all Premier League clubs combined received a total of 2.46 billion British Pounds in broadcasting payments .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] to templateTitleSubject[0] clubs from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , all templateTitleSubject[0] clubs combined received a templateTitle[2] of templateYValue[max] templateYLabel[2] British Pounds in templateYLabel[0] templateYLabel[1] .

generated_template: In the academic templateXLabel[0] templateXValue[0] there were approximately templateYValue[0] thousand templateYLabel[1] in the templateTitleSubject[0] , around 655 templateYLabel[1] less than there were in the templateXValue[last] academic templateXLabel[0] . Throughout most of this period there has been a steady decline in the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] .
generated: In the academic Year 2018/19 there were approximately 2456.01 thousand payments in the Premier League , around 655 payments less than there were in the 2010/11 academic Year . Throughout most of this period there has been a steady decline in the Broadcasting of payments in the Premier League .

Example 473:
titleEntities: {'Subject': ['Canada'], 'Date': ['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[min] arsons templateYLabel[2] 100,000 templateYLabel[4] in templateTitleSubject[0] in templateXValue[max] .

generated_template: The statistic shows the total templateYLabel[0] of templateTitle[3] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the total templateYLabel[0] from templateTitle[3] amounted to templateYValue[9] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the total Rate of rate worldwide from 2000 to 2018 . In 2018 , the total Rate from rate amounted to 39.86 arson per 100,000 .

Example 474:
titleEntities: {'Subject': ['Marathon Oil'], 'Date': ['2010', '2018']}
title: Marathon Oil 's number of employees 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Number', 'of', 'employees']: ['2400', '2300', '2117', '2611', '3330', '3359', '3367', '3322', '29677']

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

generated_template: This statistic represents the templateYLabel[0] of templateTitleSubject[0] employees from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In its templateXValue[max] fiscal templateXLabel[0] , the Switzerland-based electronics company employed templateYValue[0] people worldwide .
generated: This statistic represents the Number of Marathon Oil employees from the fiscal Year of 2010 to the fiscal Year of 2018 . In its 2018 fiscal Year , the Switzerland-based electronics company employed 2400 people worldwide .

Example 475:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Most popular social networks of U.S. teens 2016
X_Axis['Platform']: ['YouTube', 'Gmail', 'Snapchat', 'Instagram', 'Facebook', 'Kik_Messenger', 'Skype', 'Twitter', 'Vine', 'Tumblr']
Y_Axis['Percentage', 'of', 'teenagers']: ['91', '75', '66', '65', '61', '52', '43', '40', '31', '24']

gold: This statistic provides information about the most popular websites visited by teenagers in the United States as of June 2016 . During the survey period , video sharing platform YouTube was most popular among U.S. teens with a 91 percent usage rate . Snapchat was ranked third with 66 percent reporting that they accessed the photo sharing app .
gold_template: This statistic provides information about the templateTitle[0] templateTitle[1] websites visited by templateYLabel[1] in the templateTitle[4] as of 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] percent usage rate . templateXValue[2] was ranked third with templateYValue[2] percent reporting that they accessed the photo sharing app .

generated_template: This statistic gives information on the templateTitleSubject[0] templateTitle[1] online and tech templateTitle[3] in templateTitleDate[0] , based on templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Food delivery templateXLabel[0] templateXValue[0] went public in 2014 and was ranked first with a templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateYValue[max] percent .
generated: This statistic gives information on the U.S. popular online and tech networks in 2016 , based on Percentage teenagers . Food delivery Platform YouTube went public in 2014 and was ranked first with a Percentage teenagers of 91 percent .

Example 476:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Percentage of population volunteering in the U.S. in 2015 , by age
X_Axis['Year']: ['16_to_24_years', '25_to_34_years', '35_to_44_years', '45_to_54_years', '55_to_64_years', '65_years_and_over']
Y_Axis['Percentage', 'of', 'population', 'volunteering']: ['21.8', '22.3', '28.9', '28', '25.1', '23.5']

gold: This statistic displays the percentage of population volunteering in the U.S. in 2015 , by age . In 2015 , 21.8 percent of Americans 16 to 24 years old volunteered at least once during the year .
gold_template: This statistic displays the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . In templateTitleDate[0] , templateYValue[min] percent of Americans templateXValue[0] to templateXValue[0] old volunteered at least once during the templateXLabel[0] .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateYLabel[0] of templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYValue[2] templateYLabel[1] units of templateTitle[3] templateTitle[4] were sold worldwide .
generated: This statistic shows the U.S. population Percentage of U.S. 2015 from 16 to 24 years to 16 to 24 years . In 35 to 44 years , 28.9 population 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 estimated templateTitle[1] amount of strawberries produced in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around templateYValue[0] templateYLabel[1] templateYLabel[2] of strawberries were produced in the templateTitle[0] .
generated: This statistic shows the estimated sweet amount of strawberries produced in the U.S. from 2000 to 2018 . In 2018 , around 344.4 thousand tons of strawberries were produced in the U.S. .

Example 478:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Leading baby wipes vendors in the U.S. 2016 , based on sales
X_Axis['Company']: ['Private_label', 'Kimberly_Clark_Corp.', 'Procter_&_Gamble', 'Seventh_Generation', 'The_Honest_Co.', 'Johnson_&_Johnson', 'Nice-Pak_Products', 'Paper_Partners', 'Kas_Direct', 'Irish_Breeze']
Y_Axis['Million', 'U.S.', 'dollars']: ['494.4', '416.2', '276.6', '9.6', '8.6', '7.1', '6.3', '6.1', '5.7', '4.4']

gold: The statistic shows the leading baby wipes vendors in the United States in 2016 , based on sales . In that year , Kimberly Clark was the second largest U.S. baby wipes vendor with sales of 416.2 million U.S. dollars . Total sales of U.S. baby wipes vendors amounted to about 1.25 billion U.S. dollars in 2016 .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] in templateTitleDate[0] , templateTitle[6] on templateTitle[7] . In that year , templateXValue[1] was the second largest templateYLabel[1] templateTitle[1] templateTitle[2] vendor with templateTitle[7] of templateYValue[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] . Total templateTitle[7] of templateYLabel[1] templateTitle[1] templateTitle[2] templateTitle[3] amounted to about 1.25 templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleDate[0] .

generated_template: The two templateTitle[3] templateTitle[4] templateTitleSubject[0] templateTitle[6] in templateTitleDate[0] were the oil companies templateXValue[0] and templateXValue[1] , the former of which is in possession of a staggering templateYLabel[1] of templateYValue[max] templateYLabel[2] templateTitleSubject[0] pounds . This was not different in the previous year either , when templateXValue[0] ranked as the templateTitle[3] templateTitle[4] templateTitleSubject[0] templateXLabel[0] while the rest of the list had some small shifts and variations . Oil , banks and Telecom The templateTitle[3] templateTitle[4] templateTitleSubject[0] templateTitle[6] as of templateTitleDate[0] is a balanced mixture of oil and gas companies , banks and telecommunications .
generated: The two vendors U.S. based in 2016 were the oil companies Private label and Kimberly Clark Corp. , the former of which is in possession of a staggering U.S. of 494.4 dollars U.S. pounds . This was not different in the previous year either , when Private label ranked as the vendors U.S. Company while the rest of the list had some small shifts and variations . Oil , banks and Telecom The vendors U.S. based as of 2016 is a balanced mixture of oil and gas companies , banks and telecommunications .

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[0] . 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] Yankees from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Yankees was at templateYValue[0] templateYLabel[2] . • Major League Baseball average per game templateYLabel[1] • Major League Baseball total templateYLabel[1]
generated: This graph depicts the total Regular season Home attendance of the Los Angeles Rams Yankees from 2006 to 2019 . In 2019 , the Regular season Home attendance of the Los Angeles Rams Yankees was at 498605 attendance . • Major League Baseball average per game attendance • Major League Baseball total attendance

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

generated_template: The statistic 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[max] templateYLabel[3] templateYLabel[4] templateYLabel[0] templateYLabel[1] .
generated: The statistic shows the Estimated clothing Per capita sales in the U.S. capita 2000 to 2017 . In 2017 , clothing sales amounted to about 804 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: The statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] and templateTitle[4] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In that year , most templateTitle[2] templateYLabel[1] and templateTitle[4] occurred in templateXValue[0] - a total of templateYValue[max] . In templateXValue[last] , no templateTitle[2] templateYLabel[1] and templateTitle[4] occurred in templateTitleDate[0] .
generated: The statistic shows the Number of assaults aggravated and 2018 in the U.S. in 2018 , state . In that year , most assaults aggravated and 2018 occurred in California - a total of 105412 . In Vermont , no assaults aggravated and 2018 occurred in 2018 .

Example 482:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2012', '2015']}
title: Smartphone use for professional reasons among U.S. physicians 2012 to 2015
X_Axis['Month']: ['March_2015', 'March_2014', 'March_2013', 'March_2012']
Y_Axis['Percentage', 'of', 'respondents']: ['84', '79', '76', '68']

gold: This survey indicates the percentage of physicians in the United States who use smartphones for professional purposes from March 2012 to March 2015 . In March 2014 , 79 percent of surveyed physicians used smartphones for their medical practice . Use of digital health has grown in the medical industry as consumers are expressing increasing interests in mobile health technologies .
gold_template: This survey indicates the templateYLabel[0] of templateTitle[7] in the templateTitle[6] who templateTitle[1] smartphones templateTitle[2] templateTitle[3] purposes from templateXValue[0] templateXValue[last] to templateXValue[0] . In templateXValue[0] templateXValue[1] , templateYValue[1] percent of surveyed templateTitle[7] used smartphones templateTitle[2] their medical practice . templateTitle[1] of digital health has grown in the medical industry as consumers are expressing increasing interests in mobile health technologies .

generated_template: This statistic shows the results of a survey among female templateTitleSubject[0] high school students templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . According to the source , templateYValue[max] percent of female students in the templateTitle[1] were templateTitle[5] templateTitle[6] as of 2013 .
generated: This statistic shows the results of a survey among female U.S. high school students professional reasons among U.S. . According to the source , 84 percent of female students in the use were among U.S. as of 2013 .

Example 483:
titleEntities: {'Subject': ['Forecast'], 'Date': ['2014', '2020']}
title: Forecast of sulfur fertilizer demand worldwide 2014 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Total', 'demand', 'in', 'million', 'metric', 'tons']: ['69.0', '67.7', '70.38', '68.08', '65.65', '63.47', '61.44']

gold: This statistic displays a forecast of total global demand for sulfur fertilizer from 2014 to 2020 . By 2020 , the annual demand for sulfur fertilizer is expected to reach some 69 million metric tons . Increasing crop prices lead to increased fertilizer demands and has been especially noted in recent years in South Asia .
gold_template: This statistic displays a templateTitleSubject[0] of templateYLabel[0] global templateYLabel[1] for templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . By templateXValue[max] , the annual templateYLabel[1] for templateTitle[1] templateTitle[2] is expected to reach some templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Increasing crop prices lead to increased templateTitle[2] demands and has been especially noted in recent years in South Asia .

generated_template: This statistic illustrates the templateYLabel[0] of templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , in templateYLabel[1] templateYLabel[2] templateYLabel[3] . In templateXValue[max] , approximately templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[1] were produced in templateTitleSubject[0] .
generated: This statistic illustrates the Total of sulfur in Forecast from 2014 to 2020 , in demand million metric . In 2020 , approximately 61.44 demand million metric of sulfur were produced in Forecast .

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] templateYLabel[2] and is forecast to further grow by several hundred templateYLabel[2] in the next few years . China , India , and the country are the countries with the highest number of templateYLabel[0] templateYLabel[1] , with each country easily surpassing the 100 templateYLabel[2] user mark . templateYLabel[0] unit sales levelling off In the past five years , about 1.4 templateYLabel[2] smartphones were sold templateTitle[2] annually , reflecting stagnation in the templateYLabel[0] market during the last few years .

generated_template: In templateXValue[4] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] of the templateTitle[0] templateTitle[3] templateTitle[2] templateYLabel[0] was templateYValue[4] percent , and this is expected to increase significantly by templateXValue[max] . As of the date of survey it has been projected that templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] will account for templateYValue[max] percent of the entire templateTitle[3] templateTitle[2] templateYLabel[0] 's gross merchandise volume ( templateTitleSubject[1] ) . templateTitleSubject[0] 's templateTitle[3] dominance templateTitleSubject[0] is the leading online retailer in the templateTitle[0] , offering a wide range of shopping products and services to its customer base .
generated: In 2017 , Smartphone 's Smartphone users of the Smartphone 2016 worldwide Smartphone was 2.7 percent , and this is expected to increase significantly by 2021 . As of the date of survey it has been projected that Smartphone 's Smartphone users will account for 3.8 percent of the entire 2016 worldwide Smartphone 's gross merchandise volume ( Smartphone ) . Smartphone 's 2016 dominance Smartphone is the leading online retailer in the Smartphone , offering a wide range of shopping products and services to its customer base .

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] templateYLabel[2] templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[2] worldwide , up from templateYValue[1] templateYLabel[2] 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] templateYLabel[2] monthly templateTitle[1] templateTitle[2] and is one of the most popular social networks worldwide .

generated_template: This statistic shows the number of templateYLabel[0] templateYLabel[1] of the social network templateTitleSubject[0] from templateXValue[last] templateTitleDate[0] to October templateTitleDate[0] . As of the last reported period , templateTitleSubject[0] announced that templateYValue[max] templateYLabel[2] templateYLabel[0] templateYLabel[1] worldwide , down from templateYValue[5] templateYLabel[2] in templateXValue[last] templateTitleDate[0] . In 2018 , the platform banned porn and adult content , a controversial move that has sparked user outrage and caused artists and sex workers to move to other sites .
generated: This statistic shows the number of Number DAU of the social network Instagram Stories from Oct '16 16 to October 16 . As of the last reported period , Instagram Stories announced that 500 millions Number DAU worldwide , down from 150 millions in Oct '16 16 . In 2018 , the platform banned porn and adult content , a controversial move that has sparked user outrage and caused artists and sex workers to move to other sites .

Example 486:
titleEntities: {'Subject': ['Iran'], 'Date': ['2014', '2024']}
title: National debt of Iran 2014 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'in', 'billion', 'U.S.', 'dollars']: ['575.52', '436.78', '344.33', '256.69', '205.66', '167.57', '142.95', '139.13', '143.56', '101.62', '31.64']

gold: The statistic shows the national debt of Iran from 2014 to 2018 , with projections up until 2024 . In 2018 , the national debt of Iran amounted to around 142.95 billion U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was around templateYValue[6] templateYLabel[2] templateYLabel[3] templateYLabel[4] . For comparison , the Greek templateYLabel[1] amounted to approximately 375 templateYLabel[2] templateYLabel[3] templateYLabel[4] that same templateXLabel[0] .
generated: This statistic shows the National debt of Iran from 2014 to 2018 , with projections up until 2024 . In 2018 , the National debt in Iran was around 142.95 billion U.S. dollars . For comparison , the Greek debt amounted to approximately 375 billion U.S. dollars that same Year .

Example 487:
titleEntities: {'Subject': ['Latin America Caribbean'], 'Date': ['2017']}
title: Latin America & the Caribbean : homicide rates 2017 , by country
X_Axis['Country']: ['El_Salvador', 'Jamaica', 'Honduras', 'Belize', 'Bahamas', 'Brazil', 'Guatemala', 'Colombia', 'Mexico', 'Puerto_Rico', 'Guyana', 'Costa_Rica', 'Dominican_Republic', 'Grenada', 'Panama', 'Uruguay', 'Peru', 'Nicaragua', 'Ecuador', 'Suriname', 'Argentina', 'Chile']
Y_Axis['Homicides', 'per', '100,000', 'inhabitants']: ['61.8', '57.0', '41.7', '37.9', '30.9', '30.5', '26.1', '24.9', '24.8', '18.5', '14.8', '12.3', '11.3', '11.1', '9.7', '8.2', '7.7', '7.4', '5.8', '5.5', '5.2', '4.3']

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

generated_template: This statistic shows the templateTitleSubject[0] of the templateYLabel[0] templateYLabel[1] as of templateTitleDate[0] , broken down templateTitle[5] templateXLabel[0] . The largest ship templateXLabel[5] was estimated to be the templateTitle[4] templateTitle[5] 's templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the Latin America Caribbean of the Homicides per as of 2017 , broken down rates Country . The largest ship Country was estimated to be the homicide rates 's Homicides per 100,000 of 61.8 inhabitants .

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[0] to the fourth templateXLabel[0] of templateTitle[5] . In the fourth templateXLabel[0] of templateTitle[5] , the photo sharing app is projected to generate templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in global revenues , up from an templateYLabel[0] templateYValue[6] templateYLabel[2] templateYLabel[3] templateYLabel[4] in the second templateXLabel[0] of 2019 .
generated: This statistic presents and estimate of Moscow 's per from the first Quarter of 2019 to the fourth Quarter of Q1 . In the fourth Quarter of Q1 , the photo sharing app is projected to generate 760 square meter euros in global revenues , up from an Cost 613 square meter euros in the second Quarter of 2019 .

Example 489:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Average U.S. brand response rate on social media 2017 , by vertical
X_Axis['Month']: ['Utilities', 'Retail', 'Consumer_Goods', 'Banking/Finance', 'Travel/Hospitality', 'Internet/Technology', 'Marketing/Advertising', 'Automotive', 'Real_Estate', 'Healthcare', 'Professional_Services', 'Government', 'Education', 'Nonprofit', 'Media/Entertainment']
Y_Axis['Average', 'response', 'rate']: ['18', '16', '14', '13', '12', '11', '11', '11', '10', '9', '9', '8', '7', '7', '6']

gold: This statistic presents the average brand response rate on social media in the United States as of the third quarter of 2017 , by vertical . According to the findings , the retail industry had an average response rate of 16 percent to communicating back to their consumers on social media , while the consumer goods industry reported in 14 percent .
gold_template: This statistic presents the templateYLabel[0] templateTitle[2] templateYLabel[1] templateYLabel[2] on templateTitle[5] templateTitle[6] in the templateTitle[1] as of the third quarter of templateTitleDate[0] , templateTitle[8] templateTitle[9] . According to the findings , the templateXValue[1] industry had an templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateYValue[1] percent to communicating back to their consumers on templateTitle[5] templateTitle[6] , while the templateXValue[2] industry reported in templateYValue[2] percent .

generated_template: This statistic presents a ranking of leading templateTitleSubject[1] templateTitle[3] on templateYLabel[1] as of 2017 , based on the templateYLabel[0] of templateYLabel[2] . As of the survey period , templateXValue[2] ranked third with templateYValue[2] templateYLabel[3] templateYLabel[2] . templateXValue[0] Cosmetics was ranked first with templateYValue[max] templateYLabel[3] templateYLabel[2] .
generated: This statistic presents a ranking of leading U.S. response on response as of 2017 , based on the Average of rate . As of the survey period , Consumer Goods ranked third with 14 rate . Utilities Cosmetics was ranked first with 18 rate .

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[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 increase by 1.7 years , the largest increase predicted between templateXValue[2] and templateXValue[1] at 0.8 years .
generated: This statistic shows the volume of the thousand metric in the U.S. ( U.S. ) from 2013/14 to 2013/14 and visualises the predicted 'ageing 2017/18 ' _ . Over the 20 Year period , the thousand metric is expected to increase by 1.7 years , the largest increase predicted between 2015/16 and 2014/15 at 0.8 years .

Example 491:
titleEntities: {'Subject': ['UK'], 'Date': ['2018', '2018']}
title: UK : reach of top active social media platforms in Q3 2018
X_Axis['Platform']: ['Youtube', 'Facebook', 'FB_Messenger', 'Whatsapp', 'Instagram', 'Twitter', 'Snapchat', 'LinkedIn', 'Pinterest', 'Skype', 'Reddit', 'Tumblr', 'Twitch', 'WeChat', 'Viber', 'Imgur']
Y_Axis['Share', 'of', 'respondents']: ['80', '78', '60', '58', '47', '46', '27', '27', '27', '22', '14', '13', '12', '8', '8', '7']

gold: This statistic illustrates the results of a survey about the leading active social media platforms in the UK in 2018 . During the survey period , it was found that 80 percent of the respondents reported that they used Facebook . Facebook is a popular free social networking website that allows registered users to create profiles , upload photos and video , send messages and keep in touch with friends , family and colleagues .
gold_template: This statistic illustrates the results of a survey about the leading templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitleSubject[0] in templateTitle[8] . During the survey period , it was found that templateYValue[max] percent of the templateYLabel[1] reported that they used templateXValue[1] . templateXValue[1] is a popular free templateTitle[4] networking website that allows registered users to create profiles , upload photos and video , send messages and keep in touch with friends , family and colleagues .

generated_template: 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] percent , closely followed by templateXValue[1] at templateYValue[1] percent . On the opposite side , templateXValue[last] is used by four percent of templateYLabel[1] .
generated: This statistic illustrates the results of a survey about the most top active social media in UK in the third quarter of 2018 . The most used active social Platform in UK was Youtube at 80 percent , closely followed by Facebook at 78 percent . 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[0] to templateTitleDate[1] . The earthquake templateTitle[1] occurred on templateXValue[0] , templateXValue[0] in templateXValue[0] caused approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] worth of templateYLabel[0] and is the costliest earthquake on record .

generated_template: 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] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitle[4] was templateYValue[11] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Earthquakes that caused Damage in the economic in 1900 , U.S. Date, . In 1900 , the Earthquakes that caused Damage in the economic was 100 million U.S. dollars .

Example 493:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018']}
title: Population density in Italy 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Inhabitants', 'per', 'square', 'kilometer']: ['205.45', '205.81', '206.12', '206.47', '206.67', '204.78', '202.42', '201.87', '201.53', '200.91', '200.0']

gold: The statistic shows the population density in Italy from 2008 to 2018 . In 2018 , the population density in Italy amounted to about 205.45 inhabitants per square kilometer . See the population of Italy for comparison .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . See the templateTitle[0] of templateTitleSubject[0] for comparison .

generated_template: 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[max] . This number has been slowly increasing 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 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 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] percent of templateTitleSubject[0] adults indicated that they buy their shampoo/conditioner templateXValue[3] .

generated_template: A survey of online users in the templateTitle[5] revealed that templateYValue[max] percent of templateYLabel[1] felt that templateXValue[0] and messaging apps were among the three types of apps that they templateTitle[1] the most templateTitleSubject[0] on . During the third quarter templateTitleDate[0] survey , templateYValue[6] percent of templateYLabel[1] stated the same about gaming apps .
generated: A survey of online users in the shampoos revealed that 62 percent of respondents felt that "Big Box" retail store (e.g. Walmart Target) and messaging apps were among the three types of apps that they consumers the most U.S. on . During the third quarter 2014 survey , 4 percent of respondents stated the same about gaming apps .

Example 495:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Leading U.S. states in sunflower production 2019
X_Axis['State']: ['South_Dakota', 'North_Dakota', 'Minnesota', 'California', 'Colorado', 'Kansas', 'Nebraska', 'Texas']
Y_Axis['Production', 'in', 'thousand', 'pounds']: ['831600', '740700', '102630', '70680', '59400', '53925', '44850', '39650']

gold: The U.S. state with the highest production volume of sunflowers is South Dakota at 831.6 million pounds in 2019 . North Dakota came in second at 740.7 million pounds of sunflowers . Sunflower products There are several products that are derived from sunflowers .
gold_template: The templateTitleSubject[0] templateXLabel[0] with the highest templateYLabel[0] volume of sunflowers is templateXValue[0] at templateYValue[max] templateYLabel[1] templateYLabel[2] in templateTitleDate[0] . templateXValue[1] templateXValue[0] came in second at templateYValue[1] templateYLabel[1] templateYLabel[2] of sunflowers . templateTitle[3] products There are several products that are derived from sunflowers .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] of templateTitle[3] in leading countries in templateTitleSubject[0] in templateTitleDate[0] . templateXValue[0] has the highest levels of templateTitle[3] templateYLabel[0] templateTitle[0] templateTitle[1] at templateYValue[max] templateYLabel[1] , followed templateTitle[6] Gemany at templateYValue[1] templateYLabel[1] .
generated: This statistic shows the Leading U.S. Production of sunflower in leading countries in U.S. in 2019 . South Dakota has the highest levels of sunflower Production Leading U.S. at 831600 thousand , followed 2019 Gemany at 740700 thousand .

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: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] , templateTitle[7] total templateYLabel[1] of templateYLabel[0] . The templateXValue[0] templateTitle[1] brand was the top selling templateTitle[1] brand in templateTitleDate[0] , generating templateYLabel[0] of approximately templateYValue[max] templateYLabel[2] British pounds . The most expensive country in which to purchase cigarettes in Europe is the templateTitleSubject[0] ( templateTitleSubject[1] ) .
generated: This statistic shows the Leading eSports pro in the Twitter ( Twitter ) in 2016 , by total Twitter of Number . The Søren Bjerg (Bjergsen) eSports brand was the top selling eSports brand in 2016 , generating Number of approximately 604 followers British pounds . The most expensive country in which to purchase cigarettes in Europe is the Twitter ( Twitter ) .

Example 497:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2009', '2019']}
title: Population in Sweden 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Inhabitants', 'in', 'millions']: ['10.33', '10.23', '10.12', '10.0', '9.85', '9.75', '9.64', '9.56', '9.48', '9.42', '9.34']

gold: This statistic shows the total population in Sweden from 2009 to 2019 . The number of inhabitants in Sweden has increased by nearly one million in this time period . In 2009 , there were approximately 9.34 million inhabitants in Sweden and by the end of 2019 the Swedish population reached 10.33 million people .
gold_template: This statistic shows the total templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The number of templateYLabel[0] in templateTitleSubject[0] has increased by nearly one templateYLabel[1] in this time period . In templateXValue[min] , there were approximately templateYValue[min] templateYLabel[1] templateYLabel[0] in templateTitleSubject[0] and by the end of templateXValue[max] the Swedish templateTitle[0] reached templateYValue[max] templateYLabel[1] people .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[1] is based on data from the most recent census adjusted by the components of templateTitle[1] change produced since the last census , or based on templateTitle[1] registers . At the beginning of templateXValue[max] , the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] amounted to approximately templateYValue[max] templateYLabel[1] templateYLabel[0] .
generated: This statistic shows the Population Sweden of the Sweden from 2009 to 2019 . The Sweden is based on data from the most recent census adjusted by the components of Sweden change produced since the last census , or based on Sweden registers . At the beginning of 2019 , the Population Sweden of the Sweden amounted to approximately 10.33 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] templateTitle[8] to templateTitleDate[0] . In templateTitle[6] templateXLabel[0] templateTitleDate[0] , the templateTitle[3] templateTitle[4] for templateTitleSubject[0] is templateTitle[0] to be templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] greater than 2026 templateTitle[4] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of templateTitleSubject[0] amounted to approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Annual funding of the Canada of Canada from 2026-2027 to 2026-2027 . In 2026-2027 , the Annual funding of the Canada of Canada amounted to approximately 2300 increase million Canadian .

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] increased for the fourth templateXLabel[0] in a row . templateTitle[0] templateTitle[1] growth slowed down considerably though in templateXValue[3] and templateXValue[2] in comparison to templateXValue[min] and especially templateXValue[4] . In the next templateYValue[1] years , the templateTitle[0] templateTitle[1] was forecast to increase further .

generated_template: 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 increase by templateYValue[2] percent in templateXValue[3] and a further templateYValue[2] percent 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 growing every templateXLabel[0] .
generated: This statistic shows the Netherlands power change Netherlands year 2020 from 2015 to 2020 . Netherlands power change Netherlands was forecasted to increase by 0.3 percent in 2017 and a further 0.3 percent in 2018 power change - additional information power in the 21st century is no longer limited to a small geographical area , as internet communication and almost universal access to change and accommodation allow for easy national and transnational movement between companies , their partners , customers , suppliers or distributors . Although multinational corporations and power change have a long history , they have reached unprecedented levels in the modern era and seem to be growing every Year .

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] million templateYLabel[0] templateYLabel[1] templateTitle[5] . The U.S. automaker is world 's fourth largest manufacturer of templateYLabel[0] templateYLabel[1] in terms of production .

generated_template: 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[0] templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] templateYLabel[2] members , as compared to templateYValue[14] in templateXValue[14] .
generated: This statistic shows the Passenger of General Motors passenger cars produced (in 2014 1999 to 2014 . In 2014 , there were 6.64 General Motors passenger cars produced members , as compared to 5.27 in 2000 .

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 shows the templateYLabel[0] of templateXValue[0] templateTitleSubject[0] templateYLabel[1] templateTitle[4] as of 2019 , templateTitle[6] templateTitle[7] . There were templateYValue[2] templateXValue[3] templateYLabel[1] within the templateXValue[0] templateTitleSubject[0] templateXValue[0] templateXValue[2] group in templateTitleDate[0] .
generated: This statistic shows the ACSI of Pinterest ACSI score social as of 2019 , 2019 . There were 74 Instagram score within the Pinterest ACSI Pinterest Wikipedia group in 2019 .

Example 502:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. mobile device owner monthly app download rate 2018 , by age group
X_Axis['Response']: ['Teens_(13-17)', 'Millennials_(18-34)', 'Gen_X_(35-54)', 'Boomers_(55-64)']
Y_Axis['Share', 'of', 'respondents']: ['83', '74', '59', '39']

gold: This statistic gives information on the percentage of mobile device owners in the United States who download apps at least once a month or more as of April 2018 , sorted by age group . During the survey period , it was found that 74 percent of responding Millennial app users downloaded apps to their mobile device on a monthly basis .
gold_template: This statistic gives information on the percentage of templateTitle[1] templateTitle[2] owners in the templateTitle[0] who templateTitle[6] apps at least once a month or more as of 2018 , sorted templateTitle[9] templateTitle[10] templateTitle[11] . During the survey period , it was found that templateYValue[1] percent of responding Millennial templateTitle[5] users downloaded apps to their templateTitle[1] templateTitle[2] on a templateTitle[4] basis .

generated_template: This statistic shows the results of a survey among female templateTitleSubject[0] high school students templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . According to the source , templateYValue[max] percent of female students in the templateTitle[1] were templateTitle[5] templateTitle[6] as of 2013 .
generated: This statistic shows the results of a survey among female U.S. high school students owner monthly app download . According to the source , 83 percent of female students in the mobile were app download as of 2013 .

Example 503:
titleEntities: {'Subject': ['Boeing'], 'Date': ['2004', '2019']}
title: Boeing 737 - orders 2004 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Number', 'of', 'aircraft']: ['69', '837', '865', '701', '666', '1196', '1208', '1184', '625', '508', '197', '488', '850', '733', '574', '152']

gold: In 2019 , Boeing received gross orders for 69 units of its 737 narrow-body jet airliner series , but net orders after cancellations and changes ( under the ASC 606 revenue recognition standard ) came to a devastating negative 183 units . The slump in net orders came as a result of the jet maker 's 737 MAX crisis . Boeing delivered some 18 units of its 737 aircraft to Delta Air Lines in 2019 .
gold_template: In templateXValue[max] , templateTitleSubject[0] received gross templateTitle[2] for templateYValue[min] units of its templateTitle[1] narrow-body jet airliner series , but net templateTitle[2] after cancellations and changes ( under the ASC 606 revenue recognition standard ) came to a devastating negative 183 units . The slump in net templateTitle[2] came as a result of the jet maker 's templateTitle[1] MAX crisis . templateTitleSubject[0] delivered some 18 units of its templateTitle[1] templateYLabel[1] to Delta Air Lines in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[1] generated by 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] percent in templateXValue[9] , reaching 42 billion U.S. dollars in size .
generated: This statistic shows the aircraft generated by the global orders 2004 2019 from 2004 to 2019 . The orders 2004 2019 is 737 to grow by 508 percent in 2010 , reaching 42 billion U.S. dollars in size .

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: The templateTitle[4] is a major producer of soybeans worldwide , with templateXValue[0] importing the largest amount of templateTitleSubject[0] grown soybeans of any templateXLabel[0] as of templateTitleDate[0] . In that year , templateXValue[0] imported nearly templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] of soybeans from the templateTitle[4] . Soy foods Soybeans are naturally high in protein and hence a popular ingredient in vegetarian and vegan cuisine .
generated: The coffee is a major producer of soybeans worldwide , with Brazil importing the largest amount of Leading grown soybeans of any Country as of 2017 . In that year , Brazil imported nearly 1800.4 thousand hectares of soybeans from the coffee . Soy foods Soybeans are naturally high in protein and hence a popular ingredient in vegetarian and vegan cuisine .

Example 505:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2011', '2019']}
title: Facebook : worldwide quarterly revenue 2011 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11"]
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['21082', '17652', '16886', '15077', '16914', '13727', '13231', '11966', '12972', '10328', '9321', '8032', '8809', '7011', '6436', '5382', '5841', '4501', '4042', '3543', '3851', '3203', '2910', '2502', '2585', '2016', '1813', '1458', '1585', '1262', '1184', '1058', '1131']

gold: In the fourth quarter of 2019 , social network Facebook 's total revenues amounted to 21.08 billion U.S. dollars , the majority of which were generated through advertising . The company announced over seven million active advertisers on Facebook during the third quarter of 2019 . During that fiscal period , the company 's net income was 7.35 billion U.S. dollars .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[1] , social network templateTitleSubject[0] 's total revenues amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , the majority of which were generated through advertising . The company announced over seven templateYLabel[1] active advertisers on templateTitleSubject[0] during the third templateXLabel[0] of templateTitleDate[1] . During that fiscal period , the company 's net income was 7.35 templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] templateYLabel[0] of templateTitleSubject[0] Inc . The online commerce and payments platform 's templateYLabel[0] in the second templateXLabel[0] of templateTitle[5] was templateYValue[31] templateYLabel[1] US templateYLabel[3] , a 7 percent change from the first templateXLabel[0] of the previous year .
generated: This statistic shows the worldwide quarterly Revenue of Facebook Inc . The online commerce and payments platform 's Revenue in the second Quarter of 2019 was 1058 million US dollars , a 7 percent change from the first Quarter of the previous year .

Example 506:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Payment type preference when shopping at fast food restaurants in the U.S. 2018
X_Axis['Response']: ['Credit_card', 'Debit_card', 'Cash']
Y_Axis['Share', 'of', 'respondents']: ['18', '44', '32']

gold: This statistic shows the preferred form of payment when shopping at fast food restaurants among consumers in the United States in 2018 . In the study it was found that 32 percent of consumers preferred to use cash when making purchases at fast food restaurants .
gold_template: This statistic shows the preferred form of templateTitle[0] templateTitle[3] templateTitle[4] at templateTitle[5] templateTitle[6] templateTitle[7] among consumers in the templateTitle[8] in templateTitleDate[0] . In the study it was found that templateYValue[2] percent of consumers preferred to use templateXValue[last] templateTitle[3] making purchases at templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: During a survey in 2019 , it was found that just templateYValue[min] percent of templateTitle[3] adults visited a movie theater one or templateXValue[0] per month , but templateYValue[max] percent stated that they went to the cinema to watch a movie templateXValue[1] or templateXValue[last] or less . Another survey earlier in the same templateXValue[last] revealed that visits to the cinema can vary according to generation . On average , 34 percent of Gen Xers had seen one movie within the last month , compared to 43 percent of those classed as being members of Generation Z .
generated: During a survey in 2019 , it was found that just 18 percent of when adults visited a movie theater one or Credit card per month , but 44 percent stated that they went to the cinema to watch a movie Debit card or Cash or less . Another survey earlier in the same Cash revealed that visits to the cinema can vary according to generation . On average , 34 percent of Gen Xers had seen one movie within the last month , compared to 43 percent of those classed as being members of Generation Z .

Example 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 shows the templateYLabel[0] of templateXValue[0] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[3] templateTitle[4] . In templateTitleDate[0] approximately five million private templateXValue[0] were counted in templateTitleSubject[0] , of which approximately 35 percent or templateYValue[max] million were templateXValue[0] . Approximately templateYValue[1] million templateXValue[0] consisted of templateXValue[1] couples templateXValue[1] children templateXValue[1] at templateXValue[1] , whereas another 951,000 templateXValue[1] couples had no children templateXValue[1] at templateXValue[1] .
generated: This statistic shows the Average of Food in Easter U.S. in 2019 , per capita . In 2019 approximately five million private Food were counted in Easter U.S. , of which approximately 35 percent or 47.97$ million were Food . Approximately 27.29$ million Food consisted of Clothing couples Clothing children Clothing at Clothing , whereas another 951,000 Clothing couples had no children Clothing at Clothing .

Example 508:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2017', '2023']}
title: Argentina : internet user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['94', '93', '91', '88', '85', '82', '77']

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

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

Example 509:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2010', '2018']}
title: Migration balance in Belgium 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Migration', 'balance']: ['50180', '44536', '42239', '47682', '39954', '34843', '44365', '62157', '79446']

gold: In 2018 , the migration balance in Belgium was roughly 50,000 , meaning that the number of immigrants moving to Belgium outnumbered the number of people leaving the country by about 50,000 . This was an increase in comparison to the previous year , but significantly lower than for example in 2010 and 2011 , when the migration balance was 79,446 and 62,157 respectively . It was also considerably lower than in neighboring country the Netherlands , which in 2018 had a positive migration balance of over 86,000 .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was roughly 50,000 , meaning that the number of immigrants moving to templateTitleSubject[0] outnumbered the number of people leaving the country by about 50,000 . This was an increase in comparison to the previous templateXLabel[0] , but significantly lower than for example in templateXValue[min] and templateXValue[7] , when the templateYLabel[0] templateYLabel[1] was templateYValue[max] and templateYValue[7] respectively . It was also considerably lower than in neighboring country the Netherlands , which in templateXValue[max] had a positive templateYLabel[0] templateYLabel[1] of over 86,000 .

generated_template: The templateTitleSubject[0] is growing in every aspect . Over the last decade , the total templateYLabel[0] templateTitle[7] of templateTitle[3] in the templateTitleSubject[0] have more than quadrupled . In templateXValue[max] they amounted to approximately templateYValue[max] templateYLabel[3] templateYLabel[4] .
generated: The Belgium is growing in every aspect . Over the last decade , the total Migration 2018 of 2010 in the Belgium have more than quadrupled . In 2018 they amounted to approximately 79446 balance .

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 January 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] thousand 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 thousand 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] templateYLabel[2] .

generated_template: The statistic shows the total templateTitleSubject[0] templateYLabel[0] in the global templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[4] and shows a forecast through to templateXValue[max] . In templateXValue[4] , global templateTitle[2] templateTitleSubject[0] templateYLabel[0] amounted to templateYValue[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the total Switzerland Employed in the global persons Switzerland from 2010 to 2016 and shows a forecast through to 2020 . In 2016 , global persons Switzerland Employed amounted to 4.96 persons millions .

Example 512:
titleEntities: {'Subject': ['Afghanistan'], 'Date': ['2017']}
title: Fertility rate in Afghanistan 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'children', 'born', 'per', 'woman']: ['4.63', '4.8', '4.98', '5.16', '5.36', '5.56', '5.77', '5.98', '6.18', '6.37', '6.56']

gold: This timeline shows the fertility rate in Afghanistan from 2007 to 2017 . In 2017 , Afghanistan 's fertility rate amounted to 4.63 children born per woman . Today , Afghanistan is among the countries with the highest fertility rate on the world fertility rate ranking .
gold_template: This timeline shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] amounted to templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Today , templateTitleSubject[0] is among the countries with the highest templateTitle[0] templateTitle[1] on the world templateTitle[0] templateTitle[1] ranking .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[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[min] templateYLabel[1] 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 borne by one woman while being of child-bearing age . In 2017 , the Fertility rate in Afghanistan amounted to 4.63 children per woman .

Example 513:
titleEntities: {'Subject': ['GDP'], 'Date': ['2018']}
title: National debt of selected countries in relation to gross domestic product ( GDP ) 2018
X_Axis['Country']: ['Japan', 'United_States', 'France', 'Brazil', 'United_Kingdom', 'India', 'Germany', 'China', 'Russia']
Y_Axis['National', 'debt', 'in', 'relation', 'to', 'gross', 'domestic', 'product', '(GDP)']: ['237.69', '106.22', '99.31', '91.57', '85.55', '69.04', '58.58', '55.57', '16.49']

gold: This statistic shows the national debt of important industrial and emerging countries in 2019 in relation to the gross domestic product ( GDP ) . In 2019 , the national debt of China was at about 55.57 percent of the gross domestic product .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of important industrial and emerging templateTitle[3] in 2019 in templateYLabel[2] to the templateYLabel[3] templateYLabel[4] templateYLabel[5] ( templateTitleSubject[0] ) . In 2019 , the templateYLabel[0] templateYLabel[1] of templateXValue[7] was at about templateYValue[7] percent of the templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the templateTitle[4] templateTitle[5] templateYLabel[0] templateYLabel[1] templateYLabel[2] worldwide in templateTitleDate[0] , broken down templateTitle[7] templateXLabel[0] . According to the report , the templateXValue[0] accounted for approximately templateYValue[1] percent of templateTitleSubject[0] templateTitle[4] templateYLabel[0] templateYLabel[1] that year .
generated: This statistic shows the relation gross National debt relation worldwide in 2018 , broken down product Country . According to the report , the Japan accounted for approximately 106.22 percent of GDP relation National debt that year .

Example 514:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': []}
title: Liver transplants in the United Kingdom ( UK ) 2018/19
X_Axis['Country', 'of', 'residence']: ['England', 'Scotland', 'Wales', 'Northern_Ireland']
Y_Axis['Number', 'of', 'transplants']: ['779', '114', '35', '34']

gold: In the period 2018/19 , 779 liver transplants were carried out in England , followed by 114 conducted in Scotland . England has by far the largest population of the countries in the United Kingdom , so it is unsurprising it has the highest number of transplants performed in a year . State of liver transplants in the UK The number of liver transplants in the United Kingdom in 2018/19 was an five percent increase from the number that took place in the preceding year .
gold_template: In the period templateTitle[5] , templateYValue[max] templateTitle[0] templateYLabel[1] were carried out in templateXValue[0] , followed by templateYValue[1] conducted in templateXValue[1] . templateXValue[0] has by far the largest population of the countries in the templateTitleSubject[0] , so it is unsurprising it has the highest templateYLabel[0] of templateYLabel[1] performed in a year . State of templateTitle[0] templateYLabel[1] in the templateTitleSubject[1] The templateYLabel[0] of templateTitle[0] templateYLabel[1] in the templateTitleSubject[0] in templateTitle[5] was an five percent increase from the templateYLabel[0] that took place in the preceding year .

generated_template: This statistic 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] percent . templateXLabel[0] templateTitle[3] templateXValue[last] had the smallest templateYLabel[0] at just templateYValue[min] percent .
generated: This statistic shows United Kingdom transplants Kingdom UK of 2018/19 in . England Country comprised the largest Number of transplants in with 779 percent . Country Kingdom Northern Ireland had the smallest Number at just 34 percent .

Example 515:
titleEntities: {'Subject': ['Countries'], 'Date': ['2017']}
title: Countries with the highest infant mortality rate 2017
X_Axis['Country']: ['Afghanistan', 'Somalia', 'Central_African_Republic', 'Guinea-Bissau', 'Chad', 'Niger', 'Burkina_Faso', 'Nigeria', 'Mali', 'Sierra_Leone', 'Democratic_Republic_of_Congo', 'Angola', 'Mozambique', 'Equatorial_Guinea', 'South_Sudan', 'Zambia', 'Gambia', 'Comoros', 'Burundi', 'Uganda']
Y_Axis['Child', 'deaths', 'in', 'the', 'first', 'year', 'of', 'life', 'per', '1,000', 'live', 'births']: ['110.6', '94.8', '86.3', '85.7', '85.4', '81.1', '72.2', '69.8', '69.5', '68.4', '68.2', '67.6', '65.9', '65.2', '62.8', '61.1', '60.2', '60.0', '58.8', '56.1']

gold: This statistic shows the 20 countries  with the highest infant mortality rate in 2017 . An estimated 110.6 infants per 1,000 live births died in the first year of life in Afghanistan in 2017 . Infant and child mortality Infant mortality usually refers to the death of children younger than one year .
gold_template: This statistic shows the 20 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: templateTitle[4] surgery is a growing industry globally . Many templateTitleSubject[0] are known for their expertise in templateTitle[4] surgery ; however , some have greater numbers of templateYLabel[1] than others . As of templateTitleDate[0] , the U.S. had the largest templateYLabel[0] of templateTitle[4] templateYLabel[1] globally templateTitle[1] templateYValue[max] templateYLabel[1] .
generated: rate surgery is a growing industry globally . Many Countries are known for their expertise in rate surgery ; however , some have greater numbers of deaths than others . As of 2017 , the U.S. had the largest Child of rate deaths globally highest 110.6 deaths .

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 survey conducted templateTitle[3] the Hearth , Patio & Barbecue Association ( HPBA ) in July and 2018 . During the survey , templateTitleSubject[0] consumers were asked about which types of grills they owned . Approximately templateYValue[max] percent of the templateYLabel[1] indicated templateTitle[2] of a templateXValue[0] .
generated: This statistic shows the results of a survey conducted Hollywood the Hearth , Patio & Barbecue Association ( HPBA ) in July and 2018 . During the survey , Hollywood consumers were asked about which types of grills they owned . Approximately 38 percent of the respondents indicated minorities of a Do a good job of portraying racial 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[0] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] by one templateYLabel[4] while being of child-bearing age . In templateXValue[max] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] amounted to templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Fertility rate in Russia from 2007 to 2017 . The Fertility rate is the average Number of children born by one woman while being of child-bearing age . In 2017 , Russia 's Fertility rate amounted to 1.42 children born per woman .

