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 illustrates the templateTitle[0] templateTitle[1] templateTitle[2] templateTitleSubject[0] templateTitle[4] to templateTitle[5] templateTitle[6] templateXValue[0] in templateTitleDate[0] . The survey revealed templateTitle[1] templateYValue[4] percent of the templateYLabel[1] said templateTitle[1] the templateXValue[4] influences their templateTitle[6] templateXValue[0] purchases.Luggage industry in the templateTitle[3] To understand the templateXValue[0] industry , one must understand how the airline industry impacts templateXValue[0] demands . With airlines continually increasing their baggage fees , the majority of travellers are adjusting all aspects of their travel , including considerations when it comes to purchasing luggage.Consumers are n't going for the lightweight templateXValue[0] without checking out some additional templateXValue[2] .
generated: This statistic illustrates the Most used EIFS brands to U.S. 2018 Dryvit in 2018 . The survey revealed used 5.8 percent of the respondents said used the Simplex (Finestone) influences their 2018 Dryvit purchases.Luggage industry in the STUCCO To understand the Dryvit industry , one must understand how the airline industry impacts Dryvit demands . With airlines continually increasing their baggage fees , the majority of travellers are adjusting all aspects of their travel , including considerations when it comes to purchasing luggage.Consumers are n't going for the lightweight Dryvit without checking out some additional Omega Products .

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

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

generated_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[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 Pennsylvania 's Real GDP from 2000 to 2018 . In 2018 , the Real GDP of Pennsylvania was about 712.1 billion U.S. dollars . The annual Real GDP growth of the U.S. can be accessed here .

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

gold: This statistic gives information on the most popular gaming brands on Twitter , ranked by number of followers on the social network . As of September 2018 , Sony 's PlayStation was ranked first with 15.63 million Twitter followers . Ubisoft was ranked fifth with 7.1 million followers .
gold_template: This statistic gives information on the most popular templateTitle[2] templateTitle[3] on templateTitleSubject[0] , ranked templateTitle[6] number of templateTitle[7] on the social network . As of September templateTitle[5] , Sony 's templateXValue[0] was ranked first with templateYValue[max] templateYLabel[2] templateTitleSubject[0] templateTitle[7] . templateXValue[4] was ranked fifth with templateYValue[4] templateYLabel[2] templateTitle[7] .

generated_template: This statistic gives information on the most templateTitle[3] templateTitle[4] templateTitle[5] on templateTitleSubject[0] , ranked by templateTitle[1] of templateTitle[2] on the social network . As of February templateTitle[6] , personal care templateYLabel[0] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] was ranked first with close to templateYValue[max] templateYLabel[2] templateTitleSubject[0] templateTitle[2] .
generated: This statistic gives information on the most brands Twitter 2018 on Twitter , ranked by video of gaming on the social network . As of February by , personal care Brand PlayStation was ranked first with close to 15.63 millions Twitter gaming .

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

gold: There were approximately 135,000 people directly employed by the defense industry in the United Kingdom in 2018 . This was a decrease of 31 percent since the beginning of the reporting period in 2010 . According to ADS - the UK trade organization representing the aerospace/space , defense and security sectors , the country is currently the second largest exporter of defense equipment and services in the world .
gold_template: There were approximately templateYValue[min] templateTitle[1] directly templateTitle[2] by the templateTitle[3] templateTitle[4] in the United Kingdom in templateXValue[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: The templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] photovoltaics has seen an overall increase since templateXValue[min] , amounting to templateYValue[0] percent in templateXValue[max] . However , this was a minor decrease templateTitle[3] the previous templateXLabel[0] and significantly lower when compared to the templateYLabel[0] factors of other renewable sources . This can be explained by the lack of consistency in the number of sunny days recorded .
generated: The Employees thousands of employed defense industry photovoltaics has seen an overall increase since 2010 , amounting to 135 percent in 2018 . However , this was a minor decrease defense the previous Year and significantly lower when compared to the Employees factors of other renewable sources . This can be explained by the lack of consistency in the number of sunny days recorded .

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

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

generated_template: This statistic illustrates templateTitleSubject[0] 's templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] from the second templateXLabel[0] of templateTitleDate[0] to the second templateXLabel[0] of templateTitleDate[1] . In the second templateXLabel[0] of templateTitle[6] , the photo sharing app had a templateYLabel[0] templateYLabel[1] of templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] , up from templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in the first templateXLabel[0] of templateTitleDate[0] .
generated: This statistic illustrates Dublin 's office rental Cost per from the second Quarter of 2019 to the second Quarter of 2019 . In the second Quarter of 2015-Q3 , the photo sharing app had a Cost per of 538 square meter euros , up from 552 square meter euros in the first Quarter of 2019 .

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

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

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] dollars.The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] are owned by David Glass , who bought the franchise for 96 templateYLabel[1] templateYLabel[2] templateYLabel[3] in 2000 .
generated: The statistic depicts the Revenue of the Baltimore Orioles from 2001 to 2018 . In 2018 , the Revenue of the Major League Baseball franchise amounted to 253 million U.S. dollars.The Baltimore Orioles are owned by David Glass , who bought the franchise for 96 million U.S. dollars in 2000 .

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] , about 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 , about 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 illustrates the distribution of templateYLabel[1] templateTitleSubject[0] chat app templateYLabel[2] as of January templateTitleDate[0] , sorted by global region . As of that month , templateYValue[max] percent of the mobile messenger 's templateTitle[2] base were located in templateXValue[0] and templateXValue[0] templateXValue[0] . The templateXValue[3] templateXValue[3] accounted for templateYValue[3] percent of templateTitleSubject[0] messenger templateYLabel[2] .
generated: This statistic illustrates the distribution of UHNW Ultra chat app individuals as of January 2019 , sorted by global region . As of that month , 84054 percent of the mobile messenger 's net base were located in North America and North America . The China accounted for 18132 percent of Ultra messenger 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] for templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitle[7] 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] . The templateTitleSubject[0] have one of highest templateYLabel[0] templateYLabel[1] prices in the templateTitleSubject[1] .
generated: This graph depicts the Average ticket price for Chicago Blackhawks 2014/15 of the National Basketball Association from 2005/06 to 2014/15 . In the 2005/06 season , the Average ticket price was 34.88 U.S. dollars . The Chicago Blackhawks have one of highest Average ticket prices in the NHL .

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

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

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

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

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

generated_template: This statistic shows the estimated templateTitle[4] of templateTitle[0] and templateTitle[1] to templateTitle[5] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . Over this period , the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] is estimated to reach templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the estimated Great of Grocery and market to Britain in the Great Britain from 05 Nov 2017 to 28 Nov 2010 . Over this period , the Percentage growth of Grocery market growth is estimated to reach 4.2 (year-on-year) .

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

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

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] templateTitleSubject[0] The templateTitleSubject[0] templateTitleSubject[0] are a professional basketball franchise of the National Basketball Association ( NBA ) , entering the league as an expansion team in 1968 .
generated: The statistic shows the Revenue of the Brooklyn Nets franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 304 million U.S. dollars . Brooklyn Nets The Brooklyn Nets are a professional basketball franchise of the National Basketball Association ( NBA ) , entering the league as an expansion team in 1968 .

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

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

generated_template: This templateTitleDate[0] statistic shows how templateXValue[0] British templateYLabel[1] are about a templateTitle[1] attack happening in templateTitleSubject[0] . The largest templateYLabel[0] of templateYLabel[1] reported feeling templateXValue[1] templateXValue[0] , with over 40 percent , while templateYValue[min] percent reported feeling templateXValue[2] templateXValue[0] at templateXValue[last] . Yet , despite the fact that 78 percent of templateYLabel[1] confessed to worrying about terrorism , this anxiety is irrational as the total resident population in the UK is exceedingly greater than the number of fatalities resulting templateTitle[1] templateTitle[2] in recent years or the number of injuries resulting from templateTitle[1] templateTitle[2] in the UK .
generated: This 2018 statistic shows how Yes more than once British respondents are about a consumers attack happening in U.S. . The largest Share of respondents reported feeling Yes but only once Yes more than once , with over 40 percent , while 12 percent reported feeling No never Yes more than once at Don't know / can't recall . Yet , despite the fact that 78 percent of respondents confessed to worrying about terrorism , this anxiety is irrational as the total resident population in the UK is exceedingly greater than the number of fatalities resulting consumers who in recent years or the number of injuries resulting from consumers who in the UK .

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

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

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

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

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

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

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

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

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] the world 's templateTitle[4] templateTitle[5] templateTitle[3] 1900 to 2016* . 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 Economic of loss due major the world 's droughts worldwide major 1900 to 2016* . The Drought in United States January 2011 in 1973 claimed 8.0 lives . Natural disasters Natural disasters , such as earthquakes , volcanic eruption , tsunamis , floods , tornados or worldwide affect people up .

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

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

generated_template: The timeline depicts how many templateYLabel[0] templateYLabel[1] were templateYLabel[2] in each templateXLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[3] templateYLabel[0] templateYLabel[1] were templateYLabel[2] in the templateTitle[5] , a change of minus 1 percent compared to the previous templateXLabel[0] . More statistics and facts on recreational boating
generated: The timeline depicts how many Percentage employees were employees in each Year from 2000 to 2013 . In 2013 , 23.02 Percentage employees were employees in the 2000 , a change of minus 1 percent compared to the previous Year . More statistics and facts on recreational boating

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] templateYLabel[1] of 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 university 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 statistic represents the templateTitle[0] templateTitle[1] templateTitle[2] held by the templateTitle[1] from templateTitle[6] to 2019 with an additional templateTitle[3] from 2020 to templateTitle[7] as a templateYLabel[0] of the Gross Domestic Product . templateTitle[0] templateTitle[1] templateTitle[2] held by the templateTitle[1] amounted to 16.8 trillion templateTitle[0] dollars in 2019 , which was about templateYValue[11] percent of the templateTitle[0] templateYLabel[1] . The templateTitle[3] predicts an increase in templateTitle[1] templateTitle[2] up to 31.45 trillion templateTitle[0] dollars in templateTitle[7] , which would be about templateYValue[max] percent of the templateTitle[0] templateYLabel[1] .
generated: The statistic represents the Projected global median held by the global from 2100 to 2019 with an additional age from 2020 to 2100 as a Median of the Gross Domestic Product . Projected global median held by the global amounted to 16.8 trillion Projected dollars in 2019 , which was about 35.4 percent of the Projected age . The age predicts an increase in global median up to 31.45 trillion Projected dollars in 2100 , which would be about 41.9 percent of the Projected age .