Example 518:
titleEntities: {'Subject': ['King'], 'Date': ['2010', '2018']}
title: King annual income 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Operating', 'income', 'in', 'million', 'U.S.', 'dollars']: ['750.0', '700.0', '537.0', '516.78', '574.85', '567.59', '7.85', '-1.32', '1.74']

gold: This statistic shows a timeline with the global annual operating income of King.com from 2010 to 2018 . In 2018 , the company reported an income of 750 million U.S. dollars . Popular King titles include Candy Crush Saga , Farm Ville Saga as well as Bubble Witch Saga .
gold_template: This statistic shows a timeline with the global templateTitle[1] templateYLabel[0] templateYLabel[1] of King.com from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company reported an templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Popular templateTitleSubject[0] titles include Candy Crush Saga , Farm Ville Saga as well as Bubble Witch Saga .

generated_template: The statistic represents the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[1] templateTitle[2] , a franchise of the National Football League , templateTitle[4] templateXValue[min] to templateXValue[max] . In the templateXValue[max] season , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[1] templateTitle[2] were at templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic represents the Operating income of the King income , a franchise of the National Football League , 2018 2010 to 2018 . In the 2018 season , the Operating income of the King income were at 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[0] and templateTitleDate[1] , about templateYValue[7] templateYLabel[1] in templateXValue[4] templateXValue[7] were templateYLabel[2] in templateTitle[5] programs .

generated_template: This statistic shows the total templateYLabel[0] of templateTitle[1] in the templateTitle[2] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , templateYLabel[2] templateYLabel[0] of templateTitle[1] in the templateXLabel[0] of templateXValue[0] reached approximately templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows the total Number of number in the U.S. in 2017 , enrolled State . In 2017 , enrolled Number of number in the State of United States  reached approximately 1565168 children enrolled .

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 templateYLabel[0] templateYLabel[1] templateYLabel[2] of selected templateTitle[4] templateTitle[5] in templateTitleDate[0] . In that year , the templateXValue[0] templateXValue[0] templateXLabel[1] was the templateYLabel[2] templateTitle[5] with the fourth highest templateTitle[0] templateTitle[1] templateYLabel[0] . Physicians in this templateXLabel[1] averagely earned some templateYValue[max] templateYLabel[3] .
generated: This statistic shows the Price per square of selected office rent in 2019 . In that year , the Reading City was the square rent with the fourth highest UK real Price . Physicians in this City averagely earned some 468 meter .

Example 521:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1995', '2019']}
title: Number of drive-in cinema sites in the U.S. 1995 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995']
Y_Axis['Number', 'of', 'drive-in', 'cinema', 'sites']: ['321', '321', '349', '349', '349', '393', '393', '366', '366', '374', '381', '383', '383', '396', '401', '402', '400', '432', '440', '442', '446', '524', '577', '583', '593']

gold: The number of drive-in cinema sites in the United States remained at 321 in 2019 , the same as in the previous year . The figure tends to remain the same for years at a time , and is always far lower than the number of indoor sites , which make up the vast majority of cinemas in the country .
gold_template: The templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[4] remained at templateYValue[min] in templateXValue[max] , the same as in the previous templateXLabel[0] . The figure tends to remain the same for years at a time , and is always far lower than the templateYLabel[0] of indoor templateYLabel[3] , which make up the vast majority of cinemas in the country .

generated_template: In templateXValue[max] , around templateYValue[0] templateYLabel[2] templateYLabel[3] of templateTitle[5] in the templateTitle[4] stated they felt it was `` fairly easy '' or `` very easy '' to obtain templateTitleSubject[0] . This is a significant decrease from templateYValue[max] percent of templateTitle[5] templateTitle[6] who templateTitle[2] the drug to be easy to obtain in the templateXLabel[0] templateXValue[20] . templateTitleSubject[0] is a hallucinogenic drug that can alter the user 's thoughts , feelings , and perception of reality .
generated: In 2019 , around 321 cinema sites of 1995 in the U.S. stated they felt it was `` fairly easy '' or `` very easy '' to obtain U.S. . This is a significant decrease from 593 percent of 1995 2019 who cinema the drug to be easy to obtain in the Year 1999 . U.S. is a hallucinogenic drug that can alter the user 's thoughts , feelings , and perception of reality .

Example 522:
titleEntities: {'Subject': ['Hispanics'], 'Date': ['1990', '2018']}
title: Birth rate of Hispanics in the U.S. 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Births', 'per', '1,000', 'of', 'Hispanic', 'population']: ['14.8', '15.2', '16.0', '16.3', '16.5', '16.7', '17.1', '17.6', '18.7', '20.3', '21.8', '23.0', '23.3', '22.9', '22.8', '22.8', '22.7', '22.9', '23.1', '22.5', '22.7', '23.0', '23.8', '24.1', '24.7', '25.4', '26.1', '26.5', '26.7']

gold: This graph displays the birth rate of Hispanics in the United States from 1990 to 2018 . In 2018 , about 14.8 children were born per 1,000 of Hispanic population .
gold_template: This graph displays the templateTitle[0] templateTitle[1] of templateTitleSubject[0] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[min] children were born templateYLabel[1] 1,000 of templateYLabel[3] templateYLabel[4] .

generated_template: In templateXValue[max] , there were about templateYValue[0] templateYLabel[3] templateYLabel[1] templateYLabel[2] in the templateTitle[4] with a templateTitle[2] mother . This is an increase from templateXValue[min] levels , when there were about templateYValue[min] templateYLabel[3] 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 Hispanic per 1,000 in the 1990 with a Hispanics mother . This is an increase from 1990 levels , when there were about 14.8 Hispanic 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 grew in templateTitleSubject[0] during this period . In templateXValue[max] , templateYValue[max] percent of Greek templateYLabel[1] had templateTitle[1] templateTitle[2] .

generated_template: This statistic shows the percentage of templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[max] percent of the total templateYLabel[2] were living in templateTitleSubject[0] .
generated: This statistic shows the percentage of internet in Greece from 2007 to 2018 . In 2018 , about 76 percent of the total households were living in Greece .

Example 524:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2011', '2018']}
title: Number of enrolled university students in South Korea 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Enrolled', 'university', 'students', 'in', 'millions']: ['2.03', '2.05', '2.08', '2.11', '2.13', '2.13', '2.1', '2.07']

gold: This statistic illustrates the number of students enrolled in universities in South Korea from 2011 to 2018 . In 2018 , there were approximately 2.03 million students enrolled in universities in South Korea .
gold_template: This statistic illustrates the templateTitle[0] of templateYLabel[2] templateYLabel[0] in universities in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[min] templateYLabel[3] templateYLabel[2] templateYLabel[0] in universities in templateTitleSubject[0] .

generated_template: The templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( templateTitleSubject[1] ) of templateTitleSubject[0] templateTitle[1] customers in templateTitleSubject[2] has fallen steadily since templateXValue[min] , reaching an estimated 14.8 templateYLabel[4] templateYLabel[2] templateYLabel[6] in templateXValue[max] . This is a decrease of 22.9 percent on the templateTitleSubject[1] generated in templateXValue[min] . Shrinking need for templateTitleSubject[0] templateTitle[1] The decline of templateTitleSubject[0] templateTitle[1] , as demonstrated by the falling templateTitleSubject[1] and the diminishing penetration rate in countries such as the United Kingdom ( UK ) , can partially be attributed to the increased speeds and reliability that comes with 3G and 4G templateTitleSubject[0] networks .
generated: The Enrolled university students millions ( South Korea ) of South Korea enrolled customers in South Korea has fallen steadily since 2011 , reaching an estimated 14.8 millions students millions in 2018 . This is a decrease of 22.9 percent on the South Korea generated in 2011 . Shrinking need for South Korea enrolled The decline of South Korea enrolled , as demonstrated by the falling South Korea and the diminishing penetration rate in countries such as the United Kingdom ( UK ) , can partially be attributed to the increased speeds and reliability that comes with 3G and 4G South Korea networks .

Example 525:
titleEntities: {'Subject': ['Worlds'], 'Date': ['2018']}
title: Worlds ' most dangerous cities , by murder rate 2018
X_Axis['City']: ['Tijuana_-_Mexico', 'Acapulco_-_Mexico', 'Caracas_-_Venezuela', 'Ciudad_Victoria_-_Mexico', 'Ciudad_Juarez_-_Mexico', 'Irapuato_-_Mexico', 'Ciudad_Guayana_-_Venezuela', 'Natal_-_Brazil', 'Fortaleza_-_Brazil', 'Ciudad_Bolivar_-_Venezuela', 'Cape_Town_-_South_Africa', 'Belem_-_Brazil', 'Cancun_-_Mexico', 'Feira_de_Santana_-_Brazil', 'St._Louis_Missouri_-_U.S.', 'Culiacan_-_Mexico', 'Barquisimeto_-_Venezuela', 'Uruapan_-_Mexico', 'Kingston_-_Jamaica', 'Ciudad_Obregón_-_Mexico', 'Maceio_-_Brazil', 'Vitoria_da_Conquista_-_Brazil', 'Baltimore_Maryland_-_U.S.', 'San_Salvador_-_El_Salvador', 'Aracaju_-_Brazil', 'Coatzacoalcos_-_Mexico', 'Palmira_-_Colombia', 'Maturin_-_Venezuela', 'Salvador_-_Brazil', 'Macapa_-_Brazil', 'Cali_-_Colombia', 'Celaya_-_Mexico', 'San_Pedro_Sula_-_Honduras', 'Ensenada_-_Mexico', 'Campos_dos_Goytacazes_-_Brazil', 'Tepic_-_Mexico', 'Manaus_-_Brazil', 'Guatemala_City_-_Guatemala', 'Recife_-_Brazil', 'Distrito_Central_-_Honduras', 'San_Juan_-_Puerto_Rico', 'Valencia_-_Venezuela', 'Reynosa_-_Mexico', 'João_Pessoa_-_Brazil', 'Nelson_Mandela_Bay_-_South_Africa', 'Detroit_Michigan_-_U.S.', 'Durban_-_South_Africa', 'Teresina_-_Brazil', 'Chihuahua_-_Mexico', 'New_Orleans_Louisiana_-_U.S.']
Y_Axis['Murder', 'rate', 'per', '100,000', 'inhabitants']: ['138.26', '110.5', '99.98', '86.01', '85.56', '81.44', '78.3', '74.67', '69.15', '69.09', '66.36', '65.31', '64.46', '63.29', '60.59', '60.52', '56.67', '54.52', '54.12', '52.09', '51.46', '50.75', '50.52', '50.32', '48.77', '48.35', '47.97', '47.24', '47.23', '47.2', '47.03', '46.99', '46.67', '46.6', '46.28', '44.89', '44.0', '43.73', '43.72', '43.3', '42.4', '42.36', '41.48', '41.36', '39.16', '38.78', '38.51', '37.61', '37.5', '36.87']

gold: This statistic ranks the 50 most dangerous cities of 2018 , by murder rate per 100,000 inhabitants . Tijuana 's murder rate was 138.26 for every 100,000 people living in the city . The world 's most dangerous cities The Citizens ' Council for Public Security and Criminal Justice published a ranking of the world 's most dangerous cities in 2018 , ranking cities according to the number of murders per 100,000 inhabitants that year .
gold_template: This statistic ranks the templateYValue[23] templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleDate[0] , templateTitle[5] templateYLabel[0] templateYLabel[1] templateYLabel[2] 100,000 templateYLabel[4] . templateXValue[0] 's templateYLabel[0] templateYLabel[1] was templateYValue[max] for every 100,000 people living in the templateXValue[37] . The world 's templateTitle[2] templateTitle[3] templateTitle[4] The Citizens templateTitle[1] Council for Public Security and Criminal Justice published a ranking of the world 's templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] , ranking templateTitle[4] according to the number of murders templateYLabel[2] 100,000 templateYLabel[4] that year .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] and templateTitle[4] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In that year , most templateTitle[2] templateYLabel[1] and templateTitle[4] occurred in templateXValue[0] - a total of templateYValue[max] . In templateXValue[last] , no templateTitle[2] templateYLabel[1] and templateTitle[4] occurred in templateTitleDate[0] .
generated: The statistic shows the Murder of most rate and cities in the Worlds in 2018 , murder City . In that year , most rate and cities occurred in Tijuana - Mexico - a total of 138.26 . In New Orleans Louisiana - U.S. , no most rate and cities occurred in 2018 .

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 templateTitle[7] , 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows a ranking of templateTitle[0] of the templateTitleSubject[0] templateTitle[6] templateTitle[7] templateTitle[1] templateYLabel[0] ( templateTitle[3] ) templateYLabel[2] in 2019 . templateXValue[3] of FC templateXValue[1] Munich has a templateYLabel[0] ( templateTitle[3] ) templateYLabel[2] of templateYValue[3] templateYLabel[3] templateYLabel[4] . The player with the highest templateYLabel[0] templateYLabel[2] in the templateTitle[7] is templateXValue[0] of Borussia Dortmund , who was valued at templateYValue[max] templateYLabel[3] templateYLabel[4] as of 2019 .
generated: The statistic shows a ranking of Golf-Association of the Golf-Association 2013 executives Compensations ( compensation ) U.S. in 2019 . Tom Wade (PGA Tour global commercial officer) 2013 of FC Joe Steranka (Former of America CEO) 2013 Munich has a Compensations ( compensation ) U.S. of 1.17 dollars . The player with the highest Compensations U.S. in the 2013 is Tim Finchem (PGA Tour commissioner & CEO) 2013 of Borussia Dortmund , who was valued at 4.58 dollars as of 2019 .

Example 528:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2019', '2029']}
title: Forecast of population growth in Denmark 2019 to 2029
X_Axis['Year']: ['2029', '2028', '2027', '2026', '2025', '2024', '2023', '2022', '2021', '2020', '2019']
Y_Axis['Number', 'of', 'inhabitants', '(in', 'millions)']: ['6.09', '6.07', '6.04', '6.02', '6.0', '5.97', '5.95', '5.92', '5.89', '5.87', '5.83']

gold: The statistic shows a forecast of the Danish population growth from 2019 to 2029 . The total number of inhabitants will keep on increasing . According to the forecast there will be roughly over 6 million of people living in Denmark by 2029 .
gold_template: The statistic shows a templateTitle[0] of the Danish templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . The total templateYLabel[0] of templateYLabel[1] will keep on increasing . According to the templateTitle[0] there will be roughly over templateYValue[max] million of people living in templateTitleSubject[0] by templateXValue[max] .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateYLabel[0] for templateTitle[1] templateTitle[2] excluding pharmaceuticals in the templateTitle[0] had an templateYLabel[1] of templateYValue[10] ( templateXValue[6] templateYLabel[3] templateYValue[4] ) . In templateXValue[max] , the templateYLabel[1] stood at templateYValue[0] .
generated: This statistic shows the Denmark population Number inhabitants from 2019 to 2029 . In 2019 , the Number for population growth excluding pharmaceuticals in the Forecast had an inhabitants of 5.83 ( 2023 millions) 6.0 ) . In 2029 , the inhabitants stood at 6.09 .

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 displays the templateTitle[3] of templateYLabel[1] templateTitleSubject[0] chat app templateYLabel[2] templateYLabel[3] in selected templateTitle[7] as of templateTitleDate[0] . The templateTitleSubject[0] templateYLabel[1] calorie templateTitle[3] templateYLabel[3] templateYLabel[4] in the templateXValue[0] is templateYValue[max] templateYLabel[2] ( kcal ) templateYLabel[3] person . The prevalence of obesity has increased in templateTitle[7] like the templateXValue[0] and the templateXValue[0] templateXValue[2] in the last decade , with the U.S. reporting the highest rates of obesity in all OECD templateTitle[7] .
generated: This statistic displays the attacks of incidents Piracy chat app incidents in selected 2019 as of 2019 . The Piracy incidents calorie attacks incidents in the Nigeria is 35 incidents ( kcal ) incidents person . The prevalence of obesity has increased in 2019 like the Nigeria and the Nigeria Singapore Staits in the last decade , with the U.S. reporting the highest rates of obesity in all OECD 2019 .

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[0] templateYLabel[1] templateYLabel[2] of templateTitle[0] templateTitle[1] were produced . templateTitle[0] templateTitle[1] is a templateTitle[0] , low-calorie dessert , which is often served in a large variety of flavors .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Approximately templateYValue[max] templateYLabel[1] templateYLabel[2] of templateTitle[0] templateTitle[1] was produced in templateTitleSubject[0] in templateXValue[max] , up from around templateYValue[9] templateYLabel[1] templateYLabel[2] the previous templateXLabel[0] . templateTitle[0] templateTitle[1] in templateTitleSubject[0] - additional information templateTitle[1] is most commonly associated with templateTitleSubject[0] , the product 's largest producer .
generated: This statistic shows the Frozen yogurt Production in U.S. from 2006 to 2018 . Approximately 78.6 million gallons of Frozen yogurt was produced in U.S. in 2018 , up from around 46.0 million gallons the previous Year . Frozen yogurt in U.S. - additional information yogurt is most commonly associated with U.S. , the product 's largest producer .

Example 531:
titleEntities: {'Subject': ['Welsh Assembly'], 'Date': ['1999', '2011']}
title: Welsh Assembly elections : turnout rates 1999 to 2011
X_Axis['Year']: ['1999', '2003', '2007', '2011']
Y_Axis['Turnout', 'rate']: ['46.4', '38.2', '43.5', '41.8']

gold: This statistic shows the voter turnout rates for constituency votes in the Welsh Assembly elections from 1999 to 2011 . Over the last four elections there was a variation in voter turnout of 8.2 percent . The peak , in 1999 , was followed in 2003 by the lowest turnout rate of this period .
gold_template: This statistic shows the voter templateYLabel[0] templateTitle[4] for constituency votes in the templateTitleSubject[0] elections from templateXValue[min] to templateXValue[max] . Over the last four templateTitle[2] there was a variation in voter templateYLabel[0] of 8.2 percent . The peak , in templateXValue[min] , was followed in templateXValue[1] by the lowest templateYLabel[0] templateYLabel[1] of this period .

generated_template: Singaporeans consumed on average around templateYValue[0] templateYLabel[4] of templateYLabel[2] each in templateXValue[max] , which was among the lowest in Asia-Pacific . This was partly due to the heavy taxation of alcoholic beverages in templateTitleSubject[0] . Even so , health officials were concerned about a rise in unhealthy drinking habits among the young .
generated: Singaporeans consumed on average around 46.4 rate of rate each in 2011 , which was among the lowest in Asia-Pacific . This was partly due to the heavy taxation of alcoholic beverages in Welsh Assembly . Even so , health officials were concerned about a rise in unhealthy drinking habits among the young .

Example 532:
titleEntities: {'Subject': ['U.S.'], 'Date': []}
title: U.S. women who have given oral sex to a male in their lifetime , by age group
X_Axis['Age', 'group']: ['14-15', '16-17', '18-19', '20-24', '25-29', '30-39', '40-49', '50-59', '60-69', '70_and_older']
Y_Axis['Share', 'of', 'respondents']: ['13', '29', '61', '78', '89', '80', '83', '80', '73', '43']

gold: This statistic shows the share of American women who have ever given oral sex to a male in their lifetime , sorted by age group . The findings were acquired in early 2009 and published in the Journal of Sexual Medicine , a publication on sexual behavior in the United States , in 2010 . 89 percent of respondents aged 25 to 29 stated they have given oral sex to a male at some time during their life .
gold_template: This statistic shows the templateYLabel[0] of American templateTitle[1] templateTitle[2] templateTitle[3] ever templateTitle[4] templateTitle[5] templateTitle[6] to a templateTitle[7] in templateTitle[8] templateTitle[9] , sorted templateTitle[10] templateXLabel[0] templateXLabel[1] . The findings were acquired in early 2009 and published in the Journal of Sexual Medicine , a publication on sexual behavior in the templateTitle[0] , in 2010 . templateYValue[max] percent of templateYLabel[1] aged 25 to templateYValue[1] stated they templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] to a templateTitle[7] at some time during templateTitle[8] life .

generated_template: This statistic shows the templateYLabel[0] of American templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] at some point during templateTitle[7] templateTitle[8] , sorted templateTitle[9] templateXLabel[0] templateXLabel[1] . The findings were acquired in early 2009 and published in the Journal of Sexual Medicine , a publication on sexual behavior in the templateTitle[0] , in 2010 . templateYValue[max] percent of templateYLabel[1] aged 25 to 29 stated they templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] at some point during templateTitle[7] life .
generated: This statistic shows the Share of American women who have given oral sex at some point during male their , sorted lifetime Age group . The findings were acquired in early 2009 and published in the Journal of Sexual Medicine , a publication on sexual behavior in the U.S. , in 2010 . 89 percent of respondents aged 25 to 29 stated they have given oral sex at some point during male life .

Example 533:
titleEntities: {'Subject': ['Germany'], 'Date': ['2001', '2018']}
title: Share of internet users in Germany 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Share', 'of', 'internet', 'users']: ['84', '81', '79', '77.6', '76.8', '76.5', '75.6', '74.7', '72', '69.1', '65.1', '60.2', '58.2', '55.1', '52.7', '50.1', '41.7', '37']

gold: In 2018 , the share of German internet users amounted to 84 percent , an increase compared to the previous year at 81 percent . This share has only been growing in recent years . Considering current German population numbers stand at almost 83 million , such a high share of internet users is significant in itself and also for predicting future trends on digitalization and online connectivity in the country .
gold_template: In templateXValue[max] , the templateYLabel[0] of German templateYLabel[1] templateYLabel[2] amounted to templateYValue[max] percent , an increase compared to the previous templateXLabel[0] at templateYValue[1] percent . This templateYLabel[0] has only been growing in recent years . Considering current German population numbers stand at almost 83 million , such a high templateYLabel[0] of templateYLabel[1] templateYLabel[2] is significant in itself and also for predicting future trends on digitalization and online connectivity in the country .

generated_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of the Chilean population accessed the templateYLabel[3] , up from templateYValue[17] percent in templateXValue[16] . In templateXValue[max] , templateYValue[max] percent 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 percent of the Chilean population accessed the users , up from 37 percent in 2002 . In 2018 , 84 percent 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] percent of its gross domestic product ( templateYLabel[2] ) . This is an increase from templateYValue[27] percent of the templateYLabel[2] of the templateTitle[0] in templateXValue[min] . Trade and foreign relations The templateTitle[0] ' templateYLabel[2] is the largest in the world , clocking in at around 18.57 trillion templateTitle[0] dollars in 2018. International trade is a huge boon to the templateTitle[0] economy , both financially and regarding foreign relations .

generated_template: In templateXValue[max] , there were about templateYValue[0] templateYLabel[3] templateYLabel[1] templateYLabel[2] in the templateTitle[4] with a templateTitle[2] mother . This is an increase from templateXValue[min] levels , when there were about templateYValue[min] templateYLabel[3] 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 GDP percentage GDP in the 1990 with a percentage mother . This is an increase from 1990 levels , when there were about 9.04 GDP 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 million euros in templateTitleSubject[0] as of templateTitleDate[0] . The countries with the largest templateYLabel[0] of templateYLabel[1] templateYLabel[2] include templateXValue[0] ( 1.4 million of ultra-rich templateYLabel[2] ) and templateXValue[1] ( 1.3 million templateYLabel[2] ) .

generated_template: This statistic shows the an estimate of templateTitle[1] templateYLabel[0] worldwide , from the 2017 fiscal year to fiscal year 2021 , templateTitle[3] select templateXLabel[0] . The templateXValue[0] is projected to spend about templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] on drones between 2017 and 2021 , making it the templateXLabel[0] with the greatest expenditure on drones .
generated: This statistic shows the an estimate of households Number worldwide , from the 2017 fiscal year to fiscal year 2021 , Europe select Country . The Germany is projected to spend about 1433985 millionaire households on drones between 2017 and 2021 , making it the Country with the greatest expenditure on drones .

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 templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateTitle[2] templateTitle[3] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] were around templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Share retail of the filled 's share CVS Health from 2012 to 2025 . In 2019 , CVS Health 's Share retail were around 17.25 prescriptions filled .

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] 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 Zimbabwe from 1950 to 2050 . The Median age is the age that divides a population into two numerically equal groups ; that is , half the people are younger than this age and half are older . It is a single index that summarizes the age distribution of a population .

Example 538:
titleEntities: {'Subject': ['GlaxoSmithKline'], 'Date': ['2011', '2018']}
title: GlaxoSmithKline 's advertising spending 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Profit', 'in', 'million', 'British', 'pounds']: ['1376', '1351', '1265', '1059', '671', '808', '839', '910']

gold: This statistic describes the advertising spending of GlaxoSmithKline from 2011 to 2018 . In 2018 , the company reported ad spending of some 1.38 billion British pounds . GlaxoSmithKline plc is a global pharmaceutical and biotech company , headquartered in London .
gold_template: This statistic describes the templateTitle[2] templateTitle[3] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company reported ad templateTitle[3] of some templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] plc is a global pharmaceutical and biotech company , headquartered in London .

generated_template: The templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( templateTitleSubject[1] ) of templateTitleSubject[0] templateTitle[1] customers in templateTitleSubject[2] has fallen steadily since templateXValue[min] , reaching an estimated 14.8 templateYLabel[4] templateYLabel[2] templateYLabel[6] in templateXValue[max] . This is a decrease of 22.9 percent on the templateTitleSubject[1] generated in templateXValue[min] . Shrinking need for templateTitleSubject[0] templateTitle[1] The decline of templateTitleSubject[0] templateTitle[1] , as demonstrated by the falling templateTitleSubject[1] and the diminishing penetration rate in countries such as the United Kingdom ( UK ) , can partially be attributed to the increased speeds and reliability that comes with 3G and 4G templateTitleSubject[0] networks .
generated: The Profit million British pounds ( GlaxoSmithKline ) of GlaxoSmithKline 's customers in GlaxoSmithKline has fallen steadily since 2011 , reaching an estimated 14.8 pounds British pounds in 2018 . This is a decrease of 22.9 percent on the GlaxoSmithKline generated in 2011 . Shrinking need for GlaxoSmithKline 's The decline of GlaxoSmithKline 's , as demonstrated by the falling GlaxoSmithKline and the diminishing penetration rate in countries such as the United Kingdom ( UK ) , can partially be attributed to the increased speeds and reliability that comes with 3G and 4G GlaxoSmithKline networks .

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: The statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] in templateTitle[7] , templateTitle[5] templateTitle[6] of householder . In templateTitle[7] , the real templateYLabel[0] templateTitle[1] templateYLabel[1] for householder aged 15 - 24 was at templateYValue[min] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Number children in the Births in 2018 , 2018 of householder . In 2018 , the real Number children for householder aged 15 - 24 was at 469 born thousands .

Example 540:
titleEntities: {'Subject': ['Orlando Magic'], 'Date': ['2001', '2019']}
title: Orlando Magic 's revenue 2001 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['244', '223', '211', '166', '143', '143', '139', '126', '140', '108', '107', '100', '92', '89', '82', '78', '80', '82']

gold: The statistic shows the revenue of the Orlando Magic franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise amounted to 244 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Orlando Magic franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 244 million U.S. dollars .

Example 541:
titleEntities: {'Subject': ['Kickstarter'], 'Date': ['2019']}
title: Distribution of Kickstarter funding amounts raised 2019
X_Axis['Money', 'raised', '(in', 'U.S.', 'dollars)']: ['Less_than_1000', '1000_to_9999', '10000_to_19999', '20000_to_99999', '100K_to_999999', 'More_than_1M+']
Y_Axis['Number', 'of', 'projects']: ['21945', '92970', '24579', '24804', '6063', '385']

gold: The statistic shows the number of successfully funded projects on the crowdfunding platform Kickstarter as of October 2 , 2018 . It shows the number of total successfully funded projects by funds raised . As of that time , the number of successfully funded projects at Kickstarter which raised more than one million U.S. dollars amounted to 385 projects .
gold_template: The statistic shows the templateYLabel[0] of successfully funded templateYLabel[1] on the crowdfunding platform templateTitleSubject[0] as of October 2 , 2018 . It shows the templateYLabel[0] of total successfully funded templateYLabel[1] by funds templateXLabel[1] . As of that time , the templateYLabel[0] of successfully funded templateYLabel[1] at templateTitleSubject[0] which templateXLabel[1] templateXValue[last] templateXValue[0] one million templateXLabel[3] dollars amounted to templateYValue[min] templateYLabel[1] .

generated_template: 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 templateXValue[last] templateTitleDate[0] . The templateYLabel[0] of templateYLabel[1] that were templateXValue[last] templateXLabel[0] templateXLabel[1] was templateYValue[5] 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 More than 1M+ 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] 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[max] 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[0] 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 348.3 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] templateYLabel[1] likes on templateTitleSubject[0] , and ranking second was templateXValue[1] with templateYValue[1] templateYLabel[1] page likes .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] in templateTitle[4] templateTitleSubject[0] templateTitle[6] in templateTitleDate[0] . The survey was conducted among 15 to 20 year-olds and is part of the Face of Global sex Report by Durex . According to the source , the respondents in templateXValue[0] had templateYValue[max] sex templateYLabel[2] on average .
generated: This statistic shows the Followers millions of followers millions in luxury Facebook 2019 in 2019 . The survey was conducted among 15 to 20 year-olds and is part of the Face of Global sex Report by Durex . According to the source , the respondents in Louis Vuitton had 23.28 sex millions on average .

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] templateYLabel[1] British pounds was spent on templateTitle[0] templateTitle[1] and templateTitle[2] products . templateYLabel[0] rose during the period under consideration to approximately templateYValue[max] templateYLabel[1] British pounds in templateYLabel[0] in templateXValue[max] .

generated_template: In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] increased to templateYValue[0] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] . The templateTitle[0] templateTitle[1] presented a slowly growing trend over the past twenty years . The growing templateTitle[0] templateTitle[1] can be attributed to the slower growth of the whole population .
generated: In 2017 , the Fairtrade food in United Kingdom increased to 1720 Sales million 1,000 GBP . The Fairtrade food presented a slowly growing trend over the past twenty years . The growing Fairtrade food can be attributed to the slower growth of the whole population .

Example 546:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Leading prescriptions dispensed in the U.S. diabetes market 2014
X_Axis['Medicine']: ['Metformin_HCI', 'Glimepiride', 'Metformin_ER_(G)', 'Glipizide', 'Lantus_(long-acting_insulin)', 'Lantus_SoloStar_(long-acting_insulin)', 'Januvia_(sitagliptin)', 'Glipizide_ER', 'Glyburide', 'Pioglitazone']
Y_Axis['Rx', 'dispensed', 'in', 'million', 'units']: ['59.2', '12.7', '12.5', '10.4', '9.6', '9.5', '8.8', '7.1', '6.5', '5.5']

gold: The statistic shows the leading prescriptions dispensed in the U.S. diabetes market in 2014 . In that year , Metformin HCI was the leading diabetes prescription dispensed in the United States at 59.2 million units .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] templateTitle[4] templateTitle[5] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[0] templateTitle[4] prescription templateYLabel[1] in the templateTitle[3] at templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: The two templateTitle[3] templateTitle[4] templateTitleSubject[0] templateTitle[6] in templateTitleDate[0] were the oil companies templateXValue[0] and templateXValue[1] , the former of which is in possession of a staggering templateYLabel[1] of templateYValue[max] templateYLabel[2] templateTitleSubject[0] pounds . This was not different in the previous year either , when templateXValue[0] ranked as the templateTitle[3] templateTitle[4] templateTitleSubject[0] templateXLabel[0] while the rest of the list had some small shifts and variations . Oil , banks and Telecom The templateTitle[3] templateTitle[4] templateTitleSubject[0] templateTitle[6] as of templateTitleDate[0] is a balanced mixture of oil and gas companies , banks and telecommunications .
generated: The two U.S. diabetes U.S. 2014 in 2014 were the oil companies Metformin HCI and Glimepiride , the former of which is in possession of a staggering dispensed of 59.2 million U.S. pounds . This was not different in the previous year either , when Metformin HCI ranked as the U.S. diabetes U.S. Medicine while the rest of the list had some small shifts and variations . Oil , banks and Telecom The U.S. diabetes U.S. 2014 as of 2014 is a balanced mixture of oil and gas companies , banks and telecommunications .

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[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The total templateYLabel[0] templateTitle[2] of the templateTitle[3] abroad amounted to 5.95 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: In templateXValue[max] , foreign templateYLabel[0] templateTitle[1] ( FDI ) from the templateTitle[3] to other countries amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Foreign templateYLabel[0] templateTitle[1] reflects the ownership of business from templateYValue[17] country in another country . It differs from a traditional templateTitle[1] in companies located templateTitle[4] by the ownership factor in case of FDI .
generated: In 2018 , foreign Direct investment ( FDI ) from the U.S. to other countries amounted to 3.61 trillion U.S. dollars . Foreign Direct investment reflects the ownership of business from 0.77 country in another country . It differs from a traditional investment in companies located Europe by the ownership factor in case of FDI .

Example 548:
titleEntities: {'Subject': ['Carolina Panthers', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Carolina Panthers ( NFL ) 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['2400', '2300', '2300', '2075', '1560', '1250', '1057', '1048', '1002', '1037', '1049', '1040', '956', '936', '878', '760', '642', '609']

gold: This graph depicts the franchise value of the Carolina Panthers from the National Football League from 2002 to 2019 . In 2019 , the franchise value came to 2.4 billion U.S. dollars.The Carolina Panthers are owned by David Tepper , who bought the franchise for about 2.3 billion U.S. dollars in 2018 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateYLabel[2] templateYLabel[3] dollars.The templateTitleSubject[0] are owned by David Tepper , who bought the templateYLabel[0] for about templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[1] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Jerry Jones who bought the templateYLabel[0] for 150 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1989 .
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 Jerry Jones who bought the Franchise for 150 million U.S. dollars in 1989 .

Example 549:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019', '2050']}
title: U.S. production of energy from biomass forecast 2019 to 2050
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2019']
Y_Axis['Production', 'in', 'quadrillion', 'Btu']: ['5.54', '5.39', '5.27', '5.2', '5.13', '4.96', '4.74', '4.82']

gold: This statistic gives outlook figures on the production of biomass energy between 2019 and 2050 . In 2050 , U.S. biomass energy production is forecast to increase to around 5.54 quadrillion British thermal units .
gold_template: This statistic gives outlook figures on the templateYLabel[0] of templateTitle[4] templateTitle[2] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[4] templateTitle[2] templateYLabel[0] is templateTitle[5] to increase to around templateYValue[max] templateYLabel[1] British thermal units .

generated_template: This statistic shows the forecast 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[6] , the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] to templateTitle[4] templateTitle[5] amounted to approximately templateYValue[min] templateYLabel[2] templateYLabel[1] templateTitle[2] , the lowest amount in the provided time interval .
generated: This statistic shows the forecast Production of quadrillion energy from to biomass forecast in U.S. from 2019 to 2050 . In 2020 , the Production of quadrillion energy from to biomass forecast amounted to approximately 4.74 Btu quadrillion energy , the lowest amount in the provided time interval .

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 templateTitleSubject[0] templateTitle[1] of templateYLabel[0] templateYLabel[1] templateTitle[4] from templateXValue[min] to templateXValue[max] . For templateXValue[3] , the templateTitle[1] of templateYLabel[0] templateYLabel[1] templateTitle[4] is templateTitleSubject[0] to grow to templateYValue[1] templateYLabel[2] .
generated: The statistic shows the China value of Market value based from 2008 to 2015 . For 2009 , the value of Market value based is China to grow to 419.6 million .

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] percent of templateTitle[3] templateTitleSubject[0] templateYLabel[1] .

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] percent . templateXLabel[0] templateTitle[3] templateXValue[last] had the smallest templateYLabel[0] at just templateYValue[min] percent .
generated: This statistic shows Photo uploads daily upload of market in 2013 . Snapchat Platform comprised the largest Share of uploads in 2013 with 49 percent . Platform daily Flickr had the smallest Share at just 1 percent .

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 templateYLabel[2] templateYLabel[3] in templateYLabel[0] templateYLabel[1] , which is a small decrease from the templateXLabel[0] before . In the past decade , the company has made several directional shifts from handheld devices to network infrastructure in order to keep up with the quickly evolving electronics and telecommunications industry . Focus on B2B markets and products After selling its mobile and devices segment to Microsoft in templateXValue[5] , templateTitleSubject[0] has focused on its network infrastructure business .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateTitle[6] to templateYLabel[1] templateTitle[1] and templateTitle[8] in the templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] children died each templateYLabel[4] of templateTitle[1] and templateTitle[8] in the templateTitle[2] .
generated: The statistic shows the Net of sales billion euros 2019 to sales 's and 2019 in the net from 1999 to 2019 . In 2019 , about 23.32 children died each euros of 's and 2019 in the net .

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] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[7] . The second largest retailer was also German . templateXValue[1] GmbH templateXValue[1] Ko .

generated_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) of the templateXValue[1] templateTitle[4] in templateTitleDate[0] . In templateTitleDate[0] , templateYLabel[0] of templateXValue[5] amounted to approximately templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows Leading food beverage ( Billion ) of the Aldi Einkauf GmbH & Co. oHG (Germany) Europe in 2017 . In 2017 , Billion of Edeka Group (Germany) amounted to approximately 111.77 U.S. 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[0] million templateYLabel[1] were templateYLabel[2] in nursery or kindergarten programs in the templateTitle[4] .

generated_template: In templateXValue[max] , the templateTitle[3] templateTitle[4] amount of individual templateTitle[1] templateTitle[2] templateTitle[6] templateTitle[7] in the templateTitle[0] was about templateYValue[0] thousand templateYLabel[2] templateYLabel[3] . templateTitle[1] templateTitle[2] in the templateTitle[0] The economic and social purpose of templateTitle[1] templateTitle[2] is directed at handling financial guarantees to individuals and families . It provides a safety net to families in case of an unforeseen death of a bread-winning family member .
generated: In 2018 , the numbers U.S. amount of individual school enrollment 2018 in the Pre-primary was about 8.74 thousand enrolled (in . school enrollment in the Pre-primary The economic and social purpose of school enrollment is directed at handling financial guarantees to individuals and families . It provides a safety net to families in case of an unforeseen death of a bread-winning family member .

Example 555:
titleEntities: {'Subject': ['North Carolina'], 'Date': ['1992', '2018']}
title: North Carolina - Unemployment rate 1992 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92"]
Y_Axis['Unemployment', 'rate']: ['3.9', '4.5', '5.1', '5.7', '6.3', '8', '9.3', '10.3', '10.9', '10.6', '6.1', '4.7', '4.7', '5.2', '5.5', '6.4', '6.6', '5.5', '3.7', '3.2', '3.5', '3.7', '4.3', '4.3', '4.4', '5', '6']

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

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[0] percent .
generated: This statistic displays the Unemployment rate in North Carolina from 1992 to 2018 . In 2018 , Unemployment in North Carolina was 3.9 percent .

Example 556:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2019']}
title: U.S. unemployment level 1990 to 2019
X_Axis['Year']: ["'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92", "'91", "'90"]
Y_Axis['Unemployed', 'in', 'millions']: ['6.0', '6.31', '6.98', '7.75', '8.3', '9.62', '11.46', '12.51', '13.75', '14.83', '14.27', '8.92', '7.08', '7.0', '7.59', '8.15', '8.77', '8.38', '6.8', '5.69', '5.88', '6.21', '6.74', '7.24', '7.4', '8.0', '8.94', '9.61', '8.63', '7.05']

gold: This statistic shows the unemployment level in the United States from 1990 to 2019 . National unemployment level decreased to an average of six million people looking for work in 2019 . See the United States unemployment rate and the monthly unemployment rate for further information .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] in the templateTitle[0] from templateTitleDate[0] to templateTitleDate[1] . National templateTitle[1] templateTitle[2] decreased to an average of templateYValue[0] templateYLabel[1] people looking for work in templateTitleDate[1] . See the templateTitle[0] templateTitle[1] rate and the monthly templateTitle[1] rate for further information .

generated_template: 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[0] to templateTitleDate[1] . In templateTitleDate[1] , the templateYLabel[0] change of the PPI amounted to templateYValue[0] percent . The PPI for templateTitle[3] stood at 199.8 in templateTitleDate[1] .
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 percent . 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .

generated_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .
generated: 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 .

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 an estimate of templateTitle[1] templateYLabel[0] worldwide , from the 2017 fiscal year to fiscal year 2021 , templateTitle[3] select templateXLabel[0] . The templateXValue[0] is projected to spend about templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] on drones between 2017 and 2021 , making it the templateXLabel[0] with the greatest expenditure on drones .
generated: This statistic shows the an estimate of per Average worldwide , from the 2017 fiscal year to fiscal year 2021 , average select Country . The Luxembourg is projected to spend about 432221 wealth per adult on drones between 2017 and 2021 , making it the Country with the greatest expenditure on drones .

Example 559:
titleEntities: {'Subject': ['Hays'], 'Date': ['2007', '2019']}
title: Revenue of Hays worldwide 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'million', 'GBP']: ['1129.7', '1072.8', '954.6', '810.3', '764.2', '724.9', '719.0', '734.0', '672.1', '557.7', '670.8', '786.8', '633.6']

gold: This statistic shows the revenue of Hays worldwide from 2007 to 2019 . In 2019 , the UK-based recruitment specialist Hays generated over 1.1 billion British pounds in revenue worldwide , up from one billion the previous year .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the UK-based recruitment specialist templateTitleSubject[0] generated over templateYValue[max] templateYLabel[1] British pounds in templateYLabel[0] templateTitle[2] , up from templateYValue[max] templateYLabel[1] the previous templateXLabel[0] .

generated_template: British Telecommunications giant company templateTitleSubject[0] generated approximately templateYValue[max] templateYLabel[1] British pounds in templateXValue[max] , up from templateYValue[1] templateYLabel[1] the previous templateXLabel[0] . In the same templateXLabel[0] of templateXValue[max] , the Germany-based multinational engineering and electronics company employed some templateYValue[max] templateYLabel[1] British pounds . templateTitleSubject[0] bank templateTitleSubject[0] is a British banking and financial services company and one of the major players on the global banking market .
generated: British Telecommunications giant company Hays generated approximately 1129.7 million British pounds in 2019 , up from 1072.8 million the previous Year . In the same Year of 2019 , the Germany-based multinational engineering and electronics company employed some 1129.7 million British pounds . Hays bank Hays is a British banking and financial services company and one of the major players on the global banking market .

Example 560:
titleEntities: {'Subject': ['Engie'], 'Date': ['2018']}
title: Engie - revenue by region 2018
X_Axis['Country']: ['France', 'Other_EU_countries', 'Belgium', 'Asia_Middle_East_and_Oceania', 'South_America', 'North_America', 'Other_European_countries', 'Africa']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['24.98', '15.45', '5.96', '4.94', '4.2', '3.87', '0.82', '0.39']

gold: This statistic represents Engie 's revenue in the fiscal year of 2018 , by region . The French multinational energy company generated a revenue of around six billion euros in its Belgium segment . The company was formed by the merger of Gaz de France and Suez to GDF Suez and officially changed its name to Engie in April 2015 .
gold_template: This statistic represents templateTitleSubject[0] 's templateYLabel[0] in the fiscal year of templateTitleDate[0] , templateTitle[2] templateTitle[3] . The French multinational energy company generated a templateYLabel[0] of around templateYValue[2] templateYLabel[1] templateYLabel[2] in its templateXValue[2] segment . The company was formed templateTitle[2] the merger of Gaz de templateXValue[0] and Suez to GDF Suez and officially changed its name to templateTitleSubject[0] in 2015 .

generated_template: templateXValue[0] topped the ranking of countries based on templateYLabel[0] templateYLabel[1] in the templateYLabel[5] industry in templateTitleDate[0] : templateYValue[max] templateTitleSubject[0] multipurpose templateTitle[1] were installed templateYLabel[3] 10,000 templateYLabel[5] templateYLabel[6] in templateXValue[0] .
generated: France topped the ranking of countries based on Revenue billion in the euros industry in 2018 : 24.98 Engie multipurpose revenue were installed euros 10,000 euros in France .

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 templateYValue[min] there was templateYValue[0] templateYLabel[2] templateYLabel[3] in the templateTitle[2] in templateXValue[max] . This is compared to templateYValue[0] templateYLabel[2] shooting in templateXValue[min] , templateYValue[0] in templateXValue[18] , and templateYValue[max] templateYLabel[2] templateYLabel[3] in templateXValue[36] . School templateYLabel[3] The templateTitle[2] sees the most school templateYLabel[3] in the world .

generated_template: In templateXValue[max] , the templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] as a templateYLabel[2] of Gross Domestic Product was templateYValue[min] percent . Since templateXValue[min] , the templateTitleSubject[1] 's templateYLabel[0] templateYLabel[1] was at it 's highest in templateXValue[34] when templateYValue[max] percent of the templateTitleSubject[1] 's templateYLabel[3] was spent on the military . After templateXValue[34] , templateYLabel[0] templateYLabel[1] declined gradually , and then at a much faster pace after the end of the Cold War in templateXValue[27] , with the templateTitleSubject[0] only just reaching the templateYValue[min] percent benchmark set by NATO by templateXValue[max] .
generated: In 2020 , the U.S. 's Total number as a mass of Gross Domestic Product was 0 percent . Since 1982 , the U.S. 's Total number was at it 's highest in 2016 when 12 percent of the U.S. 's shootings was spent on the military . After 2016 , Total number declined gradually , and then at a much faster pace after the end of the Cold War in 2009 , with the U.S. only just reaching the 0 percent benchmark set by NATO by 2020 .

Example 562:
titleEntities: {'Subject': ['Cineplex'], 'Date': ['2010', '2018']}
title: Attendance at Cineplex cinemas 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Attendance', 'in', 'millions']: ['69.27', '70.4', '74.6', '77.0', '73.6', '72.7', '71.2', '66.1', '67.0']

gold: The timeline presents the attendance figures at Cineplex from 2010 to 2018 . In 2018 , 69.27 million people attended movies at the Canadian movie theater chain , down from 70.4 million visitors a year earlier .
gold_template: The timeline presents the templateYLabel[0] figures at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] templateYLabel[1] people attended movies at the Canadian movie theater chain , down from templateYValue[1] templateYLabel[1] visitors a templateXLabel[0] earlier .

generated_template: This graph depicts the estimated templateYLabel[1] from templateTitle[1] templateTitle[2] templateYLabel[0] as templateTitle[5] of templateTitle[6] templateYLabel[1] of Major League Baseball ( templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] percent of templateTitle[6] league templateYLabel[1] , which amounted to around 9.46 billion U.S. dollars in templateXValue[max] , was generated through gate receipts .
generated: This graph depicts the estimated millions from Cineplex cinemas Attendance as 2018 of 2018 millions of Major League Baseball ( Cineplex ) from 2010 to 2018 . In 2018 , about 69.27 percent of 2018 league millions , which amounted to around 9.46 billion U.S. dollars in 2018 , was generated through gate receipts .