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

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

generated_template: This statistic illustrates templateTitleSubject[0] 's projected templateTitle[2] templateTitle[3] between templateXValue[min] and templateXValue[max] . templateTitleSubject[0] 's templateTitle[2] templateTitle[3] is templateTitle[4] to increase from about templateYValue[min] million units in templateXValue[min] to about templateYValue[2] million units by templateXValue[max] . The company 's compound annual growth rate ( CAGR ) is estimated to stand at 5.8 percent between templateXValue[min] and templateXValue[max] .
generated: This statistic illustrates Automobile 's projected automatic transmission between 2010 and 2015 . Automobile 's automatic transmission is forecast to increase from about 25.03 million units in 2010 to about 26.72 million units by 2015 . The company 's compound annual growth rate ( CAGR ) is estimated to stand at 5.8 percent between 2010 and 2015 .

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

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

generated_template: This statistic presents the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per day in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] templateYLabel[2] templateTitle[2] templateTitle[3] templateTitle[4] were generated by the templateYLabel[0] of templateXValue[max] . The templateYLabel[0] of templateTitle[3] content is expected to amount to templateYValue[max] templateYLabel[2] templateYLabel[1] in templateXValue[max] .
generated: This statistic presents the General of Motors adjusted EBIT per day in General Motors from 2015 to 2019 . In 2015 , 8393 million adjusted EBIT 2015 were generated by the EBIT of 2019 . The EBIT of EBIT content is expected to amount to 12848 million (adjusted; in 2019 .

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

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

generated_template: In templateXValue[1] , the annual templateYLabel[0] of visitor exports is expected to reach templateYValue[max] templateYLabel[1] templateYLabel[2] . The spending of templateTitle[0] tourists in the country slightly decreased in templateXValue[3] , after the terrorist attacks which took place in Paris and Nice respectively in templateXValue[4] and templateXValue[3] . However , in templateXValue[2] , templateTitleSubject[0] had the fifth largest templateTitle[0] tourism templateTitle[3] worldwide .
generated: In 2018 , the annual Value of visitor exports is expected to reach 411.0 billion euros . The spending of Travel tourists in the country slightly decreased in 2016 , after the terrorist attacks which took place in Paris and Nice respectively in 2015 and 2016 . However , in 2017 , Germany had the fifth largest Travel tourism total worldwide .

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

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

generated_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of the Chilean population accessed the templateYLabel[3] , up from templateYValue[16] percent in templateXValue[16] . 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': ['2010', '2019']}
title: Revenue passenger miles ( RPMs ) of Southwest Airlines 2010 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['RPMs', 'in', 'billions']: ['131.35', '133.32', '129.04', '124.8', '117.5', '108.04', '104.35', '102.87', '97.58', '78.05']

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

generated_template: The total templateTitle[2] volume of templateTitle[0] templateYLabel[1] in the templateTitleSubject[0] fluctuated mainly between 400 thousand and 550 thousand from templateXValue[min] to templateXValue[max] . The highest volume was reached in templateXValue[8] with templateYValue[max] volumes templateYLabel[2] , the lowest in templateXValue[5] with templateYValue[5] . Between templateXValue[3] and templateXValue[max] , the templateYLabel[0] of templateTitle[0] templateYLabel[1] increased by approximately 63,000 .
generated: The total miles volume of Revenue billions in the Southwest Airlines fluctuated mainly between 400 thousand and 550 thousand from 2010 to 2019 . The highest volume was reached in 2011 with 133.32 volumes billions , the lowest in 2014 with 108.04 . Between 2016 and 2019 , the RPMs of Revenue billions increased by approximately 63,000 .

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

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

generated_template: There is no use denying that the way people consume newspapers and magazines has changed . As of templateXValue[max] , more than two thirds of individuals in templateTitleSubject[0] templateTitleSubject[0] were either reading or downloading templateTitle[0] templateTitle[1] , newspapers or magazines . This was more than three times the templateYLabel[0] of templateTitle[0] readers as compared to templateXValue[min] .
generated: There is no use denying that the way people consume newspapers and magazines has changed . As of 2019 , more than two thirds of individuals in Great Britain were either reading or downloading Online purchasing , newspapers or magazines . This was more than three times the Share of Online readers as compared to 2008 .

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

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

generated_template: 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 United Kingdom 's Number and university applicants from the fiscal Year of 2010 to the fiscal Year of 2018 . In the fiscal Year of 2018 , the company incurred around 636960 applicants in Number and university 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 September templateXValue[8] , templateTitleSubject[0] templateTitle[2] templateYValue[10] templateYLabel[1] templateYLabel[0] . The number of templateYLabel[0] fell to roughly templateYValue[min] templateYLabel[1] in the last period in consideration .

generated_template: This timeline illustrates templateYLabel[0] templateTitle[0] of social network templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[0] to the third templateXLabel[0] of templateTitleDate[1] , in templateYLabel[2] . During the most recently reported templateXLabel[0] , templateTitleSubject[0] had templateYValue[max] templateYLabel[2] templateTitle[2] , up from templateYValue[1] templateYLabel[2] templateTitle[2] in the preceding templateXLabel[0] . templateTitleSubject[0] – additional information templateTitleSubject[0] , which was founded in California and launched in 2003 , is a social networking service for businesses and professionals .
generated: This timeline illustrates Viewers ITV of social network ITV from the first Quarter of 2019 to the third Quarter of 2019 , in thousands . During the most recently reported Quarter , ITV had 55628 thousands reached , up from 55302 thousands reached in the preceding Quarter . ITV – additional information ITV , which was founded in California and launched in 2003 , is a social networking service for businesses and professionals .

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] templateTitle[4] club of Major League Soccer by templateTitle[0] in templateTitleDate[0] . templateXValue[0] templateXValue[0] received a salary of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in that year .
generated: The statistic shows the Player expenses ( Payroll ) of the San Jose Jose club of Major League Soccer by Player in 2019 . Valeri "Vako" Qazaishvili received a salary of 1604.04 thousand U.S. dollars in that year .

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

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

generated_template: The timeline shows 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: The timeline shows 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 graph shows the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] around the world from February templateXValue[last] to templateXValue[0] . templateXValue[0] templateXValue[0] has hit the most templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] templateTitleSubject[0] franchise history with templateYValue[max] templateYLabel[1] templateYLabel[2] .
generated: This graph shows the Number of Oakland Athletics home around the world from February Gus Zernial to Mark McGwire . Mark McGwire has hit the most home runs in 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 illustrates the templateYLabel[0] of templateTitle[1] templateYLabel[1] templateTitle[3] on templateTitleSubject[0] worldwide from templateXValue[last] to templateXValue[0] . Between templateXValue[0] and templateXValue[0] templateXValue[0] there were templateYValue[max] templateTitle[1] templateYLabel[1] templateTitle[3] on templateTitleSubject[0] , up from templateYValue[1] templateYLabel[1] templateTitle[3] in the corresponding period in templateXValue[1] .
generated: The statistic illustrates the Number of monthly views on BuzzFeed worldwide from May '16 to Apr '15 . Between Apr '15 and Apr '15 there were 7000 monthly views on BuzzFeed , up from 5000 monthly views in the corresponding period in Oct '15 .

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

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

generated_template: In the templateTitleSubject[0] templateTitleSubject[0] , the definition of templateTitle[3] is a death with an underlying cause of intentional self-harm or an injury or poisoning with undetermined intent . In templateXValue[max] , the templateTitle[3] templateTitle[4] in the templateTitleSubject[1] was templateYValue[0] deaths templateYLabel[1] 100,000 templateYLabel[3] , the highest templateTitle[4] of templateTitle[3] in the templateTitleSubject[1] since templateXValue[min] . Overall , the highest templateTitle[3] templateTitle[4] was in the templateXLabel[0] templateXValue[min] , when the templateTitle[4] was almost templateYValue[17] deaths templateYLabel[1] 100,000 .
generated: In the Canada , the definition of 2000 is a death with an underlying cause of intentional self-harm or an injury or poisoning with undetermined intent . In 2018 , the 2000 2018 in the Canada was 30582 deaths robberies 100,000 robberies , the highest 2018 of 2000 in the Canada since 2000 . Overall , the highest 2000 2018 was in the Year 2000 , when the 2018 was almost 22831 deaths robberies 100,000 .

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 graph shows data on templateTitle[3] templateTitle[4] of Suzanne Collins 's trilogy 'The templateTitleSubject[0] templateTitleSubject[0] ' in the country from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[2] sold templateYValue[0] templateYLabel[3] copies .
generated: The graph shows data on U.S. revenue of Suzanne Collins 's trilogy 'The U.S. ' in the country from 2002 to 2017 . In 2002 , the industry sold 16.43 dollars copies .

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

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

generated_template: This statistic shows the templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] ( Latino ) students in the templateTitle[7] of the templateYLabel[2] of templateTitleDate[0] . In templateTitleDate[0] , templateTitleSubject[0] reported templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] .
generated: This statistic shows the quarterly net income of Google ( Latino ) students in the 2015 of the million of 2003 . In 2003 , Google reported Net income of 4675.0 million U.S. . Google quarterly net income .

Example 39:
titleEntities: {'Subject': ['Case Shiller National Home Price Index'], 'Date': ['17', '19']}
title: U.S. housing : Case Shiller National Home Price Index 2017 to 2019
X_Axis['Month']: ['Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17']
Y_Axis['Index', 'value']: ['212.06', '211.63', '210.87', '209.65', '207.96', '206.06', '204.72', '204.46', '204.94', '205.34', '205.59', '205.6', '205.55', '205.18', '204.27', '202.66', '200.8', '198.76', '197.08', '196.29', '196.02', '195.61', '195.24', '194.97', '194.5', '193.66', '192.4', '190.65', '188.65', '186.64', '185.14']

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

generated_template: The graph shows the all-time templateTitle[8] of men 's templateTitle[1] templateTitle[0] templateTitle[2] at the templateYLabel[2] templateTitleSubject[0] . After the completion of the FIFA templateYLabel[2] templateTitleSubject[0] templateTitleDate[1] in Russiam the German templateTitle[0] team is ranked second with a total of templateYValue[1] templateYLabel[0] .
generated: The graph shows the all-time 2017 of men 's housing U.S. Case at the value Case Shiller National Home Price Index . After the completion of the FIFA value Case Shiller National Home Price Index 19 in Russiam the German U.S. team is ranked second with a total of 211.63 Index .

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

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

generated_template: This statistic illustrates the templateYLabel[0] of templateTitleSubject[0] Motor Corporation templateYLabel[1] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . templateTitleSubject[0] employed some templateYValue[max] people in the templateXValue[max] fiscal templateXLabel[0] . The templateTitleSubject[0] Motor Corporation is ranked among the largest automobile manufacturers in the world .
generated: This statistic illustrates the Number of Alibaba Motor Corporation employees from the fiscal Year of 2012 to the fiscal Year of 2019 . Alibaba employed some 101958 people in the 2019 fiscal Year . The Alibaba Motor Corporation is ranked among the largest automobile manufacturers in the world .

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

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

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] 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 2022 , 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[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . That templateXLabel[0] , almost templateYValue[0] percent of the Japanese population accessed the templateYLabel[3] , down from templateYValue[max] percent in the previous templateXLabel[0] .
generated: This statistic provides information on the millions internet in France from 2002 to 2016 . That Year , almost 55.86 percent of the Japanese population accessed the millions , down from 55.86 percent in the previous Year .

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

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

generated_template: This statistic presents a ranking of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] from RuPaul 's templateTitle[2] Race on templateTitleSubject[0] as of March templateTitleDate[0] , sorted by the templateYLabel[0] of global templateYLabel[1] . According to the source , California-native Adora templateXValue[0] was the templateTitle[0] templateTitle[1] queen with templateYValue[max] templateYLabel[2] templateTitleSubject[0] templateYLabel[1] . Adora had also participated in the seventh season of American idol under her real name , Daniel Anthony Noriega .
generated: This statistic presents a ranking of the Cities highest spending parks from RuPaul 's spending Race on U.S. as of March 2018 , sorted by the Spending of global per . According to the source , California-native Adora Minneapolis was the Cities highest queen with 346.97 resident U.S. per . Adora had also participated in the seventh season of American idol under her real name , Daniel Anthony Noriega .

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 templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] the world 's templateTitle[4] templateTitle[5] templateTitle[3] 1900 to 2016* . The templateXLabel[0] in templateXValue[0] 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 fatalities from heat the world 's waves cold heat 1900 to 2016* . The Type, in Heat wave 1980 Kansas City Missouri St Loius in 1973 claimed 670 lives . Natural disasters Natural disasters , such as earthquakes , volcanic eruption , tsunamis , floods , tornados or cold affect people waves .

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

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

generated_template: This statistic illustrates the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] in the templateTitleSubject[0] 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 November templateTitleDate[1] , an increase compared to the templateYLabel[0] a year earlier .
generated: This statistic illustrates the Amounts of notes coin in the UK ( UK ) from 17 to 19 . The Amounts of notes coin was valued at 82980 British pounds million GBP in November 19 , 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 February templateTitleDate[0] in the templateTitle[0] . During the survey period , templateYValue[2] percent of templateYLabel[1] reported having used templateXValue[2] templateXValue[0] in the previous year .

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

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

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

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] 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 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 .

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: This timeline illustrates templateYLabel[0] templateTitle[0] of social network templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[0] to the third templateXLabel[0] of templateTitleDate[1] , in templateYLabel[2] . During the most recently reported templateXLabel[0] , templateTitleSubject[0] had templateYValue[max] templateYLabel[2] templateTitle[2] , up from templateYValue[1] templateYLabel[2] templateTitle[2] in the preceding templateXLabel[0] . templateTitleSubject[0] – additional information templateTitleSubject[0] , which was founded in California and launched in 2003 , is a social networking service for businesses and professionals .
generated: This timeline illustrates Viewers MTV of social network MTV from the first Quarter of 2019 to the third Quarter of 2019 , in thousands . During the most recently reported Quarter , MTV had 11487 thousands reached , up from 9638 thousands reached in the preceding Quarter . MTV – additional information MTV , which was founded in California and launched in 2003 , is a social networking service for businesses and professionals .

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 illustrates the templateTitle[3] penetration of templateTitle[0] and templateTitle[1] apps in templateTitleSubject[0] templateTitleSubject[0] among age and gender templateTitle[8] groups . Of templateYLabel[1] aged templateXValue[4] to templateXValue[4] , templateYValue[min] percent reported regularly using a templateTitle[0] or templateTitle[1] templateTitle[2] .
generated: This statistic illustrates the 2019 penetration of Church and attendance apps in Americans among age and gender 2019 groups . Of respondents aged Never to Never , 3 percent reported regularly using a Church or attendance Americans .

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

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

generated_template: This statistic shows the templateYLabel[0] of Scottish templateYLabel[1] templateTitle[3] the rest of the United Kingdom templateTitle[3] templateXValue[min] to templateXValue[max] . With the exception of templateXValue[17] , templateXValue[8] and templateXValue[4] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] has increased templateTitle[3] templateYValue[min] templateYLabel[2] in templateXValue[min] to approximately templateYValue[max] templateYLabel[2] by templateXValue[max] .
generated: This statistic shows the Number of Scottish hospitals 2018 the rest of the United Kingdom 2018 2000 to 2018 . With the exception of 2001 , 2010 and 2014 , the Number of Belgium hospitals has increased 2018 174 hospitals in 2000 to approximately 228 hospitals by 2018 .

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

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

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

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

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

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

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

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

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

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

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

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

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

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

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around 3.48 percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Morocco from 2014 to 2017 , with projections up until 2024 . In 2018 , Morocco 's real Gross domestic product grew by around 3.48 percent compared to the previous Year .

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

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

generated_template: In templateXValue[1] , the templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was approximately templateYValue[1] 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 airlines Net profit billion in Net was approximately 25.9 U.S. . In 2020 , this was forecasted to reach around 37.6 U.S. profit billion . Net 's airlines meat Net in 2019 was above the OECD average of that Year .

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

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

generated_template: The statistic represents the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] templateTitleSubject[0] , which is the number of people living in templateYLabel[0] conditions . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] templateTitleSubject[0] stood at approximately templateYValue[max] templateYLabel[2] people .
generated: The statistic represents the Capacity megawatts in PV , which is the number of people living in Capacity conditions . In 2018 , the Capacity megawatts in PV stood at approximately 2583 megawatts people .

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

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

generated_template: The statistic shows the total templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] stood at templateYValue[max] templateYLabel[1] .
generated: The statistic shows the total Imports of American corn 2001 2019 in the 2019 from 2001 to 2019 . In 2019 , the American Imports of American corn 2001 2019 stood at 160 million .

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

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

generated_template: This statistic shows the 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 August templateTitleDate[0] survey , templateYValue[max] percent of templateYLabel[1] stated that they were aware of templateXValue[0] . templateXValue[1] was ranked second with templateYValue[1] percent templateTitle[3] rate and templateXValue[2] was in third place with templateYValue[2] percent of templateYLabel[1] stating that they knew the platform .

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

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

gold: This statistic shows the number of social network users in Thailand from 2017 to 2023 . In 2023 , it is estimated that there will be around 32.2 million social network users in the country , up from 32.1 million in 2017 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that there will be around 32.2 templateYLabel[2] templateTitle[1] templateTitle[2] templateYLabel[1] in the country , up from templateYValue[min] templateYLabel[2] in templateXValue[min] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] in templateXValue[min] and templateXValue[5] with projections until 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] .
generated: This statistic shows the Number of Thailand users in Thailand in 2017 and 2018 with projections until 2023 . In 2023 , the Number of Thailand users in Thailand is expected to reach 37.2 millions , up from 32.1 millions in 2017 .

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

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] 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] 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 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 Worldwide shipments Unit shipments 2019 millions of Worldwide was to 400.0 percent in 2019 . This was up from 380.7 percent the Year earlier , the highest millions in the past six years . What is the Unit shipments 2019 millions ? Unit shipments 2019 is essentially the amount of money a bank has on hand in case it needs to cover unexpected expenses from risky transactions .

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

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

generated_template: This statistic illustrates the templateYLabel[0] of templateYLabel[1] having already practiced templateTitle[3] on the beach or in a nudist camp worldwide in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . Thus , we note that in templateXValue[3] templateYLabel[1] were less than 10 percent to have already been entirely naked on the beach or in a naturist camp .
generated: This statistic illustrates the Change of heating having already practiced oil on the beach or in a nudist camp worldwide in 2018 , price Country . Thus , we note that in France heating were less than 10 percent to have already been entirely naked on the beach or in a naturist camp .