Example 563:
titleEntities: {'Subject': ['Angola'], 'Date': ['2019']}
title: Unemployment rate in Angola 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['7.25', '7.25', '7.14', '7.28', '7.28', '7.43', '7.45', '7.36', '7.36', '9.09', '10.61', '12.04', '14.63', '17.67', '20.53', '23.64', '23.93', '23.9', '23.12', '22.89', '20.9']

gold: This statistic shows the unemployment rate in Angola from 1999 to 2019 . In 2019 , the unemployment rate in Angola was 7.25 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Angola from 1999 to 2019 . In 2019 , the Unemployment rate in Angola was at approximately 7.25 percent .

Example 564:
titleEntities: {'Subject': ['Aston Villa'], 'Date': ['2016']}
title: Value of Aston Villa 's jersey sponsorship 2016
X_Axis['Year']: ['2015/16_(Intuit_Quickbooks)', '2014/15_(Dafabet.com)', '2013/14_(Dafabet.com)', '2012/13_(Genting)', '2011/12_(Genting)', '2010/11_(FxPro)', '2009/10_(Acorns)']
Y_Axis['Jersey', 'sponsorship', 'revenue', 'in', 'million', 'GBP']: ['5', '5', '5', '8', '8', '5', '0']

gold: The statistic shows the revenue Aston Villa generated from its jersey sponsorship deal from the 2009/10 season to the 2015/16 season . In the 2012/13 season Aston Villa received 8 million GBP from its jersey sponsor Genting .
gold_template: The statistic shows the templateYLabel[2] templateTitleSubject[0] generated from its templateYLabel[0] templateYLabel[1] deal from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[3] season templateTitleSubject[0] received templateYValue[max] templateYLabel[3] templateYLabel[4] from its templateYLabel[0] sponsor Genting .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[5] , templateYValue[5] templateYLabel[5] people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[max] templateYLabel[5] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Jersey of sponsorship million GBP in the Aston Villa from 2015/16 (Intuit Quickbooks) to 2015/16 (Intuit Quickbooks) . In 2010/11 (FxPro) , 5 GBP people accessed the million through their sponsorship revenue . In 2015/16 (Intuit Quickbooks) , this figure is projected to amount to 8 GBP sponsorship revenue million GBP .

Example 565:
titleEntities: {'Subject': ['England', 'Wales'], 'Date': ['2014']}
title: Reasons for unreported vandalism against businesses in England and Wales 2014
X_Axis['Response']: ['Trivial/no_loss', 'Lack_of_police_engagement', 'Private/dealt_with_ourselves', 'Lack_of_evidence', 'Reported_to_other_authorities', 'Inconvenient_to_report', 'Police_came', 'Common_occurrence', 'Fear_of_reprisal', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['43', '40', '13', '3', '3', '3', '3', '2', '1', '6']

gold: This survey shows the reasons businesses gave for not reporting cases of vandalism on their premises to the police in England and Wales in 2014 . Of respondents , 43 percent claimed they did not report the incident as there was no loss or damage or the crime was too trivial to report to the police .
gold_template: This survey shows the templateTitle[0] templateTitle[5] gave templateTitle[1] not reporting cases of templateTitle[3] on their premises to the templateXValue[1] in templateTitleSubject[0] and templateTitleSubject[1] in templateTitleDate[0] . Of templateYLabel[1] , templateYValue[max] percent claimed they did not templateXValue[5] the incident as there was no templateXValue[0] or damage or the crime was too trivial to templateXValue[5] to the templateXValue[1] .

generated_template: This statistic gives information on the templateTitleSubject[0] templateTitle[1] templateTitle[2] of templateXValue[0] offerings as of 2017 . During a survey of smartphone owners , templateYValue[1] percent of templateYLabel[1] stated they had templateXValue[1] a templateXValue[1] via templateXValue[0] .
generated: This statistic gives information on the England for unreported of Trivial/no loss offerings as of 2017 . During a survey of smartphone owners , 40 percent of respondents stated they had Lack of police engagement a Lack of police engagement via Trivial/no loss .

Example 566:
titleEntities: {'Subject': ['England', 'Wales'], 'Date': ['2005', '2019']}
title: Road deaths involving police pursuit in England and Wales from 2005 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05']
Y_Axis['Fatailities']: ['30', '17', '28', '13', '7', '10', '27', '12', '13', '19', '22', '17', '19', '32', '23']

gold: This statistic shows the number of road traffic fatalities related to police pursuits in England and Wales from 2004/05 to 2018/19 . During the period concerned , the number of road traffic fatalities related to police pursuits fluctuated , peaking in 2005/06 at 32 deaths .
gold_template: This statistic shows the number of templateTitle[0] traffic fatalities related to templateTitle[3] pursuits in templateTitleSubject[0] and templateTitleSubject[1] templateTitle[7] templateXValue[last] to templateXValue[0] . During the period concerned , the number of templateTitle[0] traffic fatalities related to templateTitle[3] pursuits fluctuated , peaking in templateXValue[13] at templateYValue[max] templateTitle[1] .

generated_template: This statistic shows the number of templateTitle[0] templateTitle[2] by templateTitle[1] in templateTitleSubject[0] and templateTitleSubject[1] from templateXValue[last] to templateXValue[0] . During this period , the number of templateTitle[0] templateTitle[2] by templateTitle[1] fluctuated , peaking in templateXValue[2] at templateYValue[max] templateTitle[2] . By templateXValue[0] it fell down to templateYValue[0] templateTitle[2] .
generated: This statistic shows the number of Road involving by deaths in England and Wales from 2004/05 to 2018/19 . During this period , the number of Road involving by deaths fluctuated , peaking in 2016/17 at 32 involving . By 2018/19 it fell down to 30 involving .

Example 567:
titleEntities: {'Subject': ['Burger King', '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 increased by templateYValue[max] percent .

generated_template: The statistic provides information on brands ' templateYLabel[0] on templateTitleSubject[0] templateTitle[3] and templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . According to the estimates , brands will invest templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in marketing and sponsoring templateTitleSubject[0] related events in templateXValue[1] .
generated: The statistic provides information on brands ' EBITDA on Burger King EBITDA and margin worldwide from 2011 to 2014 . According to the estimates , brands will invest 51.3 margin in marketing and sponsoring Burger King related events in 2013 .

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

generated_template: The statistic depicts 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 templateXValue[0] , the templateYLabel[0] templateTitle[1] templateYLabel[1] at the games was at templateYValue[0] templateYLabel[2] .
generated: The statistic depicts the Gate receipts at games of the Golden State Warriors Warriors NBA from the 10/11 season to the 18/19 season . In 18/19 , 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 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 2019 , templateYValue[0] new templateTitleSubject[0] cars had been templateYLabel[1] .

generated_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between 2016 and 2019 . Peaks in registration numbers were recorded in 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 2019 , templateYValue[0] new templateTitleSubject[0] templateTitle[1] templateYLabel[0] had been templateYLabel[1] , a decrease of roughly ten percent in comparison to templateYValue[17] templateYLabel[0] as of 2018 .
generated: This statistic shows the monthly amount of cars sold by Subaru car in the United Kingdom ( UK ) between 2016 and 2019 . Peaks in registration numbers were recorded in 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 2019 , 708 new Subaru car Units had been sold , a decrease of roughly ten percent in comparison to 112 Units as of 2018 .

Example 570:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2008', '2018']}
title: Number of butcher shops and meat retailers in the United Kingdom ( UK ) 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'enterprises']: ['5690', '5864', '5929', '5874', '5940', '6056', '6134', '6220', '6283', '6399', '6633']

gold: Between 2008 and 2018 , the number of stores that specialize in the sales of meat has been shrinking In the United Kingdom . During this period , the number of meat specialty stores has seen a decrease of almost 943 . Of these stores , approximately 1.75 thousand generated a turnover between one hundred thousand and 250 thousand British pounds in 2018 .
gold_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] of stores that specialize in the sales of templateTitle[3] has been shrinking In the templateTitleSubject[0] . During this period , the templateYLabel[0] of templateTitle[3] specialty stores has seen a decrease of almost 943 . Of these stores , approximately 1.75 thousand generated a turnover between one hundred thousand and 250 thousand British pounds in templateXValue[max] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] individuals emigrated templateTitle[1] templateTitleSubject[0] , whereas about 116 thousand people immigrated to Swedenin the same templateXLabel[0] .
generated: This statistic shows the Number of butcher in United Kingdom from 2008 to 2018 . In 2018 , 6633 individuals emigrated butcher United Kingdom , whereas about 116 thousand people immigrated to Swedenin the same Year .

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 decreasing to approximately templateYValue[0] thousand templateYLabel[3] yearly in templateXValue[6] . The templateYLabel[0] templateYLabel[1] wage stood at approximately templateYValue[0] thousand templateYLabel[3] in templateXValue[max] .

generated_template: 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] rose by less than a thousand templateYLabel[3] , with wage growth accelerating mainly in the period after templateXValue[8] . The 607 Euro increase recorded between templateXValue[8] and templateXValue[7] was the largest wage rise seen during this period .
generated: 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 rose by less than a thousand euros , with wage growth accelerating mainly in the period after 2010 . The 607 Euro increase recorded between 2010 and 2011 was the largest wage rise seen during this period .

Example 572:
titleEntities: {'Subject': ['Countries'], 'Date': []}
title: Countries ranked by number of ice hockey players 2018/19
X_Axis['Country']: ['Canada', 'United_States', 'Czech_Republic', 'Russia', 'Finland', 'Sweden', 'Switzerland', 'France', 'Germany', 'Japan', 'Slovakia', 'Norway', 'Great_Britain', 'Austria', 'Hungary', 'Latvia', 'Kazakhstan', 'Ukraine', 'Italy', 'Belarus']
Y_Axis['Number', 'of', 'players']: ['621026', '567908', '121613', '112236', '64641', '55431', '27867', '21667', '21340', '18837', '11394', '10353', '8162', '7670', '7106', '7000', '6915', '5384', '5210', '4580']

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

generated_template: The statistic depicts 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 depicts 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[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent . 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 Guyana from 1999 to 2019 . In 2019 , the Unemployment rate in Guyana was at approximately 12.22 percent . Guyana 's population and economy The Republic of Guyana is a sovereign state archipelago in Southeast Asia , composed of more than 17,000 islands .

Example 574:
titleEntities: {'Subject': ['Vegetables'], 'Date': ['2000', '2018']}
title: Vegetables : global production volume 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Production', 'volume', 'in', 'million', 'metric', 'tons']: ['1088.9', '1094.34', '1075.2', '1051.52', '1030.32', '997.84', '978.52', '954.89', '921.52', '900.66', '876.15', '843.23', '809.33', '779.82', '760.29', '750.86', '721.42', '700.09', '682.43']

gold: This statistic depicts the total production volume of vegetables ( including melons ) worldwide from 1990 to 2018 . In 2014 , some 1169.45 million metric tons of vegetables and melons were produced worldwide .
gold_template: This statistic depicts the total templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] ( including melons ) worldwide from 1990 to templateXValue[max] . In templateXValue[4] , some 1169.45 templateYLabel[2] templateYLabel[3] templateYLabel[4] of templateTitleSubject[0] and melons were produced worldwide .

generated_template: The templateTitleSubject[0] templateYLabel[0] of templateTitle[3] templateTitle[4] in the country in templateXValue[max] was templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . This was a decrease from around templateYValue[2] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[10] . 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 2008 . 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 increasing trend in templateYLabel[1] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( including both foreign and domestic ) at templateTitle[3] in templateTitleSubject[0] amounted to approximately templateYValue[max] templateYLabel[2] .

generated_template: Around templateYValue[max] templateYLabel[2] templateYLabel[1] were recorded at templateTitle[3] establishments in templateTitleSubject[0] in templateXValue[max] . Over the last five years templateYLabel[1] in templateTitle[2] templateTitle[3] have been increasing at a higher rate , with an extra ten templateYLabel[2] templateYLabel[1] in templateXValue[max] compared to templateXValue[5] . Tourism development in templateTitleSubject[0] The tourism industry makes a significant contribution to templateTitleSubject[0] 's economy .
generated: Around 2.8 millions arrivals were recorded at accommodation establishments in Latvia in 2018 . Over the last five years arrivals in tourist accommodation have been increasing at a higher rate , with an extra ten millions arrivals in 2018 compared to 2013 . Tourism development in Latvia The tourism industry makes a significant contribution to Latvia 's economy .

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] percent of templateTitle[2] games were 25 to 34 years old . The average templateTitle[0] of a templateTitle[2] gamer was 46.5 years .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] a templateTitle[4] templateTitle[5] a templateTitle[6] templateTitle[7] in the templateTitle[8] as of 2014 , templateTitle[10] templateTitle[11] group . During the survey , templateYValue[3] percent of templateYLabel[1] aged between templateXValue[3] and templateYValue[2] templateXValue[1] said they had bought templateTitle[6] templateTitle[5] a templateTitle[6] templateTitle[7] . In 2015 , the value of the templateTitle[6] templateTitle[7] industry in the templateTitleSubject[0] reached 856.7 templateTitleSubject[0] dollars .
generated: This statistic shows the Percentage of Age distribution mobile gamers a U.S. 2013 a 2013 in the 2013 as of 2014 , 2013 group . During the survey , 22 percent of respondents aged between 45-54 and 19 25-34 said they had bought 2013 a 2013 . In 2015 , the value of the 2013 industry in the U.S. reached 856.7 U.S. dollars .

Example 577:
titleEntities: {'Subject': ['NASA'], 'Date': ['2014', '2024']}
title: NASA - budget 2014 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'in', 'billion', 'U.S.', 'dollars']: ['21.87', '21.66', '21.44', '21.23', '21.02', '21.5', '20.74', '19.65', '19.29', '18.01', '17.65']

gold: This graph show NASA 's projected budget from 2014 to 2024 . NASA 's budget is projected to be at around 21 billion U.S. dollars in 2020 . The National Aeronautics and Space Administration ( NASA ) is the U.S. agency responsible for aeronautics and aerospace research .
gold_template: This graph show templateTitleSubject[0] 's projected templateYLabel[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] 's templateYLabel[0] is projected to be at around templateYValue[2] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[4] . The National Aeronautics and Space Administration ( templateTitleSubject[0] ) is the templateYLabel[2] agency responsible for aeronautics and aerospace research .

generated_template: The statistic shows the templateTitleSubject[0] Emirates ' ( UAE ) templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[4] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , The templateTitleSubject[0] Emirates ' templateYLabel[0] templateTitle[4] rate amounted to approximately templateYValue[min] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the NASA Emirates ' ( UAE ) NASA budget 2014 2024 from 2014 to 2017 , with projections up until 2024 . In 2017 , The NASA Emirates ' Budget 2024 rate amounted to approximately 17.65 percent U.S. to the dollars Year .

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] templateTitle[7] . During the survey , templateYValue[1] percent of templateYLabel[1] said they had made templateTitle[2] templateTitle[4] templateXValue[1] or templateXValue[1] .

generated_template: This statistic shows the results of a survey conducted templateTitle[3] the Hearth , Patio & Barbecue Association ( HPBA ) in July and 2018 . During the survey , templateTitleSubject[0] consumers were asked about which types of grills they owned . Approximately templateYValue[max] percent of the templateYLabel[1] indicated templateTitle[2] of a templateXValue[0] .
generated: This statistic shows the results of a survey conducted restaurant the Hearth , Patio & Barbecue Association ( HPBA ) in July and 2018 . During the survey , Frequency consumers were asked about which types of grills they owned . Approximately 52.8 percent of the respondents indicated online of a Yes many times .

Example 579:
titleEntities: {'Subject': ['Overwatch'], 'Date': ['2018']}
title: Number of Overwatch players worldwide 2018
X_Axis['Month']: ['May_2018', 'October_2017', 'April_2017', 'January_2017', 'October_2016', 'August_2016', 'May_2016']
Y_Axis['Number', 'of', 'players', 'in', 'millions']: ['40', '35', '30', '25', '20', '15', '7']

gold: How many people play Overwatch ? Overwatch , a team-based first-person shooter video game , launched in May 2016 and already a week later it was reported to have had seven million players . As of May 2018 , Overwatch had 40 million players worldwide . Overwatch 's eSports success While the number of gamers playing Overwatch has increased dramatically , so has the appeal of the game as an eSport .
gold_template: How many people play templateTitleSubject[0] ? templateTitleSubject[0] , a team-based first-person shooter video game , launched in templateXValue[0] templateXValue[4] and already a week later it was reported to have had templateYValue[min] templateYLabel[2] templateYLabel[1] . As of templateXValue[0] , templateTitleSubject[0] had templateYValue[max] templateYLabel[2] templateYLabel[1] templateTitle[3] . templateTitleSubject[0] 's eSports success While the templateYLabel[0] of gamers playing templateTitleSubject[0] has increased dramatically , so has the appeal of the game as an eSport .

generated_template: How many people play templateTitleSubject[0] ? Having burst onto the scene in templateXValue[4] , templateTitleSubject[0] has since become a templateTitle[4] phenomenon , amassing almost templateYValue[max] templateYLabel[2] templateYLabel[1] across the globe as of templateXValue[0] . 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: In the fall semester templateXValue[max] , templateYValue[0] templateYLabel[1] were templateTitle[1] in universities and other higher education institutions in templateTitleSubject[0] . Since 2000 , the templateYLabel[0] of individuals in templateTitleSubject[0] with an upper secondary education increased , while the individuals without decreased . In templateXValue[max] , 21.7 percent of the female population had an upper secondary education degree of three years and 24.9 percent of the male population .
generated: In the fall semester 2018/19 , 369548 students were Chinese in universities and other higher education institutions in Chinese . Since 2000 , the Number of individuals in Chinese with an upper secondary education increased , while the individuals without decreased . In 2018/19 , 21.7 percent of the female population had an upper secondary education degree of three years and 24.9 percent of the male population .

Example 581:
titleEntities: {'Subject': ['Vale'], 'Date': ['2009', '2018']}
title: Vale 's employee number 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'employees']: ['70270', '73596', '73062', '74098', '76531', '83286', '85305', '79646', '70785', '60036']

gold: This statistic shows mining company Vale 's number of employees worldwide from 2009 to 2018 . In 2018 , the company employed some 70,300 people . Vale S.A. , formerly called by the full name Companhia Vale do Rio Doce , is a Brazilian multinational metals and mining company , also specializing in logistic operations .
gold_template: This statistic shows mining company templateTitleSubject[0] templateTitle[1] templateYLabel[0] of templateYLabel[1] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company employed some 70,300 people . templateTitleSubject[0] S.A. , formerly called by the full name Companhia templateTitleSubject[0] do Rio Doce , is a Brazilian multinational metals and mining company , also specializing in logistic operations .

generated_template: templateTitleSubject[0] is an international pharmaceutical company based out of Germany . As of templateXValue[max] , the company had a total of templateYValue[0] templateYLabel[1] globally . This is a significant increase from the previous years .
generated: Vale is an international pharmaceutical company based out of Germany . As of 2018 , the company had a total of 70270 employees globally . This is a significant increase from the previous years .

Example 582:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Production of copper in Canada by province 2018
X_Axis['Month']: ['Newfoundland_and_Labrador', 'Prince_Edward_Island', 'Nova_Scotia', 'New_Brunswick', 'Quebec', 'Ontario', 'Manitoba', 'Saskatchewan', 'Alberta', 'British_Columbia', 'Yukon', 'Northwest_Territories', 'Nunavut']
Y_Axis['Production', 'in', 'metric', 'tons']: ['27456', '0', '0', '487', '35912', '135297', '33608', '0', '0', '293468', '9282', '0', '0']

gold: This statistic displays preliminary estimates of the copper production in Canada , distributed by province , in 2018 . During that year , Quebec produced some 35,912 metric tons of this mineral . Copper is often used as part of coin currency , as well as electrical equipment due to its heat and electrical conduction properties .
gold_template: This statistic displays preliminary estimates of the templateTitle[1] templateYLabel[0] in templateTitleSubject[0] , distributed templateTitle[3] templateTitle[4] , in templateTitleDate[0] . During that year , templateXValue[4] produced some templateYValue[4] templateYLabel[1] templateYLabel[2] of this mineral . templateTitle[1] is often used as part of coin currency , as well as electrical equipment due to its heat and electrical conduction properties .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] of nurse templateTitle[2] in templateTitle[3] templateTitle[4] in Canada , sorted templateTitle[5] templateTitle[6] , in templateTitleDate[0] . In templateXValue[0] , around templateYValue[max] templateYLabel[1] were part of the templateTitle[3] templateTitle[4] templateTitle[2] , while in templateXValue[1] there were almost 72,000 templateYLabel[1] .
generated: This statistic shows the copper Production of nurse Canada in by province in Canada , sorted 2018 , in 2018 . In Newfoundland and Labrador , around 293468 metric were part of the by province Canada , while in Prince Edward Island there were almost 72,000 metric .

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: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] templateTitle[4] templateTitle[5] in the middle of templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] in templateXValue[0] was templateYValue[max] percent in the middle of 2014.The templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] arises from the birth templateYLabel[1] minus the death templateYLabel[1] and without including the effects of migration.Population growthAs shown in the statistic above , the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] continues to increase on almost every templateTitle[5] in 2013.Due to medical advances , better living conditions and the increase of agricultural productivity the world templateTitle[2] is continuously rising . The development of the world templateTitle[2] from 1950 to 2030 is estimated to be tripled according to United Nations ' data.The majority of the world templateTitle[2] lives in templateXValue[4] , but the templateTitle[2] in templateXValue[0] is forecasted to rise from 1,031 in year 2010 up to 4,185 in year 2100 .
generated: The statistic shows the Number CFPs of platforms CFPs 2014 by in the middle of 2014 . The Number CFPs of platforms CFPs in Europe was 600 percent in the middle of 2014.The Number CFPs of platforms CFPs arises from the birth CFPs minus the death CFPs and without including the effects of migration.Population growthAs shown in the statistic above , the Number CFPs of platforms CFPs continues to increase on almost every by in 2013.Due to medical advances , better living conditions and the increase of agricultural productivity the world platforms is continuously rising . The development of the world platforms from 1950 to 2030 is estimated to be tripled according to United Nations ' data.The majority of the world platforms lives in Oceania , but the platforms in Europe is forecasted to rise from 1,031 in year 2010 up to 4,185 in year 2100 .

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] templateYLabel[1] 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 templateYValue[0] templateYLabel[1] 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 62862.5 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] percent of responding templateTitle[3] templateTitle[4] shoppers stated that their usual templateTitle[3] templateTitle[4] templateTitle[1] amounted to templateXValue[1] to 25 templateTitleSubject[0] dollars .

generated_template: 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] percent ) , followed by templateXValue[0] sugar ( templateYValue[1] percent ) . templateYValue[2] percent 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 0$ no expenses Up to 25$ or Up to 75$ was the Average common value of online More than 300$ among British respondents ( 34 percent ) , followed by 0$ no expenses sugar ( 26 percent ) . 34 percent of Brits liked to More than 300$ their More than 300$ 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 ranking of the templateTitle[2] templateTitle[4] who earned their fortune in the templateTitle[3] business is headed by templateXValue[0] founder templateXValue[0] , who is sitting in first place with a personal templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateXValue[1] founder templateXValue[1] was ranked second with templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Both templateXValue[0] and templateXValue[1] are also featured in the top 5 ranking of the templateTitle[2] Americans of templateTitleDate[0] .
generated: The ranking of the leaders England who earned their fortune in the New business is headed by Rob Gronkowski founder Rob Gronkowski , who is sitting in first place with a personal Touchdowns scored of 80 scored . Stanley Morgan founder Stanley Morgan was ranked second with 68 scored . Both Rob Gronkowski and Stanley Morgan are also featured in the top 5 ranking of the leaders Americans of 1960 .

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 templateYLabel[0] templateTitle[1] templateYLabel[1] templateTitle[3] from templateXValue[last] to templateXValue[1] , as well as a projection for templateXValue[0] . In templateXValue[1] , a templateYLabel[0] of templateYValue[1] templateYLabel[2] templateYLabel[3] of raisins were produced templateTitle[3] .
generated: This statistic shows the Domestic U.S. consumption raisin from 2010/2011 to 2017/2018 , as well as a projection for 2018/2019 . In 2017/2018 , a Domestic of 205000 metric tons of raisins were produced raisin .

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[max] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[max] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .
generated: This statistic shows the Per capita real Gross Domestic Product of Texas from 2000 to 2018 . In 2018 , the Per capita real GDP of Texas stood at 59674 chained 2012 U.S. dollars .

Example 589:
titleEntities: {'Subject': ['Disneyland Paris'], 'Date': ['2016']}
title: Disneyland Paris visitors spending per day 2006 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Average', 'spend', 'in', 'euros', '(excluding', 'VAT)']: ['54.0', '53.7', '50.7', '48.1', '46.4', '46.2', '45.3']

gold: This statistic displays daily expenditure per person at Disneyland Paris theme parks in France between 2006 and 2016 . Visitors spending includes entrance fee , catering , merchandise and other services in the theme parks . In the fiscal year 2016 , the average spending dipped to 54 euros ( before VAT ) .
gold_template: This statistic displays daily expenditure templateTitle[4] person at templateTitleSubject[0] theme parks in France between templateTitle[6] and templateXValue[max] . templateTitle[2] templateTitle[3] includes entrance fee , catering , merchandise and other services in the theme parks . In the fiscal templateXLabel[0] templateXValue[max] , the templateYLabel[0] templateTitle[3] dipped to templateYValue[max] templateYLabel[2] ( before VAT ) .

generated_template: The statistic shows the templateYLabel[1] of the templateTitleSubject[0] templateYLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateTitleSubject[0] templateYLabel[0] was expected to be worth templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] is a cloud operating system , providing access to various computing resources , such as compute , storage , and network .
generated: The statistic shows the spend of the Disneyland Paris Average from 2010 to 2016 . In 2014 , the Disneyland Paris Average was expected to be worth 48.1 euros (excluding VAT) . Disneyland Paris is a cloud operating system , providing access to various computing resources , such as compute , storage , and network .

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] templateTitle[2] in the templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateXLabel[0] . In templateTitleDate[0] , templateXValue[2] templateTitle[0] templateTitle[1] templateTitle[2] accounted for templateYValue[max] percent of all templateTitle[0] templateTitle[1] templateTitle[2] .
generated: The statistic above presents the distribution of U.S. winter heating in the oil in , 2005/06 Winter . In , 2017/18 U.S. winter heating accounted for 3.88 percent of all U.S. winter heating .

Example 591:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015', '2018']}
title: Share of U.S. nickel imports by country 2015 to 2018
X_Axis['Country', 'of', 'origin']: ['Other', 'Finland', 'Australia', 'Norway', 'Canada']
Y_Axis['Share', 'of', 'nickel', 'imports']: ['32', '8', '8', '11', '41']

gold: This statistic shows the percentage of nickel imports to the United States over the period between 2014 and 2018 , by country of origin . In that period , some 41 percent of all nickel imports into the United States came from Canada .
gold_template: This statistic shows the percentage of templateYLabel[1] templateYLabel[2] to the templateTitle[1] over the period between 2014 and templateTitleDate[1] , templateTitle[4] templateXLabel[0] of templateXLabel[1] . In that period , some templateYValue[max] percent of all templateYLabel[1] templateYLabel[2] into the templateTitle[1] came from templateXValue[last] .

generated_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] the most expensive templateTitle[4] templateTitle[5] in the templateTitle[6] as of templateTitleSubject[0] templateTitle[8] . According to the source , templateTitle[4] templateTitle[5] in templateXValue[0] were the most expensive out of the templateTitle[0] templateTitle[1] templateTitle[2] the templateYLabel[0] price for a templateTitle[4] room reaching templateYValue[max] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows Share U.S. nickel the most expensive by country in the 2015 as of U.S. 2018 . According to the source , by country in Other were the most expensive out of the Share U.S. nickel the Share price for a by room reaching 41 imports .

Example 592:
titleEntities: {'Subject': ['Under Armour'], 'Date': ['2009', '2019']}
title: Global revenue growth of Under Armour 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'growth']: ['1', '4', '3', '22', '28', '32', '27', '25', '38', '24', '18']

gold: This statistic depicts the growth of Under Armour 's revenue worldwide from 2009 to 2019 . In 2019 , Under Armour 's net revenue increased by one percent . Under Armour is an American sporting goods manufacturer , based in Baltimore , Maryland .
gold_template: This statistic depicts the templateYLabel[1] of templateTitleSubject[0] 's templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's net templateYLabel[0] increased by templateYValue[min] percent . templateTitleSubject[0] is an American sporting goods manufacturer , based in Baltimore , Maryland .

generated_template: 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[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] . This shows a 70 percent decrease over previous templateXLabel[0] templateTitle[3] total amounting to templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Revenue growth of The Under Armour Under Under Armour worldwide from 2009 to 2019 . Fast food chain Under Armour Under had a Revenue growth of approximately 1 growth in 2019 . This shows a 70 percent decrease over previous Year Under total amounting to 38 growth .

Example 593:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: U.S. : reported robbery cases 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Number', 'of', 'reported', 'cases']: ['282061', '320596', '332797', '328109', '322905', '345093', '355051', '354746', '369089', '408742', '443563', '447324', '449246', '417438', '401470', '414235', '420806', '422921', '408016', '409371', '447186', '497950', '535590', '580510', '618950', '659870', '672480', '687730', '639270']

gold: This graph shows the reported number of robbery cases in the United States from 1990 to 2018 . In 2018 an estimated 282,061 cases occurred nationwide .
gold_template: This graph shows the templateYLabel[1] templateYLabel[0] of templateTitle[2] templateYLabel[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] an estimated templateYValue[min] templateYLabel[2] occurred nationwide .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[4] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , an estimated templateYValue[0] templateYLabel[1] templateYLabel[2] occurred nationwide . templateYLabel[1] templateTitle[4] in the templateTitle[0] The Federal Bureau of Investigation templateYLabel[0] that templateYLabel[1] templateTitle[4] fell nationwide in the period from templateXValue[min] to templateXValue[max] .
generated: This statistic shows the Number reported 1990 in the U.S. from 1990 to 2018 . In 2018 , an estimated 282061 reported cases occurred nationwide . reported 1990 in the U.S. The Federal Bureau of Investigation Number that reported 1990 fell nationwide in the period from 1990 to 2018 .

Example 594:
titleEntities: {'Subject': ['Cincinnati Bengals', 'NFL'], 'Date': ['2001', '2018']}
title: Revenue of the Cincinnati Bengals ( NFL ) 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['380', '359', '355', '329', '296', '258', '250', '235', '236', '232', '222', '205', '194', '175', '171', '150', '141', '130']

gold: The statistic depicts the revenue of the Cincinnati Bengals , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Cincinnati Bengals was 380 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] dollars.The templateTitleSubject[0] templateTitle[1] are owned by James Pohlad , who bought the franchise for 44 templateYLabel[1] templateYLabel[2] templateYLabel[3] in 1984 .
generated: The statistic depicts the Revenue of the Cincinnati Bengals Cincinnati from 2001 to 2018 . In 2018 , the Revenue of the Major League Baseball franchise amounted to 380 million U.S. dollars.The Cincinnati Bengals Cincinnati are owned by James Pohlad , who bought the franchise for 44 million U.S. dollars in 1984 .

Example 595:
titleEntities: {'Subject': ['India'], 'Date': ['2019']}
title: Highest grossing domestic movies India 2019
X_Axis['Movie', 'Name']: ['War', 'Kabir_Singh', 'Uri-_The_Surgical_Strike', 'Bharat', 'Mission_Mangal', 'Kesari', 'Total_Dhamaal', 'Saaho', 'Chhichhore', 'Super_30']
Y_Axis['Box', 'office', 'gross', 'in', 'billion', 'Indian', 'rupees']: ['2.92', '2.76', '2.44', '1.97', '1.93', '1.52', '1.5', '1.49', '1.47', '1.47']

gold: The Bollywood movie 'War ' was the highest grossing domestic movie produced in India in 2019 with an all India net collection of almost three billion Indian rupees . This was followed by 'Kabir Singh ' at around 2.8 billion rupees worth box office collection that year .
gold_template: The Bollywood templateXLabel[0] 'War ' was the templateTitle[0] templateTitle[1] templateTitle[2] templateXLabel[0] produced in templateTitleSubject[0] in templateTitleDate[0] with an all templateTitleSubject[0] net collection of almost templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] . This was followed by 'Kabir templateXValue[1] ' at around templateYValue[1] templateYLabel[3] templateYLabel[5] worth templateYLabel[0] templateYLabel[1] collection that year .

generated_template: This statistic represents the world 's templateTitleSubject[0] templateXLabel[0] templateXLabel[1] templateTitle[3] as of 2 , templateTitleDate[0] , based on the templateYLabel[0] of templateYLabel[2] in their order books . In 2020 , templateXValue[0] had templateYValue[max] templateYLabel[2] in its order book .
generated: This statistic represents the world 's India Movie Name movies as of 2 , 2019 , based on the Box of gross in their order books . In 2020 , War had 2.92 gross in its order book .

Example 596:
titleEntities: {'Subject': ['Luxembourg'], 'Date': ['2024']}
title: Inflation rate in Luxembourg 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['1.93', '1.88', '1.95', '1.87', '1.74', '1.73', '2.02', '2.11', '0.04', '0.06', '0.7', '1.7', '2.89', '3.73', '2.8', '0.01', '4.09', '2.66', '2.96', '3.76', '3.24', '2.53', '2.06', '2.4', '3.78', '1.02', '0.97', '1.37', '1.56', '1.9', '2.2', '3.6', '3.2', '3.1', '3.7', '3.4', '1.4', '-0.1', '0.3', '4.09', '5.64']

gold: This statistic shows the average inflation rate in Luxembourg from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Luxembourg amounted to about 2.02 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

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

Example 597:
titleEntities: {'Subject': ['European'], 'Date': ['2015']}
title: Average planned spend on Christmas presents in selected European countries 2015
X_Axis['Country']: ['United_Kingdom', 'Luxembourg', 'France', 'Austria', 'Spain', 'Italy', 'Germany', 'Belgium', 'Czech_Republic', 'Romania', 'Poland', 'Netherlands']
Y_Axis['Median', 'amount', 'in', 'euros']: ['420', '300', '250', '250', '200', '200', '200', '150', '150', '110', '70', '40']

gold: This statistic displays the average amount consumers plan to spend on Christmas presents in 2015 in selected European countries . The United Kingdom ( UK ) had the highest spend , with consumers expecting to budget 420 euros for Christmas gifts .
gold_template: This statistic displays the templateTitle[0] templateYLabel[1] consumers plan to templateTitle[2] on templateTitle[3] templateTitle[4] in templateTitleDate[0] in templateTitle[5] templateTitleSubject[0] templateTitle[7] . The templateXValue[0] ( UK ) had the highest templateTitle[2] , with consumers expecting to budget templateYValue[max] templateYLabel[2] for templateTitle[3] gifts .

generated_template: 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 billion 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[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic above presents estimation data on the annual aggregate templateYLabel[1] of the American templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[3] broadcasters generated an templateYLabel[0] total templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic above presents estimation data on the annual aggregate revenue of the American picture/video production from 2005 to 2018 . In 2018 , U.S. broadcasters generated an Estimated total revenue of 69.91 billion U.S. dollars .

Example 599:
titleEntities: {'Subject': ['Sporting Goods'], 'Date': ['2006', '2018']}
title: Dick 's Sporting Goods : gross profit 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Gross', 'profit', 'in', 'million', 'U.S.', 'dollars']: ['2437', '2489', '2366', '2183', '2087', '1944', '1837', '1595', '1449', '1217', '1184', '1158', '897']

gold: The timeline depicts the gross profit of Dick 's Sporting Goods from 2006 to 2018 . The gross profit of Dick 's Sporting Goods amounted to 2,437 million U.S. dollars in 2018 .
gold_template: The timeline depicts the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: This statistic shows the templateTitle[2] earnings before interest and tax ( templateTitle[3] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] had a templateTitle[2] templateTitle[3] of approximately templateYValue[0] templateYLabel[0] templateYLabel[1] , representing an increase over the previous templateXLabel[0] .
generated: This statistic shows the Sporting earnings before interest and tax ( Goods ) of Sporting Goods from 2006 to 2018 . In 2018 , Sporting Goods had a Sporting Goods of approximately 2437 Gross profit , representing an increase over the previous Year .

Example 600:
titleEntities: {'Subject': ['Michigan'], 'Date': ['1990', '2018']}
title: Michigan - median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['60449', '57700', '57091', '54203', '52005', '48801', '50015', '48879', '46276', '45994', '49788', '49370', '48647', '45933', '42256', '45022', '42715', '45047', '45512', '46089', '41821', '38742', '39225', '36426', '35284', '32662', '32267', '32117', '29937']

gold: This statistic shows the median household income in Michigan from 1990 to 2018 . In 2018 , the median household income in Michigan amounted to 60,449 U.S. dollars .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] parent families with a female householder and no spouse present in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[28] percent of all templateTitleSubject[0] families with a templateTitle[3] mother in the templateTitle[5] lived below the templateYLabel[0] level . In templateXValue[max] , that figure was at templateYValue[min] percent .
generated: This statistic shows the Household income of Michigan income parent families with a female householder and no spouse present in the Michigan from 1990 to 2018 . In 2018 , 29937 percent of all Michigan families with a income mother in the 2018 lived below the Household level . In 2018 , that figure was at 29937 percent .

Example 601:
titleEntities: {'Subject': ['NFL'], 'Date': ['2006', '2019']}
title: Average Fan Cost Index of NFL teams 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Fan', 'Cost', 'Index', 'in', 'U.S.', 'dollars']: ['540.52', '536.04', '502.84', '480.89', '479.11', '459.73', '443.93', '427.42', '420.54', '412.64', '396.36', '367.31', '346.16']

gold: The statistic shows the average Fan Cost Index in the National Football League from 2006 to 2019 . The average Fan Cost Index was at 540.52 U.S. dollars in 2019 .
gold_template: The statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the National Football League from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[max] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: The templateTitleSubject[0] Company reported total templateTitle[0] of about templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] . Total templateTitle[0] comprises automotive templateTitle[0] , credit templateTitle[0] , and other templateTitle[0] . The templateTitleSubject[0] Company 's templateTitle[0] In templateXValue[11] , when the global economy tumbled into recession , the CEOs of all Big Three US automakers flew into Washington DC to plead for emergency government aid .
generated: The NFL Company reported total Average of about 540.52 Index U.S. dollars in 2019 . Total Average comprises automotive Average , credit Average , and other Average . The NFL Company 's Average In 2007 , when the global economy tumbled into recession , the CEOs of all Big Three US automakers flew into Washington DC to plead for emergency government aid .

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 gives information on the templateTitle[0] templateTitle[1] templateTitle[2] templateTitleSubject[0] used by internet users templateTitle[4] . During the second quarter of templateTitleDate[0] , data analysis revealed that templateYValue[max] percent of templateTitle[1] templateYLabel[1] were through a templateXValue[0] account . Approximately templateYValue[2] percent of internet users used their templateXValue[2] account to templateTitle[2] to a website .
generated: This statistic gives information on the Global online shopping Global used by internet users value . During the second quarter of 2019 , data analysis revealed that 132.6 percent of online value were through a Macintosh account . Approximately 93.52 percent of internet users used their iOS account to shopping to a website .

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] templateTitle[7] , the Russian comedy templateTitle[9] `` templateXValue[0] , '' translated as `` Serf , '' had the largest aggregate gross templateTitle[1] templateTitle[2] in Armenia , Belarus , Kazakhstan , Moldova , and templateTitleSubject[0] , measuring at approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , which made it the leading movie of the templateXLabel[0] templateTitle[8] templateYLabel[0] . The romantic comedy `` templateXValue[1] , '' where the main character was played templateTitle[8] a popular Russian singer Egor Kreed , ranked second with the templateTitle[1] templateTitle[2] of over templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: 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] percent of adults in templateXValue[17] suffered from templateYLabel[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] templateYLabel[2] 100,000 templateYLabel[4] in the templateXValue[26] in templateTitleDate[0] , templateTitle[5] 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 COPD prevalence U.S. prevalence 100,000 prevalence in the Montana in 2017 , state . In 2017 , the COPD prevalence U.S. in California was 4.4 prevalence 100,000 residents , as compared to 5.7 in Alaska Vermont .

Example 605:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2005', '2017']}
title: Mexico : number of households 2005 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2010', '2005']
Y_Axis['Number', 'of', 'households', 'in', 'millions']: ['34.07', '32.9', '31.95', '28.16', '24.8']

gold: The statistic presents a timeline with the number of households in Mexico between 2005 and 2017 . In 2017 , there were more than 34 million households in Mexico , up from nearly 33 million households a year earlier .
gold_template: The statistic presents a timeline with the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , there were more than templateYValue[max] templateYLabel[2] templateYLabel[1] in templateTitleSubject[0] , up from nearly templateYValue[1] templateYLabel[2] templateYLabel[1] a templateXLabel[0] earlier .

generated_template: This statistic 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[min] percent 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 2017 in the 2017 from 2005 to 2017 . In 2017 , 24.8 percent 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 2019 , the desktop templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] percent , which represented a 16 percent growth from the previous templateXLabel[0] . templateTitleSubject[0] does not only drive templateYLabel[0] on templateTitleSubject[0] but also on other retail platforms .

generated_template: This statistic presents the templateYLabel[0] of templateTitleSubject[0] templateYLabel[2] in templateTitleSubject[1] in templateXValue[min] , templateXValue[1] and templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[min] templateYLabel[3] templateYLabel[1] templateYLabel[2] on templateTitleSubject[0] . This figure is an increase of around 272 percent of the previous templateXLabel[0] .
generated: This statistic presents the Conversion of Amazon Prime Day rate in Amazon Prime Day in 2017 , 2018 and 2019 . In 2019 , there were approximately 11.2 rate on Amazon Prime Day . This figure is an increase of around 272 percent of the previous Year .

Example 607:
titleEntities: {'Subject': ['Saudi Arabia'], 'Date': ['1984']}
title: Inflation in Saudi Arabia since 1984
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2.09', '2.09', '2.12', '2.09', '2.23', '-1.05', '2.48', '-0.85', '2.03', '1.27', '2.2', '3.52', '2.88', '3.76', '3.78', '4.18', '6.08', '5.06', '1.9', '0.55', '0.27', '0.55', '0.14', '-1.22', '-1.08', '-2.11', '-0.39', '-0.26', '0.26', '5.25', '1.26', '1.28', '-0.98', '3.79', '-1.01', '1.17', '-0.35', '-2.39', '-3.12', '-2.31', '-0.68']

gold: The statistic shows the inflation rate in Saudi Arabia from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate amounted to 2.48 percent compared to the previous year . Oil production in Saudi Arabia Saudi Arabia 's economy relies heavily on production and export of oil and petroleum .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] amounted to templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . Oil production in templateTitleSubject[0] Saudi templateTitleSubject[0] 's economy relies heavily on production and export of oil and petroleum .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . The templateYLabel[0] templateYLabel[1] is calculated using the price increase of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the templateXLabel[0] .
generated: The statistic shows the Inflation rate in 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] percent of templateYLabel[1] stated they think templateTitle[5] is templateXValue[0] , while templateYValue[min] percent stated it templateXValue[2] on the situation .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitleSubject[0] regarding templateTitle[5] or templateTitle[6] templateTitle[7] in templateTitleDate[0] . During this survey , templateYValue[max] percent of templateYLabel[1] stated they think templateTitle[5] or templateTitle[6] templateTitle[7] are templateXValue[0] , while templateYValue[min] percent said it templateXValue[2] on the situation .
generated: This statistic shows the moral stance of Americans regarding pornography or 2018 in 2018 . During this survey , 55 percent of respondents stated they think pornography or 2018 are Morally acceptable , while 1 percent said it Depends on the situation .

Example 609:
titleEntities: {'Subject': ['BSI'], 'Date': ['2019']}
title: Top 10 strongest nation brands by BSI score 2019
X_Axis['Country']: ['Singapore', 'Switzerland', 'Netherlands', 'Germany', 'Luxembourg', 'United_Arab_Emirates', 'Finland', 'Japan', 'United_States', 'Denmark']
Y_Axis['Brand', 'Strength', 'Index', 'Score']: ['90.5', '89.9', '89.6', '88.2', '86.9', '86.6', '86.4', '85.8', '85.7', '85.6']

gold: The statistic depicts the top ten strongest nation brands of 2019 as measured by the Brand Strength Index ( BSI ) . In 2019 , Singapore received the highest BSI score of any nation with a score of 90.5 .
gold_template: The statistic depicts the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleDate[0] as measured templateTitle[5] the templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitleSubject[0] ) . In templateTitleDate[0] , templateXValue[0] received the highest templateTitleSubject[0] templateYLabel[3] of any templateTitle[3] with a templateYLabel[3] of templateYValue[max] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] in templateTitleDate[0] , sorted templateTitle[5] templateXLabel[0] . The Japanese templateTitle[1] templateTitle[2] generated a total revenue of approximately templateYValue[3] templateYLabel[2] templateYLabel[3] in templateTitleDate[0] .
generated: The statistic shows the Brand Strength of the BSI 10 strongest in 2019 , sorted by Country . The Japanese 10 strongest generated a total revenue of approximately 88.2 Index Score in 2019 .

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 percent 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] million Italians residing in the South American templateXLabel[0] . This templateTitleSubject[0] community represented a fifth of all Italians residing outside the templateXLabel[0] . Moreover , the second and third biggest groups lived in European countries : templateXValue[1] hosted templateYValue[1] thousand Italians , while templateXValue[2] templateYValue[2] thousand .
generated: In 2017 , France had the largest Europe population outside of Italy with almost 60214 million Italians residing in the South American Country . This Europe community represented a fifth of all Italians residing outside the Country . Moreover , the second and third biggest groups lived in European countries : Germany hosted 58170 thousand Italians , while United Kingdom 49824 thousand .