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: This statistic illustrates the templateYLabel[0] of templateYLabel[1] templateYLabel[2] at least one templateTitle[0] in the templateTitleSubject[0] templateTitleSubject[0] templateTitle[5] templateXLabel[0] in templateTitle[4] . templateXValue[0] and templateXValue[1] ranked highest with templateYValue[max] percent of templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitle[4] , followed templateTitle[5] the templateXValue[2] with templateYValue[2] percent .
generated: This statistic illustrates the Number of dogs thousands at least one Number in the European Union by Country in 2018 . Germany and United Kingdom ranked highest with 9400 percent of dogs thousands in 2018 , followed by the Poland with 7600 percent .

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

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] templateXLabel[1] . In that year , Coca Cola 's templateYLabel[0] templateYLabel[1] in the templateTitle[5] amounted to templateYValue[max] percent . The leading 3 templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[4] had a templateYLabel[0] templateYLabel[1] of 64.7 percent .
generated: The statistic shows the Percentage people of U.S. 2018 by in the age in 2018 , group Age group . In that year , Coca Cola 's Percentage people in the age amounted to 34 percent . The leading 3 U.S. 2018 by in the by had a Percentage people of 64.7 percent .

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 templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] manufactured in the templateTitleSubject[1] was approximately templateYValue[0] 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 Death rate of Crude death manufactured in the Sweden was approximately 9.1 thousand tons in 2018 , an increase of over eight thousand tons from 2017 . This constitutes a recovery of the production rate , which collapsed between 2008 and 2011 and fell by just under eighty percent , before it stagnated in the early 2010s . A stagnating market ? The corresponding developments in Death values give the same picture as above .

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

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

generated_template: templateTitleSubject[2] is one of the templateTitleSubject[0] American countries with the lowest templateTitleSubject[1] templateTitleSubject[1] prices . In January templateXValue[max] , it was estimated that a templateTitleSubject[1] templateTitleSubject[1] burger would cost an templateYLabel[0] of templateYValue[0] templateYLabel[2] templateYLabel[3] in templateTitleSubject[2] , up from only templateYValue[1] templateYLabel[3] a templateXLabel[0] earlier . The templateTitleSubject[1] templateTitleSubject[1] templateTitle[4] in templateTitleSubject[2] The templateTitleSubject[1] templateTitleSubject[1] templateTitle[4] is an indicator that measures an economy 's purchasing power .
generated: UK is one of the Tesco American countries with the lowest United Kingdom prices . In January 2020 , it was estimated that a United Kingdom burger would cost an Sales of 52714.03 US dollars in UK , up from only 50821.69 dollars a Year earlier . The United Kingdom for in UK The United Kingdom for is an indicator that measures an economy 's purchasing power .

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

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

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

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

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

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

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

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

generated_template: The statistics shows the templateYLabel[0] of registered templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , there were a total of templateYValue[max] registered templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] according to the International templateTitle[0] templateTitle[1] Federation .
generated: The statistics shows 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[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the value of the Phoenix Suns Franchise of the National Basketball Association from 2003 to 2020 . In 2020 , the Franchise had an estimated value of 1625 million U.S. dollars .

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

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

generated_template: The statistic shows the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] due to templateTitle[0] templateTitle[4] templateXValue[min] to templateXValue[max] . Around templateYValue[0] people died worldwide in templateXValue[max] as a result of earthquakes . Earthquakes are typically caused by the movement of the earth crusts .
generated: The statistic shows the U.S. Average electricity due to U.S. prices 1990 to 2018 . Around 10.58 people died worldwide in 2018 as a result of earthquakes . Earthquakes are typically caused by the movement of the earth crusts .

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

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

generated_template: 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'], '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 illustrates a timeline of the templateTitle[0] of templateTitle[1] templateTitle[2] templateYLabel[1] templateTitle[5] individual templateTitle[5] day in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , individuals saw on average templateYValue[max] television commercials or templateYLabel[0] templateTitle[5] day .
generated: This statistic illustrates a timeline of the Mast-Jägermeister of SE annual profit 2016 individual 2016 day in the SE from 2009 to 2016 . In 2013 , individuals saw on average 135.87 television commercials or Annual 2016 day .

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: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] templateYLabel[1] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , the Germany-based multinational engineering and electronics company employed some templateYValue[max] templateYLabel[2] templateTitleSubject[0] .
generated: This statistic shows the Revenue of Germany billion from the fiscal Year of 2000 to the fiscal Year of 2018 . In the fiscal Year of 2018 , the Germany-based multinational engineering and electronics company employed some 84.7 euros Germany .

Example 82:
titleEntities: {'Subject': ['Europe'], 'Date': ['14', '15']}
title: Number of M & A deals in Europe 2014 to 2015
X_Axis['Month']: ['Jun_15', 'May_15', 'Apr_15', 'Mar_15', 'Feb_15', 'Jan_15', 'Dec_14', 'Nov_14', 'Oct_14', 'Sep_14', 'Aug_14', 'Jul_14', 'Jun_14', 'May_14', 'Apr_14', 'Mar_14']
Y_Axis['Number', 'of', 'deals']: ['1222', '1030', '1107', '1166', '1097', '1076', '1235', '1152', '1167', '1222', '912', '1253', '1171', '1070', '1185', '1062']

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

generated_template: The timeline shows templateTitleSubject[0] templateTitle[1] templateYLabel[1] of templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitleSubject[1] between August 2018 and December templateTitleDate[0] . In December templateTitleDate[0] , templateTitleSubject[0] templateTitle[1] templateYLabel[1] of templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[7] stood at templateYValue[0] templateYLabel[2] .
generated: The timeline shows Europe M deals of Europe 2014 2015 in the Europe between August 2018 and December 14 . In December 14 , Europe M deals of Europe 2014 2015 in the 2015 stood at 1222 deals .

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

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

generated_template: 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 Turnover of Bulgari Inc. , the Montreal-based dairy company , reached approximately 194.9 million euros in 2017 . Their Turnover has gradually increased year-on-year from 96.4 million euros in 2011 . Bulgari Inc. Bulgari Inc. produces , markets and distributes dairy products .

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

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

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitle[7] 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] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] The templateTitleSubject[0] templateTitleSubject[0] are a professional basketball team that is part of the Western Conference in the National Basketball Association ( templateTitleSubject[1] ) .
generated: This graph depicts the Average ticket price for Washington Wizards 2015/16 of the National Basketball Association from 2006/07 to 2015/16 . In the 2006/07 season , the Average ticket price was 23.64 U.S. dollars . Washington Wizards The Washington Wizards are a professional basketball team that is part of the Western Conference in the National Basketball Association ( NBA ) .

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 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 Employees ' the largest number of working Average working hours 10,000 week in the 2011 in 2011 . In India , California , there were 42 working Average working hours 10,000 week in 2011 .

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

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

generated_template: This statistic illustrates the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] templateYLabel[1] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[max] templateTitleSubject[0] templateTitleSubject[0] templateYLabel[1] templateTitle[4] .
generated: This statistic illustrates the Sales of Nintendo million 2011 from 2011 to 2018 . In 2018 , there were 13.95 Nintendo million 2011 .

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

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

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

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

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

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

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitleSubject[0] templateTitle[1] market had a size of templateYValue[min] templateYLabel[1] templateYLabel[2] .
generated: The statistic shows UAE CPI Consumer 2012 2017 from 2012 to 2017 . In 2012 , the UAE CPI market had a size of 105.75 price 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 templateTitle[1] templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] .
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. .

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[1] 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 13.05 Million metric tons in the 2016 . papaya production in Asia Much of the world 's mangos come from the Asia Pacific region , specifically India and China .

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

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

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

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

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

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

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

gold: As of January 2020 , the United States had the biggest Snapchat user base in the world , with an audience of 101.25 million users . India ranked in second place with a Snapchat audience base of 22.95 million users . The photo-sharing platform is projected to surpass 365 million global users in 2023 .
gold_template: As of January templateTitle[5] , the templateXValue[0] had the biggest templateTitleSubject[0] user base in the world , templateTitle[1] an templateYLabel[0] of templateYValue[max] templateYLabel[2] templateTitle[4] . templateXValue[1] ranked in second place templateTitle[1] a templateTitleSubject[0] templateYLabel[0] base of templateYValue[1] templateYLabel[2] templateTitle[4] . The photo-sharing platform is projected to surpass 365 templateYLabel[2] global templateTitle[4] in 2023 .

generated_template: templateTitle[4] templateYLabel[1] are popular among people that want to enhance select features of their body through surgical and nonsurgical methods . As of templateTitleDate[0] , the templateXValue[0] templateXValue[0] had the templateTitle[2] templateYLabel[0] of templateTitle[4] templateYLabel[1] during that year templateTitle[1] over 4.3 million templateYLabel[1] . templateXValue[1] had the second templateTitle[2] templateYLabel[0] of templateTitle[4] templateYLabel[1] templateTitle[1] almost templateYValue[1] templateYLabel[2] templateYLabel[1] .
generated: 2020 size are popular among people that want to enhance select features of their body through surgical and nonsurgical methods . As of 2020 , the United States had the Snapchat Audience of 2020 size during that year most over 4.3 million size . India had the second Snapchat Audience of 2020 size most almost 22.95 millions size .

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

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

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

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

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

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

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

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

generated_template: 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 Armenia from 1999 to 2019 . In 2019 , the Unemployment rate in Armenia was at approximately 9.81 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 2050*.The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of the templateTitle[2] of templateTitleSubject[0] was templateYValue[7] templateYLabel[2] .

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

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

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

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

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

generated_template: The statistic illustrates the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] was around templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] templateTitleSubject[0] – additional information templateTitleSubject[0] templateTitleSubject[0] , along with Wells Fargo , Bank of America and Citigroup , is one of the leading banks in the country .
generated: The statistic illustrates the Attendance of U.S. from 2003 to 2013 . In 2013 , the Attendance of U.S. was around 73.54 millions . U.S. – additional information U.S. , along with Wells Fargo , Bank of America and Citigroup , is one of the leading banks in the country .