Example 613:
titleEntities: {'Subject': ['Norway'], 'Date': ['2019']}
title: Leading companies in Norway 2019 , by number of employees
X_Axis['Month']: ['Helse_Sør-Øst_RHF', 'Telenor_ASA', 'Aker_ASA', 'Equinor_ASA_/_Statoil', 'Posten_Norge_AS', 'Orkla_ASA', 'Yara_International_ASA', 'Aker_Solutions_ASA', 'Tallyman_AS', 'Norges_Statsbaner_AS', 'Norsk_Hydro_ASA', 'Marine_Harvest_ASA', 'Strawberry_Holding_AS', 'Nordic_Choice_Hospitality_Group_AS', 'Kongsberg_Automotive_ASA', 'DNB_ASA', 'Hfn_Group_AS', 'Evry_ASA', 'Hospitality_Invest_AS', 'Nokas_AS']
Y_Axis['Number', 'of', 'employees']: ['60368', '31000', '20753', '20245', '18327', '18154', '14736', '14300', '13760', '13006', '12911', '12717', '10412', '10320', '9791', '9561', '9172', '9100', '9001', '8273']

gold: This statistic shows the 20 biggest companies in Norway as of March 2019 , by number of employees . Helse Sør-Øst RHF was ranked first with over 60 thousand employees , while Telenor ASA was ranked second with 31 thousand employees .
gold_template: This statistic shows the 20 biggest templateTitle[1] in templateTitleSubject[0] as of 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: Ranked as the templateTitle[0] employer among the Stockholm-based templateTitle[1] in templateTitleDate[0] was the security templateXValue[14] templateXValue[0] , the employer of over templateYValue[max] thousand people . templateXValue[1] M and the information and communication technology templateXValue[14] templateXValue[2] had the second and third templateTitle[0] templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] , amounting to over 123 and 97 thousand templateYLabel[1] respectively . The three templateTitle[0] employers in templateTitleSubject[1] The three templateTitle[1] did not only rank as the three templateTitle[0] employers based in templateTitleSubject[0] , but also formed the top three the templateTitle[0] employers among all templateTitle[1] based in SwedenEmployees in Europe , North and Latin America templateXValue[0] is the templateTitle[0] employer based in templateTitleSubject[1] , but templateYLabel[1] of the templateXValue[14] are spread around the world , in Europe , North America .
generated: Ranked as the Leading employer among the Stockholm-based companies in 2019 was the security Kongsberg Automotive ASA Helse Sør-Øst RHF , the employer of over 60368 thousand people . Telenor ASA M and the information and communication technology Kongsberg Automotive ASA Aker ASA had the second and third Leading Number of employees in Norway , amounting to over 123 and 97 thousand employees respectively . The three Leading employers in Norway The three companies did not only rank as the three Leading employers based in Norway , but also formed the top three the Leading employers among all companies based in SwedenEmployees in Europe , North and Latin America Helse Sør-Øst RHF is the Leading employer based in Norway , but employees of the Kongsberg Automotive ASA are spread around the world , in Europe , North America .

Example 614:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2009']}
title: Golf industry in the U.S. : total economic output by state 2009
X_Axis['State']: ['California', 'Florida', 'Texas', 'New_York', 'North_Carolina', 'Georgia', 'Ohio', 'Illinois', 'Michigan', 'Arizona', 'Virginia', 'New_Jersey', 'Massachusetts', 'Oregon', 'Hawaii', 'Washington', 'Wisconsin', 'Minnesota', 'Pennsylvania', 'South_Carolina', 'Colorado', 'Indiana', 'Connecticut', 'New_Mexico', 'Louisiana', 'Iowa', 'Kentucky']
Y_Axis['Total', 'economic', 'output', '(in', 'billion', 'U.S.', 'dollars)']: ['15.1', '13.8', '7.4', '5.3', '5.3', '5.1', '4.8', '4.8', '4.2', '3.4', '3.1', '2.8', '2.8', '2.5', '2.5', '2.5', '2.4', '2.4', '2.3', '2.3', '1.7', '1.7', '1.1', '0.99', '0.81', '0.77', '0.71']

gold: This graph depicts the total economic output of the golf industry in the U.S. by state as of 2009 . In New Mexico , the total economic output was at 985 million U.S. dollars in 2006 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitle[0] templateTitle[1] in the templateYLabel[5] templateTitle[6] templateXLabel[0] as of templateTitleDate[0] . In templateXValue[3] templateXValue[23] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at 985 templateYLabel[4] templateYLabel[5] dollars in 2006 .

generated_template: This graphic 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] percent of templateTitle[3] surveyed said that they templateYLabel[3] templateYLabel[4] walking templateTitle[5] the streets at templateTitle[7] templateTitle[6] .
generated: This graphic 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 percent 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] are expected .

generated_template: This statistic shows the templateYLabel[0] of Major templateTitleSubject[0] Baseball ( templateTitleSubject[0] ) from templateTitle[3] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] and its templateTitle[2] generated templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] from sponsorships .
generated: This statistic shows the Revenue of Major North America Baseball ( North America ) from revenue between 2007 and 2015 . In 2015 , the North America and its cartridge generated 23886 million U.S. dollars in Revenue from sponsorships .

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 increase . The peak was in templateXValue[0] at templateYValue[max] templateYLabel[3] British pounds ( templateYLabel[4] ) .

generated_template: In templateXValue[0] income templateTitle[4] templateYLabel[1] in the templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[2] British pounds , which when compared with templateXValue[last] was a net increase of 74.2 templateYLabel[2] pounds . The amount which workers in the templateTitleSubject[0] pay in templateYLabel[0] templateYLabel[1] is determined by how much they earn and falls into four templateYLabel[0] templateYLabel[1] bands . All workers in the templateTitleSubject[0] are entitled to earn a personal allowance of 12.5 thousand pounds before they are charged templateYLabel[0] templateYLabel[1] .
generated: In 2018/19 income HMRC tax in the United Kingdom amounted to 5.36 receipts British pounds , which when compared with 2000/01 was a net increase of 74.2 receipts pounds . The amount which workers in the United Kingdom pay in Inheritance tax is determined by how much they earn and falls into four Inheritance tax bands . All workers in the United Kingdom are entitled to earn a personal allowance of 12.5 thousand pounds before they are charged Inheritance tax .

Example 617:
titleEntities: {'Subject': ['LVMH Group'], 'Date': ['2008', '2019']}
title: LVMH Group 's R & D expenditure worldwide 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['R&D', 'expenditure', 'in', 'million', 'euros']: ['140', '130', '130', '111', '97', '79', '71', '68', '63', '46', '45', '43']

gold: This statistic highlights the trend in research and development ( R & D ) expenditure of the LVMH Group worldwide from 2008 to 2019 . In 2019 , LVMH Group 's global R & D expenditure amounted to about 140 million euros .
gold_template: This statistic highlights the trend in research and development ( templateTitleSubject[0] templateTitle[4] templateTitle[5] ) templateYLabel[1] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's global templateTitleSubject[0] templateTitle[4] templateTitle[5] templateYLabel[1] amounted to about templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: In templateXValue[max] , templateTitleSubject[0] Lines had approximately templateYValue[max] billion templateYLabel[0] templateYLabel[1] templateYLabel[2] , which continued the increase of the past five years . templateYLabel[0] templateYLabel[1] templateYLabel[2] report the total flight passenger capacity of an airline in templateYLabel[2] , by multiplying the total number of seats templateYLabel[0] to the total number of templateYLabel[2] in which those seats were flown . templateTitleSubject[0] : a profitable airline The airline company had a passenger load factor of around 86.3 percent in templateXValue[max] , which is slightly higher than the national average .
generated: In 2019 , LVMH Group Lines had approximately 140 billion R&D expenditure million , which continued the increase of the past five years . R&D expenditure million report the total flight passenger capacity of an airline in million , by multiplying the total number of seats R&D to the total number of million in which those seats were flown . LVMH Group : a profitable airline The airline company had a passenger load factor of around 86.3 percent in 2019 , which is slightly higher than the national average .

Example 618:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018', '2018']}
title: Murder in the U.S. : number of offenders by age 2018
X_Axis['Age', 'of', 'offender', 'in', 'years']: ['Infant_(<1)', '1_to_4', '5_to_8', '9_to_12', '13_to_16', '17_to_19', '20_to_24', '25_to_29', '30_to_34', '35_to_39', '40_to_44', '45_to_49', '50_to_54', '55_to_59', '60_to_64', '65_to_69', '70_to_74', '75+', 'Unknown']
Y_Axis['Number', 'of', 'offenders']: ['0', '1', '1', '8', '496', '1479', '2254', '1998', '1440', '1161', '651', '495', '439', '346', '186', '106', '82', '93', '5099']

gold: 2,254 murderers in the United States in 2018 were individuals between the ages of 20 and 24 . In the same year , the youngest murder offender was between the ages of one and four , and there were 93 murder offenders over the age of 75 . Murder rate in the United States Despite some feeling that violent crime in the United States is on the rise , perhaps due to sensationalized media coverage , the murder and nonnegligent manslaughter rate has declined steeply since 1990 .
gold_template: templateYValue[6] murderers in the templateTitle[1] in templateTitle[6] were individuals between the ages of templateXValue[6] and templateXValue[6] . In the same year , the youngest templateTitle[0] templateXLabel[1] was between the ages of templateXValue[1] and templateXValue[1] , and there were templateYValue[17] templateTitle[0] templateYLabel[1] over the templateXLabel[0] of 75 . templateTitle[0] rate in the templateTitle[1] Despite some feeling that violent crime in the templateTitle[1] is on the rise , perhaps due to sensationalized media coverage , the templateTitle[0] and nonnegligent manslaughter rate has declined steeply since 1990 .

generated_template: This statistic shows the templateYLabel[0] of 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 Number of offenders number in 2018 . In 2018 , at least 1 people were executed in 1 to 4 . The figures represent minimum values according to Amnesty International .

Example 619:
titleEntities: {'Subject': ['Panama'], 'Date': ['2024']}
title: Unemployment rate in Panama 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Unemployment', 'rate']: ['5.77', '5.77', '5.77', '5.8', '5.91', '6.11', '5.96', '6.13', '5.49', '5.05', '4.82']

gold: This statistic shows the unemployment rate in Panama from 2014 to 2018 , with projections up until 2024 . In 2018 , the unemployment rate in Panama was 5.96 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[6] percent .

generated_template: 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 percentage 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] increased from about templateYValue[min] thousand templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] in templateXValue[min] to about templateYValue[0] thousand templateYLabel[1] in templateXValue[max] .

generated_template: In the fall semester templateXValue[max] , templateYValue[0] templateYLabel[1] were templateTitle[1] in universities and other higher education institutions in templateTitleSubject[0] . Since 2000 , the templateYLabel[0] of individuals in templateTitleSubject[0] with an upper secondary education increased , while the individuals without decreased . In templateXValue[max] , 21.7 percent of the female population had an upper secondary education degree of three years and 24.9 percent of the male population .
generated: In the fall semester 2018 , 148551 students were students in universities and other higher education institutions in Denmark . Since 2000 , the Number of individuals in Denmark with an upper secondary education increased , while the individuals without decreased . In 2018 , 21.7 percent of the female population had an upper secondary education degree of three years and 24.9 percent of the male population .

Example 621:
titleEntities: {'Subject': ['UK'], 'Date': ['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] , June and July to templateYValue[max] , templateYValue[18] and templateYValue[17] templateYLabel[1] respectively . Unsurprisingly it was the end of each year when templateTitle[2] templateYLabel[1] were lowest .

generated_template: The templateYLabel[1] of the DJIA templateYLabel[0] amounted to templateYValue[0] on 31 , templateTitleDate[1] . templateTitleSubject[0] Industrial templateTitleSubject[0] templateYLabel[0] – additional information The templateTitleSubject[0] Industrial templateTitleSubject[0] templateYLabel[0] is a price-weighted templateTitleSubject[0] of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ , including Goldman Sachs , IBM and Walt Disney . This templateYLabel[0] is considered to be a barometer of the state of the American economy .
generated: The hours of the DJIA Number amounted to 46.2 on 31 , 2019 . UK Industrial UK Number – additional information The UK Industrial UK Number is a price-weighted UK of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ , including Goldman Sachs , IBM and Walt Disney . This Number is considered to be a barometer of the state of the American economy .

Example 622:
titleEntities: {'Subject': ['Countries'], 'Date': ['2017']}
title: Countries with the lowest access to electricity 2017
X_Axis['Country']: ['Burundi', 'Chad', 'Malawi', 'Dem._Republic_of_the_Congo', 'Niger', 'Liberia', 'Uganda', 'Sierra_Leone', 'Madagascar', 'South_Sudan', 'Burkina_Faso', 'Guinea-Bissau', 'Mozambique', 'Central_African_Republic', 'Tanzania', 'Somalia', 'Lesotho', 'Rwanda', 'Guinea', 'Zambia', 'Global_average']
Y_Axis['Access', 'rate']: ['9.3', '10.9', '12.7', '19.1', '20', '21.5', '22', '23.4', '24.1', '25.4', '25.5', '26', '27.4', '30', '32.8', '32.9', '33.7', '34.1', '35.4', '40.3', '88.8']

gold: This statistic shows the countries with the lowest access to electricity in 2017 based on access rate . As of that time , about 12.7 percent of the population in Malawi had access to electricity .
gold_template: This statistic shows the templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] to templateTitle[4] in templateTitleDate[0] based on templateYLabel[0] templateYLabel[1] . As of that time , about templateYValue[2] percent of the population in templateXValue[2] had templateYLabel[0] to templateTitle[4] .

generated_template: This statistic represents the amount of templateTitle[1] templateTitle[2] templateYLabel[1] generated worldwide in 2016 , broken down templateTitle[5] select countries . The templateXValue[0] generated templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] of templateTitle[1] templateTitle[2] templateYLabel[1] based on the latest reported templateYLabel[6] .
generated: This statistic represents the amount of lowest access rate generated worldwide in 2016 , broken down 2017 select countries . The Burundi generated 88.8 rate of lowest access rate based on the latest reported 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 templateTitle[7] to the third templateXLabel[0] of templateTitle[9] . Automated transaction templateYLabel[2] ( ATMs ) are banking outlets designed for maximum access to banking transactions and information , outside of bank working hours and without the assistance of a clerk . It can be seen that the total templateYLabel[0] of templateYLabel[1] templateYLabel[2] increased between the first templateXLabel[0] of templateTitle[7] and the second templateXLabel[0] of 2016 , reaching a total of more than templateYValue[9] thousand as of the second templateXLabel[0] of 2016 .

generated_template: In the fourth templateXLabel[0] of templateTitleDate[1] , templateTitleSubject[0] 's net templateYLabel[1] templateYLabel[2] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] , representing a 22 percent year-on-year growth . This templateYLabel[1] templateYLabel[2] was generated through the over 3.46 templateYLabel[3] transactions which templateTitleSubject[0] processed during that period . In 2018 , the templateYLabel[1] provider 's annual templateYLabel[1] templateYLabel[2] came to 578 templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: In the fourth Quarter of 2019 , United Kingdom 's net cash machines amounted to 70682 machines , representing a 22 percent year-on-year growth . This cash machines was generated through the over 3.46 machines transactions which United Kingdom processed during that period . In 2018 , the cash provider 's annual cash machines came to 578 machines .

Example 624:
titleEntities: {'Subject': ['CPG'], 'Date': ['2016']}
title: Global operating margin of CPG companies 2016 , by company
X_Axis['Company']: ['Kraft_Heinz', 'Kimberly-Clark', 'General_Mills', 'PepsiCo', 'Nestlé']
Y_Axis['Operating', 'margin']: ['21.9', '18.2', '15.9', '15.6', '14.7']

gold: This statistic shows the operating margins of consumer packaged goods ( CPG ) companies worldwide in 2016 , sorted by company . In that year , Kraft Heinz had an operating margin of 21.9 percent , the highest among the referenced CPG companies .
gold_template: This statistic shows the templateYLabel[0] margins of consumer packaged goods ( templateTitleSubject[0] ) templateTitle[4] worldwide in templateTitleDate[0] , sorted templateTitle[6] templateXLabel[0] . In that year , templateXValue[0] had an templateYLabel[0] templateYLabel[1] of templateYValue[max] percent , the highest among the referenced templateTitleSubject[0] templateTitle[4] .

generated_template: This statistic 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] percent of templateTitleSubject[0] 's templateYLabel[0] coming from Germany.IKEAIKEA is an internationally known home furnishing retailer . It has grown rapidly since it was founded in 1943 .
generated: This statistic shows the Global operating margin CPG of CPG 2016 in 2016 . In that year , Kraft Heinz was the Global margin Company of CPG 2016 , with 21.9 percent of CPG 's Operating coming from Germany.IKEAIKEA is an internationally known home furnishing retailer . It has grown rapidly since it was founded in 1943 .

Example 625:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Cities with the largest parkland percentage in the U.S. 2018
X_Axis['State']: ['Anchorage', 'Fremont', 'Irvine', 'Scottsdale', 'North_Las_Vegas', 'Chesapeake', 'New_Orleans', 'Albuquerque', 'New_York', 'Washington_D.C.', 'San_Francisco', 'Las_Vegas', 'El_Paso', 'San_Diego', 'Jersey_City']
Y_Axis['Share', 'of', 'parkland']: ['84.2', '49.4', '27.4', '26.9', '26.2', '26', '25.9', '23.2', '21.7', '21.1', '19.6', '19.4', '19.2', '19.1', '18.1']

gold: This statistic shows the cities with the largest parkland percentage of the city area in the United States in 2018 . In Anchorage , Alaska , 84.2 percent of the city 's area was comprised of parkland in 2018 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[1] templateTitle[4] of the templateXValue[last] area in the templateTitle[5] in templateTitleDate[0] . In templateXValue[0] , Alaska , templateYValue[max] percent of the templateXValue[last] 's area was comprised of templateYLabel[1] in templateTitleDate[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] in templateTitle[4] templateTitleSubject[0] templateTitle[6] in templateTitleDate[0] . The survey was conducted among 15 to 20 year-olds and is part of the Face of Global sex Report by Durex . According to the source , the respondents in templateXValue[0] had templateYValue[max] sex templateYLabel[2] on average .
generated: This statistic shows the Share parkland of parkland in U.S. 2018 in 2018 . The survey was conducted among 15 to 20 year-olds and is part of the Face of Global sex Report by Durex . According to the source , the respondents in Anchorage had 84.2 sex parkland on average .

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 templateXValue[max] , according to the templateYLabel[0] templateYLabel[1] , templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[0] was templateYValue[max] . This figure was at templateYValue[min] in templateTitleDate[0] , which indicates an increase in templateTitle[2] inequality in the templateTitle[0] over the past 30 years . What is the templateYLabel[0] templateYLabel[1] ? The templateYLabel[0] templateYLabel[1] , or templateYLabel[0] index , is a statistical measure of economic inequality and wealth templateTitle[3] among a population .
generated: In 2016 , according to the Number sports , sports Summer Olympic in the Number was 35 . This figure was at 9 in 2016 , which indicates an increase in Summer inequality in the Number over the past 30 years . What is the Number sports ? The Number sports , or Number index , is a statistical measure of economic inequality and wealth Olympic among a population .

Example 627:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2018']}
title: Urbanization in Denmark 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['87.87', '87.76', '87.64', '87.53', '87.41', '87.29', '87.14', '86.96', '86.8', '86.65', '86.49']

gold: This statistic shows the degree of urbanization in Denmark from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 87.87 percent of Denmark 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in 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 .

Example 628:
titleEntities: {'Subject': ['Canada'], 'Date': ['2000', '2019']}
title: Number of births in Canada 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Number', 'of', 'births']: ['382533', '378848', '379675', '383579', '383315', '382281', '381607', '378840', '376951', '379373', '379290', '373695', '360916', '346082', '339270', '337762', '330523', '328155', '327107']

gold: In 2018 , there were an estimated 382,533 babies born in Canada . This is an increase from 327,107 births in the year 2001 . Births in Canada In 2018 , there were more male babies born than female babies , and overall births have been increasing since 2000 .
gold_template: In templateXValue[1] , there were an estimated templateYValue[0] babies born in templateTitleSubject[0] . This is an increase from templateYValue[min] templateYLabel[1] in the templateXLabel[0] templateXValue[min] . templateYLabel[1] in templateTitleSubject[0] In templateXValue[1] , there were more male babies born than female babies , and overall templateYLabel[1] have been increasing since templateTitleDate[0] .

generated_template: There were templateYValue[0] thousand templateYLabel[1] templateYLabel[2] recorded in the templateTitleSubject[0] in templateXValue[max] , a decline of almost 24 thousand templateYLabel[2] when compared with the previous templateXLabel[0] . Between templateXValue[min] and templateXValue[max] the templateXLabel[0] with the highest templateYLabel[0] of templateYLabel[1] templateYLabel[2] was templateXValue[6] when there were almost templateYValue[max] thousand templateYLabel[2] . Birth rate at a 15-year low At 11.4 templateYLabel[2] per 1,000 people , the birth rate of the templateTitleSubject[0] in templateXValue[1] marked a fifteen-year low .
generated: There were 382533 thousand births recorded in the Canada in 2019 , a decline of almost 24 thousand births when compared with the previous Year . Between 2001 and 2019 the Year with the highest Number of births was 2013 when there were almost 383579 thousand births . Birth rate at a 15-year low At 11.4 births per 1,000 people , the birth rate of the Canada in 2018 marked a fifteen-year low .

Example 629:
titleEntities: {'Subject': ['Indonesia'], 'Date': ['2006', '2019']}
title: Per capita poultry consumption in Indonesia 2006 to 2019
X_Axis['Year']: ['2025', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Consumption', 'per', 'capita', 'in', 'kilograms']: ['8.39', '7.57', '7.57', '7.68', '7.91', '7.05', '6.81', '6.54', '6.25', '6.07', '5.68', '5.27', '5.17', '5.08', '4.93']

gold: In 2019 , Indonesians consumed around 7.6 kilograms of poultry meat per capita . In 2025 , this was expected to increase to 8.4 kilograms per capita . Indonesia 's meat consumption had been increasing in the last few years , indicating improved economic prosperity for the population .
gold_template: In templateXValue[1] , Indonesians consumed around templateYValue[1] templateYLabel[3] of templateTitle[2] meat templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this was expected to increase to templateYValue[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] 's meat templateYLabel[0] had been increasing in the last few years , indicating improved economic prosperity for the population .

generated_template: In templateXValue[1] , the templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was approximately templateYValue[1] templateYLabel[3] . In templateXValue[max] , this was forecasted to reach around templateYValue[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] 's templateTitle[2] meat templateYLabel[0] in templateXValue[1] was above the OECD average of that templateXLabel[0] .
generated: In 2019 , the poultry Consumption per capita in Indonesia was approximately 7.57 kilograms . In 2025 , this was forecasted to reach around 8.39 kilograms per capita . Indonesia 's poultry meat Consumption in 2019 was above the OECD average of that Year .

Example 630:
titleEntities: {'Subject': ['United States'], 'Date': ['2011']}
title: Geographic distance between grandparents and their grandchildren in the United States in 2011
X_Axis['Year']: ['10_miles_or_less', '11_-_50_miles', '51_-_100_miles', '101_-_150_miles', '151_-_200_miles', 'More_than_200_miles', 'Only_have_grandchildren_who_live_with_me', "Don't_know"]
Y_Axis['Percentage', 'of', 'respondents']: ['21', '17', '7', '4', '4', '43', '1', '2']

gold: This statistic shows the results of a survey among grandparents in the United States in 2011 on the geographic distance between themselves and their grandchildren . In 2011 , 43 percent of the respondents stated they live more than 200 miles away from their grandchildren , whereas 21 percent said they live 10 or less miles away from their grandchildren .
gold_template: This statistic shows the results of a survey among templateTitle[3] in the templateTitleSubject[0] in templateTitleDate[0] on the templateTitle[0] templateTitle[1] templateTitle[2] themselves and templateTitle[4] templateXValue[6] . In templateTitleDate[0] , templateYValue[max] percent of the templateYLabel[1] stated they templateXValue[6] templateXValue[5] 200 templateXValue[0] away from templateTitle[4] templateXValue[6] , whereas templateYValue[0] percent said they templateXValue[6] templateXValue[0] or templateXValue[0] away from templateTitle[4] templateXValue[6] .

generated_template: Among templateXValue[0] templateTitle[11] groups templateTitle[2] , those aged templateXValue[3] to templateYValue[2] templateXValue[1] had the highest templateYLabel[0] of templateTitle[8] mobile app users . It was estimated that in templateTitleDate[0] , around templateYValue[max] percent of those aged templateXValue[3] to templateYValue[2] templateXValue[1] templateTitle[4] templateTitle[8] tracking apps .
generated: Among 10 miles or less 2011 groups between , those aged 101 - 150 miles to 7 11 - 50 miles had the highest Percentage of 2011 mobile app users . It was estimated that in 2011 , around 43 percent of those aged 101 - 150 miles to 7 11 - 50 miles their 2011 tracking apps .

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] % of templateYLabel[1] rated templateTitle[3] templateTitleSubject[0] 's templateTitle[1] as templateXValue[0] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] survey templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] with sensitive information templateTitle[8] or templateTitle[7] , while traveling . templateYValue[max] percent of templateYLabel[1] templateTitle[4] templateTitle[8] or templateTitle[5] a templateXValue[0] templateTitle[7] whilst traveling .
generated: The statistic shows the Share of Armour survey respondents Under Armour footwear United with sensitive information 2014 or States , while traveling . 72 percent of respondents Armour 2014 or footwear a Extremely positive States whilst traveling .

Example 632:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2020']}
title: Public opinion on the most important problem facing the U.S. 2020
X_Axis['Response']: ['Dissatisfaction_with_government/Poor_leadership', 'Immigration', 'Healthcare', 'Ethics/moral/religious/family_decline', 'Unifying_the_country', 'Poverty/Hunger/Homelessness', 'Lack_of_respect_for_each_other', 'Environment/Pollution/Climate_change', 'Race_relations/Racism', 'Situation_in_Iraq/ISIS', 'Foreign_policy/Foreign_aid/Focus_overseas', 'Economy_in_general', 'Guns/Gun_control', 'Gap_between_rich_and_poor', 'Education', 'Wars/War_(nonspecific)/Fear_of_war']
Y_Axis['Share', 'of', 'respondents']: ['28', '6', '6', '5', '5', '5', '4', '4', '3', '2', '2', '2', '2', '2', '2', '2']

gold: This statistic represents American adults ' view of the most important problem facing the United States . In January 2020 , 28 percent of the participants stated that poor leadership and a general dissatisfaction with the government were the most important problems facing the U.S .
gold_template: This statistic represents American adults ' view of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] the templateTitle[6] . In 2020 , templateYValue[max] percent of the participants stated that templateXValue[13] templateXValue[0] and a templateXValue[11] templateXValue[0] the government were the templateTitle[2] templateTitle[3] problems templateTitle[5] the templateTitle[6] .

generated_template: This statistic shows the results of a survey about the most templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] in the third quarter of templateTitleDate[0] . The most used templateTitle[3] templateTitle[4] templateXLabel[0] in templateTitleSubject[0] was templateXValue[0] at templateYValue[max] percent , closely followed by templateXValue[1] at templateYValue[1] percent . On the opposite side , templateXValue[last] is used by four percent of templateYLabel[1] .
generated: This statistic shows the results of a survey about the most important problem facing in U.S. in the third quarter of 2020 . The most used important problem Response in U.S. was Dissatisfaction with government/Poor leadership at 28 percent , closely followed by Immigration at 6 percent . On the opposite side , Wars/War (nonspecific)/Fear of war is used by four percent of respondents .

Example 633:
titleEntities: {'Subject': ['Yemen'], 'Date': ['2002', '2019']}
title: U.S. airstrikes in Yemen 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2002']
Y_Axis['Number', 'of', 'airstrikes']: ['8', '35', '125', '44', '23', '23', '26', '42', '10', '4', '2', '1']

gold: This statistic shows the number of U.S. airstrikes in Yemen from 2002 to 2019 . In 2018 , there were 35 United States airstrikes in Yemen .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , there were templateYValue[1] templateTitle[0] templateYLabel[1] in templateTitleSubject[0] .

generated_template: 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[0] 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 Number of Yemen and 2002 2019 in Indonesian territorial waters from 2002 to 2019 . In 2019 , there were 8 Yemen and 2002 2019 in Yemen . 2019 in Yemen 2019 in Indonesian waters spiked in 2015 , when 125 airstrikes were reported .

Example 634:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2024']}
title: Budget balance in Mexico in relation to gross domestic product ( GDP ) 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'balance', 'to', 'GDP', 'ratio']: ['-2.4', '-2.3', '-2.3', '-2.2', '-2.6', '-2.8', '-2.2', '-1.07', '-2.77', '-4', '-4.54']

gold: The statistic shows the budget balance in Mexico from 2014 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 . In 2018 , the state deficit of Mexico was at around 2.2 percent of the gross domestic product .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , the state deficit of templateTitleSubject[0] was at around 2.2 percent of the templateTitle[4] templateTitle[5] templateTitle[6] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , the state deficit of templateTitleSubject[0] amounted to about 6.4 percent 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 percent 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[0] to templateTitle[6] . In templateTitle[6] , the templateYLabel[0] templateYLabel[1] on templateTitle[2] and home furnishings in templateYLabel[3] templateTitle[4] was about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of each templateTitleSubject[0] club in the templateTitle[6] season . templateXValue[0] Munich will receive templateYValue[max] templateYLabel[3] templateYLabel[4] from its templateYLabel[0] sponsor Telekom in the templateTitle[6] season .
generated: This statistic shows the Gross margin billion of each U.S. club in the 2017 season . 17 Munich will receive 27.92 U.S. dollars from its Gross sponsor Telekom in the 2017 season .

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: The two templateTitle[3] templateTitle[4] templateTitleSubject[0] templateTitle[6] in templateTitleDate[0] were the oil companies templateXValue[0] and templateXValue[1] , the former of which is in possession of a staggering templateYLabel[1] of templateYValue[max] templateYLabel[2] templateTitleSubject[0] pounds . This was not different in the previous year either , when templateXValue[0] ranked as the templateTitle[3] templateTitle[4] templateTitleSubject[0] templateXLabel[0] while the rest of the list had some small shifts and variations . Oil , banks and Telecom The templateTitle[3] templateTitle[4] templateTitleSubject[0] templateTitle[6] as of templateTitleDate[0] is a balanced mixture of oil and gas companies , banks and telecommunications .
generated: The two affected worldwide Volcanic 2016 in 2016 were the oil companies Volcanic eruption in the Philippines (June 9 1991) and Volcano eruption in Ecuador (August 14 2015) , the former of which is in possession of a staggering victims of 1036065 victims Volcanic pounds . This was not different in the previous year either , when Volcanic eruption in the Philippines (June 9 1991) ranked as the affected worldwide Volcanic Location while the rest of the list had some small shifts and variations . Oil , banks and Telecom The affected worldwide Volcanic 2016 as of 2016 is a balanced mixture of oil and gas companies , banks and telecommunications .

Example 637:
titleEntities: {'Subject': ['Black Friday'], 'Date': ['2017']}
title: U.S. consumer sentiments towards Black Friday shopping 2017
X_Axis['Response']: ['It_is_a_great_opportunity_to_buy_gifts_for_the_holidays', "It's_a_tradition", 'I_like_it_even_more_now_that_I_can_shop_online', 'It_is_the_best_opportunity_to_buy_expensive_items_at_a_discount', 'It_is_when_you_find_promotions_that_are_not_available_at_any_other_time_of_year', 'It_is_a_good_way_to_spend_quality_time_with_friends/family', 'I_will_wait_until_Cyber_Monday_to_do_most_of_my_shopping', 'Promotions_are_never_on_products_I_am_interested_in', 'Retailers_just_discount_their_worst_brands', 'None_of_these']
Y_Axis['Share', 'of', 'respondents']: ['42', '39', '37', '33', '31', '19', '18', '16', '9', '5']

gold: This statistic shows the results of a 2017 survey in which U.S. consumers were asked about their attitude towards Black Friday shopping . According to the survey , 42 percent of respondents said that Black Friday is a great opportunity to buy gifts for the holidays .
gold_template: This statistic shows the results of a templateTitleDate[0] survey in which templateTitle[0] consumers were asked about templateXValue[8] attitude templateTitle[3] templateTitleSubject[0] shopping . According to the survey , templateYValue[max] percent of templateYLabel[1] said templateXValue[2] templateTitleSubject[0] is a templateXValue[0] to templateXValue[0] for the templateXValue[0] .

generated_template: 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] percent 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 percent 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] templateYLabel[3] lifetime video templateYLabel[2] , making the elementary schooler the templateTitle[1] templateTitle[2] templateTitleSubject[0] templateYLabel[1] owner of all time . This achievement unseated former first-ranked video game commentator Felix Kjellberg , who produces videos under the name templateXValue[1] . Ryan has been uploading templateTitleSubject[0] videos since 2015 , and mainly features in videos where he is playing with and reviews templateXValue[5] `` for templateXValue[3] , by a kid '' .

generated_template: This statistic shows the templateYLabel[1] of templateTitleSubject[0] 's templateTitle[1] templateTitle[3] based on the templateXValue[5] private templateTitle[2] templateXValue[5] for MAT 2015 . Pharma company templateXValue[0] showed the highest templateYLabel[1] with a templateYValue[max] templateYLabel[0] increase during this period .
generated: This statistic shows the channel of YouTube 's most YouTube based on the Fun Toys Collector Disney private viewed Fun Toys Collector Disney for MAT 2015 . Pharma company Ryan's World showed the highest channel with a 35.18 All-time increase during this period .

Example 639:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2018']}
title: Brazil : most popular music genres 2018
X_Axis['Response']: ['Pop', 'Brazilian_pop', 'Sertanejo', 'Rock', 'Samba/pagode', 'Electronic/dance_music', 'Dance', 'Gospel', 'Hip_hop', 'Reggae', 'Forró', 'Funk/soul', 'Blues', 'Latin', 'Rap', 'Country', 'Metal', 'Techno/EDM', 'R&B/soul', 'Jazz', 'Heavy_metal', 'Classical/opera', 'Reggaeton', 'Easy_listening', 'Punk', 'Folk', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['55.5', '54', '50.3', '48.8', '38.1', '37.9', '35.6', '35.1', '31.8', '31', '30.2', '25.2', '24.7', '23.4', '23.2', '22.6', '17.6', '17.4', '17.3', '16.8', '14.5', '14.3', '13.2', '10.7', '10.4', '8.9', '4.8']

gold: This statistic shows the results of a Deezer survey on music listening habits among adults in Brazil as of 2018 . That year , 55.5 percent of Brazilian respondents claimed to listen to pop music , whereas 54 percent said they listened to Brazilian pop .
gold_template: This statistic shows the results of a Deezer survey on templateXValue[5] templateXValue[23] habits among adults in templateTitleSubject[0] as of templateTitleDate[0] . That year , templateYValue[max] percent of templateXValue[1] templateYLabel[1] claimed to listen to templateXValue[0] templateXValue[5] , whereas templateYValue[1] percent said they listened to templateXValue[1] templateXValue[0] .

generated_template: 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] percent of templateYLabel[1] stating that they used templateXValue[0] and templateYValue[1] percent 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 2018 , Pop and Brazilian pop were the Brazil commonly used music genres 2018 software 2018 around the world , with nearly 55.5 percent of respondents stating that they used Pop and 54 percent 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[max] templateYLabel[3] in templateXValue[max] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of U.S. 2000 in the Per from 2000 to 2018 . According to the report , the U.S. Per capita consumption of U.S. 2000 amounted to approximately 5.8 pounds in 2018 .

Example 641:
titleEntities: {'Subject': ['Daily'], 'Date': ['2018']}
title: Daily online video usage in selected countries 2018
X_Axis['Country']: ['Saudi_Arabia', 'Turkey', 'Brazil', 'New_Zealand', 'Australia', 'Mexico', 'Spain', 'Canada', 'United_States', 'South_Korea', 'France', 'Philippines', 'India', 'Germany', 'Japan', 'China', 'Indonesia', 'South_Africa', 'Nigeria']
Y_Axis['Share', 'of', 'respondents']: ['64', '64', '62', '61', '60', '56', '53', '50', '50', '44', '42', '34', '33', '32', '32', '30', '21', '19', '16']

gold: This statistic gives information on the share of internet users in selected countries who watch online videos every day as of January 2018 . During the survey , it was found that 50 percent of U.S. internet users watched online video content on a daily basis . Additionally , more than half of the internet users in Mexico watched online videos every day .
gold_template: This statistic gives information on the templateYLabel[0] of internet users in templateTitle[4] templateTitle[5] who watch templateTitle[1] videos every day as of 2018 . During the survey , it was found that templateYValue[7] percent of U.S. internet users watched templateTitle[1] templateTitle[2] content on a templateTitleSubject[0] basis . Additionally , more than half of the internet users in templateXValue[5] watched templateTitle[1] videos every day .

generated_template: This statistic presents 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] percent of survey templateYLabel[1] who have experienced templateXValue[13] abuse stated that they had experienced templateTitleSubject[0] via templateXValue[0] .
generated: This statistic presents 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 percent 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[0] percent of the labor force aged 15 to 24 years in templateTitleSubject[0] were unemployed .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at about templateYValue[0] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Youth unemployment rate in Singapore from 2005 to 2018 . In 2018 , the Youth unemployment rate in Singapore was at about 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[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[max] . The major nations conducting templateYLabel[1] templateYLabel[2] include Russia , the country and the member states of ESA .

generated_template: In templateXValue[max] , the templateYLabel[1] templateYLabel[2] of hospitals in the templateTitleSubject[0] stood at templateYValue[0] percent . Hospitals are a cornerstone of health services in any country and also contribute considerably to the economy . Nevertheless , the number of hospitals in the templateTitleSubject[0] has decreased in recent years .
generated: In 2017 , the space launches of hospitals in the Worldwide stood at 33 percent . Hospitals are a cornerstone of health services in any country and also contribute considerably to the economy . Nevertheless , the number of hospitals in the Worldwide has decreased in recent years .

Example 644:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2008', '2018']}
title: Number of divorces in Denmark 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'divorces']: ['14936', '15169', '17146', '16290', '19387', '18858', '15709', '14484', '14460', '14940', '14695']

gold: In 2017 and 2018 , most Danes were never married ; the number of never married inhabitants was around 2.8 million in the fourth quarter of 2018 . By contrast , among all Danes , the fewest were divorced . In general , the number of divorces between different sexes fluctuated in recent years , peaking in 2014 at about 19 thousand divorces .
gold_template: In templateXValue[1] and templateXValue[max] , most Danes were never married ; the templateYLabel[0] of never married inhabitants was around 2.8 million in the fourth quarter of templateXValue[max] . By contrast , among all Danes , the fewest were divorced . In general , the templateYLabel[0] of templateYLabel[1] between different sexes fluctuated in recent years , peaking in templateXValue[4] at about templateYValue[max] thousand templateYLabel[1] .

generated_template: The templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] peaked in templateXValue[5] , when almost templateYValue[max] thousand couples got divorced . Since then the divorce templateYLabel[0] decreased until templateXValue[max] , when it again increased and amounted to almost templateYValue[0] thousand templateYLabel[1] . A similar trend can be seen in the neighbor country Norway , where the templateYLabel[0] of templateYLabel[1] decreased for several years but increased again in templateXValue[1] .
generated: The Number of divorces in Denmark peaked in 2013 , when almost 19387 thousand couples got divorced . Since then the divorce Number decreased until 2018 , when it again increased and amounted to almost 14936 thousand divorces . A similar trend can be seen in the neighbor country Norway , where the Number of divorces decreased for several years but increased again in 2017 .

Example 645:
titleEntities: {'Subject': ['Germany'], 'Date': ['2018']}
title: GDP of Germany 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['GDP', 'in', 'billion', 'euros']: ['3386.0', '3277.34', '3159.75', '3048.86', '2938.59', '2826.24', '2758.26', '2703.12', '2580.06', '2460.28', '2561.74', '2513.23']

gold: In 2018 , Germany 's gross domestic product ( GDP ) amounted to 3,386 billion euros . Germany is thus among the leading five countries in the world GDP ranking . Ze Germans are living large Germany 's economy is not only one of the largest worldwide , it is also the largest in Europe and predicted to be among the countries with the largest GDP by the year 2030 .
gold_template: In templateXValue[max] , templateTitleSubject[0] 's gross domestic product ( templateYLabel[0] ) amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] is thus among the leading five countries in the world templateYLabel[0] ranking . Ze Germans are living large templateTitleSubject[0] 's economy is not only one of the largest worldwide , it is also the largest in Europe and predicted to be among the countries with the largest templateYLabel[0] by the templateXLabel[0] 2030 .

generated_template: The statistic shows the total templateYLabel[0] of templateTitle[0] templateTitleSubject[0] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company 's templateYLabel[0] of templateTitle[0] templateTitle[1] amounted to approximately templateYValue[max] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] is a chain of discount variety stores that operates in 44 templateTitle[5] templateTitleSubject[0] .
generated: The statistic shows the total GDP of GDP Germany in the 2018 from 2007 to 2018 . In 2018 , the company 's GDP of GDP Germany amounted to approximately 3386.0 billion euros . Germany is a chain of discount variety stores that operates in 44 2018 Germany .

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 templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in Russia . templateXValue[0] was the templateTitleSubject[0] soccer team with the oldest templateYLabel[0] templateYLabel[1] ( templateYValue[max] templateYLabel[2] ) , followed by templateXValue[1] with team players averaging templateYValue[1] templateYLabel[2] old .

generated_template: Most templateTitleSubject[0] templateTitle[3] templateTitle[4] require the use of templateYLabel[1] templateYLabel[2] , largely coming from Asia . About templateYValue[max] percent of templateTitle[3] photovoltaic templateTitle[2] into the templateTitle[0] came from templateXValue[0] between January and 2019 . About 58 percent of voters in the templateTitle[0] stated that they opposed imposing tariffs on these templateYLabel[1] energy templateYLabel[2] .
generated: Most Latin American Cup average require the use of age years , largely coming from Asia . About 29.8 percent of Cup photovoltaic World into the 2018 came from Costa Rica between January and 2019 . About 58 percent of voters in the 2018 stated that they opposed imposing tariffs on these age energy years .

Example 647:
titleEntities: {'Subject': ['PlayStation'], 'Date': ['2014']}
title: Suggested retail price of a PlayStation 4 in 2014 , by country
X_Axis['Country']: ['Brazil', 'Argentina', 'India', 'Indonesia', 'Thailand', 'South_Africa', 'United_Kingdom', 'Philippines', 'Germany', 'Malaysia', 'Russia', 'Singapore', 'South_Korea', 'Australia', 'United_Arab_Emirates', 'Canada', 'Hong_Kong', 'Taiwan', 'United_States', 'Japan']
Y_Axis['Price', 'in', 'U.S.', 'dollars']: ['1702.43', '1387.9', '653.54', '619.76', '614.77', '585.79', '580.94', '559.51', '557.07', '550.76', '523.85', '505.7', '466.82', '492.84', '462.56', '451.42', '435.23', '427.83', '399.99', '392.38']

gold: The ranking shows the suggested retail price of a PlayStation 4 in selected countries worldwide as of March 2014 . Brazil ranked first with a suggested retail price of more than 1,702 U.S. dollars , almost four times as much as the price in the United States ( 399.99 dollars ) . Global unit sales data from 2014 and 2015 shows that PlayStation 4 was the highest selling platform worldwide in those years .
gold_template: The ranking shows the templateTitle[0] templateTitle[1] templateYLabel[0] of a templateTitleSubject[0] templateTitleDate[0] in selected countries worldwide as of 2014 . templateXValue[0] ranked first with a templateTitle[0] templateTitle[1] templateYLabel[0] of more than templateYValue[max] templateYLabel[1] templateYLabel[2] , almost templateTitleDate[0] times as much as the templateYLabel[0] in the templateXValue[6] templateXValue[18] ( templateYValue[18] templateYLabel[2] ) . Global unit sales data from templateTitleDate[0] and 2015 shows that templateTitleSubject[0] templateTitleDate[0] was the highest selling platform worldwide in those years .

generated_template: 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 billion 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 percent less than in the templateXValue[4] templateXValue[last] .

generated_template: The European Union for installed templateTitleSubject[0] templateTitle[1] in templateTitleDate[0] were templateXValue[0] , the templateXValue[1] , and templateXValue[2] . templateXValue[0] was leading in templateTitleSubject[0] templateTitle[1] installations with a templateYLabel[0] of around templateYValue[max] templateYLabel[1] . The templateXValue[1] , in second place , had a templateYLabel[0] of around templateYValue[1] templateYLabel[1] .
generated: The European Union for installed Manufacturing costs in 2016 were Mexico , the Canada , and Netherlands . Mexico was leading in Manufacturing costs installations with a Manufacturing of around 100 costs . The Canada , in second place , had a Manufacturing of around 88.8 costs .

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 increase to templateYValue[10] templateYLabel[1] templateYLabel[2] templateYLabel[3] . In the following decade templateTitle[4] will likely slow down , with the templateTitle[6] up until templateXValue[max] remaining constant . By the end of the period in consideration , templateTitle[4] will amount to an estimated templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] for templateTitle[2] templateTitle[3] in the templateYLabel[2] from templateXValue[min] to templateXValue[max] . In templateXValue[10] , templateTitle[2] templateTitle[3] sold for an templateYLabel[0] templateYLabel[1] of templateYValue[3] templateYLabel[2] templateYLabel[3] , a templateYLabel[1] increase of 10 percent compared to templateXValue[4] . More statistics and facts on recreational boating
generated: The statistic shows the Volume thousand for poultry meat in the metric from 2015 to 2028 . In 2018 , poultry meat sold for an Volume thousand of 11976 metric tons , a thousand increase of 10 percent compared to 2024 . More statistics and facts on recreational boating

Example 650:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2003', '2013']}
title: Great Britain : Households that use WiFi to access the Internet 2003 to 2013
X_Axis['Year']: ['2013', '2011', '2009', '2007', '2005', '2003']
Y_Axis['Share', 'of', 'respondents']: ['96', '80', '54', '30', '6', '1']

gold: This survey presents the percentage of British households that use WiFi at home to access the Internet from 2003 to 2013 . In 2009 , 54 percent of respondents reported accessing the internet via WiFi , whereas in 2013 the share of respondents increased to 96 percent .
gold_template: This survey presents the percentage of British templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] at home to templateTitle[6] the templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYValue[2] percent of templateYLabel[1] reported accessing the templateTitle[7] via templateTitle[5] , whereas in templateXValue[max] the templateYLabel[0] of templateYLabel[1] increased to templateYValue[max] percent .

generated_template: 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 percent 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 percent 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 templateYLabel[0] of templateYLabel[1] in the templateXValue[26] of America as of templateTitleDate[0] , in the last 30 days templateTitle[5] templateXLabel[0] . As of that year , templateYValue[22] percent of templateTitle[4] in templateXValue[22] consumed more than 4 ( women ) or 5 ( men ) alcoholic beverages on a single occasion within the preceding 30 days .

generated_template: This statistic shows the templateTitle[0] and templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] 100,000 templateYLabel[3] in the templateXValue[23] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[0] of templateXValue[0] had the highest templateYLabel[0] of templateTitle[0] and templateTitle[1] templateTitle[2] with a templateYLabel[0] of templateYValue[max] templateYLabel[1] 100,000 templateYLabel[3] .
generated: This statistic shows the U.S. and binge drinking Percentage binge 100,000 drinkers in the California in 2018 , adults State . In 2018 , the District of Columbia of District of Columbia had the highest Percentage of U.S. and binge drinking with a Percentage of 25.9 binge 100,000 drinkers .

Example 652:
titleEntities: {'Subject': ['Austria'], 'Date': ['2018']}
title: Urbanization in Austria 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['58.3', '58.09', '57.91', '57.72', '57.53', '57.34', '57.15', '57.12', '57.4', '57.68', '57.97']

gold: This statistic shows the degree of urbanization in Austria from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 58.3 percent of Austria 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in 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 .

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] templateYLabel[2] templateYLabel[3] , followed by templateYValue[1] templateYLabel[2] templateYLabel[3] for templateXValue[1] and templateYValue[2] templateYLabel[2] templateYLabel[3] for RB templateXValue[2] .

generated_template: The statistic shows templateYLabel[0] professional templateXLabel[0] templateYLabel[1] templateYLabel[2] templateTitle[5] templateXLabel[1] in templateTitle[7] . templateYLabel[0] templateYLabel[2] paid to templateXValue[0] players amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] for the templateTitle[7] season .
generated: The statistic shows Market professional Club value million clubs Name in 2020 . Market million paid to FC Bayern München players amounted to 933.15 euros for the 2020 season .

Example 654:
titleEntities: {'Subject': ['France'], 'Date': ['2017']}
title: Distribution of women practicing nudism at the beach in France 2017 , by age
X_Axis['Year']: ['18_to_24_years', '25_to_34_years', '35_to_49_years', '50_to_59_years', '60_years_and_older']
Y_Axis['Share', 'of', 'women', 'surveyed']: ['6', '5', '9', '11', '13']

gold: This statistic indicates the share of French women who have already practiced naturism on the beach or in a nudist camp in 2017 , by age group . We can see that more than 10 percent of women aged 50 to 59 had already practiced nudism at the beach or in a naturist camp . Discover also the level of interest of the French for naturism .
gold_template: This statistic indicates the templateYLabel[0] of French templateYLabel[1] who have already practiced naturism on the templateTitle[4] or in a nudist camp in templateTitleDate[0] , templateTitle[7] templateTitle[8] group . We can see that more than 10 percent of templateYLabel[1] aged templateXValue[3] to templateXValue[3] had already practiced templateTitle[3] at the templateTitle[4] or in a naturist camp . Discover also the level of interest of the French for naturism .

generated_template: This statistic shows the templateTitle[3] of the templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[0] to templateTitleDate[1] and visualises the predicted 'ageing templateTitle[6] ' _ . Over the 20 templateXLabel[0] period , the templateYLabel[1] templateYLabel[2] is expected to increase by 1.7 years , the largest increase predicted between templateXValue[2] and templateXValue[1] at 0.8 years .
generated: This statistic shows the nudism of the women surveyed in the France ( France ) from 2017 to 2017 and visualises the predicted 'ageing 2017 ' _ . Over the 20 Year period , the women surveyed is expected to increase by 1.7 years , the largest increase predicted between 35 to 49 years and 25 to 34 years at 0.8 years .

Example 655:
titleEntities: {'Subject': ['Dell'], 'Date': ['1996', '2019']}
title: Dell : Number of employees 1996 to 2019
X_Axis['Year']: ["'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96"]
Y_Axis['Number', 'of', 'employees', 'in', 'thousands']: ['157.0', '145.0', '138.0', '102.0', '98.3', '108.3', '111.3', '109.4', '103.3', '96.0', '78.9', '88.2', '90.5', '65.2', '55.2', '46.0', '39.1', '34.6', '40.0', '36.5', '24.4', '16.2', '10.35', '8.4']

gold: As of early 2019 , Dell 's employee count was 157 thousand . The majority , approximately 145 thousand , of these employees are full-time employees . 37 percent of Dell 's full-time employees are located in the company 's home market , the United States .
gold_template: As of early templateTitleDate[1] , templateTitleSubject[0] 's employee count was templateYValue[max] thousand . The majority , approximately templateYValue[1] thousand , of these templateYLabel[1] are full-time templateYLabel[1] . templateYValue[19] percent of templateTitleSubject[0] 's full-time templateYLabel[1] are located in the company 's home market , the country .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] of people in the templateTitleSubject[0] of America from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , there were templateYValue[19] people employed by the templateTitleSubject[0] .
generated: This statistic shows the Dell Number of people in the Dell of America from 1996 to 2019 . In 2019 , there were 36.5 people employed by the 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[0] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[0] percent .
generated: The statistic shows the Youth unemployment rate in Bhutan from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated Youth unemployment rate in Bhutan was at 9.69 percent .

Example 657:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2017']}
title: Facebook reactions on top shared content 2017
X_Axis['Response']: ['Love', 'Haha', 'Wow', 'Sad', 'Angry']
Y_Axis['Share', 'of', 'reactions']: ['41', '28', '15', '12', '5']

gold: This statistic presents the reaction usage in top shared posts on Facebook in September 2017 . During the measured period , Love was the most popular Facebook reaction on top shared posts on the social network .
gold_template: This statistic presents the reaction usage in templateTitle[2] templateTitle[3] posts on templateTitleSubject[0] in 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: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , the most important templateTitle[1] partner templateTitle[3] templateTitleSubject[0] was templateXValue[0] , accounting templateTitle[3] templateYValue[max] percent of all templateYLabel[2] .
generated: The statistic shows the Facebook reactions top shared Facebook in 2017 . In 2017 , the most important reactions partner shared Facebook was Love , accounting shared 41 percent of all reactions .

Example 658:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Household income of black families in the U.S. 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Median', 'income', 'in', '2018', 'CPI-U-RS', 'adjusted', 'U.S.', 'dollars']: ['41361', '40324', '40339', '38178', '36689', '37232', '35641', '35203', '36195', '37319', '39054', '40196', '38963', '38828', '39151', '39607', '39661', '40902', '42348', '41192', '38212', '38269', '36649', '35880', '34503', '32721', '32210', '33103', '34068']

gold: This statistic shows the household income of black families in the United States from 1990 to 2018 . The median income in 2018 was at 41,361 U.S. dollars for black households .
gold_template: This statistic shows the templateTitle[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] in templateXValue[max] was at templateYValue[0] templateYLabel[5] templateYLabel[6] for templateTitle[2] households .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[0] was templateYValue[min] out of every 1,000 templateYLabel[6] . This is a significant decrease from templateXValue[min] , when templateYLabel[0] templateYLabel[1] was at templateYValue[max] 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 2018 in the Household was 32210 out of every 1,000 dollars . This is a significant decrease from 1990 , when Median income was at 42348 deaths out of every 1,000s dollars . What is Median income ? The Median income 2018 is the number of deaths of 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateXValue[1] and templateXValue[2] rounded off the templateTitle[1] three templateTitle[2] templateTitle[3] .

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] templateYLabel[1] 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 Sales men 's leading toy companies in the worldwide in 2013 . In that year , Mattel was the Sales men 's leading toy Company in the worldwide with Sales of about 6300 million U.S. dollars . Additional information on the leading toy market leading toy is used by men in order to facilitate leading , creating a more comfortable process and a better finish .

Example 660:
titleEntities: {'Subject': ['Denver Broncos', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Denver Broncos ( NFL ) 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['3000', '2650', '2600', '2400', '1940', '1450', '1161', '1132', '1046', '1049', '1081', '1061', '994', '975', '907', '815', '683', '604']

gold: This graph depicts the franchise value of the Denver Broncos from the National Football League from 2002 to 2019 . In 2019 , the franchise value came to three billion U.S. dollars . The Denver Broncos are owned by the Pat Bowlen Trust , who bought the franchise for 78 million U.S. dollars in 1984 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by the Pat Bowlen Trust , who bought the templateYLabel[0] for 78 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1984 .

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] Giants templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Rays are owned by Stuart Sternberg , who bought the templateYLabel[0] for 200 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[15] .
generated: This graph depicts the value of the Denver Broncos Giants Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 3000 million U.S. dollars . The Denver Broncos Rays are owned by Stuart Sternberg , who bought the Franchise for 200 million U.S. dollars in 2004 .

Example 661:
titleEntities: {'Subject': ['Aramark'], 'Date': ['2008', '2019']}
title: Facilities management industry - Aramark worldwide revenue 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['16227.3', '15789.6', '14604.4', '14415.8', '14329.1', '14832.9', '13945.7', '13505.4', '13082.4', '12419.1', '12138.1', '13252.1']

gold: This statistic shows the annual total worldwide revenue of Aramark from 2008 to 2019 . In 2019 , Aramark had total revenues of over 16.2 billion U.S. dollars . The Aramark Corporation is an American foodservice , facilities , and clothing provider headquartered in Philadelphia , Pennsylvania .
gold_template: This statistic shows the annual total templateTitle[4] templateYLabel[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] had total revenues of over templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . The templateTitleSubject[0] Corporation is an American foodservice , templateTitle[0] , and clothing provider headquartered in Philadelphia , Pennsylvania .

generated_template: 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 templateYLabel[1] templateYLabel[2] templateYLabel[3] . The coupon portal had close to 43.6 templateYLabel[1] 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] for only the second time in the past 15 years . The company , previously known as Research in Motion ( RIM ) , has transitioned from developing BlackBerry brand smartphones to enterprise software and services involving the internet of things . Transitioning to the Enterprise of Things In the past decade , BlackBerry has seen a significant decline in their templateYLabel[0] figures and share of the global and templateYLabel[2] smartphone market .

generated_template: This statistic represents templateTitleSubject[0] 's , formerly known as GSI Commerce , templateTitle[2] templateTitle[3] templateYLabel[0] from templateXValue[min] to templateXValue[max] , in templateYLabel[1] templateYLabel[2] templateYLabel[3] . In templateXValue[11] , GSI Commerce reported a templateTitle[3] templateYLabel[0] of templateYValue[max] templateYLabel[1] 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] percent 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] percent 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 percent 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[max] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[2] templateYLabel[1] in short-stay templateTitle[3] in templateTitleSubject[0] have generally increased over this period , from around templateYValue[9] templateYLabel[2] in templateXValue[min] to approximately templateYValue[max] templateYLabel[2] by templateXValue[max] .
generated: This statistic shows the Number of tourist arrivals in Bulgaria from 2006 to 2018 . tourist arrivals in short-stay accommodation in Bulgaria have generally increased over this period , from around 4.3 millions in 2006 to approximately 7.8 millions by 2018 .

Example 665:
titleEntities: {'Subject': ['Florida'], 'Date': ['2000', '2018']}
title: Per capita real GDP of Florida 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'real', 'GDP', 'in', 'chained', '2012', 'U.S.', 'dollars']: ['43423', '42719', '42013', '41491', '40547', '40080', '39806', '40001', '40656', '40652', '43353', '45507', '45926', '45193', '43471', '42074', '41062', '40267', '40049']

gold: This statistic shows the per capita real Gross Domestic Product of Florida from 2000 to 2018 . In 2018 , the per capita real GDP of Florida stood at 43,423 chained 2012 U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[0] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[max] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .
generated: This statistic shows the Per capita real Gross Domestic Product of Florida from 2000 to 2018 . In 2018 , the Per capita real GDP of Florida stood at 45926 chained 2012 U.S. dollars .

Example 666:
titleEntities: {'Subject': ['Scotland'], 'Date': ['2014', '2039']}
title: Scotland : forecasted median age of population 2014 to 2039
X_Axis['Year']: ['2039', '2034', '2029', '2024', '2019', '2014']
Y_Axis['Age']: ['45.2', '44.3', '43.5', '42.9', '42.4', '41.9']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , according to the templateYLabel[0] Finance valuation methodology . The ranking , provided by The Banker Magazine , is an independent , publicly reported measure of the strength of a templateYLabel[0] and its impact across all business lines and customer groups . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was valued at templateYValue[5] templateYLabel[2] templateYLabel[3] templateYLabel[4] and the bank was ranked third in its peer group .
generated: The statistic shows the Age of Scotland from 2014 to 2039 , according to the Age Finance valuation methodology . The ranking , provided by The Banker Magazine , is an independent , publicly reported measure of the strength of a Age and its impact across all business lines and customer groups . In 2039 , the Age of Scotland was valued at 41.9 Age and the bank was ranked third in its peer group .

Example 667:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: U.S. company data loss prevention methods 2017
X_Axis['Response']: ['Training_and_awareness_programs', 'Expanded_use_of_encryption', 'Endpoint_security_solutions', 'Identity_and_access_management_solutions', 'Additional_manual_procedures_and_controls', 'Data_loss_prevention_(DLP)_solutions', 'Security_intelligence_solutions', 'Other_system_control_practices', 'Security_certification_or_audit', 'Strenghtening_of_perimeter_controls']
Y_Axis['Share', 'of', 'respondents']: ['60', '55', '49', '44', '39', '36', '35', '26', '19', '16']

gold: This statistic presents a ranking of common data loss prevention controls and activities of organizations in the United States in 2017 . During the survey period , it was found that 35 percent of U.S. companies had implemented security intelligence solutions .
gold_template: This statistic presents a ranking of common templateXValue[5] prevention templateXValue[4] and activities of organizations in the templateTitle[0] in templateTitleDate[0] . During the survey period , it was found that templateYValue[6] percent of templateTitleSubject[0] companies had implemented templateXValue[2] templateXValue[6] templateXValue[2] .

generated_template: This statistic shows , 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] percent 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 percent 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] percent of surveyed bank templateYLabel[1] in the templateXValue[0] indicated high levels of templateTitle[8] , ranking the templateXLabel[0] highest among templateTitleSubject[0] templateTitle[6] locations in templateTitleDate[0] . This was followed templateTitle[4] the templateXValue[1] , templateYLabel[2] templateYValue[1] percent of bank templateYLabel[1] templateYLabel[2] a templateYLabel[3] templateYLabel[4] throughout the year .

generated_template: This statistic shows the an estimate of templateTitle[1] templateYLabel[0] worldwide , from the 2017 fiscal year to fiscal year 2021 , templateTitle[3] select templateXLabel[0] . The templateXValue[0] is projected to spend about templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] on drones between 2017 and 2021 , making it the templateXLabel[0] with the greatest expenditure on drones .
generated: This statistic shows the an estimate of European Share worldwide , from the 2017 fiscal year to fiscal year 2021 , ranked select Country . The Netherlands is projected to spend about 70.6 customers positive experience on drones between 2017 and 2021 , making it the Country with the greatest expenditure on drones .

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

generated_template: This statistic shows the year-over-year growth of templateTitle[3] templateYLabel[2] templateYLabel[3] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[3] templateYLabel[2] templateYLabel[3] were forecast to grow by approximately templateYValue[0] percent compared to the previous templateXLabel[0] . templateTitle[3] Season in the templateTitle[6] The templateTitle[3] season is just around the corner and it 's truly the best time of the templateXLabel[0] for both consumers and retailers of all shapes and sizes .
generated: This statistic shows the year-over-year growth of U.S. dollars in the services from 2000 to 2019 . In 2019 , U.S. dollars were forecast to grow by approximately 3.12 percent compared to the previous Year . U.S. Season in the services The U.S. season is just around the corner and it 's truly the best time of the Year for both consumers and retailers of all shapes and sizes .

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 graph shows templateXLabel[0] and templateTitle[1] government templateYLabel[0] in the templateTitle[3] as a templateYLabel[1] of Gross Domestic Product for the templateTitleDate[0] fiscal year , templateTitle[7] templateXLabel[0] . In templateTitleDate[0] , total templateXLabel[0] and templateTitle[1] government templateYLabel[0] in the templateXLabel[0] of templateXValue[1] amounted to templateYValue[1] percent of the annual Gross Domestic Product of the templateXLabel[0] . The national templateYLabel[0] of the United Stated can be found here .
generated: This graph shows State and number government Number in the by as a murder of Gross Domestic Product for the 2018 fiscal year , 2018 State . In 2018 , total State and number government Number in the State of Texas amounted to 1322 percent of the annual Gross Domestic Product of the State . The national Number of the United Stated can be found here .

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] percent 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 percent 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 million people .

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

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 the templateYLabel[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's templateYLabel[0] of templateTitleSubject[0] amounted to about templateYValue[min] percent of the templateTitle[4] templateTitle[5] templateTitle[6] .
generated: The statistic shows the Price of Global from 2013 to 2018 in price to the index 2013 2030 ( real ) , with projections up until 2030 . In 2018 , Global 's Price of Global amounted to about 55.1 percent of the index 2013 2030 .

Example 674:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Most used paint brands in the U.S. 2018
X_Axis['Brand']: ['Sherwin-Williams', 'Benjamin_Moore', 'Behr_Paint_Cooperation', 'Kelly_Moore', 'Valspar', 'PPG_Pittsburgh_Paints', 'Zar_(United_Gilsonite_Labs)', 'Devoe_&_Raynolds', 'Dutch_Boy', 'Olympic', 'None_of_these']
Y_Axis['Share', 'of', 'respondents']: ['49.5', '22.4', '12.1', '2.8', '1.9', '1.9', '1.9', '0.9', '0.9', '0.9', '4.7']

gold: This statistic depicts paints used the most by U.S. construction firms in 2018 . The survey revealed that 49.5 percent of the respondents used Sherwin-Williams brand paints the most .
gold_template: This statistic depicts templateXValue[5] templateTitle[1] the templateTitle[0] by templateTitleSubject[0] construction firms in templateTitleDate[0] . The survey revealed that templateYValue[max] percent of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] templateXValue[5] the templateTitle[0] .

generated_template: This statistic depicts templateTitle[2] templateTitle[3] templateTitle[1] the templateTitle[0] by templateTitleSubject[0] construction firms in templateTitleDate[0] . The survey revealed that templateYValue[max] percent of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] 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 percent 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] templateYLabel[0] templateYLabel[1] increase while templateXValue[4] 's templateXValue[4] Rocket templateXValue[1] experienced a templateYValue[max] templateYLabel[0] increase compared to templateXValue[1] year .

generated_template: 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[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateTitle[1] templateTitle[2] is a type of inflammatory bowel disease ( IBD ) which is characterized by the formation of ulcers in the colon .

generated_template: The statistic shows a growth forecast for the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] in templateXValue[max] and shows the actual projected templateYLabel[0] value of the templateTitle[1] templateTitle[2] templateYLabel[0] in templateXValue[min] . For templateXValue[max] , the templateTitleSubject[0] smart templateTitle[1] templateTitle[2] templateYLabel[0] is forecasted to reach templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The predicted compound annual growth rate between templateXValue[min] and templateXValue[max] should be 26.3 percent .
generated: The statistic shows a growth forecast for the Global ulcerative colitis Market in 2022 and shows the actual projected Market value of the ulcerative colitis Market in 2012 . For 2022 , the Global smart ulcerative colitis Market is forecasted to reach 6.6 billion U.S. dollars . The predicted compound annual growth rate between 2012 and 2022 should be 26.3 percent .

Example 677:
titleEntities: {'Subject': ['Estonia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Estonia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.8', '2.8', '2.8', '2.8', '2.9', '3.2', '4.76', '5.75', '2.63', '1.85', '2.99']

gold: The statistic shows the growth in real GDP in Estonia from 2014 to 2018 , with projections up until 2024 . In 2018 , Estonia 's real gross domestic product grew by around 4.76 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Estonia from 2014 to 2018 , with projections up until 2024 . In 2018 , Estonia 's real Gross domestic product grew by around 4.76 percent compared to the previous Year .

Example 678:
titleEntities: {'Subject': ['Northern Ireland', 'UK'], 'Date': ['2013', '2019']}
title: Youth unemployment rate in Northern Ireland ( UK ) 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Unemployment', 'rate']: ['7.6', '8.4', '12.6', '13.9', '19.5', '19.4', '20.4']

gold: This statistic shows the unemployment rate of young people ( aged 18 to 24 ) in Northern Ireland from 2013 to 2019 . At the start of this period the youth unemployment rate stood at over 20 percent , but by 2019 this had decreased to 7.6 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of young people ( aged 18 to 24 ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . At the start of this period the templateTitle[0] templateYLabel[0] templateYLabel[1] stood at over templateYValue[4] percent , but by templateXValue[max] this had decreased to templateYValue[min] percent .

generated_template: 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] percent .
generated: This statistic shows the Unemployment rate in Northern Ireland from 2013 to 2019 . In 2019 , the Unemployment rate in Northern Ireland was at 7.6 percent .

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[13] templateYLabel[4] 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] templateTitle[1] templateTitle[2] of the templateTitleSubject[0] ( templateTitleSubject[0] ) templateYLabel[1] templateYLabel[2] from templateTitleDate[0] to templateTitleDate[1] . The templateTitle[0] templateTitle[1] templateTitle[2] had more than templateYValue[max] templateYLabel[3] templateYLabel[2] in 2017 .
generated: This statistic shows the Viki number monthly of the Viki ( Viki ) monthly active from 2012 to 2015 . The Viki number monthly had more than 40.1 users active in 2017 .

Example 680:
titleEntities: {'Subject': ['Citigroup'], 'Date': ['2011', '2018']}
title: Total direct staff of Citigroup 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Number', 'of', 'direct', 'staff', 'in', 'thousands']: ['204', '209', '219', '231', '241', '251', '259', '266']

gold: This statistic shows the number of direct employees of Citigroup from 2011 to 2018 . In 2018 , the direct staff of Citigroup amounted to approximately 204,000 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] employees of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] amounted to approximately templateYValue[min] .

generated_template: The templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( templateTitleSubject[1] ) of templateTitleSubject[0] templateTitle[1] customers in templateTitleSubject[2] has fallen steadily since templateXValue[min] , reaching an estimated 14.8 templateYLabel[4] templateYLabel[2] templateYLabel[6] in templateXValue[max] . This is a decrease of 22.9 percent on the templateTitleSubject[1] generated in templateXValue[min] . Shrinking need for templateTitleSubject[0] templateTitle[1] The decline of templateTitleSubject[0] templateTitle[1] , as demonstrated by the falling templateTitleSubject[1] and the diminishing penetration rate in countries such as the United Kingdom ( UK ) , can partially be attributed to the increased speeds and reliability that comes with 3G and 4G templateTitleSubject[0] networks .
generated: The Number direct staff thousands ( Citigroup ) of Citigroup direct customers in Citigroup has fallen steadily since 2011 , reaching an estimated 14.8 thousands staff thousands in 2018 . This is a decrease of 22.9 percent on the Citigroup generated in 2011 . Shrinking need for Citigroup direct The decline of Citigroup direct , as demonstrated by the falling Citigroup and the diminishing penetration rate in countries such as the United Kingdom ( UK ) , can partially be attributed to the increased speeds and reliability that comes with 3G and 4G Citigroup networks .

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

generated_template: The templateYLabel[0] initial public offering ( templateTitleSubject[0] ) in the templateTitle[4] was templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[max] . This figure gives an idea of how willing speculators in the templateTitle[4] are to invest in a company going public , which is the process of being listed on a stock exchange for the first time . Who goes public ? Most IPOs come from relatively new firms that have grown quickly .
generated: The Gross initial public offering ( Mattel ) in the Mattel was 3478.88 U.S. dollars in 2019 . This figure gives an idea of how willing speculators in the Mattel are to invest in a company going public , which is the process of being listed on a stock exchange for the first time . Who goes public ? Most IPOs come from relatively new firms that have grown quickly .

Example 682:
titleEntities: {'Subject': ['March'], 'Date': ['2', '2020']}
title: COVID-19 cases worldwide as of March 2 , 2020 , by country
X_Axis['Country']: ['Total_(worldwide)', 'China', 'Republic_of_Korea', 'Italy', 'Iran_(Islamic_Republic_of)', 'Cases_on_an_international_conveyance_(Japan)', 'Japan', 'Germany', 'Singapore', 'France', 'United_States_of_America', 'Kuwait', 'Bahrain', 'Spain', 'Thailand', 'United_Kingdom', 'Australia', 'Malaysia', 'Switzerland', 'United_Arab_Emirates', 'Norway', 'Iraq', 'Canada', 'Viet_Nam', 'Sweden', 'Netherlands', 'Lebanon', 'Austria', 'Israel', 'Croatia', 'Greece', 'Oman', 'Finland', 'Mexico', 'Pakistan', 'Denmark', 'India', 'Czechia', 'Romania', 'Georgia', 'Philippines', 'Azerbaijan', 'Qatar', 'Indonesia', 'Iceland', 'Egypt', 'Brazil', 'Russian_Federation', 'Armenia', 'Ecuador', 'Dominican_Republic', 'Estonia', 'Ireland', 'Lithuania', 'Luxembourg', 'Monaco', 'Algeria', 'New_Zealand', 'Cambodia', 'North_Macedonia', 'San_Marino', 'Nepal', 'Sri_Lanka', 'Afghanistan', 'Nigeria', 'Belarus', 'Belgium']
Y_Axis['Number', 'of', 'cases']: ['88948', '80174', '4212', '1689', '978', '706', '254', '129', '106', '100', '62', '56', '47', '45', '42', '36', '27', '24', '24', '21', '19', '19', '19', '16', '14', '13', '10', '10', '7', '7', '7', '6', '6', '5', '4', '4', '3', '3', '3', '3', '3', '3', '3', '2', '2', '2', '2', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1']

gold: As of March 2 , 2020 , the outbreak of the coronavirus disease ( COVID-19 ) had been confirmed in 65 countries , with the overwhelming majority of cases reported in China . The virus had infected 88,948 people worldwide , and the number of deaths had totaled 3,043 . The most severely affected countries outside of China were the Republic of Korea and Italy .
gold_template: As of templateTitleSubject[0] templateYValue[43] , templateTitle[5] , the outbreak of the coronavirus disease ( templateTitle[0] ) had been confirmed in 65 countries , with the overwhelming majority of templateXValue[5] reported in templateXValue[1] . The virus had infected templateYValue[max] people templateTitle[2] , and the templateYLabel[0] of deaths had totaled 3,043 . The most severely affected countries outside of templateXValue[1] were the templateXValue[2] of templateXValue[2] and templateXValue[3] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in the templateTitle[1] in templateTitleDate[0] , templateTitle[3] templateXLabel[0] . In templateTitleDate[0] , there were a total of templateYValue[max] templateYLabel[1] in the templateTitle[1] , compared to templateYValue[min] templateYLabel[1] templateYLabel[2] in templateXValue[1] .
generated: This statistic shows the Number of cases in the cases in 2 , March Country . In 2 , there were a total of 88948 cases in the cases , compared to 1 cases in China .

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[0] templateTitleSubject[0] Navy members in the templateTitle[2] Department of Defense . In templateXValue[14] , there were templateYValue[14] templateTitleSubject[0] members .

generated_template: The templateYLabel[0] of the men 's lifestyle and entertainment magazine templateTitleSubject[0] has dropped significantly in recent years , falling to just over 200 thousand in templateXValue[max] from templateYValue[4] templateYLabel[1] templateYValue[11] years earlier . The magazine reported its highest templateTitle[1] templateYLabel[0] in templateXValue[12] , when an average issue sold templateYValue[max] templateYLabel[1] copies . templateTitleSubject[0] templateYLabel[0] numbers – additional information Founded in 1953 by the late Hugh Hefner , templateTitleSubject[0] is a men 's lifestyle and entertainment magazine .
generated: The Number of the men 's lifestyle and entertainment magazine Active Duty Navy has dropped significantly in recent years , falling to just over 200 thousand in 2018 from 321599 Navy 332269 years earlier . The magazine reported its highest Duty Number in 2006 , when an average issue sold 429630 Navy copies . Active Duty Navy Number numbers – additional information Founded in 1953 by the late Hugh Hefner , Active Duty Navy is a men 's lifestyle and entertainment magazine .

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: The templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] reached its highest in templateXValue[max] , amounting to approximately 1.26 thousand templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] . The South Asian country was the tenth most densely populated countries in the world that templateXLabel[0] . Within the Asia Pacific region , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] was only exceeded by the Maldives , Hong Kong , Singapore and Macao .
generated: The Price per in United Kingdom reached its highest in 2015 , amounting to approximately 1.26 thousand tonne GBP . The South Asian country was the tenth most densely populated countries in the world that Year . Within the Asia Pacific region , United Kingdom 's Price per was only exceeded by the Maldives , Hong Kong , Singapore and Macao .

Example 685:
titleEntities: {'Subject': ['England'], 'Date': ['2019']}
title: Share of the population who gave to charity in England 2019 , by age
X_Axis['Year']: ['16_to_24', '25_to_34', '35_to_49', '50_to_64', '65_to_74', '75_and_over']
Y_Axis['Share', 'of', 'respondents']: ['59', '69', '76', '79', '82', '83']

gold: This statistic shows the share of the population who said they gave to charity in the last four weeks in 2018/19 , by age group . Proportionally , those aged 75 and more gave most to charity . At 59 percent , 16 to 24 year olds had the smallest proportion of charitable givers .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] said they templateTitle[3] to templateTitle[4] in the last four weeks in 2018/19 , templateTitle[7] templateTitle[8] group . Proportionally , those aged templateXValue[last] and more templateTitle[3] most to templateTitle[4] . At templateYValue[min] percent , templateXValue[0] to templateXValue[0] templateXLabel[0] olds had the smallest proportion of charitable givers .

generated_template: 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 percent 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 percent 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: There were templateYValue[min] templateTitle[1] templateTitle[2] recorded in templateTitleSubject[0] in templateXValue[max] . templateTitleSubject[0] had been able to decrease the templateYLabel[0] of people fatally injured on its roads by nearly half since templateXValue[min] . templateXValue[7] and templateXValue[3] were the only years in which the templateYLabel[0] of fatal accidents increased .
generated: There were 1818 road deaths recorded in Romania in 2018 . Romania had been able to decrease the Number of people fatally injured on its roads by nearly half since 2006 . 2011 and 2015 were the only years in which the Number of fatal accidents increased .

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] templateYLabel[2] units sold in templateXValue[max] . Meanwhile , revenue generated amounted to 155 templateYLabel[2] euros in the same templateXLabel[0] , a decrease on the one before . Storage media templateTitleSubject[0] templateTitle[3] templateTitle[4] revolutionized data storage for computer users everywhere when they emerged on the market , joining such well-known storage media as floppy discs , hard templateTitle[4] and optical storage units like CD-R and CD-RW discs .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] reached its lowest level since templateXValue[min] . templateTitleSubject[0] 's templateYLabel[1] templateYLabel[2] was forecasted to reach templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] , as compared to the previous templateXLabel[0] . The base rate in the templateTitle[4] refers to grow in the general decades .
generated: In 2018 , the Sales volume millions in USB reached its lowest level since 2004 . USB 's volume millions was forecasted to reach 16.17 millions , as compared to the previous Year . The base rate in the drives refers to grow in the general decades .

Example 688:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2009', '2018']}
title: Working age population in Vietnam 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Working', 'age', 'population', 'in', 'millions']: ['72.59', '71.89', '70.94', '69.74', '69.34', '68.69', '68.19', '67.38', '65.71', '64.44']

gold: In 2018 , the working age population in Vietnam amounted to approximately 72.59 million people . In that year , the labor participation rate was at 76.7 percent while the employment rate was at 75.2 percent .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to approximately templateYValue[max] templateYLabel[3] people . In that templateXLabel[0] , the labor participation rate was at 76.7 percent while the employment rate was at 75.2 percent .

generated_template: The templateTitleSubject[0] is growing in every aspect . Over the last decade , the total templateYLabel[0] templateTitle[7] of templateTitle[3] in the templateTitleSubject[0] have more than quadrupled . In templateXValue[max] they amounted to approximately templateYValue[max] templateYLabel[3] templateYLabel[4] .
generated: The Vietnam is growing in every aspect . Over the last decade , the total Working 2018 of Vietnam in the Vietnam have more than quadrupled . In 2018 they amounted to approximately 72.59 millions .

Example 689:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Most followed sports leagues in the U.S. 2019
X_Axis['Response']: ['NFL', 'MLB', 'NBA', 'NHL', 'MLS', "I_don't_follow_any_of_these_leagues"]
Y_Axis['Share', 'of', 'respondents']: ['33', '16', '10', '5', '3', '32']

gold: There are widely considered to be four major professional men 's sports leagues in the United States and Canada - NFL , NBA , MLB , and NHL . The professional soccer league ( MLS ) has also achieved some popularity in the United States in recent years . During a 2019 survey , 33 percent of respondents stated that the National Football League , NFL , was their favorite men 's U.S. professional sports league to follow .
gold_template: There are widely considered to be four major professional men 's templateTitle[2] templateXValue[last] in the templateTitle[4] and Canada - templateXValue[0] , templateXValue[2] , templateXValue[1] , and templateXValue[3] . The professional soccer league ( templateXValue[4] ) has also achieved some popularity in the templateTitle[4] in recent years . During a templateTitleDate[0] survey , templateYValue[max] percent of templateYLabel[1] stated that the National Football League , templateXValue[0] , was their favorite men 's templateTitleSubject[0] professional templateTitle[2] league to templateXValue[last] .

generated_template: The statistic shows the templateTitle[0] of templateTitle[1] to Disney 's new online video streaming service ( templateTitleSubject[0] ) among adults in the templateTitle[3] as of 2019 . The data reveals templateXValue[2] just templateYValue[min] percent of surveyed templateTitleSubject[0] adults said templateXValue[2] they were templateXValue[0] to sign up to templateTitleSubject[0] upon its launch in templateTitleDate[0] , and templateYValue[max] percent said templateXValue[2] they were templateXValue[2] at templateXValue[3] templateXValue[0] to subscribe to the streaming service .
generated: The statistic shows the Most of followed to Disney 's new online video streaming service ( U.S. ) among adults in the leagues as of 2019 . The data reveals NBA just 3 percent of surveyed U.S. adults said NBA they were NFL to sign up to U.S. upon its launch in 2019 , and 33 percent said NBA they were NBA at NHL NFL to subscribe to the streaming service .

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] percent 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] . During that period of time , templateYValue[max] percent of templateXValue[0] respondents stated that they 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 . During that period of time , 41 percent of Under $30000 respondents stated that they used the social networking site .

Example 691:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2024']}
title: Total population of South Korea 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['52.91', '52.69', '52.48', '52.27', '52.06', '51.85', '51.64', '51.43', '51.22', '51.02', '50.75']

gold: The statistic shows the total population of South Korea from 2014 to 2018 , with projections up until 2024 . In 2018 , the total population of South Korea was about 51.64 million people . Population of South Korea South Korea , also called Republic of Korea , has one of the highest population densities worldwide , i.e .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was about templateYValue[6] templateYLabel[1] people . templateTitle[1] of templateTitleSubject[0] South templateTitleSubject[0] , also called Republic of templateTitleSubject[0] , has one of the highest templateTitle[1] densities worldwide , i.e .

generated_template: 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 estimated at almost templateYValue[6] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] has a surprisingly low ( and decreasing ) templateTitle[1] growth rate ; despite it being home to the largest number of Catholics in the world , the majority of women in templateTitleSubject[0] use some form of contraception , which is often government-subsidized or free , even though the Catholic Church retains its stance that the use of contraceptives is inherently wrong .
generated: This 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 estimated at almost 51.64 millions Inhabitants . population of South Korea has a surprisingly low ( and decreasing ) population growth rate ; despite it being home to the largest number of Catholics in the world , the majority of women in South Korea use some form of contraception , which is often government-subsidized or free , even though the Catholic Church retains its stance that the use of contraceptives is inherently wrong .

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[0] to the third templateXLabel[0] templateTitleDate[1] . The templateYLabel[0] cap of top templateTitle[3] global templateTitle[4] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] in the third templateXLabel[0] of templateTitleDate[1] .

generated_template: The templateYLabel[0] of solar templateTitle[3] templateTitle[4] in the templateTitle[0] has seen a fairly consistent decrease over the last few years . In templateXValue[0] templateTitleDate[1] , module templateTitle[2] averaged templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[5] , in comparison to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[5] in templateXValue[2] templateTitleDate[0] . Solar system pricing has fallen in all markets , including residential , non-residential , and utility markets .
generated: The Market of solar 100 banks in the Market has seen a fairly consistent decrease over the last few years . In Q3 '19 2019 , module leading averaged 4.1 capitalization trillion Euros , in comparison to 5.6 capitalization trillion Euros in Q1 '19 2016 . Solar system pricing has fallen in all markets , including residential , non-residential , and utility markets .

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 templateXValue[0] templateYValue[max] percent of the templateYLabel[1] answered that their family eat templateTitle[4] templateTitle[5] at templateTitle[6] on templateXValue[last] to templateXValue[last] templateXValue[0] a week .

generated_template: The statistic illustrates the answers to the following survey question : `` The templateTitle[2] templateXValue[0] templateTitle[5] will probably cost a thousand euros . templateXValue[last] you willing to pay that ? '' As of templateTitleDate[0] , roughly 20 percent of the templateYLabel[1] said to templateXValue[0] the templateXValue[0] from templateTitleSubject[0] when it is released , even if it templateTitle[6] them a thousand euros . However , more than half of the templateYLabel[1] said the price is templateXValue[1] absurd for an templateTitleSubject[0] templateXValue[0] .
generated: The statistic illustrates the answers to the following survey question : `` The families 0 to 3 nights together will probably cost a thousand euros . 6 to 7 nights you willing to pay that ? '' As of 2013 , roughly 20 percent of the respondents said to 0 to 3 nights the 0 to 3 nights from American when it is released , even if it home them a thousand euros . However , more than half of the respondents said the price is 4 to 5 nights absurd for an American 0 to 3 nights .

Example 694:
titleEntities: {'Subject': ['Iran'], 'Date': ['2024']}
title: Iran 's national debt in relation to gross domestic product 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Share', 'in', 'GDP']: ['30.26', '29.18', '28.24', '28.06', '28.79', '30.67', '32.18', '39.53', '47.47', '38.42', '11.82']

gold: This statistic shows the national debt of Iran in relation to gross domestic product ( GDP ) from 2014 to 2018 , with projections up until 2024 . In 2018 , Iran 's national debt amounted to 32.18 percent of gross domestic product .
gold_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitleSubject[0] in templateTitle[4] to templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[1] ) from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] amounted to templateYValue[6] percent of templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the country from templateXValue[min] to templateXValue[6] in templateYLabel[2] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[3] ) , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of the country was at around templateYValue[min] percent of the templateTitle[4] templateTitle[5] templateTitle[6] . See the templateTitleSubject[0] templateYLabel[3] for further information .
generated: The statistic shows the Share GDP of the country from 2014 to 2018 in GDP to the relation gross domestic ( GDP ) , with projections up until 2024 . In 2018 , the Share GDP of the country was at around 11.82 percent of the relation gross domestic . See the Iran GDP for further information .

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[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: templateTitle[4] occurs when a number of private individuals each pays a small templateYLabel[0] of money to support a project . As of templateXValue[max] , this process had templateTitle[2] templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[5] . How does templateTitle[4] work ? There are three main types of templateTitle[4] : peer-to-peer lending , rewards and donation templateTitle[4] , and equity templateTitle[4] .
generated: GDP occurs when a number of private individuals each pays a small GDP of money to support a project . As of 2026 , this process had contribution 20.9 contribution billion U.S. UAE . How does GDP work ? There are three main types of GDP : peer-to-peer lending , rewards and donation GDP , and equity GDP .

Example 696:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2018']}
title: Participants in hunting in the U.S. from 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'participants', 'in', 'millions']: ['15.69', '15.63', '15.47', '15.53', '14.85', '13.53', '14.71', '14.89', '14.01', '15.27', '13.98', '14.14', '15.1']

gold: This statistic shows the number of participants in hunting in the United States from 2006 to 2018 . In 2018 , the number of participants ( aged six years and older ) in hunting amounted to approximately 15.69 million .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] in the templateTitle[2] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] amounted to approximately templateYValue[max] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] 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[0] templateYLabel[2] .
generated: This statistic shows the Number of participants in hunting in 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 decrease during this period , the templateTitle[1] volumes were still over 400 templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[max] . The region which generated the largest volume of residual templateTitle[2] templateYLabel[1] templateTitle[1] was the North East of templateTitleSubject[0] , where an average of 601 templateYLabel[0] of templateTitle[2] was generated templateYLabel[1] templateYLabel[2] in 2017/2018 .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateYLabel[0] in crop years templateXValue[min] to templateXValue[max] . In crop templateXLabel[0] templateXValue[max] , there were around templateYValue[max] templateYLabel[1] templateYLabel[2] of rice-cultivated area worldwide . In 2016/2017 , India was estimated to be the leading global producer of templateTitle[1] and to harvest about 44.5 templateYLabel[1] templateYLabel[2] of templateTitle[1] .
generated: This statistic shows the England household Kilograms in crop years 2010 to 2017 . In crop Year 2017 , there were around 425 per person of rice-cultivated area worldwide . In 2016/2017 , India was estimated to be the leading global producer of household and to harvest about 44.5 per person of household .

Example 698:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Preferred modes of transportation when taking a family vacation in the U.S. 2015
X_Axis['Preferred', 'mode', 'of', 'travel']: ['Car', 'Plane', 'RV', 'Train', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['63', '31', '3', '2', '1']

gold: The statistic shows the preferred ways to travel when taking a family vacation in the United States in 2015 . The survey revealed that 63 percent of respondents prefer to travel by car .
gold_template: The statistic shows the templateXLabel[0] ways to templateXLabel[2] templateTitle[3] templateTitle[4] a templateTitle[5] templateTitle[6] in the templateTitle[7] in templateTitleDate[0] . The survey revealed that templateYValue[max] percent of templateYLabel[1] prefer to templateXLabel[2] by templateXValue[0] .

generated_template: 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] percent 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 percent 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] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . After a severe drop below templateYValue[9] percent in templateXValue[9] , templateTitleSubject[0] 's templateYLabel[0] seems to have stabilized again and is expected to level off at around templateYValue[0] percent in the next few years . templateTitleSubject[0] 's economic struggles Around templateXValue[12] , templateTitleSubject[0] suffered the consequences of the global economic crisis and domestic economic mismanagement , which saw enterprises going bankrupt , templateYLabel[0] peaking at over templateYValue[12] percent , and gross domestic product slumping to a dramatic low .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Vietnam from 1984 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Vietnam amounted to about 3.54 percent compared to the previous Year .

Example 700:
titleEntities: {'Subject': ['Global'], 'Date': ['2016', '2022']}
title: Global smart augmented reality glasses revenue 2016 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017', '2016']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['19718.88', '15686.56', '10936.67', '7275.43', '4421.74', '409.67', '138.61']

gold: The statistic shows smart AR glasses revenue worldwide from 2016 to 2022 . Smart augmented reality glasses revenue reached 138.6 million U.S. dollars in 2016 and is forecast to amount to around 19.7 billion U.S. dollars by 2022 .
gold_template: The statistic shows templateTitle[1] AR templateTitle[4] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] reached templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[min] and is forecast to amount to around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] by templateXValue[max] .

generated_template: This statistic represents templateYLabel[2] templateYLabel[0] generated with online apparel and templateTitle[2] retail sales between templateXValue[min] and templateXValue[max] . In templateXValue[5] , retail e-commerce revenues from apparel and templateTitle[2] sales amounted to templateYValue[5] templateYLabel[1] templateYLabel[2] templateYLabel[3] and are projected to increase to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . E-commerce templateYLabel[0] from apparel in the templateYLabel[2] – additional information With retail e-commerce sales worldwide expected to reach just over four templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[3] , the global e-commerce industry appears to show no signs of slowing down .
generated: This statistic represents U.S. Revenue generated with online apparel and augmented retail sales between 2016 and 2022 . In 2017 , retail e-commerce revenues from apparel and augmented sales amounted to 409.67 million U.S. dollars and are projected to increase to 19718.88 million U.S. dollars in 2022 . E-commerce Revenue from apparel in the U.S. – additional information With retail e-commerce sales worldwide expected to reach just over four million U.S. dollars in 2019 , the global e-commerce industry appears to show no signs of slowing down .

Example 701:
titleEntities: {'Subject': ['West Virginia'], 'Date': ['1990', '2018']}
title: West Virginia - Median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['50573', '45392', '44354', '42824', '39552', '40241', '43553', '41821', '42777', '40490', '37994', '42091', '38419', '36445', '33373', '32763', '29359', '29673', '29411', '29297', '26704', '27488', '25247', '24880', '23564', '22421', '20271', '23147', '22137']

gold: This statistic shows the median household income in West Virginia from 1990 to 2018 . In 2018 , the median household income in West Virginia amounted to 50,573 U.S. dollars .
gold_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Virginia Household income in West Virginia from 1990 to 2018 . In 2018 , the Virginia Household income in West Virginia amounted to 50573 U.S. 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 templateTitle[6] to the second templateXLabel[0] of the fiscal year templateTitle[8] . In the second fiscal templateXLabel[0] of templateTitle[8] , which ended on 30 , 2019 , templateTitleSubject[0] generated a net templateYLabel[0] of templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Here you can find information about EA 's templateTitle[0] net income .

generated_template: The timeline shows the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] ! in the period from the first templateXLabel[0] of templateTitleDate[0] to the first templateXLabel[0] of templateTitle[4] . In the most recently reported templateXLabel[0] , templateTitleSubject[0] 's GAAP templateYLabel[0] amounted to templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The timeline shows the Quarterly Revenue of Electronic Arts ! in the period from the first Quarter of 2010 to the first Quarter of from . In the most recently reported Quarter , Electronic Arts 's GAAP Revenue amounted to 1348 million U.S. dollars .

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] templateYLabel[3] .

generated_template: This statistic presents the estimated templateYLabel[0] templateYLabel[1] of the 20 templateTitle[0] templateTitle[1] in templateTitleSubject[0] as of templateTitleDate[0] . At this time templateXValue[0] was the templateTitle[0] man in the country with an estimated templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateTitle[0] templateTitle[1] in the country - additional information Every year since 1982 , the American business magazine Forbes has been compiling lists of the 400 templateTitle[0] templateTitle[1] in the country , known as the `` Forbes 400 .
generated: This statistic presents the estimated Number site of the 20 Leading global in Leading as of 2014 . At this time Booking.com was the Leading man in the country with an estimated Number site of 166.0 visits millions . Leading global in the country - additional information Every year since 1982 , the American business magazine Forbes has been compiling lists of the 400 Leading global in the country , known as the `` Forbes 400 .