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

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

generated_template: The timeline shows the templateTitle[1] of templateYLabel[0] templateYLabel[1] per templateXLabel[0] of the website Yelp.com from templateTitleDate[0] to templateTitleDate[1] . The local search and review site 's visitor numbers have displayed a steady growth , reaching templateYValue[max] templateYLabel[2] templateYLabel[0] templateYLabel[1] in the first templateXLabel[0] of templateTitleDate[1] .
generated: The timeline shows the Telecommunications of Average revenue per Quarter of the website Yelp.com from to . The local search and review site 's visitor numbers have displayed a steady growth , reaching 39.6 per Average revenue in the first Quarter of .

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 worldwide templateYLabel[0] revenue made by video games company templateTitleSubject[0] from fiscal templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] , up from templateYValue[1] templateYLabel[2] a templateXLabel[0] earlier .
generated: This statistic shows the worldwide Net revenue made by video games company Regal Entertainment Group from fiscal 2006 to 2017 . In 2017 , Regal Entertainment Group 's Net income amounted to 112.3 million U.S. dollars , up from 170.5 million a Year earlier .

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

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

generated_template: The statistic shows the templateYLabel[1] of the real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a change in it is a sign of economic templateYLabel[1] .
generated: The statistic shows the growth of the real Gross domestic product ( GDP ) in Peru from 2014 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a Year . It is considered to be a very important indicator of the economic strength of a country and a change in it is a sign of economic growth .

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 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 garage door used the Most by U.S. construction firms in 2018 . The survey revealed that 50 percent of the respondents used Liftmaster Brand garage the Most . Residential construction involves the building and selling of both individual and multi-family dwellings .

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

gold: Inflation in Argentina was 34 percent in 2018 , expected to rise to nearly 44 percent the following year before declining to 6 percent by 2024 . A major factor in this is the legacy of poor fiscal discipline left by the economic depression from 1998 to 2002 . What causes inflation ? Inflation is a rise in price levels for all goods .
gold_template: templateYLabel[0] in templateTitleSubject[0] was templateYValue[6] 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] had 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 had 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: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) as of October templateTitleDate[0] . Of templateYLabel[1] templateYValue[max] percent reported action or thriller .
generated: This statistic presents the Tourism industry growth expectations in the Tourism ( Tourism ) as of October 2018 . Of respondents 56 percent reported action or thriller .

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] templateXValue[1] templateXValue[1] templateXValue[1] in their free time templateXValue[1] . Even though templateXValue[0] is a very popular 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 in their free time Unemployment . Even though Crime is a very popular 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] templateTitleSubject[0] templateTitle[6] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[0] .
generated: This graph depicts the total Regular season Home attendance of the Los Angeles Chargers Chargers franchise of the National Football League from 2006 to 2019 . In 2019 , the Regular season Home attendance of the franchise was 177755 .

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

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

generated_template: The highest templateTitle[0] of templateTitleSubject[0] templateTitle[2] templateYLabel[0] for one templateYLabel[4] of templateTitle[4] in templateXValue[max] was templateYValue[0] templateYLabel[1] templateYLabel[2] . The global templateTitle[4] industry templateTitle[4] is a soft , white , malleable metal that has the highest electrical and thermal conductivity of all metals . templateTitle[4] is most commonly used to make coins , jewelry , and silverware .
generated: The highest Annual of CAC 40 Index for one points of 1995 in 2019 was 5978.06 points . The global 1995 industry 1995 is a soft , white , malleable metal that has the highest electrical and thermal conductivity of all metals . 1995 is most commonly used to make coins , jewelry , and silverware .

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

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

generated_template: This statistic shows the templateYLabel[0] of hospital admission templateYLabel[1] for templateTitle[0] bites and strikes in templateTitleSubject[0] from April 2017 to March 2018 , templateTitle[5] templateXLabel[0] templateXLabel[1] . People aged between 50 and 59 were the most likely templateTitle[2] of templateTitle[0] bites and strikes , with just over templateYValue[max] thousand admissions in this reporting year .
generated: This statistic shows the Installed of hospital admission base for Video bites and strikes in U.S. from April 2017 to March 2018 , base Console . People aged between 50 and 59 were the most likely systems of Video bites and strikes , with just over 21 thousand admissions in this reporting year .

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

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

generated_template: This statistic represents templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] through templateXValue[max] . In the most recently reported period , the internet company 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateYLabel[3] US dollars , up from 12.92 templateYLabel[3] US dollars in templateXValue[12] . That templateXLabel[0] , templateTitleSubject[0] 's revenue came to 66 templateYLabel[3] US dollars .
generated: This statistic represents U.S. 's Capital expenditure from 2004 through 2018 . In the most recently reported period , the internet company 's Capital expenditure amounted to 33200 U.S. US dollars , up from 12.92 U.S. US dollars in 2006 . That Year , U.S. 's revenue came to 66 U.S. US dollars .

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: First released in templateXValue[1] 2013 , templateTitleSubject[0] templateTitleDate[0] is a multiplayer online battle arena game that was created as a sequel to Defense of the Ancients ( templateTitleSubject[0] ) . Published by Valve Corporation , which is also the creator of the gaming platform Steam , templateTitleSubject[0] templateTitleDate[0] had an estimated templateYValue[0] templateYLabel[2] monthly active users templateTitle[3] in templateXValue[0] templateXValue[0] . The templateYLabel[1] figure peaked back in templateXValue[3] templateXValue[3] , when an average of over templateYValue[max] templateYLabel[2] players enjoyed the game every templateXLabel[0] .
generated: First released in January 2007 2013 , Number 2006 is a multiplayer online battle arena game that was created as a sequel to Defense of the Ancients ( Number ) . Published by Valve Corporation , which is also the creator of the gaming platform Steam , Number 2006 had an estimated 17.8 (in monthly active users who in September 2006 . The players figure peaked back in Spring 2009 , when an average of over 22.2 (in players enjoyed the game every Month .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] a templateTitle[3] templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that the templateTitle[2] templateTitle[3] templateTitle[4] will have a templateYLabel[0] templateYLabel[1] of around templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Average price of for a full set in the gel from 2009 to 2014 . In 2014 , it is estimated that the for full set 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 February 2014 , social network templateXValue[1] acquired the templateXValue[3] app for 19 templateYLabel[3] U.S. dollars .

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

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

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] of templateTitle[2] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] and templateTitleDate[1] . The highest templateYLabel[0] of templateTitle[2] was valued at templateYValue[0] percent in June templateTitleDate[0] .
generated: This statistic shows the rate Inflation of CPI in the United Kingdom ( UK ) in 17 and 19 . The highest Inflation of CPI was valued at 1.5 percent in June 17 .

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 worldwide templateYLabel[0] of templateTitleSubject[0] market mergers and acquisitions ( templateTitleSubject[0] templateTitle[3] A ) templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , based on the values of disclosed templateTitle[4] , the templateTitleSubject[0] 's templateTitleSubject[0] templateTitle[3] A deals amounted to templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the worldwide Spending of Global market mergers and acquisitions ( Global sponsorships A ) 2017 from 2011 to 2017 . In 2016 , based on the values of disclosed 2011 , the Global 's Global sponsorships A deals amounted to 5.58 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] templateTitle[2] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] templateTitle[2] amounted to approximately templateYValue[0] templateYLabel[2] .
generated: This statistic shows the Number of participants in indoor soccer in the U.S. from 2006 to 2018 . In 2018 , the Number of participants ( aged six years and older ) in indoor soccer amounted to approximately 5.23 millions .

Example 120:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. per capita consumption of fresh cucumbers 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['8.0', '7.4', '8.1', '7.5', '7.4', '7.3', '7.1', '6.4', '6.7', '6.8', '6.4', '6.4', '6.1', '6.2', '6.4', '6.2', '6.6', '6.3', '6.4']

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

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[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[1] .
generated: The timeline shows the Per capita consumption of fresh cucumbers 2000 in the U.S. from 2000 to 2018 . According to the report , the U.S. Per capita consumption of fresh cucumbers 2000 amounted to about 8.0 pounds in 2017 .

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: In the first two months of templateXValue[max] , templateYValue[min] thousand migrants arrived by sea in templateTitleSubject[0] . In templateXValue[1] , templateYValue[1] thousand migrants templateTitle[1] on the Italian coasts . Between templateXValue[min] and templateXValue[1] , the templateYLabel[0] of migrants setting foot in the country peaked in templateXValue[4] at templateYValue[max] thousand templateYLabel[1] .
generated: In the first two months of 2019 , 524.3 thousand migrants arrived by sea in Mattel . In 2018 , 524.3 thousand migrants ad on the Italian coasts . Between 2013 and 2018 , the Ad of migrants setting foot in the country peaked in 2015 at 750.2 thousand expenditure .

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

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

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

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

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

generated_template: This survey details a distribution of the templateTitle[0] number of templateXValue[0] templateTitle[2] on templateTitle[3] templateXValue[last] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] . During the survey , templateYValue[1] percent of templateYLabel[1] said that they watched templateXValue[last] templateXValue[1] to templateXValue[1] templateXValue[0] per week .
generated: This survey details a distribution of the U.S. number of Less than 35K USD Prime on membership 150000 USD and over in the Amazon ( Amazon ) in 2018 . During the survey , 41.6 percent of respondents said that they watched 150000 USD and over 35000 - 74999 USD to 35000 - 74999 USD Less than 35K USD per week .

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: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitleSubject[0] templateTitle[3] templateTitle[4] were living below the templateYLabel[0] line in the templateTitle[5] . templateYLabel[0] is the state of one who lacks a certain amount of material possessions or money .
generated: This statistic shows the Number arrests of USA for all in the USA from 1990 to 2018 . In 2018 , 10310960 percent of USA for all were living below the Number line in the offenses . Number is the state of one who lacks a certain amount of material possessions or money .