Example 704:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2009', '2019']}
title: Unemployment rate in the Netherlands 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Share', 'of', 'individuals']: ['4.3', '4.8', '5.9', '7.3', '8.6', '9', '8.9', '7.1', '6.1', '6.1', '5.5']

gold: In 2019 , the unemployment rate in the Netherlands was just over four percent . Unemployment peaked in 2013 and 2014 . At the height of the financial crisis , the annual unemployment rate in the country reached 8.9 and 9 percent respectively .
gold_template: In templateXValue[max] , the templateTitle[0] templateTitle[1] in the templateTitleSubject[0] was just over templateYValue[min] percent . templateTitle[0] peaked in templateXValue[6] and templateXValue[5] . At the height of the financial crisis , the annual templateTitle[0] templateTitle[1] in the country reached templateYValue[6] and templateYValue[4] percent respectively .

generated_template: The templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] manufactured in the templateTitleSubject[1] was approximately 40 thousand tons in templateXValue[max] , an increase of over eight thousand tons from templateXValue[1] . This constitutes a recovery of the production templateYLabel[1] , which collapsed between templateXValue[min] and templateXValue[7] and fell by just under eighty percent , before it stagnated in the early 2010s . A stagnating market ? The corresponding developments in templateYLabel[0] values give the same picture as above .
generated: The Share individuals of Unemployment rate manufactured in the Netherlands was approximately 40 thousand tons in 2019 , an increase of over eight thousand tons from 2018 . This constitutes a recovery of the production individuals , which collapsed between 2009 and 2012 and fell by just under eighty percent , before it stagnated in the early 2010s . A stagnating market ? The corresponding developments in Share values give the same picture as above .

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 templateXValue[7] 2019 templateXValue[8] find out about the situation of templateXValue[7] and templateXValue[6] ( in ) equality across 27 countries . When asked which were the main templateTitle[3] templateXValue[13] templateXValue[7] and templateXValue[7] were templateTitle[4] in templateTitleSubject[0] , templateYValue[max] percent of templateYLabel[1] answered templateXValue[0] .

generated_template: This graph shows the templateTitle[0] templateTitle[4] of templateTitle[1] templateTitleSubject[0] templateTitle[3] templateTitle[5] regularly in the templateTitle[6] as of 2015 . During a survey , templateYValue[max] percent of templateYLabel[1] stated they regularly watched templateXValue[0] or templateXValue[0] shows on templateTitleSubject[0] during templateTitle[1] . templateXValue[1] , part of the billion-dollar film industry , are the second most common genre on templateTitle[1] templateTitleSubject[0] templateTitle[3] .
generated: This graph shows the Mexico facing of most Mexico issues women regularly in the girls as of 2015 . During a survey , 40 percent of respondents stated they regularly watched Sexual harassment or Sexual harassment shows on Mexico during most . Sexual violence , part of the billion-dollar film industry , are the second most common genre on most Mexico issues .

Example 706:
titleEntities: {'Subject': ['Nicaragua'], 'Date': ['2024']}
title: Inflation rate in Nicaragua 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['5.04', '4.94', '4.79', '4.67', '4.19', '5.63', '4.97', '3.85', '3.52', '4', '6.04', '7.14', '7.19', '8.08', '5.46', '3.69', '19.83', '11.13', '9.14', '9.6', '8.47', '5.3', '3.75', '7.36', '11.55', '11.21', '13.05', '9.19', '11.65', '11.12', '3.7', '13.5', '21.9', '116.6', '3004.1', '7428.7', '4775.2', '13109.5', '885.2', '571.4', '141.3']

gold: This statistic shows the average inflation rate in Nicaragua from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Nicaragua amounted to about 4.97 percent compared to the previous year . Nicaragua 's economy Nicaragua 's inflation rate has been on the decline since 2011 , but it is expected to rise again in 2016 .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . templateTitleSubject[0] 's economy templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] has been on the decline since templateXValue[13] , but it is expected to rise again in templateXValue[8] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] had amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . templateTitleSubject[0] 's slowing economy The templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] has fluctuated from a low of templateYValue[14] percent in templateXValue[14] to a high of 4.39 percent as of templateXValue[10] .
generated: This statistic shows the average Inflation rate in Nicaragua from 1984 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Nicaragua had amounted to about 4.97 percent compared to the previous Year . Nicaragua 's slowing economy The Inflation rate in Nicaragua has fluctuated from a low of 5.46 percent in 2010 to a high of 4.39 percent as of 2014 .

Example 707:
titleEntities: {'Subject': ['Chile'], 'Date': ['2014', '2018']}
title: Chile : gender gap index 2014 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014']
Y_Axis['Index', 'score']: ['0.72', '0.7', '0.7', '0.7', '0.7']

gold: The graph presents the gender gap index in Chile from 2014 to 2018 . In 2018 , Chile scored 0.72 points , which shows a gender gap of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . In 2018 , the gender gap in the area of political empowerment in Chile amounted to 69 percent .
gold_template: The graph presents the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] scored templateYValue[max] points , which shows a templateTitle[1] templateTitle[2] of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . In templateXValue[max] , the templateTitle[1] templateTitle[2] in the area of political empowerment in templateTitleSubject[0] amounted to 69 percent .

generated_template: The graph presents the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] scored templateYValue[max] , which shows a templateTitle[1] templateTitle[2] of approximately 29 percent ( women are 29 percent less likely than men to have equal opportunities ) . That same templateXLabel[0] , the templateTitle[1] templateTitle[2] in the area of political empowerment in templateTitleSubject[0] amounted to 83 percent .
generated: The graph presents the gender gap Index in Chile from 2014 to 2018 . In 2018 , Chile scored 0.72 , which shows a gender gap of approximately 29 percent ( women are 29 percent less likely than men to have equal opportunities ) . That same Year , the gender gap in the area of political empowerment in Chile amounted to 83 percent .

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] percent of templateTitle[4] adults aged between 18 and 29 years used the photo sharing app templateTitleSubject[0] . Furthermore , it was found that 43 percent of female adults in the templateTitleSubject[1] used templateTitleSubject[0] compared to only 31 percent of adult men . templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] StatesInstagram is one of the most popular social networks in the templateTitleSubject[1] with a 37 percent templateTitle[1] templateTitle[2] among the adult population .

generated_template: This statistic shows the templateYLabel[0] of adults in the templateTitleSubject[0] who were using templateTitle[0] as of 2019 , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During that period of time , templateYValue[0] percent of templateYLabel[1] aged between 18 and 29 years stated that they used the visual blogging site .
generated: This statistic shows the Share of adults in the Instagram who were using Instagram as of 2019 , sorted by age group . During that period of time , 67 percent of respondents aged between 18 and 29 years stated that they used the visual blogging site .

Example 709:
titleEntities: {'Subject': ['Colombia'], 'Date': ['1990', '2018']}
title: District of Colombia - median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['85750', '83382', '70982', '70071', '68277', '60675', '65246', '55251', '56928', '53141', '55590', '50783', '48477', '44993', '43451', '45044', '39070', '41169', '41222', '38670', '33433', '31860', '31966', '30748', '30116', '27304', '30247', '29885', '27392']

gold: This statistic shows the median household income in the District of Colombia from 1990 to 2018 . In 2018 , the median household income in the District of Colombia amounted to 85,750 U.S. dollars .
gold_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in the templateTitle[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in the templateTitle[0] of templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Colombia Household income in Colombia from 1990 to 2018 . In 2018 , the Colombia Household income in Colombia amounted to 85750 U.S. 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] templateYLabel[1] templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was estimated at almost templateYValue[7] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] has a surprisingly low ( and decreasing ) templateTitle[1] growth rate ; despite it being home to the largest number of Catholics in the world , the majority of women in templateTitleSubject[0] use some form of contraception , which is often government-subsidized or free , even though the Catholic Church retains its stance that the use of contraceptives is inherently wrong .
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 was estimated at almost 37.14 millions Inhabitants . population of Iraq has a surprisingly low ( and decreasing ) population growth rate ; despite it being home to the largest number of Catholics in the world , the majority of women in Iraq use some form of contraception , which is often government-subsidized or free , even though the Catholic Church retains its stance that the use of contraceptives is inherently wrong .

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: The statistic shows the top 25 templateTitle[0] at the templateTitle[6] templateTitle[8] templateTitle[9] templateTitle[7] in templateTitleSubject[0] according to their current templateYLabel[0] / templateTitle[4] templateYLabel[1] . templateXValue[0] of Argentina is the templateTitle[2] valued player , templateTitle[1] a templateTitle[4] templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the top 25 European at the 2013/14 in European according to their current Average / attendance . Boussia Dortmund of Argentina is the clubs valued player , football a attendance Average attendance of 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] percent of the population unemployed , compared with just templateYValue[min] percent in the most recent reporting templateXLabel[0] of templateXValue[max] .

generated_template: The templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] in templateXValue[max] was templateYValue[min] percent , 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] percent , before it rose suddenly in templateXValue[10] to templateYValue[6] percent . After peaking at templateYValue[max] percent in templateXValue[8] , 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 percent , 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 rose suddenly in 2009 to 7.5 percent . After peaking at 7.5 percent in 2011 , 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The number of templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] has increased steadily in recent years , and is expected to keep rising until at least templateXValue[max] . By templateXValue[max] , there will be an expected templateYValue[max] templateYLabel[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] . Smartphones : reduced barriers to ownership Just as they reach a higher share of the global population , smart phones also ( there is an `` are '' missing in between those two ) becoming more accessible .
generated: The number of Market million in Video has increased steadily in recent years , and is expected to keep rising until at least 2022 . By 2022 , there will be an expected 2997.8 U.S. Market million in Video . Smartphones : reduced barriers to ownership Just as they reach a higher share of the global population , smart phones also ( there is an `` are '' missing in between those two ) becoming more accessible .

Example 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[0] .

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] , down from templateYValue[1] the previous templateXLabel[0] .
generated: This statistic shows the American Customer Satisfaction Customer Satisfaction Index scores for American Customer Satisfaction restaurants in the 2007 from 2007 to 2019 . In 2019 , the ACSI for American Customer Satisfaction restaurants in the 2007 was 78 , down from 81 the previous Year .

Example 715:
titleEntities: {'Subject': ['Eastman Chemical'], 'Date': ['2008', '2018']}
title: Eastman Chemical 's revenue 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['10151', '9549', '9008', '9648', '9527', '9350', '8102', '7178', '5842', '4396', '5936']

gold: This statistic shows the revenues of Eastman Chemical from 2007 to 2018 . United States-based Eastman Chemical Company is engaged in the manufacture and sale of chemicals , fibers , and plastics . In 2018 , the company generated approximately 10.15 billion U.S. dollars of sales revenues .
gold_template: This statistic shows the revenues of templateTitleSubject[0] from 2007 to templateXValue[max] . United States-based templateTitleSubject[0] Company is engaged in the manufacture and sale of chemicals , fibers , and plastics . In templateXValue[max] , the company generated approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] of sales revenues .

generated_template: British oil and gas equipment company templateTitleSubject[0] generated a templateYLabel[0] of templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . The origin story templateTitleSubject[0] emerged as the result of a merger in templateXValue[1] between the American company , FMC Technologies Inc. ( founded in 2001 ) , and the French company , Technip SA ( founded in 1958 ) . Headquartered in Houston , Paris , and London , templateTitleSubject[0] has grown to become one of the leading companies in the global oil and gas equipment and services technology sector .
generated: British oil and gas equipment company Eastman Chemical generated a Revenue of 10151 million U.S. dollars in 2018 . The origin story Eastman Chemical emerged as the result of a merger in 2017 between the American company , FMC Technologies Inc. ( founded in 2001 ) , and the French company , Technip SA ( founded in 1958 ) . Headquartered in Houston , Paris , and London , Eastman Chemical has grown to become one of the leading companies in the global oil and gas equipment and services technology sector .

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] templateYLabel[1] templateTitle[4] . The company was founded in 2005 in Hoffman Estates , Illinois and is the tenth largest retailer by annual revenue in the country .

generated_template: This statistic shows the templateYLabel[0] of specialized templateYLabel[1] for the templateTitle[2] sale of templateTitle[1] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] templateTitle[1] templateTitle[2] templateYLabel[1] in the templateTitleSubject[1] , an increase of 44 templateYLabel[1] on the previous templateXLabel[0] .
generated: This statistic shows the Number of specialized stores for the Sears sale of stores in the Sears Holdings ( Sears Holdings ) from 2009 to 2017 . In 2017 , there were 1002 stores Sears stores in the Sears Holdings , an increase of 44 stores on the previous Year .

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 decreasing throughout recent years . In templateXValue[max] , approximately templateYValue[min] percent of Colombians were living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] percent of the country 's templateYLabel[1] in 2005.Moreover , it was recently found that the incidence rate of templateTitle[1] in templateTitleSubject[0] is higher in families whose heads of household were women .

generated_template: The templateTitle[1] rate in templateTitleSubject[0] reached the lowest point in a ten-year period from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[7] percent of the templateYLabel[1] lived in templateTitleSubject[0] . This figure is expected to be templateYValue[0] percent of the country in templateXValue[max] .
generated: The poverty rate in Colombia reached the lowest point in a ten-year period from 2005 to 2017 . In 2017 , about 18.3 percent of the population lived in Colombia . This figure is expected to be 10.8 percent of the country in 2017 .

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 January 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: The statistic depicts 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[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Oil of Iran ( magnesium compounds ) oil as of 2011 , exports major 2011 . At this point , Iran Oil in Germany amounted to approximately 11 imports thousand barrels .

Example 719:
titleEntities: {'Subject': ['Samsung Electronics'], 'Date': ['2009', '2019']}
title: Samsung Electronics ' operating profit 2009 - 2019 , by quarter
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11", "Q3_'11", "Q2_'11", "Q1_'11", "Q4_'10", "Q3_'10", "Q2_'10", "Q1_'10", "Q4_'09", "Q3_'09", "Q2_'09", "Q1_'09"]
Y_Axis['Operating', 'profit', 'in', 'trillion', 'South', 'Korean', 'won']: ['7.16', '7.78', '6.6', '6.23', '10.8', '17.57', '14.87', '15.64', '15.15', '14.53', '14.07', '9.9', '9.22', '5.2', '8.14', '6.68', '6.14', '7.39', '6.9', '5.98', '5.29', '4.06', '7.2', '8.5', '8.3', '10.2', '9.5', '8.8', '8.8', '8.1', '6.5', '5.7', '4.7', '4.3', '3.8', '2.8', '3.0', '4.9', '5.0', '4.4', '3.4', '4.2', '2.7', '0.6']

gold: In the fourth quarter of 2019 , Korean consumer electronics company Samsung Electronics reported an operating profit of nearly 7.16 trillion Korean Won or around 6.5 billion U.S. dollars . Many of the company 's segments experienced increases in their overall sales numbers in the third quarter of 2019 , but growing competition throughout the consumer electronics industry meant that profitability fell . Samsung Samsung ranks among the largest companies in the world in terms of market value and is perhaps best known for its various lines of successful consumer electronics products .
gold_template: In the fourth templateXLabel[0] of templateTitle[6] , templateYLabel[4] consumer templateTitleSubject[0] company templateTitleSubject[0] reported an templateYLabel[0] templateYLabel[1] of nearly templateYValue[0] templateYLabel[2] templateYLabel[4] templateYLabel[5] or around templateYValue[30] templateYLabel[2] U.S. dollars . Many of the company 's segments experienced increases in their overall sales numbers in the third templateXLabel[0] of templateTitle[6] , but growing competition throughout the consumer templateTitleSubject[0] industry meant that profitability fell . templateTitleSubject[0] ranks among the largest companies in the world in terms of market value and is perhaps best known for its various lines of successful consumer templateTitleSubject[0] products .

generated_template: In the fourth templateXLabel[0] of templateTitleDate[1] , adjusted templateYLabel[3] templateTitle[3] templateYLabel[0] templateYLabel[1] amounted to over templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateTitle[3] templateYLabel[0] templateYLabel[1] in the templateTitle[1] are estimated from samples used for the Monthly templateTitle[3] Trade Survey and exclude online travel services , ticket templateYLabel[1] agencies and financial brokers . Online templateTitle[3] templateYLabel[1] currently account for one tenth of total templateTitle[3] and approximately 5 percent of annual templateYLabel[0] revenue in the templateTitle[1] .
generated: In the fourth Quarter of 2019 , adjusted South operating profit amounted to over 17.57 trillion South Korean . operating profit in the Electronics are estimated from samples used for the Monthly operating Trade Survey and exclude online travel services , ticket profit agencies and financial brokers . Online operating profit currently account for one tenth of total operating and approximately 5 percent of annual Operating revenue in the Electronics .

Example 720:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013', '2018']}
title: Retail sales of the vision care market in the U.S. 2013 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Retail', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['35725.0', '34782.0', '40357.8', '39767.0', '37595.7', '36236.2']

gold: This statistic depicts retail sales of the vision care market in the United States from 2013 to 2018 . In 2016 , the U.S. vision care market generated approximately 40.36 billion U.S. dollars , up from 39.77 billion U.S. dollars the previous year .
gold_template: This statistic depicts templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateYLabel[3] templateTitle[2] templateTitle[3] templateTitle[4] generated approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] , up from templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] the previous templateXLabel[0] .

generated_template: The 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 templateYLabel[2] templateYLabel[3] templateYLabel[4] from its cold chain templateTitleSubject[0] worldwide .
generated: The statistic shows the Retail sales of U.S. worldwide from 2013 to 2018 . In 2018 , the American company reported Retail sales to the value of almost 1.15 million U.S. dollars from its cold chain U.S. worldwide .

Example 721:
titleEntities: {'Subject': ['UFC'], 'Date': ['2012', '2018']}
title: UFC : number of events 2012 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'UFC', 'events']: ['39', '39', '41', '41', '46', '33', '31']

gold: In 2018 , a total of 39 Ultimate Fighting Championship ( UFC ) events were hosted around the world featuring 474 fights . The highest live attendance in 2018 was at UFC Fight Night 136 – Hunt vs Oleinik with 22,603 ringside spectators and the highest attendance to date was at UFC 193 – Rousey vs Holm in 2015 with 56,214 attendees . Pay-Per-View In 2017 , the UFC was among the 10 most valuable sports business brands worldwide which is in part thanks to its ' Pay-Per-View ( PPV ) business model .
gold_template: In templateXValue[max] , a total of templateYValue[0] Ultimate Fighting Championship ( templateYLabel[1] ) templateYLabel[2] were hosted around the world featuring 474 fights . The highest live attendance in templateXValue[max] was at templateYLabel[1] Fight Night 136 – Hunt vs Oleinik with 22,603 ringside spectators and the highest attendance to date was at templateYLabel[1] 193 – Rousey vs Holm in templateXValue[3] with 56,214 attendees . Pay-Per-View In templateXValue[1] , the templateYLabel[1] was among the 10 most valuable sports business brands worldwide which is in part thanks to its ' Pay-Per-View ( PPV ) business model .

generated_template: The global templateTitle[3] templateTitle[4] templateYLabel[0] templateYLabel[1] was projected to reach templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] . As a relative newcomer to the competitive consumer electronics landscape , templateTitle[3] templateTitle[4] have carved out a large space for itself , with templateTitle[5] shipments templateTitleSubject[0] to amount to 279 templateYLabel[2] units by 2023 . templateTitle[3] templateTitle[4] As the name suggests , wearables are gadgets that can be worn .
generated: The global 2012 2018 Number UFC was projected to reach 46 events in 2018 . As a relative newcomer to the competitive consumer electronics landscape , 2012 2018 have carved out a large space for itself , with 2018 shipments UFC to amount to 279 events units by 2023 . 2012 2018 As the name suggests , wearables are gadgets that can be worn .

Example 722:
titleEntities: {'Subject': ['United States'], 'Date': []}
title: Ratio of government expenditure to gross domestic product ( GDP ) in the United States
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Ratio', 'of', 'government', 'expenditure', 'to', 'GDP']: ['36.77', '36.75', '36.73', '36.51', '36.41', '36.19', '35.14', '35.25', '35.46', '35.15', '35.47']

gold: The statistic shows the ratio of government expenditure to gross domestic product ( GDP ) in the United States from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure amounted to 35.14 percent of the gross domestic product . See the US GDP for further information .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] amounted to templateYValue[min] percent of the templateTitle[3] templateTitle[4] templateTitle[5] . See the US templateYLabel[3] for further information .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to about templateYValue[6] percent of the country 's templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The statistic shows the Ratio of government expenditure to gross domestic product ( GDP ) in United States from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure in United States amounted to about 35.14 percent of the country 's gross domestic product .

Example 723:
titleEntities: {'Subject': ['Croatia'], 'Date': ['2006', '2018']}
title: Croatia : Number of road deaths 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'fatalities']: ['317', '331', '307', '348', '308', '368', '393', '418', '426', '548', '664', '619', '614']

gold: This statistic illustrates the number of road traffic fatalities per year in Croatia between 2006 and 2018 . In the period of consideration , road fatalities presented an overall trend of decline . The year with the lowest amount of fatalities was 2016 , with a total of 207 road traffic fatalities in Croatia .
gold_template: This statistic illustrates the templateYLabel[0] of templateTitle[2] traffic templateYLabel[1] per templateXLabel[0] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In the period of consideration , templateTitle[2] templateYLabel[1] presented an overall trend of decline . The templateXLabel[0] with the lowest amount of templateYLabel[1] was templateXValue[2] , with a total of 207 templateTitle[2] traffic templateYLabel[1] in templateTitleSubject[0] .

generated_template: There were templateYValue[min] templateTitle[1] templateTitle[2] recorded in templateTitleSubject[0] in templateXValue[max] . templateTitleSubject[0] had been able to decrease the templateYLabel[0] of people fatally injured on its roads by nearly half since templateXValue[min] . templateXValue[7] and templateXValue[3] were the only years in which the templateYLabel[0] of fatal accidents increased .
generated: There were 307 Number road recorded in Croatia in 2018 . Croatia had been able to decrease the Number of people fatally injured on its roads by nearly half since 2006 . 2011 and 2015 were the only years in which the Number of fatal accidents increased .

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] , the templateYLabel[1] templateYLabel[2] of hospitals in the templateTitleSubject[0] stood at templateYValue[0] percent . Hospitals are a cornerstone of health services in any country and also contribute considerably to the economy . Nevertheless , the number of hospitals in the templateTitleSubject[0] has decreased in recent years .
generated: In 2017 , the per 100,000 of hospitals in the U.S. stood at 16.48 percent . Hospitals are a cornerstone of health services in any country and also contribute considerably to the economy . Nevertheless , the number of hospitals in the U.S. has decreased in recent years .

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] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Sox are owned by Jerry Reinsdorf , who bought the templateYLabel[0] for 20 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1981 .

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] Jays templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Jays are owned by Rogers Communications , who bought the templateYLabel[0] for 137 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 2000 .
generated: This graph depicts the value of the Chicago White Sox Jays Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 1600 million U.S. dollars . The Chicago White Sox Jays are owned by Rogers Communications , who bought the Franchise for 137 million U.S. dollars in 2000 .

Example 726:
titleEntities: {'Subject': ['Bitcoins'], 'Date': ['2012', '2019']}
title: Number of Bitcoins in circulation 2012 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12"]
Y_Axis['Number', 'of', 'Bitcoins', 'in', 'millions']: ['18.13', '17.97', '17.79', '17.62', '17.45', '17.3', '17.12', '16.95', '16.78', '16.6', '16.42', '16.25', '16.08', '15.9', '15.72', '15.38', '15.03', '14.67', '14.33', '14.0', '13.67', '13.33', '12.97', '12.59', '12.2', '11.77', '11.35', '10.97', '10.61']

gold: In the fourth quarter of 2019 , there were 18.13 million Bitcoins in the world . Bitcoin is a virtual currency . It can be bought and sold through online exchanges or ATMs .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[1] , there were templateYValue[max] templateYLabel[2] templateYLabel[1] in the world . Bitcoin is a virtual currency . It can be bought and sold through online exchanges or ATMs .

generated_template: Messaging app templateTitle[0] is templateTitleSubject[0] 's most popular online communication tool , peaking at approximately templateYValue[max] templateYLabel[3] templateYLabel[1] actives templateYLabel[2] in templateTitleSubject[0] during the fourth templateXLabel[0] of templateTitleDate[1] . templateTitle[0] 's main target group are young adults aged 15 to 34 years , representing more than 50 percent 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 percent 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[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] with , among others , HBO , CNN and Cartoon templateTitle[2] . Due to the acquisition of Time Warner by AT & T and its subsequent renaming ( now templateTitleSubject[0] ) , results for previous years are not considered meaningful and as such were not reported by AT & T in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] cloud-based online service templateYLabel[1] templateTitle[4] . In templateXValue[max] , approximately templateYValue[max] templateYLabel[2] internet templateYLabel[1] are projected to access templateTitle[1] templateTitle[2] services , up from templateYValue[min] templateYLabel[2] templateYLabel[1] in templateXValue[min] .
generated: The statistic shows the Revenue of WarnerMedia cloud-based online service billion 2018 . In 2018 , approximately 10.58 U.S. internet billion are projected to access television network services , up from 10.58 U.S. billion in 2018 .

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 increase to templateYValue[2] templateYLabel[3] templateYLabel[4] in templateXValue[5] and again to templateYValue[max] templateYLabel[4] in templateXValue[max] .

generated_template: The statistic shows the templateYLabel[0] of templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , in templateYLabel[1] templateYLabel[2] templateYLabel[3] . In templateXValue[max] , approximately templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitle[1] were produced in templateTitleSubject[0] .
generated: The statistic shows the Average of global in Average from 2009 to 2015 , in hotel rate U.S. . In 2015 , approximately 155 hotel rate U.S. of global were produced in Average .

Example 729:
titleEntities: {'Subject': ['Algeria'], 'Date': ['2019']}
title: Unemployment rate in Algeria 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['12.35', '12.15', '12', '10.2', '11.21', '10.21', '9.82', '10.97', '9.96', '9.96', '10.16', '11.33', '13.79', '12.27', '15.27', '17.65', '23.72', '25.9', '27.3', '29.77', '28.45']

gold: This statistic shows the unemployment rate in Algeria from 1998 to 2019 . In 2019 , the unemployment rate in Algeria was 12.35 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from 1998 to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Algeria from 1999 to 2019 . In 2019 , the Unemployment rate in Algeria was at approximately 12.35 percent .

Example 730:
titleEntities: {'Subject': ['Kenya'], 'Date': ['2024']}
title: Total population of Kenya 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['56.43', '54.96', '53.52', '52.11', '50.72', '49.36', '48.03', '46.73', '45.45', '44.2', '43.0']

gold: This statistic shows the total population of Kenya from 2014 to 2024 . In 2018 , the total population of Kenya was estimated at approximately 48.03 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was estimated at approximately templateYValue[6] templateYLabel[1] templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was at approximately templateYValue[7] templateYLabel[1] templateYLabel[0] .
generated: This statistic shows the Total population of Kenya from 2014 to 2017 , with projections up until 2024 . In 2017 , the Total population of Kenya was at approximately 46.73 millions Inhabitants .

Example 731:
titleEntities: {'Subject': ['Qatar'], 'Date': ['2018']}
title: Urbanization in Qatar 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['99.14', '99.08', '99.02', '98.95', '98.87', '98.79', '98.7', '98.6', '98.5', '98.34', '98.14']

gold: This statistic shows the degree of urbanization in Qatar from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 99.14 percent of Qatar 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in 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 .

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[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The annual templateYLabel[0] templateYLabel[1] growth of the templateYLabel[3] can be accessed here .
generated: This statistic shows the development of Michigan 's Real GDP from 2000 to 2018 . In 2018 , the Real GDP of Michigan was about 468.39 billion U.S. dollars . The annual Real GDP growth of the U.S. can be accessed here .

Example 733:
titleEntities: {'Subject': ['Kuwait'], 'Date': ['2018']}
title: Urbanization in Kuwait 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['100', '100', '100', '100', '100', '100', '100', '100', '100', '100', '100']

gold: This statistic shows the degree of urbanization in Kuwait from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 100 percent of Kuwait 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[2] lived in cities .
generated: This statistic shows the Urbanization of Kuwait in Kuwait from 2008 to 2018 . In 2018 , 100 percent of Kuwait 's population lived in cities .

Example 734:
titleEntities: {'Subject': ['NCAA'], 'Date': ['2019']}
title: NCAA division I men 's basketball attendance leaders 2019
X_Axis['State']: ['Syracuse', 'Kentucky', 'North_Carolina', 'Tennessee', 'Wisconsin', 'Louisville', 'Kansas', 'Creighton', 'Marquette', 'Nebraska', 'Arkansas', 'Indiana', 'Michigan_St.', 'Perdue', 'Iowa_St.', 'Virginia', 'Memphis', 'Maryland', 'Ohio_St.', 'NC_State', 'Arizona', 'Dayton', 'Iowa', 'Michigan', 'Illinois', 'Texas_Tech', 'BYU', 'South_Carolina', 'Cincinnati', 'New_Mexico']
Y_Axis['Average', 'attendance']: ['21992', '21695', '19715', '19034', '17170', '16601', '16236', '15980', '15611', '15341', '15278', '15206', '14797', '14467', '14099', '14087', '14065', '14009', '13922', '13897', '13744', '12957', '12869', '12505', '12456', '12098', '11958', '11472', '11256', '11107']

gold: While the players on the court might still be college students , the National Collegiate Athletic Association men 's basketball top division still draws in big crowds . The Syracuse Orange men 's basketball , which represents Syracuse University in New York , attracted the highest average attendance during the 2019 season . The team , traditionally known as the Syracuse Orangemen , had an average home audience of almost 22 thousand in 2019 .
gold_template: While the players on the court might still be college students , the National Collegiate Athletic Association templateTitle[3] templateTitle[4] templateTitle[5] top templateTitle[1] still draws in big crowds . The templateXValue[0] Orange templateTitle[3] templateTitle[4] templateTitle[5] , which represents templateXValue[0] University in templateXValue[last] York , attracted the highest templateYLabel[0] templateYLabel[1] during the templateTitleDate[0] season . The team , traditionally known as the templateXValue[0] Orangemen , had an templateYLabel[0] home audience of almost templateYValue[max] thousand in templateTitleDate[0] .

generated_template: The 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: The 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[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[2] line in templateXValue[max]

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the Jersey poverty in New Jersey from 2000 to 2018 . In 2018 , about 9.5 percent of New Jersey 's population lived below the Jersey line .

Example 736:
titleEntities: {'Subject': ['Croatian'], 'Date': []}
title: Leading Croatian national team players at FIFA World Cup 2018 , by market value
X_Axis['Month']: ['Ivan_Rakitic', 'Ivan_Perisic', 'Mateo_Kovacic', 'Andrej_Kramaric', 'Marcelo_Brozovic', 'Luka_Modric', 'Sime_Vrsaljko', 'Dejan_Lovren', 'Mario_Mandzukic', 'Milan_Badelj', 'Marko_Pjaca', 'Nikola_Kalinic', 'Ante_Rebic', 'Duje_Caleta–Car', 'Domagoj_Vida', 'Lovre_Kalinic', 'Tin_Jedvaj', 'Danijel_Subasic', 'Vedran_Corluka', 'Ivan_Strinic', 'Filip_Bradaric', 'Josip_Pivaric', 'Dominik_Livakovic']
Y_Axis['Market', 'value', 'in', 'million', 'euros']: ['50.0', '40.0', '30.0', '27.0', '27.0', '25.0', '25.0', '20.0', '18.0', '15.0', '15.0', '14.0', '10.0', '10.0', '7.0', '6.5', '5.0', '4.5', '4.0', '4.0', '3.5', '2.0', '1.5']

gold: The statistic displays the leading players of the national football team of Croatia at FIFA World Cup as of June 2018 , by market value . The most valuable player was Ivan Rakitic , with a market value of 50 million euros .
gold_template: The statistic displays the templateTitle[0] templateTitle[4] of the templateTitle[2] football templateTitle[3] of Croatia at templateTitle[5] templateTitle[6] templateTitle[7] as of 2018 , templateTitle[9] templateYLabel[0] templateYLabel[1] . The most valuable player was templateXValue[0] , with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the top-25 templateTitle[0] at the templateTitle[6] templateTitle[8] templateTitle[9] templateTitle[7] in templateTitleSubject[0] according to their current templateYLabel[0] / templateTitle[4] templateYLabel[1] . templateXValue[0] of Argentina is the templateTitle[2] valued player templateTitle[1] a templateTitle[4] templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] . templateYLabel[0] values of templateTitle[0] at the templateTitle[8] templateTitle[9] templateTitle[7] - additional information The market/transfer templateYLabel[1] of a football player is determined by a number of statistics , including the player 's age , salary , playing stats and recent performances .
generated: The statistic shows the top-25 Leading at the World 2018 by Cup in Croatian according to their current Market / players value . Ivan Rakitic of Argentina is the national valued player Croatian a players Market value of 50.0 million euros . Market values of Leading at the 2018 by Cup - additional information The market/transfer value of a football player is determined by a number of statistics , including the player 's age , salary , playing stats and recent performances .

Example 737:
titleEntities: {'Subject': ['Distribution'], 'Date': ['2018']}
title: Distribution of consumer transactions worldwide 2018 , by payment channel
X_Axis['Response']: ['In-store', 'Other_online', 'Buy_buttons', 'Other_mobile_transfers', 'P2P_transfer', 'Mobile_messenger_apps', 'QR_codes', 'Other_in-app_payments', 'Smart_home_device', 'Wearables_/_contactless', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['41', '14', '9', '8', '7', '7', '5', '4', '2', '2', '2']

gold: This statistic presents the most popular payment methods for everyday transactions according to internet users worldwide as of June 2018 . When asked to think about they payment methods for their ten most recent transactions , it was found that seven percent were made via P2P transfer . In-store still accounted for the single largest share of everyday transactions with 41 percent .
gold_template: This statistic presents the most popular templateTitle[6] methods for everyday templateTitle[2] according to internet users templateTitle[3] as of 2018 . When asked to think about they templateTitle[6] methods for their ten most recent templateTitle[2] , it was found that templateYValue[4] percent were made via templateXValue[4] . templateXValue[0] still accounted for the single largest templateYLabel[0] of everyday templateTitle[2] with templateYValue[max] percent .

generated_template: The statistic shows data on the templateTitle[0] of templateXValue[last] genres in the templateTitle[4] as of 2016 . During the survey , templateYValue[max] percent of templateYLabel[1] stated they watched templateXValue[0] templateXValue[last] shows .
generated: The statistic shows data on the Distribution of Other genres in the 2018 as of 2016 . During the survey , 41 percent of respondents stated they watched In-store Other shows .

Example 738:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: U.S. preterm birth rate 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1990']
Y_Axis['Percentage', 'of', 'all', 'births']: ['10.02', '9.93', '9.85', '9.63', '9.57', '9.62', '9.76', '9.81', '9.98', '10.07', '10.36', '10.44', '12.8', '12.73', '11.64', '10.62']

gold: This statistic depicts the percentage of births that were preterm births in the United States from 1990 to 2018 . In 1990 , some 10.6 percent of all births in the United States were preterm births . A preterm birth means that a child was delivered after less than 37 weeks of gestation .
gold_template: This statistic depicts the templateYLabel[0] of templateYLabel[2] that were templateTitle[1] templateYLabel[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , some templateYValue[15] percent of templateYLabel[1] templateYLabel[2] in the templateTitle[0] were templateTitle[1] templateYLabel[2] . A templateTitle[1] templateTitle[2] means that a child was delivered after less than 37 weeks of gestation .

generated_template: In templateXValue[max] , the templateYLabel[1] templateYLabel[2] of hospitals in the templateTitleSubject[0] stood at templateYValue[0] percent . Hospitals are a cornerstone of health services in any country and also contribute considerably to the economy . Nevertheless , the number of hospitals in the templateTitleSubject[0] has decreased in recent years .
generated: In 2018 , the all births of hospitals in the U.S. stood at 10.02 percent . Hospitals are a cornerstone of health services in any country and also contribute considerably to the economy . Nevertheless , the number of hospitals in the U.S. has decreased in recent years .

Example 739:
titleEntities: {'Subject': ['PV'], 'Date': ['2018']}
title: Solar PV capacity - new installations worldwide by country 2018
X_Axis['Country']: ['China', 'India', 'US', 'Japan', 'Australia', 'Germany', 'Mexico', 'Republic_of_Korea', 'Turkey', 'Netherlands']
Y_Axis['Percentage', 'of', 'newly', 'installed', 'capacity']: ['45', '11', '11', '7', '4', '3', '3', '2', '2', '1']

gold: This statistic shows the share of new installed solar PV capacity worldwide in 2018 , by country . In 2018 , new solar PV capacity installations in China accounted for around 45 percent of the world 's total new installed grid-connected PV capacity .
gold_template: This statistic shows the share of templateTitle[3] templateYLabel[2] templateTitle[0] templateTitleSubject[0] templateYLabel[3] templateTitle[5] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , templateTitle[3] templateTitle[0] templateTitleSubject[0] templateYLabel[3] templateTitle[4] in templateXValue[0] accounted for around templateYValue[max] percent of the world 's total templateTitle[3] templateYLabel[2] grid-connected templateTitleSubject[0] templateYLabel[3] .

generated_template: The statistic shows a ranking on the leading templateTitle[0] based on their templateYLabel[0] of templateYLabel[4] templateYLabel[5] worldwide as of 2018 . During the measured period , the templateXValue[1] accounted for templateYValue[1] percent of the templateYLabel[1] templateYLabel[4] population . templateXValue[0] accounted for templateYValue[max] percent of total templateYLabel[4] templateYLabel[5] worldwide , having finally overtaken the templateXValue[1] in terms of templateYLabel[4] templateTitle[4] figures .
generated: The statistic shows a ranking on the leading Solar based on their Percentage of capacity worldwide as of 2018 . During the measured period , the India accounted for 11 percent of the newly capacity population . China accounted for 45 percent of total capacity worldwide , having finally overtaken the India in terms of capacity installations figures .

Example 740:
titleEntities: {'Subject': ['Groupon'], 'Date': ['2009', '2019']}
title: Groupon : annual net income 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['-11.7', '1.99', '26.63', '-183.32', '33.68', '-63.92', '-88.95', '-51.03', '-297.76', '-413.39', '-1.34']

gold: The statistic above shows the annual net income of Groupon from 2008 to 2019 . In 2019 , the coupon site accumulated a net loss of more than 11.6 million dollars , an decline from the previous year 's net income of two million US dollars .
gold_template: The statistic above shows the templateTitle[1] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from 2008 to templateXValue[max] . In templateXValue[max] , the coupon site accumulated a templateYLabel[0] loss of more than 11.6 templateYLabel[2] templateYLabel[4] , an decline from the previous templateXLabel[0] 's templateYLabel[0] templateYLabel[1] of templateYValue[1] templateYLabel[2] US templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of 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[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] . This shows a 70 percent decrease over previous templateXLabel[0] templateTitle[3] total amounting to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Net income of The Groupon income Groupon 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 percent 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] templateYLabel[1] smartphones were shipped in templateTitleSubject[0] .

generated_template: The statistic shows the templateYLabel[1] of templateTitle[1] templateYLabel[0] in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , the revenue from templateTitle[1] templateYLabel[0] in templateTitleSubject[0] amounted to templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the million of smartphone Shipments in China from 2013 to Q1 2018 . In Q1 2018 , the revenue from smartphone Shipments in China amounted to 109.6 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[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Kazakhstan from 2008 to 2018 . Urbanization means the Share of urban population in the total population of a country . In 2018 , 57.43 percent of Kazakhstan 's total population lived in urban areas and cities .

Example 743:
titleEntities: {'Subject': ['eBay'], 'Date': ['2014', '2019']}
title: eBay : quarterly classifieds revenue 2014 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14"]
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['269', '265', '271', '256', '263', '254', '259', '246', '244', '235', '219', '199', '201', '197', '207', '186', '183', '178', '180', '162', '180']

gold: eBay 's classifieds revenue in the fourth quarter of 2019 amounted to 269 million U.S. dollars . This represents a three percent year-on-year change . The classifieds revenue is counted towards the company 's marketing services and other revenues segment .
gold_template: templateTitleSubject[0] 's templateTitle[2] templateYLabel[0] in the fourth templateXLabel[0] of templateTitleDate[1] amounted to templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . This represents a three percent year-on-year change . The templateTitle[2] templateYLabel[0] is counted towards the company 's marketing services and other revenues segment .

generated_template: In the fourth templateXLabel[0] of templateTitleDate[1] , California-based web company templateTitleSubject[0] had an templateYLabel[0] templateYLabel[1] of almost templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] , up from 10.89 templateYLabel[2] templateYLabel[3] templateYLabel[4] in the preceding fiscal templateXLabel[0] . templateTitleSubject[0] operates under the parent company Alphabet Inc .
generated: In the fourth Quarter of 2019 , California-based web company eBay had an Revenue million of almost 271 U.S. dollars , up from 10.89 U.S. dollars in the preceding fiscal Quarter . eBay operates under the parent company Alphabet Inc .

Example 744:
titleEntities: {'Subject': ['Countries'], 'Date': ['2017']}
title: Countries with the highest population decline rate 2017
X_Axis['Country']: ['Cook_Islands', 'Puerto_Rico', 'American_Samoa', 'Lebanon', 'Saint_Pierre_and_Miquelon', 'Latvia', 'Lithuania', 'Moldova', 'Bulgaria', 'Estonia', 'Federated_States_of_Micronesia', 'Northern_Mariana_Islands', 'Croatia', 'Serbia', 'Ukraine', 'Romania', 'Slovenia', 'Cuba', 'Montenegro', 'Virgin_Islands']
Y_Axis['Population', 'decline', 'compared', 'to', 'the', 'previous', 'year']: ['2.79', '1.74', '1.3', '1.1', '1.08', '1.08', '1.08', '1.05', '0.61', '0.57', '0.52', '0.51', '0.5', '0.46', '0.41', '0.33', '0.31', '0.29', '0.28', '0.25']

gold: This statistic shows the 20 countries with the highest population decline rate in 2017 . In the Cook Islands , the population decreased by about 2.8 percent compared to the previous year , making it the country with the highest population decline rate in 2017 . The population decline of Syria Due to the conflicts and instability in Syria in 2014 , thousands of people have fled have fled the country to cross into surrounding countries such as Turkey .
gold_template: This statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitleDate[0] . In the templateXValue[0] , the templateYLabel[0] decreased by about templateYValue[max] percent templateYLabel[2] to the templateYLabel[3] templateYLabel[4] , making it the templateXLabel[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] of Syria Due to the conflicts and instability in Syria in 2014 , thousands of people have fled have fled the templateXLabel[0] to cross into surrounding templateTitleSubject[0] such as Turkey .

generated_template: This statistic shows the templateTitle[4] of templateTitle[1] templateTitle[2] templateYLabel[0] in selected templateTitleSubject[0] countries , as of the first quarter templateTitleDate[0] . templateYLabel[0] companies usually decide to specialize in templateYValue[7] of the markets : residential or templateTitle[1] templateTitle[2] . Residential real estate investments can be seen as less risky , but the templateTitle[1] investments can also be understood as safer , due to higher cash flow potential , offering better returns on templateYLabel[0] .
generated: This statistic shows the rate of highest population in selected Countries , as of the first quarter 2017 . Population companies usually decide to specialize in 1.05 of the markets : residential or highest population . Residential real estate investments can be seen as less risky , but the highest investments can also be understood as safer , due to higher cash flow potential , offering better returns on Population .

Example 745:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010']}
title: Percentage of U.S. companies using self-insured health plans for employees 2010
X_Axis['Number', 'of', 'employees']: ['3_to_49', '50_to_199', '200_to_999', '1000_and_more']
Y_Axis['Share', 'of', 'companies']: ['8', '20', '48', '80']

gold: This statistic shows the percentage of U.S. companies using self-insured health plans for employees in 2010 , by the number of employees . 80 percent of companies with 1,000 and more employees used self-insured health plans in 2010 .
gold_template: This statistic shows the templateTitle[0] of templateTitleSubject[0] templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateXLabel[1] in templateTitleDate[0] , by the templateXLabel[0] of templateXLabel[1] . templateYValue[max] percent of templateYLabel[1] with templateXValue[last] and templateXValue[last] templateXLabel[1] used templateTitle[4] templateTitle[5] templateTitle[6] in templateTitleDate[0] .

generated_template: This statistic shows the results of a survey among female templateTitleSubject[0] high school students templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . According to the source , templateYValue[max] percent of female students in the templateTitle[1] were templateTitle[5] templateTitle[6] as of 2013 .
generated: This statistic shows the results of a survey among female U.S. high school students using self-insured health plans . According to the source , 80 percent of female students in the U.S. were health plans as of 2013 .

Example 746:
titleEntities: {'Subject': ['BP'], 'Date': ['2010', '2018']}
title: BP 's revenue - Upstream segment 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['56399', '45440', '33188', '43235', '65424', '70374', '72225', '75754', '66266']

gold: This statistic shows the revenue of the BP Upstream segment from 2010 to 2018 . In 2018 , BP Upstream reported some 56.4 billion U.S. dollars of revenue . BP is a globally operating oil and gas company , and the eighth largest company generally worldwide as of 2017 , measured by revenue .
gold_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[3] reported some templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateYLabel[0] . templateTitleSubject[0] is a globally operating oil and gas company , and the eighth largest company generally worldwide as of templateXValue[1] , measured by templateYLabel[0] .

generated_template: templateTitle[2] spending on the National Football League ( templateTitleSubject[0] ) and its teams has increased annually since templateXValue[min] , reaching templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide during the templateXValue[max] season . Nike is one of the league 's main sponsors , providing uniform and apparel to all 32 templateTitleSubject[0] teams since templateXValue[6] – the two parties agreed a long-term extension to their rights partnership in templateXValue[max] . Why are templateTitle[2] deals worth so much ? Sponsors are prepared to spend large amounts of money to get their brand displayed on big screens and pitch-side hoardings in stadiums across the templateTitleSubject[0] .
generated: revenue spending on the National Football League ( BP ) and its teams has increased annually since 2010 , reaching 75754 million U.S. dollars worldwide during the 2018 season . Nike is one of the league 's main sponsors , providing uniform and apparel to all 32 BP teams since 2012 – the two parties agreed a long-term extension to their rights partnership in 2018 . Why are revenue deals worth so much ? Sponsors are prepared to spend large amounts of money to get their brand displayed on big screens and pitch-side hoardings in stadiums across the BP .

Example 747:
titleEntities: {'Subject': ['U.S. Instagram'], 'Date': ['2015', '2015']}
title: Share of U.S. teenagers who use Instagram 2015 , by gender and age
X_Axis['Response']: ['Boys_13-14', 'Boys_15-17', 'Girls_13-14', 'Girls_15-17']
Y_Axis['Share', 'of', 'respondents']: ['33', '51', '56', '64']

gold: This statistic shows the share of teenagers in the United States who were Instagram users as of March 2015 , sorted by gender and age group . During that period of time , 64 percent of female U.S. teens aged 15 to 17 years used the social networking app .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] in the templateTitle[1] templateTitle[3] were templateTitleSubject[0] users as of 2015 , sorted templateTitle[7] templateTitle[8] and templateTitle[9] group . During that period of time , templateYValue[max] percent of female templateTitleSubject[0] teens aged 15 to 17 years used the social networking app .

generated_template: This statistic shows the results of a survey among female templateTitleSubject[0] high school students templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . According to the source , templateYValue[max] percent of female students in the templateTitle[1] were templateTitle[5] templateTitle[6] as of 2013 .
generated: This statistic shows the results of a survey among female U.S. Instagram high school students who use Instagram 2015 . According to the source , 64 percent of female students in the U.S. were Instagram 2015 as of 2013 .

Example 748:
titleEntities: {'Subject': ['EU-28'], 'Date': ['2017']}
title: Proportion of individuals who have tried waterpipe , shisha or hooka in EU-28 2017
X_Axis['Response']: ['Yes', 'Never', 'Spontaneous']
Y_Axis['Share', 'of', 'respondents']: ['13', '87', '0']

gold: This statistic displays the proportion of individuals who have tried water pipe , shisha or hookah in EU-28 countries in 2017 . A majority of 87 percent of respondents said they have never tried water pipe , shisha or hookah products . Additionally , the proportion of individuals who have tried oral , nasal or chewing tobacco can be found at the following .
gold_template: This statistic displays the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] water pipe , templateTitle[6] or hookah in templateTitleSubject[0] countries in templateTitleDate[0] . A majority of templateYValue[max] percent of templateYLabel[1] said they templateTitle[3] templateXValue[1] templateTitle[4] water pipe , templateTitle[6] or hookah products . Additionally , the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] oral , nasal or chewing tobacco can be found at the following .

generated_template: This statistic shows the templateYLabel[0] of 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] percent 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 percent 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 templateTitleDate[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleDate[0] , templateTitle[4] templateYLabel[0] of templateYLabel[1] . The templateTitle[1] templateTitle[2] attack in templateTitleDate[0] occurred on 10 , templateTitleDate[0] , was carried out templateTitle[4] the Taliban in templateXValue[0] , templateXValue[0] , and caused templateYValue[max] templateYLabel[1] .

generated_template: There were the templateTitle[3] leader in templateTitleSubject[0] in templateTitleDate[0] with templateYValue[max] templateYLabel[0] templateYLabel[1] , followed by templateXValue[1] and templateXValue[2] with templateYValue[1] and templateYValue[2] templateYLabel[0] templateYLabel[1] . Is templateXValue[0] are the largest templateTitle[1] templateXLabel[0] in the templateTitleSubject[0] with a templateYLabel[0] of templateYValue[max] templateYLabel[0] .
generated: There were the attacks leader in The in 2018 with 466 Number fatalities , followed by Farah Afghanistan (Taliban) (5/15/2018) and Darengarh Pakistan (Khorasan Chapter of the Islamic State) (7/13/2018) with 330 and 150 Number fatalities . Is Ghazni Afghanistan (Taliban) (8/10/2018) are the largest worst City, in the with a Number of 466 Number .

Example 750:
titleEntities: {'Subject': ['Finland'], 'Date': ['2007', '2017']}
title: Number of hospitals in Finland 2007 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'hospitals']: ['247', '262', '268', '258', '259', '263', '275', '280', '298', '320', '325']

gold: The number of hospitals in Finland was down at the lowest point of the observed period in 2017 , when there were 247 hospitals . At the beginning of the observed period , in 2007 , the number of hospitals amounted to 325 .
gold_template: The templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] was down at the lowest point of the observed period in templateXValue[max] , when there were templateYValue[min] templateYLabel[1] . At the beginning of the observed period , in templateXValue[min] , the templateYLabel[0] of templateYLabel[1] amounted to templateYValue[max] .

generated_template: The templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] peaked in templateXValue[5] , when almost templateYValue[max] thousand couples got divorced . Since then the divorce templateYLabel[0] decreased until templateXValue[max] , when it again increased and amounted to almost templateYValue[0] thousand templateYLabel[1] . A similar trend can be seen in the neighbor country Norway , where the templateYLabel[0] of templateYLabel[1] decreased for several years but increased again in templateXValue[1] .
generated: The Number of hospitals in Finland peaked in 2012 , when almost 325 thousand couples got divorced . Since then the divorce Number decreased until 2017 , when it again increased and amounted to almost 247 thousand hospitals . A similar trend can be seen in the neighbor country Norway , where the Number of hospitals decreased for several years but increased again in 2016 .

Example 751:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Leading men 's hair coloring brands in the U.S. 2019
X_Axis['Brand']: ['Just_For_Men', 'Just_For_Men_Autostop', 'Just_For_Men_Control_GX', 'Just_For_Men_Touch_of_Gray', 'Softsheen-Carson_Dark_&_Natural', 'Private_label', 'Grecian_Formula_16', 'Just_For_Men_Original_Formula', 'Creme_of_Nature', 'Grecian_5']
Y_Axis['Sales', 'in', 'million', 'U.S.', 'dollars']: ['138.0', '27.2', '18.2', '9.7', '5.7', '2.8', '2.3', '0.5', '0.3', '0.1']

gold: In 2019 , Just For Men was the leading men 's hair coloring brand in the United States with sales of approximately 138 million U.S. dollars . Ranked second , the Just For Men Autostop brand generated sales of around 27.2 million U.S. dollars that year .
gold_template: In templateTitleDate[0] , templateXValue[0] Men was the templateTitle[0] templateXValue[0] templateTitle[2] templateTitle[3] templateTitle[4] templateXLabel[0] in the templateTitle[6] with templateYLabel[0] of approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Ranked second , the templateXValue[0] Men templateXValue[1] templateXLabel[0] generated templateYLabel[0] of around templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] that year .

generated_template: The templateTitle[0] name templateXLabel[0] of templateTitle[1] templateTitle[2] and templateTitle[1] templateTitle[3] in the templateTitle[5] is templateXValue[1] Sheer , which generated some templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] in templateTitleDate[0] . However , templateXValue[0] suntan lotions had higher dollar templateYLabel[0] than any name templateXLabel[0] in that year , at around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Sunscreen and skin protection Dermatologists highly recommend sunscreen to everyone , regardless of age or complexion .
generated: The Leading name Brand of men 's and men hair in the brands is Just For Men Autostop Sheer , which generated some 27.2 million U.S. dollars in Sales in 2019 . However , Just For Men suntan lotions had higher dollar Sales than any name Brand in that year , at around 138.0 million U.S. dollars . Sunscreen and skin protection Dermatologists highly recommend sunscreen to everyone , regardless of age or complexion .

Example 752:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018']}
title: Birth rate in Italy 2018 , by region
X_Axis['Month']: ['Trentino-South_Tyrol', 'Campania', 'Sicily', 'Calabria', 'Lombardy', 'Emilia-Romagna', 'Lazio', 'Apulia', 'Aosta_Valley', 'Veneto', 'Abruzzo', 'Piedmont', 'Tuscany', 'Marche', 'Umbria', 'Basilicata', 'Friuli-Venezia_Giulia', 'Molise', 'Liguria', 'Sardinia']
Y_Axis['Birth', 'rate', 'per', 'thousand', 'inhabitants']: ['9.0', '8.3', '8.1', '7.8', '7.5', '7.3', '7.2', '7.2', '7.2', '7.2', '6.8', '6.7', '6.7', '6.7', '6.6', '6.6', '6.4', '6.2', '5.8', '5.7']

gold: In 2018 , Trentino-South Tyrol was the region in Italy with the highest birth rate nationwide , with nine births per every 1,000 inhabitants . The following three positions of the ranking were occupied by Southern regions : Campania , Sicily , and Calabria . Indeed , South-Italy was the macro-region with the largest birth-rate in Italy .
gold_template: In templateTitleDate[0] , templateXValue[0] was the templateTitle[5] in templateTitleSubject[0] with the highest templateYLabel[0] templateYLabel[1] nationwide , with templateYValue[max] births templateYLabel[2] every 1,000 templateYLabel[4] . The following three positions of the ranking were occupied templateTitle[4] Southern regions : templateXValue[1] , templateXValue[2] , and templateXValue[3] . Indeed , South-Italy was the macro-region with the largest birth-rate in templateTitleSubject[0] .

generated_template: In templateTitleDate[0] , the highest templateYLabel[0] templateYLabel[1] was registered in the South of templateTitleSubject[0] . templateXValue[0] , templateXValue[1] , and templateXValue[2] , the three regions where the shares of citizens without a job exceeded templateYValue[2] percent , led in the ranking of Italian regions with the highest templateYLabel[0] rates . The disparities in templateYLabel[0] indicators can be observed not only on the regional level , but also among genders .
generated: In 2018 , the highest Birth rate was registered in the South of Italy . Trentino-South Tyrol , Campania , and Sicily , the three regions where the shares of citizens without a job exceeded 8.1 percent , led in the ranking of Italian regions with the highest Birth rates . The disparities in Birth indicators can be observed not only on the regional level , but also among genders .

Example 753:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2018']}
title: Ad blocker usage in the United Kingdom ( UK ) 2018
X_Axis['Response']: ['Use_ad_blocker', "Don't_use_ad_blocker", "Don't_know"]
Y_Axis['Share', 'of', 'respondents']: ['41', '53', '6']

gold: This statistic shows the survey on ad blocker usage in the United Kingdom in 2018 . According to the survey , 41 percent of the respondents used an ad blocker , while 53 percent did not . Six percent of respondents said they did n't know if they used ad blocking software .
gold_template: This statistic shows the survey on templateXValue[0] usage in the templateTitleSubject[0] in templateTitleDate[0] . According to the survey , templateYValue[0] percent of the templateYLabel[1] used an templateXValue[0] , while templateYValue[max] percent did not . templateYValue[min] percent of templateYLabel[1] said they did n't templateXValue[last] if they used templateXValue[0] blocking software .

generated_template: This statistic shows how consumers rate their templateTitle[2] to templateXValue[0] when templateTitle[0] in the templateTitleSubject[0] ( templateTitleSubject[1] ) over the 12 months prior to survey ( 2013 templateXValue[0] 2014 ) . Of templateYLabel[1] , templateYValue[max] percent said they felt templateXValue[0] to templateXValue[0] while templateYValue[1] percent felt their level of templateXValue[0] templateTitle[2] remained templateXValue[1] .
generated: This statistic shows how consumers rate their usage to Use ad blocker when Ad in the United Kingdom ( UK ) over the 12 months prior to survey ( 2013 Use ad blocker 2014 ) . Of respondents , 53 percent said they felt Use ad blocker to Use ad blocker while 53 percent felt their level of Use ad blocker usage remained Don't use ad blocker .

Example 754:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2018']}
title: Number of pubs in the United Kingdom ( UK ) 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '48', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Number', 'of', 'pubs', 'in', 'thousands']: ['47.6', '48.35', '50.3', '50.8', '51.9', '52.5', '53.8', '54.7', '55.4', '52.5', '54.8', '56.8', '58.2', '58.6', '59.0', '59.4', '60.1', '60.7', '60.8']

gold: How many pubs are there in the UK ? There were approximately 47,600 pubs operating in the United Kingdom in 2018 . This represented a decrease of approximately 7,200 pubs in the last ten years , and over 13,200 pubs since 2000 . Pubs in decline Several factors have been suggested for the decline in pubs in the UK .
gold_template: How many templateYLabel[1] are there in the templateTitleSubject[1] ? There were approximately templateYValue[min] templateYLabel[1] operating in the templateTitleSubject[0] in templateXValue[max] . This represented a decrease of approximately 7,200 templateYLabel[1] in the last ten years , and over 13,200 templateYLabel[1] since templateXValue[18] . templateYLabel[1] in decline Several factors have been suggested for the decline in templateYLabel[1] in the templateTitleSubject[1] .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] in templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[4] templateYLabel[5] templateYLabel[2] templateYLabel[3] . The templateYLabel[0] gate templateYLabel[1] of one templateYLabel[3] of templateTitle[4] templateTitle[5] in templateTitleSubject[0] has fluctuated over the past eighteen years , having hit a high of templateYValue[max] templateYLabel[4] templateYLabel[5] in templateXValue[min] . The volume of templateTitle[4] templateTitle[5] produced in templateTitleSubject[0] has also varied quite dramatically in the last few years .
generated: In 2018 , the Number pubs of UK 2000 in United Kingdom amounted to 47.6 thousands . The Number gate pubs of one thousands of UK 2000 in United Kingdom has fluctuated over the past eighteen years , having hit a high of 60.8 thousands in 48 . The volume of UK 2000 produced in United Kingdom has also varied quite dramatically in the last few years .

Example 755:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Household income of white families in the U.S. 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Median', 'income', 'in', '2018', 'CPI-U-RS', 'adjusted', 'U.S.', 'dollars']: ['70642', '69851', '66440', '65133', '62453', '63588', '60979', '60526', '61361', '62374', '63378', '65089', '63892', '63900', '63627', '63832', '64084', '62773', '63609', '63654', '62480', '60548', '59128', '58184', '56297', '55914', '55842', '55568', '56917']

gold: This statistic shows the household income of white families in the U.S. from 1990 to 2018 . The median income in 2018 was at 70,642 U.S. dollars for white , non-Hispanic families . The median household income of the United States can be accessed here .
gold_template: This statistic shows the templateTitle[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the templateYLabel[5] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] in templateXValue[max] was at templateYValue[max] templateYLabel[5] templateYLabel[6] for templateTitle[2] , non-Hispanic templateTitle[3] . The templateYLabel[0] templateTitle[0] templateYLabel[1] of the templateTitle[4] can be accessed here .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[0] was templateYValue[min] out of every 1,000 templateYLabel[6] . This is a significant decrease from templateXValue[min] , when templateYLabel[0] templateYLabel[1] was at templateYValue[max] 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 2018 in the Household was 55568 out of every 1,000 dollars . This is a significant decrease from 1990 , when Median income was at 70642 deaths out of every 1,000s dollars . What is Median income ? The Median income 2018 is the number of deaths of 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[max] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic depicts templateTitleSubject[0] 's templateYLabel[0] on research and development from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company 's templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] stood at approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] is a globally operating oil and gas company , and the eighth largest company generally worldwide as of templateXValue[1] , measured by revenue .
generated: This statistic depicts U.S. 's Expenditure on research and development from 2010 to 2018 . In 2018 , the company 's Average annual food Expenditure stood at approximately 3459 U.S. dollars . U.S. is a globally operating oil and gas company , and the eighth largest company generally worldwide as of 2017 , measured by revenue .

Example 757:
titleEntities: {'Subject': ['Turkey'], 'Date': []}
title: Ratio of government expenditure in relation to gross domestic product ( GDP ) in Turkey
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budgetary', 'balance', 'in', 'relation', 'to', 'the', 'gross', 'domestic', 'product']: ['35.66', '35.68', '35.64', '35.61', '35.17', '34.81', '34.61', '33.62', '35.08', '33.37', '33.23']

gold: This statistic shows the ratio of government expenditure to gross domestic product ( GDP ) in Turkey from 2014 to 2018 , with projections up until 2024 . In 2018 , the ratio in relation to the GDP in Turkey was at approximately 34.61 percent .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] to templateYLabel[3] templateYLabel[4] templateYLabel[5] ( templateTitle[7] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] in templateYLabel[2] to the templateTitle[7] in templateTitleSubject[0] was at approximately templateYValue[6] percent .

generated_template: The statistic shows the templateTitleSubject[0] 's templateYLabel[0] of the templateYLabel[1] templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[2] ) adjusted for Purchasing Power Parity ( PPP ) from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitleSubject[0] 's templateYLabel[0] of templateYLabel[1] templateYLabel[2] ( in relation to PPP dollars ) amounted to about templateYValue[6] percent .
generated: The statistic shows the Turkey 's Budgetary of the balance domestic product GDP ( relation ) adjusted for Purchasing Power Parity ( PPP ) from 2014 to 2024 . In 2018 , the Turkey 's Budgetary of balance relation ( in relation to PPP dollars ) amounted to about 34.61 percent .

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] percent of templateXValue[0] said that they found templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: A 2017 survey of templateTitleSubject[0] adults found that approximately templateYValue[max] percent of American templateXValue[0] and templateYValue[min] percent of American templateXValue[last] currently smoke templateTitle[4] . Impact of Legalizing Cannabis in the templateTitle[0] . Since Washington and Colorado legalized recreational templateTitle[4] in 2012 , several more states have followed suit .
generated: A 2017 survey of U.S. adults found that approximately 74 percent of American Women and 67 percent of American Men currently smoke family . Impact of Legalizing Cannabis in the Travelers . Since Washington and Colorado legalized recreational family in 2012 , several more states have followed suit .

Example 759:
titleEntities: {'Subject': ['El Salvador'], 'Date': ['2024']}
title: Inflation rate in El Salvador 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['1', '1', '1.1', '1.1', '1.06', '0.89', '1.09', '1.01', '0.6', '-0.73', '1.14', '0.76', '1.73', '5.13', '1.18', '0.54', '7.26', '4.58', '4.04', '4.69', '4.45', '2.12', '1.87', '3.75', '2.27', '0.51', '2.55', '4.49', '9.79', '10.03', '10.58', '18.51', '11.22', '14.41', '28.29', '17.65', '19.77', '24.85', '31.95', '22.32', '11.71']

gold: This statistic shows the average inflation rate in El Salvador from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in El Salvador amounted to about 1.09 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

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

Example 760:
titleEntities: {'Subject': ['Global'], 'Date': ['2019']}
title: Global exports of broiler meat 2019 , by country
X_Axis['Country']: ['Brazil', 'United_States', 'EU', 'Thailand', 'China', 'Turkey', 'Ukraine', 'Belarus', 'Russia', 'Argentina', 'Canada', 'Others']
Y_Axis['Export', 'volume', 'in', 'thousand', 'metric', 'tons']: ['3775', '3248', '1500', '900', '475', '400', '350', '185', '180', '145', '130', '331']

gold: This statistic depicts the export volume of broiler meat worldwide in 2019 , by leading country , in thousand metric tons . The broiler meat exports of the United States amounted to approximately 3.25 million metric tons in that year .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] worldwide in templateTitleDate[0] , templateTitle[5] leading templateXLabel[0] , in thousand templateYLabel[3] templateYLabel[4] . The templateTitle[2] templateTitle[3] templateTitle[1] of the templateXValue[1] amounted to approximately templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in that year .

generated_template: 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 Global Export meat in 2018/2019 , broken down 2019 Country . According to the report , the Brazil was responsible for over a third of the world 's Global Export that year . Most of the U.S. Export is attributable to the Global Belt , which is located in the Midwest of the Brazil .

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 templateTitle[9] , it appears that the majority of French teenagers were in middle school when they had templateTitle[5] templateTitle[6] templateTitle[7] . Love appears to be an important area of life at a templateTitle[1] templateTitle[4] , with more than 50 percent of templateTitle[1] French templateTitle[2] stating that love relationships were important for them . templateTitle[6] love experiences Even though new technologies and smartphones may have changed the way teenagers live templateTitle[5] love life , it seems that the templateTitle[4] for templateTitle[6] love and sex experiences has templateXValue[last] really changed templateXValue[5] the templateXValue[0] .

generated_template: This statistic 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] percent 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 percent 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] templateYLabel[2] 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] templateYLabel[2] 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] rose templateTitle[4] almost 50 percent between templateXValue[4] and templateXValue[0] . Year-to-date templateYLabel[1] increased to between 367,000 and 368,000 templateYLabel[2] in templateXValue[0] , and templateTitleSubject[0] delivered around templateYValue[max] vehicles during the fourth templateXLabel[0] of templateXValue[0] alone . The quarterly figure represents a new record following the electric carmaker templateTitle[1] previous templateXLabel[0] which set the record at templateYValue[1] templateYLabel[1] worldwide .

generated_template: The time series shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from the second templateXLabel[0] of templateTitle[3] to the first templateXLabel[0] of templateTitle[4] . During the last reported period , the templateYLabel[0] templateYLabel[1] of the Rakuten-owned cashback website templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The time series shows the Number deliveries of Tesla from the second Quarter of deliveries to the first Quarter of by . During the last reported period , the Number deliveries of the Rakuten-owned cashback website Tesla amounted to 112000 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] percent in templateXValue[7] – and a real , significant decrease is nowhere in sight . The bigger problem is its unsteadiness , however : An templateYLabel[0] templateYLabel[1] that is bouncing all over the place , like this one , is usually a sign of a struggling economy , causing prices to fluctuate , and unemployment and poverty to increase . templateTitleSubject[0] 's economy - a so-called `` mixed economy '' , which means the market economy is at least in part regulated by the state – is not entirely in bad shape , though .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Nigeria from 2004 to 2017 , with projections up until 2024 . In 2017 , the average Inflation rate in Nigeria amounted to about 12.09 percent 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 templateTitle[5] , templateTitle[6] age and gender . templateTitle[2] templateTitleSubject[0] was most common among individuals within the 35 to 44 age templateTitle[7] , at templateYValue[max] percent of templateYLabel[1] .

generated_template: A 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] percent of templateXValue[0] aged 16 years or older reported accessing the templateTitle[2] this way , compared with templateYValue[1] percent of templateXValue[1] . Smartphone ownership As of 2018 , 95 percent of people aged 16 to 34 years owned a smartphone .
generated: A slightly lower Share of Women than Men access the purchasing via Online Great Britain Online in Online Great Britain , according to figures from the United Kingdom 's ( UK ) Office for National Statistics ( ONS ) . 15 percent of Men aged 16 years or older reported accessing the purchasing this way , compared with 18 percent of Women . Smartphone ownership As of 2018 , 95 percent of people aged 16 to 34 years owned a smartphone .

Example 767:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2006', '2018']}
title: Number of road deaths in the Netherlands 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'fatalities']: ['678', '613', '629', '621', '570', '570', '650', '661', '640', '720', '750', '791', '811']

gold: In 2018 , 678 people were killed on roads in the Netherlands . Between 2006 and 2018 , road traffic fatalities had seen a net decline of 16 percent , with the peak recorded at the beginning of the reporting period in 2006 . The Dutch Government is dedicated to making its roads even safer in the future and aims to bring the number of road deaths down to below 500 by 2020 .
gold_template: In templateXValue[max] , templateYValue[0] people were killed on roads in the templateTitleSubject[0] . Between templateXValue[min] and templateXValue[max] , templateTitle[1] traffic templateYLabel[1] had seen a net decline of 16 percent , with the peak recorded at the beginning of the reporting period in templateXValue[min] . The Dutch Government is dedicated to making its roads even safer in the future and aims to bring the templateYLabel[0] of templateTitle[1] templateTitle[2] down to below 500 by 2020 .

generated_template: There were templateYValue[0] reported templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateXValue[max] . This was a slight increase compared to the previous templateXLabel[0] , but an increase of 14 incidents compared to the low reported in templateXValue[4] . The Northern European island state is known for enforcing a strict templateTitle[1] safety policy in order to ensure the security of its residents and tourists in the country .
generated: There were 678 reported road deaths in Netherlands in 2018 . This was a slight increase compared to the previous Year , but an increase of 14 incidents compared to the low reported in 2014 . The Northern European island state is known for enforcing a strict road safety policy in order to ensure the security of its residents and tourists in the country .

Example 768:
titleEntities: {'Subject': ['Balfour Beatty Group'], 'Date': ['2011', '2018']}
title: Balfour Beatty Group 's average number of employees 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Average', 'number', 'of', 'employees']: ['26000', '28000', '22450', '23316', '39751', '41221', '50304', '50301']

gold: Balfour Beatty was employer to some 26,000 people in 2018 . The United Kingdom based heavy construction company let go 2,000 employees between 2017 and 2018 , whilst the overall decline in the period of consideration was even more prominent . Overall , employment figures have fallen by nearly 50 percent since a high of 50,304 was reported in 2012 .
gold_template: templateTitleSubject[0] was employer to some templateYValue[0] people in templateXValue[max] . The United Kingdom based heavy construction company let go 2,000 templateYLabel[2] between templateXValue[1] and templateXValue[max] , whilst the overall decline in the period of consideration was even more prominent . Overall , employment figures have fallen by nearly 50 percent since a high of templateYValue[max] was reported in templateXValue[6] .

generated_template: The templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( templateTitleSubject[1] ) of templateTitleSubject[0] templateTitle[1] customers in templateTitleSubject[2] has fallen steadily since templateXValue[min] , reaching an estimated 14.8 templateYLabel[4] templateYLabel[2] templateYLabel[6] in templateXValue[max] . This is a decrease of 22.9 percent on the templateTitleSubject[1] generated in templateXValue[min] . Shrinking need for templateTitleSubject[0] templateTitle[1] The decline of templateTitleSubject[0] templateTitle[1] , as demonstrated by the falling templateTitleSubject[1] and the diminishing penetration rate in countries such as the United Kingdom ( UK ) , can partially be attributed to the increased speeds and reliability that comes with 3G and 4G templateTitleSubject[0] networks .
generated: The Average number employees ( Balfour Beatty Group ) of Balfour Beatty Group Beatty customers in Balfour Beatty Group has fallen steadily since 2011 , reaching an estimated 14.8 employees in 2018 . This is a decrease of 22.9 percent on the Balfour Beatty Group generated in 2011 . Shrinking need for Balfour Beatty Group Beatty The decline of Balfour Beatty Group Beatty , as demonstrated by the falling Balfour Beatty Group and the diminishing penetration rate in countries such as the United Kingdom ( UK ) , can partially be attributed to the increased speeds and reliability that comes with 3G and 4G Balfour Beatty Group networks .

Example 769:
titleEntities: {'Subject': ['Cintas'], 'Date': ['2012', '2019']}
title: Cintas - annual revenue 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['6.89', '6.48', '5.32', '4.8', '4.37', '4.09', '3.88', '3.76']

gold: This statistic depicts the annual revenue of Cintas Corporation between the fiscal year of 2012 and the fiscal year of 2019 . For the fiscal year of 2019 , the Cincinnati-based specialized facility services company reported an annual revenue of just under 6.9 billion U.S. dollars .
gold_template: This statistic depicts the templateTitle[1] templateYLabel[0] of templateTitleSubject[0] Corporation between the fiscal templateXLabel[0] of templateXValue[min] and the fiscal templateXLabel[0] of templateXValue[max] . For the fiscal templateXLabel[0] of templateXValue[max] , the Cincinnati-based specialized facility services company reported an templateTitle[1] templateYLabel[0] of just under templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: 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[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[2] . In templateXValue[max] , 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 2019 , 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[0] to templateTitleDate[1] . In templateTitleDate[1] , the templateTitle[3] , templateTitle[4] , and distilled alcoholic beverages templateYLabel[0] of templateYLabel[2] merchant wholesalers amounted to about templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Alcohol in the templateTitle[0] During the recession , alcohol-related industries were some of the few seeing continued growth , proving alcohol to be a frequent purchase for many Americans .

generated_template: This statistic represents the templateTitle[0] templateYLabel[0] of templateTitle[2] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateTitle[0] templateTitle[2] templateYLabel[0] came to templateYValue[2] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic represents the U.S. Sales of sales in the wholesale from 02 to 17 . In 15 , U.S. sales came to 152.57 billion U.S. dollars .

Example 771:
titleEntities: {'Subject': ['Christmas U.S'], 'Date': []}
title: Average spending on Christmas gifts in the U.S .
X_Axis['Year']: ['2006', '2007', '2008', '2009', '2010', '2011']
Y_Axis['Average', 'estimated', 'amount', 'in', 'U.S.', 'dollars']: ['907', '909', '801', '740', '715', '712']

gold: The statistic depicts the results of a survey about the average Christmas spending of U.S. consumers from 2006 to 2011 . October of 2007 was the most generous regarding spending on gifts among Americans , estimating a likelihood of spending around 909 U.S. dollars ( on average ) . Since then , the amount reserved for Christmas presents has steadily declined .
gold_template: The statistic depicts the results of a survey about the templateYLabel[0] templateTitleSubject[0] templateTitle[1] of templateYLabel[3] consumers from templateXValue[min] to templateXValue[max] . October of templateXValue[1] was the most generous regarding templateTitle[1] on templateTitle[3] among Americans , estimating a likelihood of templateTitle[1] around templateYValue[max] templateYLabel[3] templateYLabel[4] ( on templateYLabel[0] ) . Since then , the templateYLabel[2] reserved for templateTitleSubject[0] presents has steadily declined .

generated_template: 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] were spent templateTitle[2] the Supplemental Nutrition Assistance Program .
generated: The statistic shows total amount Average Christmas the Supplemental Nutrition Assistance Program ( gifts , formerly called Christmas U.S ) from 2006 to 2011 . In 2008 , about 801 estimated amount U.S. were spent Christmas the Supplemental Nutrition Assistance Program .

Example 772:
titleEntities: {'Subject': ['Advance Publications'], 'Date': ['2006', '2014']}
title: Advance Publications ' revenue 2006 to 2014
X_Axis['Year']: ['2006', '2007', '2008', '2009', '2011', '2012', '2013', '2014']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['7.14', '7.97', '7.36', '7.16', '6.55', '6.78', '6.56', '8.0']

gold: The timeline shows estimated data on the revenue of the American media corporation Advance Publications , Inc. from 2006 to 2014 . Advance Publications is a privately-owned , unlisted company and owns such brands as Reddit - a social news and entertainment website and Conde Nast - a magazine publisher . Its 2006 revenue is estimated to have amounted to 7.14 billion US dollars .
gold_template: The timeline shows estimated data on the templateYLabel[0] of the American media corporation templateTitleSubject[0] , Inc. from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] is a privately-owned , unlisted company and owns such brands as Reddit - a social news and entertainment website and Conde Nast - a magazine publisher . Its templateXValue[min] templateYLabel[0] is estimated to have amounted to templateYValue[0] templateYLabel[1] US templateYLabel[3] .

generated_template: This statistic shows the global templateYLabel[0] of templateTitle[1] templateTitle[2] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] of templateTitle[1] templateTitle[2] reached approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from templateYValue[5] templateYLabel[1] the previous templateXLabel[0] . In templateXValue[max] , the templateTitle[1] with the highest lottery templateYLabel[0] was New York , with around 9.7 templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] .
generated: This statistic shows the global Revenue of Publications ' in the revenue from 2006 to 2014 . In 2014 , Revenue of Publications ' reached approximately 8.0 billion U.S. dollars , up from 6.78 billion the previous Year . In 2014 , the Publications with the highest lottery Revenue was New York , with around 9.7 billion U.S. dollars in Revenue .

Example 773:
titleEntities: {'Subject': ['Nike'], 'Date': ['2016', '2020']}
title: Global brand value of Nike from 2016 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016']
Y_Axis['Brand', 'value', 'in', 'million', 'U.S.', 'dollars']: ['34792', '32421', '28030', '31762', '28041']

gold: In 2020 , the Nike brand was valued at approximately 34.8 billion U.S. dollars , which was an increase of over two billion U.S. dollars from 2019 . Nike 's popularity Nike 's footwear segment was the source of the most revenue for the company in 2019 , netting over 24 billion U.S. dollars that year . Among U.S. consumers , Nike was the most popular sports shoe , ahead of its main competitors Adidas .
gold_template: In templateXValue[max] , the templateTitleSubject[0] templateYLabel[0] was valued at approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] , which was an increase of over two templateYLabel[2] templateYLabel[3] templateYLabel[4] templateTitle[4] templateXValue[1] . templateTitleSubject[0] 's popularity templateTitleSubject[0] 's footwear segment was the source of the most revenue for the company in templateXValue[1] , netting over 24 templateYLabel[2] templateYLabel[3] templateYLabel[4] that templateXLabel[0] . Among templateYLabel[3] consumers , templateTitleSubject[0] was the most popular sports shoe , ahead of its main competitors Adidas .

generated_template: 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: Global brand in the from has undergone a period of decline in recent years . As of 2020 estimates suggest that there were just 34792 million U.S. of or produced in the Nike Global brand is an important mineral substance that is used in the Global and steel industry . Global brand Global brand is mined in about 34792 countries globally and is used almost exclusively to create steel .

Example 774:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2008']}
title: Stationery retail sales turnover in the United Kingdom ( UK ) 2008 to 207
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Turnover', 'in', 'million', 'GBP']: ['4784', '4892', '4372', '4304', '4620', '4397', '4432', '4025', '4423', '4446']

gold: This statistic shows the total annual turnover of the retail sales of stationery and drawing materials and miscellaneous printed matter in the United Kingdom , from 2008 to 2017 . In 2017 , turnover from stationery and drawing material retail sales reached 4.78 billion British pounds which was the highest point of turnover over the nine year period .
gold_template: This statistic shows the total annual templateYLabel[0] of the templateTitle[1] templateTitle[2] of templateTitle[0] and drawing materials and miscellaneous printed matter in the templateTitleSubject[0] , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] from templateTitle[0] and drawing material templateTitle[1] templateTitle[2] reached templateYValue[0] templateYLabel[1] British pounds which was the highest point of templateYLabel[0] over the nine templateXLabel[0] period .

generated_template: The statistic illustrates the annual amount of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] generated templateYLabel[0] person templateYLabel[0] templateYLabel[5] between templateXValue[min] and templateXValue[max] . In the templateTitle[0] , an average of templateYValue[0] templateYLabel[3] of templateTitle[1] templateTitle[2] templateTitle[3] were generated daily templateYLabel[0] person in templateXValue[max] . templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[2] in the United StatesNon-hazardous templateTitle[3] consists of industrial templateTitle[3] and templateTitle[1] templateTitle[2] templateTitle[3] .
generated: The statistic illustrates the annual amount of United Kingdom retail sales turnover generated Turnover person Turnover GBP between 2008 and 2017 . In the Stationery , an average of 4784 GBP of retail sales turnover were generated daily Turnover person in 2017 . retail sales turnover GBP in the United StatesNon-hazardous turnover consists of industrial turnover and retail sales turnover .

Example 775:
titleEntities: {'Subject': ['Norway'], 'Date': ['2018', '2024']}
title: Forecast of smartphone user numbers in Norway 2018 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018']
Y_Axis['Smartphone', 'users', 'in', 'millions']: ['5.19', '5.15', '5.11', '5.0', '4.89', '4.77', '4.64']

gold: This statistic displays the development in smartphone user numbers in Norway in 2018 with a forecast from 2019 to 2024 . In 2018 , the number of smartphone users amounted to 4.64 million . In the same year , smartphone penetration rate was at 86.95 percent .
gold_template: This statistic displays the development in templateYLabel[0] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateXValue[min] with a templateTitle[0] from templateXValue[5] to templateXValue[max] . In templateXValue[min] , the number of templateYLabel[0] templateYLabel[1] amounted to templateYValue[min] templateYLabel[2] . In the same templateXLabel[0] , templateYLabel[0] penetration rate was at 86.95 percent .

generated_template: The statistic depicts the total number of templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . It is expected that the number of templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] will reach templateYValue[4] templateYLabel[2] in templateXValue[max] .
generated: The statistic depicts the total number of Smartphone users in Norway from 2018 to 2024 . It is expected that the number of Smartphone users in Norway will reach 4.89 millions in 2024 .

Example 776:
titleEntities: {'Subject': ['Haiti'], 'Date': ['2018']}
title: Infant mortality rate in Haiti 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Deaths', 'per', '1,000', 'live', 'births']: ['49.5', '50.8', '52.1', '53.3', '54.5', '55.7', '56.8', '57.9', '85.6', '60.2', '61.4']

gold: The statistic shows the infant mortality rate in Haiti from 2008 to 2018 . In 2018 , the infant mortality rate in Haiti was at about 49.5 deaths per 1,000 live births .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[min] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] was at about templateYValue[min] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Infant mortality in Haiti from 2008 to 2018 . In 2018 , the Infant mortality 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] percent of templateYLabel[1] stated that the templateXValue[0] of templateXValue[0] was the most templateTitle[0] feature of templateXValue[0] templateXValue[5] templateTitle[4] .

generated_template: templateXValue[2] than half of templateTitleSubject[1] wanted to start templateTitleDate[0] by saving templateXValue[0] and by getting in templateXValue[1] . The most popular templateXValue[5] templateTitleSubject[0] templateTitle[2] templateTitle[3] ever – `` templateXValue[7] '' – was not as high up on the list as it used to be , but at least templateYValue[7] percent decided to finally give up templateXValue[7] once and templateTitle[5] all . templateTitle[3] makers , templateTitle[3] keepers ? While some might say that they do not need templateXValue[5] templateTitleSubject[0] templateTitle[2] Eve to finally turn their life around , making resolutions on 31 is a common , well-liked tradition , especially in the Western world .
generated: The ability to listen on multiple divices than half of U.S. wanted to start 2018 by saving The variety of music available and by getting in Low price point . The most popular The ability to combine your music library with your streaming service library U.S. music streaming ever – `` Curated playlists '' – was not as high up on the list as it used to be , but at least 52 percent decided to finally give up Curated playlists once and U.S. all . streaming makers , streaming keepers ? While some might say that they do not need The ability to combine your music library with your streaming service library U.S. music Eve to finally turn their life around , making resolutions on 31 is a common , well-liked tradition , especially in the Western world .

Example 778:
titleEntities: {'Subject': ['Gannett'], 'Date': ['2013', '2018']}
title: Gannett 's revenue 2013 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['2.92', '3.15', '3.05', '2.89', '3.17', '3.32']

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

generated_template: The 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 fiscal templateXLabel[0] of templateXValue[max] , the Germany-based website generated around templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateYLabel[1] U.S. dollars in the preceding templateXLabel[0] .
generated: The statistic shows Gannett 's Revenue from the fiscal Year of 2013 to the fiscal Year of 2018 . In the fiscal Year of 2018 , the Germany-based website generated around 2.89 billion U.S. dollars , up from 3.15 billion U.S. dollars in the preceding Year .

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 increase by templateYValue[max] percent . templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[5] It is projected that the growth of templateTitle[6] templateYLabel[1] the templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] will slow , templateYLabel[1] a rate of templateYValue[max] percent in templateXValue[min] to templateYValue[min] percent in templateXValue[1] , and grow again to templateYValue[max] percent in templateXValue[max] .

generated_template: This statistic 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 increase by 1.7 years , the largest increase predicted between templateXValue[2] and templateXValue[1] at 0.8 years .
generated: This statistic shows the refinery of the from year in the U.S. ( 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 0.8 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] templateYLabel[2] templateYLabel[3] in templateXValue[5] .

generated_template: The statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[1] worth approximately templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] had been exported templateTitle[2] templateTitleSubject[0] . templateTitle[0] of templateTitle[1] templateTitle[2] templateTitleSubject[0] – additional information According to the graph at hand , templateTitleSubject[0] 's templateYLabel[0] have been growing steadily over the past decade , except in templateXValue[8] when financial crisis and global economic downturn slowed down global trade .
generated: The statistic shows the Chocolate of cocoa products Spanish from 2008 to 2018 . In 2018 , cocoa worth approximately 164.9 consumption million kilograms had been exported products Spanish . Chocolate of cocoa products Spanish – additional information According to the graph at hand , Spanish 's Total have been growing steadily over the past decade , except in 2010 when financial crisis and global economic downturn slowed down global trade .

Example 781:
titleEntities: {'Subject': ['Burundi'], 'Date': ['2024']}
title: Inflation rate in Burundi 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['8.97', '8.97', '8.97', '8.97', '8.97', '7.32', '1.24', '16.64', '5.53', '5.55', '4.42', '7.94', '18.18', '9.58', '6.5', '10.56', '24.41', '8.41', '2.74', '13.25', '8.18', '10.57', '-1.26', '7.87', '25.52', '3.52', '12.47', '31.06', '26.42', '19.36', '14.71', '9.71', '5.33', '9.01', '6.99', '11.67', '4.49', '7.11', '1.67', '3.82', '14.3']

gold: This statistic shows the average inflation rate in Burundi from 1984 to 2017 , with projections up until 2024 . In 2017 , the average inflation rate in Burundi amounted to about 16.64 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[7] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

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

Example 782:
titleEntities: {'Subject': ['U.S. April'], 'Date': ['2014', '2014']}
title: Methods of ordering food for takeout or delivery in the U.S. as of April 2014
X_Axis['Response']: ['By_phone', 'In_person', 'Online', 'Via_mobile_app', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['50.5', '43.5', '32.1', '3.9', '0.2']

gold: This statistic shows the methods which consumers used to order food for takeout or delivery in the United States as of April 2014 . During the survey , 32.1 percent of respondents said they ordered food for takeout or delivery online .
gold_template: This statistic shows the templateTitle[0] which consumers used to order templateTitle[2] templateTitle[3] templateTitle[4] or templateTitle[5] in the templateTitle[6] as of templateTitleSubject[0] templateTitle[8] . During the survey , templateYValue[2] percent of templateYLabel[1] said they ordered templateTitle[2] templateTitle[3] templateTitle[4] or templateTitle[5] templateXValue[2] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] survey templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] with sensitive information templateTitle[8] or templateTitle[7] , while traveling . templateYValue[max] percent of templateYLabel[1] templateTitle[4] templateTitle[8] or templateTitle[5] a templateXValue[0] templateTitle[7] whilst traveling .
generated: The statistic shows the Share of U.S. April survey respondents for takeout delivery U.S. with sensitive information 2014 or April , while traveling . 50.5 percent of respondents takeout 2014 or delivery a By phone April whilst traveling .

Example 783:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2019']}
title: Public sector expenditure as a share of GDP in the United Kingdom ( UK ) 2000 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02', '00/01']
Y_Axis['Expenditure', 'as', 'share', 'of', 'GDP']: ['34.1', '34.4', '34.8', '35.7', '36.4', '37.3', '38.3', '39.1', '39.7', '39.4', '36.9', '34.9', '34.5', '34.7', '34.9', '34', '32.8', '32.1', '31.8']

gold: This statistic shows total public sector current expenditure as a share of GDP in the United Kingdom ( UK ) from 2000/01 to 2018/19 . During this period public sector spending fluctuated , peaking in 2010/11 at 39.7 percent of GDP .
gold_template: This statistic shows total templateTitle[0] templateTitle[1] current templateYLabel[0] as a templateYLabel[1] of templateYLabel[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from 2000/01 to 2018/19 . During this period templateTitle[0] templateTitle[1] spending fluctuated , peaking in 2010/11 at templateYValue[max] percent of templateYLabel[2] .

generated_template: This statistic shows the total templateTitleSubject[0] ( UK ) templateTitleSubject[0] templateYLabel[0] and templateYLabel[1] templateYLabel[2] templateYLabel[3] from fiscal templateXLabel[0] 2000/01 to fiscal templateXLabel[0] 2018/19 . Over the period there were a number of fluctuations but overall there was an increase . In the most recent recorded period , templateYLabel[0] and templateYLabel[1] templateYLabel[2] templateYLabel[3] totaled almost templateYValue[max] templateYLabel[4] British pounds , which was also the peak .
generated: This statistic shows the total United Kingdom ( UK ) United Kingdom Expenditure and share GDP from fiscal Year 2000/01 to fiscal Year 2018/19 . Over the period there were a number of fluctuations but overall there was an increase . In the most recent recorded period , Expenditure and share GDP totaled almost 39.7 GDP British pounds , which was also the peak .

Example 784:
titleEntities: {'Subject': ['Luxembourg'], 'Date': ['2006', '2018']}
title: Number of arrivals in tourist accommodation Luxembourg 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012_', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'arrivals', 'in', 'thousands']: ['1139.0', '1156.0', '1161.8', '1196.1', '1142.9', '1044.3', '1021.7', '935.0', '854.72', '907.53', '936.65', '979.21', '967.88']

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Luxembourg from 2006 to 2018 . There were around 1.1 million arrivals at accommodation establishments in Luxembourg in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . There were around templateYValue[0] templateYLabel[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] in templateXValue[0] .

generated_template: The annual templateTitle[3] templateTitle[4] has been on decline in templateTitleSubject[0] and the templateTitleSubject[1] since templateXValue[min] . In templateXValue[max] , this region 's templateTitle[3] templateTitle[4] amounted to an average of templateYValue[min] live templateYLabel[1] templateYLabel[2] 1,000 population , down from almost templateYValue[11] templateYLabel[1] templateYLabel[2] 1,000 templateYLabel[4] in templateXValue[min] . The population growth templateTitle[4] in templateTitleSubject[0] and templateTitleSubject[1] has started to decrease in the last few years .
generated: The annual accommodation Luxembourg has been on decline in Luxembourg and the Luxembourg since 2018 . In 2018 , this region 's accommodation Luxembourg amounted to an average of 854.72 live arrivals thousands 1,000 population , down from almost 979.21 arrivals thousands 1,000 thousands in 2018 . The population growth Luxembourg in Luxembourg and Luxembourg has started to decrease in the last few years .