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

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

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

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

gold: This statistic shows the responses on a study about social network usage in Canada as of January 2015 . During the reported period , Facebook reached 75 percent of Canadians aged 18 to 34 , while among Canadians aged 65 and older Facebook had a 32 percent penetration rate . In general the platform reached 59 percent of people in Canada .
gold_template: This statistic shows the responses on a study about social network usage in templateTitleSubject[0] as of January templateTitleDate[0] . During the reported period , templateTitleSubject[1] reached templateYValue[max] percent of Canadians aged 18 to 34 , while among Canadians aged 65 and older templateTitleSubject[1] had a templateYValue[min] percent templateTitle[3] rate . In general the platform reached templateYValue[5] percent of people in templateTitleSubject[0] .

generated_template: This statistic shows the share of internet users in the templateTitleSubject[0] templateTitleSubject[0] who were using templateTitle[0] templateTitle[1] as of January templateTitleDate[0] , sorted templateTitle[6] annual templateTitle[7] . We Are Flint found that templateYValue[0] percent of internet users with templateTitle[7] earnings of templateXValue[0] templateXValue[0] 30,000 US dollars per templateXLabel[0] used the social networking site .
generated: This statistic shows the share of internet users in the Canada who were using Canada Facebook as of January 2015 , sorted age annual age . We Are Flint found that 75 percent of internet users with age earnings of 18-34 30,000 US dollars per Year used the social networking site .

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

gold: Today , the vast majority of the world uses what is known as the Gregorian calendar , Named after Pope Gregory XIII , who introduced it in 1582 . The Gregorian calendar replaced the Julian calendar , which had been the most used calendar in Europe until this point . The Gregorian calendar lasts for approximately 365.24 days ; this means that most years have 365 days , with one extra day being added every fourth year , unless the year is divisible by 100 but not 400 ( for example , the year 2000 was a leap year , whereas 2100 and 2200 will not be ) .
gold_template: Today , the vast majority of the templateTitle[4] uses what is known as the templateXValue[5] templateXLabel[0] , Named after Pope Gregory XIII , who introduced it in 1582 . The templateXValue[5] templateXLabel[0] replaced the templateXValue[3] templateXLabel[0] , which had been the most used templateXLabel[0] in Europe until this point . The templateXValue[5] templateXLabel[0] lasts for approximately 365.24 days ; this means that most years have 365 days , with one extra day being added every fourth templateYLabel[1] , unless the templateYLabel[1] is divisible by 100 but not 400 ( for example , the templateYLabel[1] 2000 was a leap templateYLabel[1] , whereas 2100 and 2200 will not be ) .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] the world 's templateTitle[4] templateTitle[5] templateTitle[3] 1900 to 2016* . The templateXLabel[0] in templateXValue[0] 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 Current of year various historical the world 's world calendars historical 1900 to 2016* . The Calendar in Assyrian in 1973 claimed 4718 lives . Natural disasters Natural disasters , such as earthquakes , volcanic eruption , tsunamis , floods , tornados or calendars affect people 2020 .

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

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

generated_template: This statistic shows the amount of templateYLabel[0] templateYLabel[1] templateXValue[last] on 31st of March , templateTitleDate[0] , templateTitle[7] templateXValue[last] of templateXValue[last] . There were a total of 82,634 templateYLabel[0] templateYLabel[1] templateXValue[last] in templateTitleSubject[0] and templateTitleSubject[1] on this date , the largest share of whom , templateYValue[max] , were serving templateXValue[2] of templateXValue[0] or templateXValue[0] templateXValue[0] .
generated: This statistic shows the amount of Number Coast 1995 on 31st of March , 1995 , from 1995 of 1995 . There were a total of 82,634 Number Coast 1995 in Coast Guard and Coast Guard on this date , the largest share of whom , 42426 , were serving 2008 of 2010 or 2010 .

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

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

generated_template: This statistic illustrates the templateTitle[0] templateYLabel[2] of the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] volume from templateTitle[6] exporters in templateTitleDate[0] . In that year , templateXValue[1] accounted for a templateYLabel[2] of templateYValue[1] percent of templateYLabel[0] templateTitle[4] to the templateTitle[2] .
generated: This statistic illustrates the Physicians 10,000 of the Physicians per volume from 2013 exporters in 2013 . In that year , Americas accounted for a 10,000 of 21.5 percent of Physicians region to the worldwide .

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

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

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

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

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

generated_template: Swedish e-shoppers cared about fashion and templateXValue[1] in templateTitleDate[0] . The templateTitle[0] frequently templateTitle[1] templateXValue[1] templateTitle[3] by Swedish consumers , were templateXValue[0] and templateXValue[0] items , with templateYValue[max] percent of templateYLabel[1] ' vote . templateXValue[1] and templateXValue[1] templateXValue[1] followed , with templateYValue[1] percent of templateYLabel[1] ' vote .
generated: Swedish e-shoppers cared about fashion and Colleague from work in 2017 . The Persons frequently whom Colleague from work cheat by Swedish consumers , were Friend and Friend items , with 25.4 percent of respondents ' vote . Colleague from work and Colleague from work followed , with 22.6 percent of respondents ' vote .

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

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

generated_template: This statistic shows the approximate templateYLabel[0] of templateTitle[0] enrollments in templateTitle[3] across the templateTitle[2] from the academic templateXLabel[0] templateXValue[last] to templateXValue[0] . In templateXValue[0] , around templateYValue[min] templateYLabel[2] templateYLabel[1] enrolled for templateTitle[0] degrees in the templateTitle[2] .
generated: This statistic shows the approximate Cars of Car enrollments in total across the U.S. from the academic Year 2012 to 2002 . In 2002 , around 1617 thousands service enrolled for Car degrees in the U.S. .

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

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

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

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

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

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

generated_template: This statistic shows the templateYLabel[0] of templateXValue[0] templateTitleSubject[0] templateYLabel[1] templateTitle[4] as of November templateTitleDate[0] , 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 Share of European Union Share market as of November 2015 , region 2015 . There were 15 South America market within the European Union Share European Union China group in 2015 .

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[min] percent .
generated: This statistic shows the Unemployment rate in Tonga from 1999 to 2019 . In 2019 , the Unemployment rate in Tonga was at approximately 0.96 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: The statistic represents the templateTitleSubject[0] templateYLabel[0] templateTitle[2] of templateTitle[2] and templateTitle[3] templateTitle[4] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] templateXLabel[1] . In that year , the templateXValue[0] templateXValue[0] templateXValue[0] ' templateTitle[2] templateTitleSubject[0] templateYLabel[0] of around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic represents the Major Production groups of groups and global aquaculture in 2017 , 2017 Month . In that year , the Carps barbels and other cyprinids ' groups Major Production of around 28345 thousand metric tons .

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 August templateXValue[max] . Following the templateTitleSubject[0] accident in Japan in March templateXValue[8] , templateYValue[3] reactors at Fukushima Daiichi templateTitleSubject[0] templateTitle[1] plant were shut down permanently . In total , there were templateYValue[1] templateTitle[3] templateYLabel[1] in templateXValue[1] .

generated_template: The templateYLabel[0] initial public offering ( templateTitleSubject[0] ) in the templateTitle[4] was templateYValue[0] 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 Number initial public offering ( Nuclear ) in the shutdowns was 3 shutdowns in 2019 . This figure gives an idea of how willing speculators in the shutdowns 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 142:
titleEntities: {'Subject': ['U.S. Black Friday'], 'Date': ['2015', '2019']}
title: Share of U.S. consumers shopping on Black Friday 2015 to 2019
X_Axis['Year']: ['2015', '2016', '2017', '2018', '2019']
Y_Axis['Share', 'of', 'respondents']: ['59', '51', '35', '37', '36']

gold: Some 36 percent of survey respondents stated that they planned to do most of their holiday shopping on Black Friday in the United States in 2019 . This is a decrease of 23 percent since 2015 , when some 59 percent of respondents were planning to do the majority of their shopping on Black Friday .
gold_template: Some templateYValue[4] percent of survey templateYLabel[1] stated that they planned to do most of their holiday templateTitle[3] on templateTitleSubject[0] in the templateTitle[1] in templateXValue[max] . This is a decrease of 23 percent since templateXValue[min] , when some templateYValue[max] percent of templateYLabel[1] were planning to do the majority of their templateTitle[3] on templateTitleSubject[0] .

generated_template: 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: 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 BMW Group 's Production and units from the fiscal Year of 2010 to the fiscal Year of 2018 . In the fiscal Year of 2018 , the company incurred around 162687 units in Production and 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 April templateTitleDate[0] to September templateTitleDate[1] . As of the last reported period , the mobile templateTitleSubject[0] had templateYValue[max] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide , ranking second among mobile chat apps worldwide . templateTitle[0] templateTitleSubject[0] templateYLabel[3] – additional information Mobile templateTitleSubject[0] apps are on the rise , with optimistic projections for this market in the coming years .

generated_template: This statistic shows a timeline with the amount of monthly templateYLabel[2] templateTitleSubject[0] templateYLabel[3] worldwide as of December templateTitleDate[1] . As of that templateXLabel[0] , the mobile messaging app announced more than templateYValue[max] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from over templateYValue[1] templateYLabel[4] MAU in February 2016 .
generated: This statistic shows a timeline with the amount of monthly active Messenger users worldwide as of December 2017 . As of that Month , the mobile messaging app announced more than 1300 millions monthly active users , up from over 1200 millions MAU in February 2016 .

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

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

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

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

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

generated_template: The statistics shows the templateYLabel[0] for templateTitle[0] templateTitleSubject[0] templateTitleSubject[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitleSubject[0] templateTitleSubject[0] templateTitle[3] generated a total amount of templateYValue[max] templateYLabel[2] templateYLabel[3] , up from templateYValue[max] templateYLabel[2] a templateXLabel[0] earlier .
generated: The statistics shows the Revenue for Sporting Easton Bell Sports (in from 2006 to 2013 . In 2013 , Sporting Easton Bell Sports Revenue generated a total amount of 834.9 million U.S. , up from 834.9 million a Year earlier .

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

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

generated_template: This statistic illustrates the templateYLabel[0] of templateTitle[2] templateTitleSubject[0] chat app users from June templateTitleDate[0] to March templateTitleDate[1] . As of that templateXLabel[0] , the mobile messenger had almost templateYValue[max] templateYLabel[2] templateTitle[2] users worldwide . The chat app had been acquired by Japanese e-commerce company Rakuten in February 2014 for 900 templateYLabel[2] U.S. dollars , making it the second-most expensive acquisition of the e-commerce platform up to date .
generated: This statistic illustrates the Number of vehicle Mexico chat app users from June 2019 to March 2019 . As of that Automaker , the mobile messenger had almost 174706 sold vehicle users worldwide . The chat app had been acquired by Japanese e-commerce company Rakuten in February 2014 for 900 sold U.S. dollars , making it the second-most expensive acquisition of the e-commerce platform up to date .

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

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

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

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

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

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] 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: The statistic shows Bombardier transportation Revenue between the 2007 and 2018 fiscal years . In the 2018 fiscal Year , the manufacturer of regional aircraft , business jets , mass transportation equipment and recreational equipment had revenues of 8.91 billion U.S. dollars .

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

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

generated_template: 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': ['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] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] 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': ['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': ['08', '19']}
title: Twitter : number of employees 2008 to 2019
X_Axis['Month']: ["Dec_'19", "Dec_'18", "Dec_'17", "Dec_'16", "Dec_'15", "Dec_'14", "Dec_'13", "Jan_'11", "Jan_'10", "Jan_'09", "Jan_'08"]
Y_Axis['Number', 'of', 'employees']: ['4900', '3920', '3372', '3583', '3900', '3638', '2712', '350', '130', '29', '8']

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

generated_template: This statistic displays the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] around the world as of January templateTitleDate[0] , templateTitle[5] templateTitle[6] . As of that templateXLabel[0] , there were templateYValue[max] templateTitleSubject[0] templateYLabel[1] in the templateTitleSubject[1] templateTitleSubject[1] , up from templateYValue[1] templateYLabel[1] .
generated: This statistic displays the Number of Twitter employees around the world as of January 08 , 2019 . As of that Month , there were 4900 Twitter employees in the Twitter , up from 3920 employees .

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

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

generated_template: The statistic shows how templateXValue[4] people in the templateTitleSubject[0] templateTitleSubject[0] use templateTitle[2] in templateTitleDate[0] according to a Statista survey . templateYValue[max] percent of templateYLabel[1] said that they use templateTitle[2] on a templateXValue[0] basis .
generated: The statistic shows how Not at all people in the Americans use level in 2018 according to a Statista survey . 47 percent of respondents said that they use level on a Extremely basis .

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: 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 Bulgaria from 1999 to 2019 . In 2019 , the Unemployment rate in Bulgaria was at approximately 4.82 percent .

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

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

generated_template: In templateXValue[max] , approximately templateYValue[0] templateYLabel[1] templateYLabel[0] in templateTitleSubject[0] disposed of one television or more . In templateXValue[max] , the templateTitle[0] of French templateYLabel[0] owning at least one television amounted to templateYValue[max] thousand . Due to the implementation of new broadcasting resolution technologies , such as the 4K UHD , and to the growing affordability of high-end televisions , this figure could increase in the coming years .
generated: In 2018 , approximately 2984.2 value Retail in United Kingdom disposed of one television or more . In 2018 , the Retail of French Retail owning at least one television amounted to 3063.4 thousand . Due to the implementation of new broadcasting resolution technologies , such as the 4K UHD , and to the growing affordability of high-end televisions , this figure could increase in the coming years .

Example 158:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2010', '2019']}
title: Ice hockey players in Sweden 2010 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11']
Y_Axis['Number', 'of', 'players']: ['55431', '62701', '63901', '60408', '60089', '56839', '64214', '69921', '62003']

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

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

Example 159:
titleEntities: {'Subject': ['Guatemala'], 'Date': ['2015']}
title: Median age of the population in Guatemala 2015
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2015', '2010', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960', '1955', '1950']
Y_Axis['Median', 'age', 'in', 'years']: ['33.0', '31.3', '29.6', '28.0', '26.3', '24.5', '22.9', '21.3', '19.7', '18.5', '17.7', '17.2', '16.9', '16.6', '16.8', '17.0', '16.9', '16.8', '17.2', '17.8', '18.4']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] is the templateYLabel[1] that divides a templateTitle[2] into two numerically equal groups ; that is , half the people are younger than this templateYLabel[1] and half are older . It is a single index that summarizes the templateYLabel[1] distribution of a templateTitle[2] .
generated: This statistic shows the Median age of the population in Guatemala from 1950 to 2050 . The Median age is the age that divides a population into two numerically equal groups ; that is , half the people are younger than this age and half are older . It is a single index that summarizes the age distribution of a population .

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

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

generated_template: The statistic shows the templateTitle[0] of templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[6] through the Supplemental Nutrition Assistance Program ( templateTitleSubject[0] , formerly called templateTitleSubject[0] templateTitleSubject[0] ) in templateTitleDate[0] , by templateXLabel[0] . About templateYValue[2] percent of the templateTitle[3] in templateXValue[5] is templateTitle[4] templateTitle[6] through the Supplemental Nutrition Assistance Program .
generated: The statistic shows the Number of Obamacare 2019 Obamacare enrollment through the Supplemental Nutrition Assistance Program ( Obamacare , formerly called Obamacare ) in 2019 , by State . About 1087240 percent of the 2019 in Pennsylvania is Obamacare enrollment through the Supplemental Nutrition Assistance Program .

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

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

generated_template: This graph shows the templateXValue[1] templateYLabel[1] templateYLabel[2] to the Gross Domestic Product ( templateTitle[1] ) of templateTitleSubject[0] templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[9] templateXLabel[0] templateYLabel[2] 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 Finance insurance real estate rental and leasing value added to the Gross Domestic Product ( GDP ) of GDP Michigan in 2018 , by Industry . In 2018 , the Information Industry added 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: This statistic shows the results of a templateTitleDate[0] survey among templateTitleSubject[0] templateTitle[1] aged 18 to 60 about their stance on templateXValue[0] on templateTitle[4] . They were asked how they would manage to templateXValue[0] templateXValue[0] on templateTitle[4] templateTitle[5] templateTitle[6] a templateXValue[0] with others . templateYValue[max] percent of the templateTitle[1] stated templateXValue[1] they would templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] is in the templateXValue[0] .
generated: This statistic shows the results of a 2012 survey among American friends aged 18 to 60 about their stance on Parents on teenagers . They were asked how they would manage to Parents on teenagers ' decisions a Parents with others . 38 percent of the friends stated Friends they would Parents is in the Parents .

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

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

generated_template: 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 2017 IT 2017 who access selected functions outsourced as of the third quarter 2017 . During the survey period , it was found that 64 percent of 2017 in the 2017 used Software application development .

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 forecasted templateYLabel[0] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] , a comparison between templateXValue[0] and templateXValue[last] . In templateXValue[last] , the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] is expected to have a cargo templateYLabel[0] of around templateYValue[max] templateYLabel[1] templateYLabel[2] ( twenty-foot equivalent units ) .
generated: The statistic shows the forecasted Equipment of the sales U.S. sleeping bags , a comparison between 2010 and 2011 . In 2011 , the sales U.S. sleeping bags is expected to have a cargo Equipment of around 210.38 sales million ( twenty-foot equivalent units ) .

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: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Average sales of Outback Steakhouse from 2015 to 2018 . In 2018 , Outback Steakhouse 's Average sales amounted to approximately 3580 thousand U.S. dollars .

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

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

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

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

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

generated_template: The graph shows the member states of the European Union templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] templateTitle[8] in templateTitle[8] . templateXValue[0] ranked first , on templateYValue[max] percent of the templateYLabel[1] had the highest result of templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in the European Union , making it '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 graph shows the member states of the European Union 2018 by country in country . New Zealand ranked first , on 87 percent of the score had the highest result of 2018 by country in the European Union , making it 's leading member state in terms of 2018 by score . The dawn of 2018 payments The practice of paying without cash has its roots in the New Zealand States and was first used in the 1940s as a means of paying for automobile fuel .

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

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

gold: The statistic shows gross domestic product ( GDP ) per capita in Nigeria from 1994 to 2024 . GDP is the total value of all goods and services produced in a country in a year . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
gold_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
generated: The statistic shows the Gross domestic product ( GDP ) per capita in 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']}
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 templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) of the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] amounted to approximately templateYValue[6] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows Gross domestic product ( GDP ) of the Latin America from 2014 to 2018 , with projections up until 2024 . In 2018 , the Gross domestic product GDP amounted to 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] templateYLabel[3] templateTitle[5] templateTitle[7] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[8] templateXLabel[0] . The templateYLabel[0] templateYLabel[1] of templateTitle[2] templateYLabel[2] in the templateXValue[0] templateXValue[0] was at templateYValue[max] percent .
generated: The statistic shows the Number hostages of hostages taken region in the Terrorism in , region Country . The Number hostages of hostages taken in the Africa was at 2651 percent .

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 illustrates the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] templateTitle[5] templateYLabel[4] templateYLabel[5] templateTitle[6] top templateTitleSubject[0] templateTitle[7] . During the first quarter of templateTitle[8] , an templateXValue[10] templateYLabel[1] templateYLabel[2] templateYLabel[5] templateTitle[6] templateXValue[0] templateTitle[5] templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . In constrast , top templateXValue[0] influencer Cara Delevigne templateTitle[5] an templateXValue[10] user templateTitle[4] of 662,894 interactions templateYLabel[4] templateYLabel[5] .
generated: This statistic illustrates the Number of social media actions generated per post by top Luxury top . During the first quarter of influencers , an Hermes (Xenia Tchoumi) social media post by Valentino (Demi Lovato) generated 1385467 social media actions . In constrast , top Valentino (Demi Lovato) influencer Cara Delevigne generated an Hermes (Xenia Tchoumi) user engagement of 662,894 interactions per post .