Example 785:
titleEntities: {'Subject': ['Iberdrola'], 'Date': ['2009', '2018']}
title: Iberdrola - revenue 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'in', 'million', 'euros']: ['35075.9', '31263.26', '28759.15', '31418.69', '30032.27', '31077.11', '34201.0', '31648.0', '30431.0', '25891.9']

gold: This statistic represents Iberdrola 's global revenue between the fiscal year of 2009 and the fiscal year of 2018 . The Spain-based multinational electric utility company with headquarters in Bilbao generated around 35 billion euros in revenue in the fiscal year of 2018 .
gold_template: This statistic represents templateTitleSubject[0] 's global templateYLabel[0] between the fiscal templateXLabel[0] of templateXValue[min] and the fiscal templateXLabel[0] of templateXValue[max] . The Spain-based multinational electric utility company with headquarters in Bilbao generated around templateYValue[max] templateYLabel[1] templateYLabel[2] in templateYLabel[0] in the fiscal templateXLabel[0] of templateXValue[max] .

generated_template: templateTitleSubject[0] has managed to increase its annual templateYLabel[0] from templateXValue[1] by three percent , meaning that annual templateYLabel[0] surpassed templateYValue[max] templateYLabel[1] templateYLabel[2] in the templateXValue[max] fiscal templateXLabel[0] . Restructure and redirection templateYLabel[0] grew in templateXValue[max] at templateTitleSubject[0] in spite of a struggling power and gas division , which will be spun off by the end of 2020 . In fact , templateYLabel[0] from this division dropped from around 18 templateYLabel[1] templateYLabel[2] in templateXValue[1] to about 17.66 templateYLabel[1] templateYLabel[2] in templateXValue[max] .
generated: Iberdrola has managed to increase its annual Revenue from 2017 by three percent , meaning that annual Revenue surpassed 35075.9 million euros in the 2018 fiscal Year . Restructure and redirection Revenue grew in 2018 at Iberdrola in spite of a struggling power and gas division , which will be spun off by the end of 2020 . In fact , Revenue from this division dropped from around 18 million euros in 2017 to about 17.66 million euros in 2018 .

Example 786:
titleEntities: {'Subject': ['United States'], 'Date': ['2012']}
title: Reasons for opposing same-sex marriage in the United States in 2012
X_Axis['Month']: ['Religion/Bible_says_it_is_wrong', 'Marriage_should_be_between_a_man_and_a_woman', 'Morally_wrong/Have_traditional_beliefs', 'Civil_unions_are_sufficient', 'Unnatural/Against_laws_of_nature', 'Undermines_traditional_family_structure/Mother_and_father', 'Other', 'No_opinion']
Y_Axis['Share', 'of', 'respondents']: ['47', '20', '16', '6', '5', '5', '7', '4']

gold: This statistic shows the results of a 2012 survey among American adults opposing legal same-sex marriage . They were asked to give reasons for this decision . 47 percent of respondents stated that they oppose same-sex marriage because their religion and/or the Bible says it 's wrong .
gold_template: This statistic shows the results of a templateTitleDate[0] survey among American adults templateTitle[2] legal templateTitle[3] templateXValue[1] . They were asked to give templateTitle[0] templateTitle[1] this decision . templateYValue[max] percent of templateYLabel[1] stated that they oppose templateTitle[3] templateXValue[1] because their religion and/or the Bible templateXValue[0] it 's templateXValue[0] .

generated_template: As of the third quarter of templateTitleDate[0] , it was found that templateYValue[max] percent of templateTitleSubject[0] users in the templateTitleSubject[1] accessed the photo sharing app templateXValue[0] a templateXValue[0] . A further templateYValue[1] percent of templateYLabel[1] claimed that they used templateTitleSubject[0] on a templateXValue[1] basis .
generated: As of the third quarter of 2012 , it was found that 47 percent of United States users in the United States accessed the photo sharing app Religion/Bible says it is wrong a Religion/Bible says it is wrong . A further 20 percent of respondents claimed that they used United States on a Marriage should be between a man and a woman basis .

Example 787:
titleEntities: {'Subject': ['Nissan', 'Europe'], 'Date': ['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 rose from 400 thousand templateYLabel[1] templateYLabel[2] in templateXValue[min] to over templateYValue[max] thousand templateYLabel[1] templateYLabel[2] by templateXValue[1] . In templateXValue[max] , there were templateYValue[0] thousand templateYLabel[1] of templateTitleSubject[0] cars templateYLabel[2] in templateTitleSubject[1] .

generated_template: In templateXValue[max] , the templateYLabel[1] templateYLabel[2] of hospitals in the templateTitleSubject[0] stood at templateYValue[0] percent . Hospitals are a cornerstone of health services in any country and also contribute considerably to the economy . Nevertheless , the number of hospitals in the templateTitleSubject[0] has decreased in recent years .
generated: In 2018 , the units sold of hospitals in the Nissan stood at 487017 percent . Hospitals are a cornerstone of health services in any country and also contribute considerably to the economy . Nevertheless , the number of hospitals in the Nissan has decreased in recent years .

Example 788:
titleEntities: {'Subject': ['Annual'], 'Date': ['2010', '2018']}
title: Annual growth in average global hotel rates 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Year-over-year', 'growth', 'in', 'average', 'hotel', 'rates']: ['3.7', '2.5', '2.5', '2.6', '1.8', '0', '-1.5', '7.4', '4.7']

gold: This statistic shows annual growth in average global hotel rates from 2010 to 2018 . Global hotel rates were forecasted to increase by 3.7 percent in 2018 . The average daily rate of the hotel industry in the Americas reached around 123.37 U.S. dollars in 2016 .
gold_template: This statistic shows templateTitleSubject[0] templateYLabel[1] in templateYLabel[2] templateTitle[3] templateYLabel[3] templateYLabel[4] from templateXValue[min] to templateXValue[max] . templateTitle[3] templateYLabel[3] templateYLabel[4] were forecasted to increase by templateYValue[0] percent in templateXValue[max] . The templateYLabel[2] daily rate of the templateYLabel[3] industry in the Americas reached around 123.37 U.S. dollars in templateXValue[2] .

generated_template: 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] decreased by 0.6 percent compared to the previous templateXLabel[0] . templateTitle[3] templateTitle[4] were forecasted to increase by templateYValue[0] percent in templateXValue[max] .
generated: This statistic shows Annual growth in average global hotel from 2010 to 2018 . In 2014 , the average global hotel decreased by 0.6 percent compared to the previous Year . global hotel were forecasted to increase by 3.7 percent in 2018 .

Example 789:
titleEntities: {'Subject': ['Muslims', 'Spain'], 'Date': ['2018']}
title: Muslims in Spain 2018 , by nationality
X_Axis['Country']: ['Spain', 'Morocco', 'Pakistan', 'Senegal', 'Algeria', 'Nigeria', 'Mali', 'Gambia', 'Bangladesh', 'Guinea', 'Others']
Y_Axis['Number', 'of', 'Muslims']: ['847801', '769050', '82738', '66046', '60820', '39374', '23685', '19381', '15979', '10186', '58615']

gold: This statistic presents the number of Muslims in Spain in 2018 , broken down by nationality . That year , there were a total of approximately two million Muslims in Spain . Almost 848 thousand had Spanish nationality , followed by Muslims with a Moroccan nationality with figures that almost reached 770 thousand individuals .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] in templateXValue[0] in templateTitleDate[0] , broken down templateTitle[3] templateTitle[4] . That year , there were a total of approximately two million templateYLabel[1] in templateXValue[0] . Almost templateYValue[max] thousand had Spanish templateTitle[4] , followed templateTitle[3] templateYLabel[1] with a Moroccan templateTitle[4] with figures that almost reached 770 thousand individuals .

generated_template: The statistic shows the countries with the templateTitleSubject[0] templateTitle[1] templateYLabel[1] in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[0] suffered templateYValue[max] templateTitle[1] templateYLabel[1] , the same templateYLabel[0] as templateXValue[1] .
generated: The statistic shows the countries with the Muslims Spain Muslims in 2018 . In 2018 , Spain suffered 847801 Spain Muslims , the same Number as Morocco .

Example 790:
titleEntities: {'Subject': ['Tampa Bay Buccaneers', 'NFL'], 'Date': ['2001', '2018']}
title: Revenue of the Tampa Bay Buccaneers ( NFL ) 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['400', '383', '367', '341', '313', '275', '267', '258', '245', '246', '241', '224', '205', '203', '195', '175', '168', '151']

gold: The statistic depicts the revenue of the Tampa Bay Buccaneers , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Tampa Bay Buccaneers was 400 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] Buccaneers , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] Buccaneers was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Tampa Bay Buccaneers , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the Revenue of the Tampa Bay Buccaneers was 400 million U.S. dollars .

Example 791:
titleEntities: {'Subject': ['Afghanistan'], 'Date': ['2018']}
title: Population growth in Afghanistan 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Population', 'growth', 'compared', 'to', 'previous', 'year']: ['2.38', '2.55', '2.78', '3.08', '3.36', '3.49', '3.41', '3.14', '2.75', '2.4', '2.27']

gold: This timeline shows the population growth in Afghanistan from 2008 to 2018 . In 2018 , Afghanistan 's population grew by an estimated 2.38 percent compared to the previous year . See Afghanistan 's population figures for comparison .
gold_template: This timeline shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] grew by an estimated templateYValue[0] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . See templateTitleSubject[0] 's templateYLabel[0] figures for comparison .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] had reached about templateYValue[max] years.Demographic development in templateTitleSubject[0] – additional information templateYLabel[0] templateYLabel[1] at templateYLabel[2] refers to the average number of years a group of people born in the same templateXLabel[0] would live , assuming constant mortality rates . The country with the highest templateYLabel[0] templateYLabel[1] at templateYLabel[2] was Japan , while templateTitleSubject[0] had reached a templateYLabel[0] templateYLabel[1] above global average .
generated: The statistic shows the Population growth at compared in Afghanistan from 2008 to 2018 . In 2018 , the average Population growth at compared in Afghanistan had reached about 3.49 years.Demographic development in Afghanistan – additional information Population growth at compared 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 Population growth at compared was Japan , while Afghanistan had reached a Population growth above global average .

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] percent in templateXValue[min] , amounting to about templateYValue[1] million people using a templateTitle[1] network in the templateTitleSubject[0] as of templateXValue[5] . Considering that the number of internet users in the templateTitleSubject[0] was at just under 70 million in that templateXLabel[0] , the templateTitle[1] templateTitle[2] templateTitle[4] was projected to increase to templateYValue[max] percent of the templateYLabel[1] by templateXValue[max] . templateTitle[1] templateTitle[2] in the templateTitleSubject[0] The templateTitleSubject[0] are an archipelagic country , which poses logistical problems for templateTitle[1] interaction and communication between residents from the various islands .

generated_template: This statistic gives information on the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the Singaporean templateYLabel[1] were using the templateTitle[1] . In templateXValue[max] , this figure is projected to grow to templateYValue[max] percent .
generated: This statistic gives information on the social media user in Philippines from 2017 to 2023 . In 2017 , 49 percent of the Singaporean population were using the social . In 2023 , this figure is projected to grow to 55 percent .

Example 793:
titleEntities: {'Subject': ['USA'], 'Date': ['1990', '2018']}
title: USA - reported burglary rate 1990 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92", "'91", "'90"]
Y_Axis['Rate', 'per', '100,000', 'population']: ['376.0', '429.7', '468.9', '494.7', '537.2', '610.5', '672.2', '701.3', '701.0', '717.7', '733.0', '726.1', '733.1', '726.9', '730.3', '741.0', '747.0', '740.8', '728.8', '770.4', '863.0', '919.6', '944.8', '987.1', '1042.0', '1099.2', '1168.2', '1252.0', '1235.9']

gold: This graph shows the reported burglary rate in the United States from 1990 to 2018 . In 2018 , the nationwide rate was 376 cases per 100,000 of the population .
gold_template: This graph shows the templateTitle[1] templateTitle[2] templateYLabel[0] in the country from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , the nationwide templateYLabel[0] was templateYValue[min] cases templateYLabel[1] 100,000 of the templateYLabel[3] .

generated_template: This graph shows the templateTitle[1] templateTitle[2] and non-negligent templateTitle[4] templateYLabel[0] in the country from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , 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[20] templateYLabel[4] templateYLabel[1] one templateYLabel[2] of upland templateTitle[0] .

generated_template: This statistic represents the total templateTitle[1] templateYLabel[0] templateYLabel[1] in the templateTitle[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] of templateTitle[1] were generated in the templateTitle[0] . templateTitleSubject[0] templateTitle[1] templateYLabel[1] In templateXValue[max] , the templateTitle[0] generated some 4.2 petawatt templateYLabel[4] of templateTitle[1] .
generated: This statistic represents the total price per in the Cotton between 1990 and 2018 . In 2018 , approximately 88.02 pound U.S. cents of price were generated in the Cotton . U.S. price per In 2018 , the Cotton generated some 4.2 petawatt cents of price .

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: There were templateYValue[min] templateTitle[1] templateTitle[2] recorded in templateTitleSubject[0] in templateXValue[max] . templateTitleSubject[0] had been able to decrease the templateYLabel[0] of people fatally injured on its roads by nearly half since templateXValue[min] . templateXValue[7] and templateXValue[3] were the only years in which the templateYLabel[0] of fatal accidents increased .
generated: There were 371478 deaths Spain recorded in Spain in 2018 . Spain had been able to decrease the Number of people fatally injured on its roads by nearly half since 2006 . 2011 and 2015 were the only years in which the Number of fatal accidents increased .

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[21] athletes participated . This figure grew to templateYValue[1] participating athletes from 82 nations during the templateXValue[1] Winter Olympics .

generated_template: This statistic shows the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] worldwide from templateTitleDate[0] to templateTitle[5] . By templateTitle[5] , the worldwide templateYLabel[0] templateYLabel[1] at templateYLabel[2] is templateTitleSubject[0] to be templateYValue[max] templateYLabel[3] .
generated: This statistic shows the Winter Olympic Games Number participants worldwide from 2014 to 2014 . By 2014 , the worldwide Number participants at participants is Winter Olympic Games to be 2800 participants .

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

generated_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of templateTitleSubject[0] was about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the development of California 's Real GDP from 2000 to 2018 . In 2018 , the GDP of California was about 2677.94 billion U.S. dollars .

Example 798:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in South Korea 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['1988.11', '1884.06', '1789.96', '1702.14', '1626.55', '1629.53', '1720.49', '1623.9', '1500.48', '1465.77', '1484.32', '1305.61', '1222.81', '1202.46', '1094.5', '901.94', '1002.22', '1122.68', '1011.8', '898.14', '764.88', '680.52', '609.02', '533.05', '561.63', '485.25', '374.24', '557.5', '598.1', '556.13', '455.61', '386.3', '350.05', '325.73', '279.35', '243.53', '196.97', '146.13', '115.54', '100.27', '96.6']

gold: The statistic shows gross domestic product ( GDP ) of South Korea from 1984 to 2018 , with projections up until 2024 . GDP or gross domestic product is the sum of all goods and services produced in a country in a year ; it is a strong indicator of economic strength . In 2018 , South Korea 's GDP was around 1.72 trillion U.S. dollars .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateTitle[3] or templateYLabel[0] templateYLabel[1] templateYLabel[2] is the sum of all goods and services produced in a country in a templateXLabel[0] ; it is a strong indicator of economic strength . In templateXValue[6] , templateTitleSubject[0] 's templateTitle[3] was around templateYValue[6] templateYLabel[3] templateYLabel[4] templateYLabel[5] .

generated_template: The statistic shows 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 templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[4] templateTitle[5] in templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[4] templateYLabel[5] templateYLabel[2] templateYLabel[3] . The templateYLabel[0] gate templateYLabel[1] of one templateYLabel[3] of templateTitle[4] templateTitle[5] in templateTitleSubject[0] has fluctuated over the past eighteen years , having hit a high of templateYValue[max] templateYLabel[4] templateYLabel[5] in templateXValue[min] . The volume of templateTitle[4] templateTitle[5] produced in templateTitleSubject[0] has also varied quite dramatically in the last few years .
generated: In 2018 , the Imports thousand of Iraq 2000 in Iraq amounted to 521 day barrels per . The Imports gate thousand of one per of Iraq 2000 in Iraq has fluctuated over the past eighteen years , having hit a high of 795 day in 2000 . The volume of Iraq 2000 produced in Iraq has also varied quite dramatically in the last few years .

Example 800:
titleEntities: {'Subject': ['Cree'], 'Date': ['2015', '2019']}
title: Cree 's revenue 2015 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015']
Y_Axis['Net', 'revenue', 'in', 'million', 'U.S.', 'dollars']: ['1080.0', '924.9', '771.5', '727.5', '726.0']

gold: This statistic represents Cree 's revenue from the fiscal year of 2015 to the fiscal year of 2019 . In the fiscal year of 2019 , the LED technology company reported revenue of about 1.08 billion U.S. dollars .
gold_template: This statistic represents templateTitleSubject[0] templateTitle[1] templateYLabel[1] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , the LED technology company reported templateYLabel[1] of about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The templateTitleSubject[0] is the leading country in Europe in terms of online shopping penetration . By now a mature market , templateTitleSubject[1] templateTitle[0] templateYLabel[0] reached a value of templateYValue[max] templateYLabel[1] British pounds in templateXValue[max] . Between templateXValue[2] and templateXValue[1] alone , templateTitle[0] templateYLabel[0] went up by 80 templateYLabel[1] British pounds .
generated: The Cree is the leading country in Europe in terms of online shopping penetration . By now a mature market , Cree Net reached a value of 1080.0 revenue British pounds in 2019 . Between 2017 and 2018 alone , Cree Net went up by 80 revenue British pounds .

Example 801:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2018']}
title: Population growth in Malaysia 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Population', 'growth', 'compared', 'to', 'previous', 'year']: ['1.35', '1.36', '1.36', '1.34', '1.34', '1.37', '1.45', '1.56', '1.69', '1.82', '1.91']

gold: This statistic shows the population growth in Malaysia from 2008 to 2018 . In 2018 , Malaysia 's population increased by approximately 1.35 percent compared to the previous year .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] increased by approximately templateYValue[0] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateTitle[0] of templateTitle[1] to templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[1] worth approximately templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] were imported to templateTitleSubject[0] . templateYLabel[0] to templateTitleSubject[0] – additional information In templateXValue[4] , templateTitleSubject[0] had surpassed the country as the world 's largest templateTitle[1] trader .
generated: The statistic shows the Population of growth to Malaysia from 2008 to 2018 . In 2018 , growth worth approximately 1.35 growth compared previous were imported to Malaysia . Population to Malaysia – additional information In 2014 , Malaysia had surpassed the country as the world 's largest growth trader .

Example 802:
titleEntities: {'Subject': ['Nepal'], 'Date': ['2024']}
title: Total population of Nepal 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['30.36', '29.97', '29.59', '29.2', '28.83', '28.46', '28.09', '27.63', '27.26', '27.02', '26.91']

gold: This statistic represents the total population of Nepal from 2014 to 2015 , with projections up until 2024 . In 2018 , the estimated total population of Nepal amounted to around 28.09 million people .
gold_template: This statistic represents the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[9] , with projections up until templateXValue[max] . In templateXValue[6] , the estimated templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateYLabel[1] people .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to around templateYValue[7] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] is the ten largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .
generated: The statistic shows the Total population of Nepal from 2014 to 2017 , with projections up until 2024 . In 2017 , the Total population of Nepal amounted to around 27.63 millions Inhabitants . population of Nepal is the ten largest nation in the Middle East and one of the founding members of the United Nations , NAM , OIC and OPEC .

Example 803:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Leading U.S. consumer e-mail providers 2016
X_Axis['Response']: ['Google_(Gmail)', 'Yahoo', 'Outlook_(Hotmail)', 'AOL', 'Other', 'iCloud', 'Comcast']
Y_Axis['Share', 'of', 'respondents']: ['53', '18', '14', '8', '4', '2', '1']

gold: This statistic shows the most popular e-mail providers according to consumers in the United States as of 2016 . During the consumer survey , 53 percent of respondents stated that they used Gmail as their primary e-mail provider . Yahoo was ranked second with 18 percent .
gold_template: This statistic shows the most popular templateTitle[3] templateTitle[4] according to consumers in the templateTitle[1] as of templateTitleDate[0] . During the templateTitle[2] survey , templateYValue[max] percent of templateYLabel[1] stated that they used Gmail as their primary templateTitle[3] provider . templateXValue[1] was ranked second with templateYValue[1] percent .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among templateTitleSubject[0] templateTitle[1] aged 18 to 60 about their stance on templateXValue[0] on templateTitle[4] . They were asked how they would manage to templateXValue[0] on templateTitle[4] templateTitle[5] templateTitle[6] a templateXValue[0] with others . templateYValue[max] percent of the templateTitle[1] stated templateXValue[1] they would templateXValue[0] when templateXValue[0] is in the templateXValue[0] .
generated: This statistic shows the results of a 2016 survey among U.S. aged 18 to 60 about their stance on Google (Gmail) on providers . They were asked how they would manage to Google (Gmail) on providers 2016 a Google (Gmail) with others . 53 percent of the U.S. stated Yahoo they would Google (Gmail) when Google (Gmail) is in the Google (Gmail) .

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: 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: U.S. 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 , China and United States are the two countries leading the list of cat owners in the EU , with the former 's narrow win . While China also ranks as the top EU Country with the highest Price of pet dogs , U.S. still win in Global households .

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] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .

generated_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .
generated: This graph depicts the annual National Hockey League Revenue of the Minnesota Wilds from the 2005/06 season to the 2018/19 season . The Revenue of the Minnesota Wilds 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] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: In templateXValue[max] , the templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] Group reported a templateYLabel[0] of almost templateYValue[min] templateYLabel[1] templateYLabel[2] . Despite the impressive figure , the templateYLabel[0] of templateXValue[max] represented a decrease for templateTitle[4] compared to the previous years . Indeed , in templateXValue[min] , the first templateXLabel[0] considered in this graph , the templateYLabel[0] of the templateTitle[3] amounted to approximately templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: In 2018 , the U.S. volume U.S. asset-backed Group reported a Volume of almost 126 billion U.S. . Despite the impressive figure , the Volume of 2018 represented a decrease for asset-backed compared to the previous years . Indeed , in 2000 , the first Year considered in this graph , the Volume of the U.S. amounted to approximately 550 billion U.S. .

Example 807:
titleEntities: {'Subject': ['Luxottica'], 'Date': ['2018']}
title: Share of global net sales of Luxottica by geographical area 2018
X_Axis['Geographical', 'area']: ['North_America', 'Europe', 'Asia-Pacific', 'Latin_America', 'Rest_of_the_world']
Y_Axis['Share', 'of', 'net', 'sales']: ['58', '21', '13', '6', '2']

gold: This statistic depicts the share of net sales of Luxottica worldwide in 2018 , by geographical area . In that year , 58 percent of Luxottica 's global net sales came from North America . Founded in 1961 in Agordo , Italy , the Luxottica Group S.p.A. is the world 's largest eyewear company .
gold_template: This statistic depicts the templateYLabel[0] of templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] worldwide in templateTitleDate[0] , templateTitle[5] templateXLabel[0] templateXLabel[1] . In that year , templateYValue[max] percent of templateTitleSubject[0] 's templateTitle[1] templateYLabel[1] templateYLabel[2] came from templateXValue[0] . Founded in 1961 in Agordo , Italy , the templateTitleSubject[0] Group S.p.A. is the templateXValue[last] 's largest eyewear company .

generated_template: The statistic depicts templateTitle[4] figures for total templateTitle[3] templateTitleSubject[0] templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleDate[0] . The templateXValue[0] Cup templateXValue[0] in templateTitleDate[0] is templateTitle[4] to draw around templateYValue[max] templateYLabel[1] templateTitleSubject[0] viewers templateTitle[3] . templateYLabel[0] for templateTitle[0] templateTitle[1] templateTitle[2] – additional information The templateXValue[0] , an international soccer competition organized by templateXValue[0] , is one of the biggest sports templateTitle[2] in the templateXValue[0] .
generated: The statistic depicts Luxottica figures for total sales Luxottica Share of Share global net in 2018 . The North America Cup North America in 2018 is Luxottica to draw around 58 net Luxottica viewers sales . Share for Share global net – additional information The North America , an international soccer competition organized by North America , is one of the biggest sports net in the North America .

Example 808:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2011', '2017']}
title: Retail sales of the frame market for eyewear in the U.S. 2011 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Retail', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['9732.2', '9710.0', '9627.0', '9153.0', '8871.0', '8475.0', '8213.0']

gold: This statistic depicts the retail sales of the frame market for eyewear in the United States from 2011 to 2017 . In 2017 , the U.S. frame market for eyewear generated about 9.73 billion U.S. dollars in retail sales .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[3] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] generated about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateYLabel[0] templateYLabel[1] .

generated_template: 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 sales Retail of sales enrolled in Canadian colleges in 2011 , for eyewear U.S. . In the academic Year 2011 , around 9732.2 sales aged between 2017 and 2016 2017 were enrolled in Canadian colleges .

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] percent of templateTitleSubject[0] templateTitle[1] are templateXValue[0] that templateXValue[0] is a templateXValue[0] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] survey templateYLabel[1] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] with sensitive information templateTitle[8] or templateTitle[7] , while traveling . templateYValue[max] percent of templateYLabel[1] templateTitle[4] templateTitle[8] or templateTitle[5] a templateXValue[0] templateTitle[7] whilst traveling .
generated: The statistic shows the Share of American survey respondents belief existence God with sensitive information God or God , while traveling . 54 percent of respondents existence God or God a Absolutely certain that there is a God God whilst traveling .

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[max] 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[max] . This number has been slowly increasing 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[min] templateYLabel[3] templateYLabel[4] dollars .

generated_template: The statistic illustrates the templateYLabel[0] of templateTitleSubject[0] & Cie. from templateXValue[last] to templateXValue[0] . In its fiscal templateXLabel[0] templateXValue[1] , templateTitleSubject[0] made total templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] worldwide , a record high . templateTitleSubject[0] 's annual sales have witnessed continuous growth during the measured period .
generated: The statistic illustrates the Vended of Vending & Cie. from 2010 to 1999 . In its fiscal Year 2000 , Vending made total Vended of 47.5 volume (in worldwide , a record high . Vending 's annual sales have witnessed continuous growth during the measured period .

Example 812:
titleEntities: {'Subject': ['Ohio'], 'Date': ['1990', '2018']}
title: Ohio - Median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['61633', '59768', '53985', '53301', '49644', '46398', '44375', '44648', '45886', '45879', '46934', '49099', '45900', '44203', '43055', '43520', '42684', '41785', '42962', '39489', '38925', '36134', '34070', '34941', '31855', '31285', '31404', '29790', '30013']

gold: This statistic shows the median household income in Ohio from 1990 to 2018 . In 2018 , the median household income in Ohio amounted to 61,633 U.S. dollars .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Median Household income in Ohio from 1990 to 2018 . In 2018 , the Median Household income in Ohio amounted to 61633 U.S. 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[16] templateTitle[0] arrived in the templateTitle[4] aged templateXValue[last] templateXValue[1] or templateXValue[last] . The total templateYLabel[0] of refugee arrivals amounted to 22,405 .

generated_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] of templateTitle[4] templateTitle[5] in the templateTitle[7] between templateTitleDate[0] and templateTitleDate[1] . templateTitleSubject[0] templateTitle[1] templateTitle[2] had a templateYLabel[0] of templateYValue[max] templateYLabel[1] .
generated: This statistic shows U.S. arriving by Number of U.S. 2018 in the 2018 between 2018 and 2018 . U.S. arriving by had a Number of 2914 persons .

Example 814:
titleEntities: {'Subject': ['Cyber'], 'Date': ['2019']}
title: Cyber bullying : common types of bullying 2019
X_Axis['Response']: ['I_have_been_cyber_bullied', 'Mean_or_hurtful_comments_online', 'Rumors_online', 'Threatened_to_hurt_me_through_a_cell_phone_text', 'Posted_mean_names_or_comments_online_about_me_with_a_sexual_meaning', 'Threatened_to_hurt_me_online', 'Posted_a_mean_or_hurtful_picture_online_of_me', 'Pretended_to_be_me_online', 'Posted_mean_names_or_comments_about_my_race_or_color', 'Posted_a_mean_or_hurtful_video_online_of_me', 'Posten_mean_names_or_comments_online_about_my_religion', 'Created_a_mean_or_hurtful_web_page_about_me', 'One_or_more_of_above_two_or_more_times']
Y_Axis['Share', 'of', 'respondents']: ['17.4', '24.9', '22.2', '12.2', '12', '11.7', '10.8', '10.1', '9.5', '7.1', '6.7', '6.4', '30.1']

gold: This statistic presents the percentage of middle and high school students in the United States who were cyber bullied , divided by the type of cyber bullying endured . During the April 2019 survey , 10.1 percent of cyber bullying victims had been impersonated online during the last 30 days . Cyber bullying includes unwanted contact by threatening or insulting the respondent via digital communications as well as sharing private information online .
gold_template: This statistic presents the percentage of middle and high school students in the country who were templateXValue[0] , divided by the type of templateXValue[0] templateTitle[1] endured . During the 2019 survey , templateYValue[7] percent of templateXValue[0] templateTitle[1] victims had templateXValue[0] impersonated templateXValue[1] during the last templateYValue[max] days . templateXValue[0] templateTitle[1] includes unwanted contact by threatening or insulting the respondent via digital communications as well as sharing private information templateXValue[1] .

generated_template: This statistic shows the results of a survey that asked British adults which types of templateXValue[last] , if any , they ever templateXValue[last] in templateTitleSubject[0] ( GB ) in templateTitleDate[0] . templateXValue[0] tea ranked highest with a templateYValue[max] percent templateYLabel[0] of templateYLabel[1] , followed by templateXValue[1] templateXValue[last] ( templateYValue[1] percent ) and templateXValue[2] ( templateYValue[2] percent ) . A majority of British consumers templateXValue[last] on a daily basis , with earlier times in the day the peak period for templateXValue[last] drinking .
generated: This statistic shows the results of a survey that asked British adults which types of One or more of above two or more times , if any , they ever One or more of above two or more times in Cyber ( GB ) in 2019 . I have been cyber bullied tea ranked highest with a 30.1 percent Share of respondents , followed by Mean or hurtful comments online One or more of above two or more times ( 24.9 percent ) and Rumors online ( 22.2 percent ) . A majority of British consumers One or more of above two or more times on a daily basis , with earlier times in the day the peak period for One or more of above two or more times drinking .

Example 815:
titleEntities: {'Subject': ['LEGO Group'], 'Date': ['2009', '2018']}
title: LEGO Group operating profit 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Operating', 'profit', 'in', 'million', 'Euros']: ['1440.6', '1391.21', '1674.3', '1645.35', '1302.45', '1117.43', '1019.49', '762.19', '667.1', '389.5']

gold: This statistic shows the operating profit of the LEGO Group from 2009 to 2018 . In 2015 , the LEGO Group 's operating profit amounted to approximately 1.65 billion euros .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[3] templateYLabel[2] templateYLabel[3] .

generated_template: The templateTitleSubject[0] is growing in every aspect . Over the last decade , the total templateYLabel[0] templateTitle[7] of templateTitle[3] in the templateTitleSubject[0] have more than quadrupled . In templateXValue[max] they amounted to approximately templateYValue[max] templateYLabel[3] templateYLabel[4] .
generated: The LEGO Group is growing in every aspect . Over the last decade , the total Operating 2018 of profit in the LEGO Group have more than quadrupled . In 2018 they amounted to approximately 1674.3 Euros .

Example 816:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013', '2019']}
title: Mobile share of U.S. organic search engine visits 2013 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13"]
Y_Axis['Share', 'of', 'organic', 'search', 'visits']: ['58', '60', '59', '59', '57', '56', '55', '53', '53', '53', '51', '53', '51', '48', '46', '45', '43', '45', '45', '45', '39', '38', '34', '34', '33', '27']

gold: This statistic highlights the mobile share of organic search engine visits in the United States . As of the fourth quarter of 2019 , it was found that mobile devices accounted for 58 percent of organic search engine visits .
gold_template: This statistic highlights the templateTitle[0] templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[5] templateYLabel[3] in the templateTitle[2] . As of the fourth templateXLabel[0] of templateTitleDate[1] , it was found that templateTitle[0] devices accounted for templateYValue[0] percent of templateYLabel[1] templateYLabel[2] templateTitle[5] templateYLabel[3] .

generated_template: templateYLabel[0] templateYLabel[1] rates in templateTitleSubject[0] decreased by half between the first templateXLabel[0] of 2013 and the second templateXLabel[0] of templateTitle[7] . Over the period under consideration , the highest templateYLabel[1] templateYLabel[2] was found in the first and third quarters of 2013 , when the value recorded was templateYValue[max] percent . After the third templateXLabel[0] of 2013 , templateYLabel[0] templateYLabel[1] rates experienced a steady decrease , reaching a value of templateYValue[min] percent as of the second templateXLabel[0] of templateTitle[7] , which represented the lowest templateYLabel[1] templateYLabel[2] during the period considered .
generated: Share organic rates in U.S. decreased by half between the first Quarter of 2013 and the second Quarter of 2013 . Over the period under consideration , the highest organic search was found in the first and third quarters of 2013 , when the value recorded was 60 percent . After the third Quarter of 2013 , Share organic rates experienced a steady decrease , reaching a value of 27 percent as of the second Quarter of 2013 , which represented the lowest organic search during the period considered .

Example 817:
titleEntities: {'Subject': ['China'], 'Date': ['2000', '2018']}
title: Direct investment position of the U.S. in China 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Direct', 'investments', 'in', 'billion', 'U.S.', 'dollars']: ['116.52', '107.56', '97.29', '92.15', '82.24', '60.45', '54.51', '53.66', '59.0', '54.07', '53.93', '29.71', '26.46', '19.02', '17.62', '11.26', '10.57', '12.08', '11.14']

gold: This statistic shows the direct investment position of the United States in China from 2000 to 2018 , on a historical-cost basis . In 2018 , the U.S. investments made in China were valued at approximately 117 billion U.S. dollars . Direct investment position of the United States - additional information Foreign direct investment ( FDI ) , simply put , is an investment of one company into another company located in a different country .
gold_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitleSubject[0] were valued at approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] - additional information Foreign templateYLabel[0] templateTitle[1] ( FDI ) , simply put , is an templateTitle[1] of one company into another company located in a different country .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitleSubject[0] were valued at approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateYLabel[3] templateYLabel[0] templateTitle[1] abroad is defined as ownership by a templateYLabel[3] investor of at least templateYValue[12] percent of a foreign business .
generated: This statistic shows the Direct investment position of the U.S. in China from 2000 to 2018 , on a historical-cost basis . In 2018 , the U.S. investments made in China were valued at approximately 116.52 billion U.S. dollars . U.S. Direct investment abroad is defined as ownership by a U.S. investor of at least 26.46 percent of a foreign business .

Example 818:
titleEntities: {'Subject': ['R.R Martin'], 'Date': ['2011', '2016']}
title: George R.R . Martin - earnings 2011 to 2016
X_Axis['Month']: ['June_2015_to_June_2016', 'June_2014_to_June_2015', 'June_2013_to_June_2014', 'June_2012_to_June_2013', 'May_2011_to_May_2012']
Y_Axis['Earnings', 'in', 'million', 'U.S.', 'dollars']: ['9.5', '12.0', '12.0', '12.0', '15.0']

gold: The statistic presents data on the annual earnings of George R.R . Martin from May 2011 to June 2016 . The author earned 12 million U.S. dollars in the period June 2014 to June 2015 .
gold_template: The statistic presents data on the annual templateYLabel[0] of templateTitle[0] templateTitleSubject[0] . templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . The author earned templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the period templateXValue[0] templateXValue[1] to templateXValue[0] .

generated_template: The statistic presents 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 percent of templateTitleSubject[0] 's traffic templateTitle[2] .
generated: The statistic presents information on the Earnings of R.R Martin million Martin in 2011 , 2011 Month . According to the source , the June 2015 to June 2016 account for over 20 percent of R.R Martin 's traffic Martin .

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 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 2019 , templateYValue[max] new templateTitleSubject[0] cars were templateYLabel[1] in the templateTitleSubject[2]

generated_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between 2016 and 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 2019 , templateYValue[5] new templateTitleSubject[0] templateTitle[1] templateYLabel[0] had been templateYLabel[1] , a decrease of roughly ten percent in comparison to templateYValue[17] templateYLabel[0] as of 2018 .
generated: This statistic shows the monthly amount of cars sold by Lexus car in the United Kingdom ( UK ) between 2016 and 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 2019 , 1118 new Lexus car Units had been sold , a decrease of roughly ten percent in comparison to 784 Units as of 2018 .

Example 820:
titleEntities: {'Subject': ['Banco Santander'], 'Date': ['2012', '2019']}
title: Banco Santander : customer numbers globally 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'customers', 'in', 'millions']: ['145.0', '144.0', '133.0', '125.0', '121.0', '117.0', '106.6', '102.1']

gold: Between 2018 and 2019 , the Banco Santander Group increased by one million customers worldwide . In 2019 , the Spanish giant had approximately196 thousand employees , across nearly12 thousand branches looking after the needs of its customers globally . As of 2019 , Banco Santander 's largest contributor to the company 's profit was Europe .
gold_template: Between templateXValue[1] and templateXValue[max] , the templateTitleSubject[0] Group increased by one templateYLabel[2] templateYLabel[1] worldwide . In templateXValue[max] , the Spanish giant had approximately196 thousand employees , across nearly12 thousand branches looking after the needs of its templateYLabel[1] templateTitle[4] . As of templateXValue[max] , templateTitleSubject[0] 's largest contributor to the company 's profit was Europe .

generated_template: The templateYLabel[0] of the templateTitleSubject[0] luxury brand templateTitle[4] templateTitle[5] has increased twofold over the period surveyed , growing from roughly templateYValue[min] templateYLabel[1] templateYLabel[2] in templateXValue[min] to templateYValue[max] templateYLabel[1] templateYLabel[2] in the templateXLabel[0] templateXValue[max] . Despite the steady increase in templateYLabel[0] during the period considered , the templateTitle[3] reported a net loss of approximately 25 templateYLabel[1] templateYLabel[2] in templateXValue[max] . Worldwide recognition Founded in 1978 in Milan , templateTitle[4] templateTitle[5] is one of the leading international templateTitle[2] design houses .
generated: The Number of the Banco Santander luxury brand globally 2012 has increased twofold over the period surveyed , growing from roughly 102.1 customers millions in 2012 to 145.0 customers millions in the Year 2019 . Despite the steady increase in Number during the period considered , the numbers reported a net loss of approximately 25 customers millions in 2019 . Worldwide recognition Founded in 1978 in Milan , globally 2012 is one of the leading international customer design houses .

Example 821:
titleEntities: {'Subject': ['Guatemala'], 'Date': ['2019']}
title: Unemployment rate in Guatemala 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['2.75', '2.73', '2.68', '2.83', '2.51', '2.72', '3.02', '2.77', '4.13', '3.5', '3.31', '2.84', '2.8', '2.89', '2.99', '2.97', '2.81', '2.85', '2.78', '2.9', '2.92']

gold: This statistic shows the unemployment rate in Guatemala from 1999 to 2019 . In 2019 , the unemployment rate in Guatemala was 2.75 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[0] percent .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Guatemala from 1999 to 2019 . In 2019 , the Unemployment rate in Guatemala was at approximately 2.75 percent .

Example 822:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2001', '2018']}
title: Unemployment in U.S. motion picture and recording industries 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Unemployment', 'rate']: ['5.5', '6.2', '7.3', '5.6', '7.1', '9.3', '12.9', '10.7', '13', '13.8', '9', '6.9', '5.9', '8.5', '8.7', '11.2', '10.3', '9.2']

gold: The statistic above presents the yearly unemployment rate for the U.S. motion picture and sound recording industry from 2001 to 2018 . In this industry , 5.5 percent of all private wage and salary workers were unemployed in 2018 .
gold_template: The statistic above presents the yearly templateYLabel[0] templateYLabel[1] for the templateTitleSubject[0] templateTitle[2] templateTitle[3] and sound templateTitle[4] industry from templateXValue[min] to templateXValue[max] . In this industry , templateYValue[min] percent of all private wage and salary workers were unemployed in templateXValue[max] .

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

Example 823:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2012']}
title: Survey on amount of money spent on boating in the U.S. 2012
X_Axis['Yearly', 'expenses']: ['under_500$', '$500_to_$999', '$1000_to_$1999', '$2000_to_$4999', 'over_$5000']
Y_Axis['Percentage', 'of', 'boat', 'owners']: ['30.5', '15.5', '18.3', '17.4', '18.3']

gold: The statistic depicts the amount of money boat owners in the U.S. spent on boating in 2012 . 18.3 percent of the respondents stated that they spent between $ 1,000 and $ 1,999 on boating in 2012 .
gold_template: The statistic depicts the templateTitle[1] of templateTitle[2] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] templateTitle[3] on templateTitle[4] in templateTitleDate[0] . templateYValue[2] percent of the respondents stated that they templateTitle[3] between $ 1,000 and $ 1,999 on templateTitle[4] in templateTitleDate[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , sorted templateTitle[5] templateXLabel[0] templateXLabel[1] . In that year , there were approximately templateYValue[1] percent of templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] .
generated: This statistic shows the Percentage boat of the U.S. amount money spent in 2012 , sorted U.S. Yearly expenses . In that year , there were approximately 15.5 percent of U.S. amount money spent .

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] percent of the male templateYLabel[1] named watching templateXValue[0] as their templateTitle[0] preferred activity during templateTitle[2] templateXValue[4] .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] problems templateTitle[4] templateTitle[5] templateXValue[4] in the templateTitleSubject[0] in 2020 . During the survey , about templateYValue[max] percent of the templateYLabel[1] stated that the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateXValue[4] was templateXValue[0] .
generated: This statistic shows the popular leisure problems among men Spending time with families and friends in the U.S. in 2020 . During the survey , about 43 percent of the respondents stated that the popular leisure activities among men Spending time with families and friends was Watch TV .

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 stores 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 stores 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[0] to templateTitleDate[1] , by templateXLabel[1] type . In this period , templateYValue[max] percent of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] were caused by templateXValue[0] during this period .

generated_template: The templateXLabel[0] templateXLabel[1] with the largest templateYLabel[0] of individuals on the transplant templateTitle[3] templateTitle[4] in the templateTitleSubject[0] in 2019 was those aged 50 - 64 templateXValue[1] . This templateXLabel[0] templateXLabel[1] had templateYValue[max] patients templateTitle[3] to receive transplants at that time . There is an extensive need for templateTitle[2] donations in the templateTitle[5] .
generated: The Fossil Fuel with the largest Share of individuals on the transplant carbon monoxide in the United Kingdom in 2019 was those aged 50 - 64 Solid . This Fossil Fuel had 35 patients carbon to receive transplants at that time . There is an extensive need for unintentional donations in the poisoning .

Example 827:
titleEntities: {'Subject': ['El Salvador'], 'Date': ['2019']}
title: Unemployment rate in El Salvador 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['4.35', '4.39', '4.39', '4.42', '4', '4.16', '3.69', '3.85', '4.3', '4.89', '7.33', '5.88', '6.41', '6.57', '7.22', '6.05', '6.26', '5.73', '6.96', '6.96', '6.68']

gold: This statistic shows the unemployment rate in El Salvador from 1999 to 2019 . In 2019 , the unemployment rate in El Salvador amounted to approximately 4.35 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to approximately templateYValue[0] percent .

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent , a slight decrease from previous years . templateTitleSubject[0] 's economy is in good shape Although on a steady downward trend after peaking at over templateYValue[6] percent in templateXValue[6] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] is still quite high but not alarmingly high in global comparison . More than half of the island 's population live in urban areas and cities already , and the numbers are rising .
generated: In 2019 , the Unemployment rate in El Salvador was at approximately 4.35 percent , a slight decrease from previous years . El Salvador 's economy is in good shape Although on a steady downward trend after peaking at over 3.69 percent in 2013 , El Salvador 's Unemployment rate is still quite high but not alarmingly high in global comparison . More than half of the island 's population live in urban areas and cities already , and the numbers are rising .

Example 828:
titleEntities: {'Subject': ['Canada'], 'Date': ['2024']}
title: Total population in Canada 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['39.22', '38.87', '38.52', '38.17', '37.81', '37.46', '36.99', '36.49', '36.05', '35.68', '35.39']

gold: The statistic shows the total population in Canada from 2014 to 2018 , with projections up until 2024 . In 2018 , the total population in Canada amounted to about 36.99 million inhabitants . Population of Canada Canada ranks second among the largest countries in the world in terms of area size , right behind Russia , despite having a relatively low total population .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] ranks second among the largest countries in the world in terms of area size , right behind Russia , despite having a relatively low templateTitle[0] templateTitle[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[7] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] amounted to templateYValue[7] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] Saudi templateTitleSubject[0] , the ten largest Arab state , is a nation in development .
generated: This statistic shows the Total population of Canada from 2014 to 2018 , with projections up until 2024 . In 2017 , Canada 's Total population amounted to 36.49 millions Inhabitants . population of Canada Saudi Canada , the ten largest Arab state , is a nation in development .

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] increased 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[0] equal to templateYValue[37] .

generated_template: Cosmetics and templateTitle[1] are bought in larger quantities each passing templateXLabel[0] in the UK . Most recently , in 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[0] , 2017 and the first half of 2018 .
generated: Cosmetics and watch are bought in larger quantities each passing Month in the UK . Most recently , in 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[min] templateYLabel[1] people . In templateXValue[max] , the number of templateYLabel[0] increased to around templateYValue[max] templateYLabel[1] .

generated_template: templateTitleSubject[0] is the largest city in the Netherlands , with a templateTitle[1] amounting to nearly 863,000 templateYLabel[1] . In the last ten years , templateTitleSubject[0] 's templateTitle[1] increased rapidly , and the end is not yet in sight . By 2030 , the templateYLabel[0] of templateYLabel[1] is forecast to reach over templateYValue[max] million .
generated: Norway is the largest city in the Netherlands , with a Norway amounting to nearly 863,000 millions . In the last ten years , Norway 's Norway increased rapidly , and the end is not yet in sight . By 2030 , the Inhabitants of millions is forecast to reach over 5.33 million .

Example 831:
titleEntities: {'Subject': ['Americans'], 'Date': ['2019']}
title: Share of Americans who believe in true love in 2019
X_Axis['Response']: ['Yes', 'No']
Y_Axis['Share', 'of', 'respondents']: ['94', '6']

gold: This statistic shows the results of a survey conducted in the United States in 2017 on whether the respondents believe in love , or not . During the survey , some 94 percent of respondents stated they believe in true love .
gold_template: This statistic shows the results of a survey conducted in the country in 2017 on whether the templateYLabel[1] templateTitle[3] in templateTitle[5] , or not . During the survey , some templateYValue[max] percent of templateYLabel[1] stated they templateTitle[3] in templateTitle[4] templateTitle[5] .

generated_template: 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] percent 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 percent of respondents stated they do not regret getting any of their who .