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

gold: The statistic shows the inflation rate in the Euro area countries in June 2019 . Inflation or currency devaluation ( drop in the value of money ) , is characterized by a steady rise in the prices of finished products ( consumer goods , capital goods ) . The consumer price index tracks price trends of private consumption expenditure , and shows an increase in the index 's current level of inflation .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in the templateXValue[12] countries in templateTitle[6] 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[3] ranked 4th templateTitle[1] an estimated templateYLabel[0] templateYLabel[1] of about 27.6 percent templateYLabel[2] to the templateYLabel[3] templateYLabel[4] . templateYLabel[0] in industrialized and in emerging templateTitleSubject[0] Higher templateYLabel[0] rates are more present in third world or developing templateTitleSubject[0] , because they often lack a sufficient central bank , which in turn results in the manipulation of currency to achieve short term economic goals .
generated: The statistic lists the 20 Euro inflation the rate Inflation rate in 2019 . In 2019 , Estonia ranked 4th inflation an estimated Inflation rate of about 27.6 percent compared to the previous year . Inflation in industrialized and in emerging Euro Higher Inflation rates are more present in third world or developing Euro , because they often lack a sufficient central bank , which in turn results in the manipulation of currency to achieve short term economic goals .

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

gold: The price of one pound of seedless grapes ( Thompson ) in the United States was 2.39 U.S. dollars in 2019 . U.S. seedless grape prices peaked in 2009 at 3.14 U.S. dollars per pound . Grape Production in the United States Grapes have the highest production volume of any fruit in the United States .
gold_template: The templateYLabel[0] of one templateYLabel[4] of seedless templateTitle[3] ( Thompson ) in the templateTitle[0] was templateYValue[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: The statistic shows the percentage of templateYLabel[0] templateYLabel[1] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . Home templateYLabel[1] value is calculated by subtracting the value of remaining mortgage debt from the market value of the real estate property . That means that the value of home templateYLabel[1] increases as the debtor pays off the mortgage .
generated: The statistic shows the percentage of Price U.S. in the grapes from 1995 to 2019 . Home U.S. value is calculated by subtracting the value of remaining mortgage debt from the market value of the real estate property . That means that the value of home U.S. increases as the debtor pays off the mortgage .

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

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

generated_template: 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 Bolivia from 2000 to 2017 . In 2017 , 43.83 percent of the Chilean population accessed the internet , up from 2.12 percent in 2001 . In 2016 , 43.83 percent of the Chilean population acceded to internet .

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 represents the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] is calculated as the ratio of templateYLabel[0] templateYLabel[1] to disposable templateYLabel[0] income . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] templateTitleSubject[0] amounted to templateYValue[0] percent .
generated: The statistic represents the Life expectancy birth in the China from 1960 to 2017 . Life expectancy birth is calculated as the ratio of Life expectancy to disposable Life income . In 2017 , the Life expectancy birth in the China amounted to 78.83 percent .

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

gold: This statistic shows the result of a survey question designed to find out what is most important to young people ( 16 - 24 ) in Great Britain , as of November 2013 . The majority of respondents said that their family is the most important thing to them .
gold_template: This statistic shows the result of a survey question designed to find out what is templateTitle[0] templateTitle[1] to templateTitle[4] templateTitle[5] ( templateTitle[6] - templateTitle[7] ) in templateTitleSubject[0] , as of November templateTitle[10] . The majority of templateYLabel[1] said that their templateXValue[0] is the templateTitle[0] templateTitle[1] thing to them .

generated_template: This statistic shows the leading reasons why teenagers in the templateTitle[6] feel that templateTitle[4] templateTitle[5] has a mostly templateTitle[2] effect on people their own age as of April templateTitleDate[0] . The findings show that templateYValue[max] percent of surveyed templateTitleSubject[0] teens feel that the most templateTitle[2] effect of templateTitle[4] people on people their age is templateXValue[0] templateXValue[0] friends and/or family .
generated: This statistic shows the leading reasons why teenagers in the 16 feel that young people has a mostly things effect on people their own age as of April 2013 . The findings show that 51 percent of surveyed Great Britain teens feel that the most things effect of young people on people their age is Family friends and/or family .

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

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

generated_template: This statistic presents the templateTitle[2] templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , companies detected and reported templateYValue[1] templateYLabel[2] IT templateTitle[1] breaches . This templateYLabel[0] increased by 38 percent in the following templateXLabel[0] .
generated: This statistic presents the & R&D of Global R expenditure from 2013 to 2019 . In 2018 , companies detected and reported 54.0 million IT R breaches . This R&D increased by 38 percent in the following Year .

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

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

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

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

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

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a trade deficit . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] trade deficit amounted to around 7.23 percent of templateYLabel[3] .
generated: The statistic shows Argentina 's Budget balance in relation to GDP between 2014 and 2018 , with projections up until 2024 . A positive value indicates a Budget surplus , a negative value indicates a trade deficit . In 2018 , Argentina 's trade deficit amounted to around 7.23 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: The statistic shows the templateYLabel[0] of worldwide denial of service templateYLabel[1] templateYLabel[2] between November 2017 and April templateTitleDate[0] , sorted by originating templateTitle[2] . During that period of time , templateYValue[max] percent of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] originated from IP addresses in the templateXValue[0] templateXValue[0] .
generated: The statistic shows the Import of worldwide denial of service value billion between November 2017 and April 2018 , sorted by originating 10 . During that period of time , 77 percent of Value billion originated from IP addresses in the European Union (28) .

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

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

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] are owned by 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 Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 1900 million U.S. dollars . The Los Angeles Angels are owned by 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] templateTitleSubject[0] templateTitleSubject[0] templateTitle[7] 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] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] The templateTitleSubject[0] templateTitleSubject[0] are a professional basketball team that is part of the Western Conference in the National Basketball Association ( templateTitleSubject[1] ) .
generated: This graph depicts the Average ticket price for Detroit Pistons 2015/16 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 The Detroit Pistons are a professional basketball team that is part of the Western Conference in the National Basketball Association ( NBA ) .

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

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

generated_template: This graph shows the statutory 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[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This graph shows the statutory Average age in U.S. from 2013–2015 to 2013–2015 . In 2013–2015 , the Average age in U.S. was at 14.64 years .

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

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

generated_template: This graph shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[3] percent of the templateTitle[2] templateTitle[3] in the templateTitle[4] , a change of minus 1 percent compared to the previous templateXLabel[0] .
generated: This graph shows the Number companies of market United in the Kingdom from 2004 to 2017 . In 2017 , 526 percent of the market United in the Kingdom , a change of minus 1 percent compared to the previous Year .

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

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

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] are owned by 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 Rams Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 3800 million U.S. dollars . The Los Angeles Rams are owned by Rogers Communications , who bought the Franchise for 137 million U.S. dollars in 2000 .

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

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

generated_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] on templateTitle[3] , templateTitle[4] , and templateTitle[5] preparation templateTitle[6] amounted to approximately templateYValue[0] templateYLabel[4] per consumer unit in the templateTitle[0] . This figure represents the highest templateYLabel[2] in over a decade . Purchasing behavior in the templateTitle[0] The templateTitle[0] is the most lucrative beauty and personal care market in the world .
generated: In 2018 , the Value added percentage on percentage , GDP , and 2007 preparation 2018 amounted to approximately 11.4 GDP per consumer unit in the Value . This figure represents the highest percentage in over a decade . Purchasing behavior in the Value The Value is the most lucrative beauty and personal care market in the world .

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

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

generated_template: This statistic displays the templateYLabel[0] of templateTitleSubject[0] templateTitle[4] templateTitleSubject[0] templateTitle[2] templateTitle[1] in selected countries templateTitle[5] in templateTitleDate[0] . During the survey period , it was found that templateYValue[max] percent of templateTitleSubject[0] templateTitle[4] templateTitleSubject[0] users in templateXValue[0] and templateYValue[max] percent were followed by templateXValue[1] .
generated: This statistic displays the Share of Search penetration Search regular engines in selected countries 2013 in 2013 . During the survey period , it was found that 85 percent of Search penetration Search users in Belgium and 85 percent were followed by Norway .

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

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

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

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

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

generated_template: In templateXValue[max] , templateTitleSubject[0] was the fifth largest European country in terms of templateTitle[1] size , with an templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] , up from templateYValue[1] templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] . The company 's average templateTitle[1] mix has shifted in the last few years , with the company 's Japanese a templateYLabel[0] templateYLabel[1] of only year .
generated: In 2019 , Chained was the fifth largest European country in terms of consumer size , with an Chained Consumer of 144.73 Price Index , up from 141.7 Price Index in the previous December . The company 's average consumer mix has shifted in the last few years , with the company 's Japanese a Chained Consumer of only year .

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

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

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

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[min] 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 2.4 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 the first half of templateXValue[17] , household templateTitle[0] templateTitle[1] in templateTitleSubject[0] have seen an overall increase , rising to templateYValue[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( kWh ) in the first half of templateXValue[0] . This was the highest price during the reporting period . templateTitle[1] dropped noticeably between templateXValue[9] and the first half of templateXValue[7] , with templateTitle[3] paying less than templateYValue[min] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .
generated: Since the first half of 2010 S2 , household Electricity prices in Germany have seen an overall increase , rising to 30.0 Euro cents per kilowatt-hour ( kWh ) in the first half of 2019 S1 . This was the highest price during the reporting period . prices dropped noticeably between 2014 S2 and the first half of 2015 S2 , with households paying less than 23.75 Euro cents per kWh .

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

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

generated_template: The statistic shows the templateYLabel[1] of the real templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a change in it is a sign of economic templateYLabel[1] .
generated: The statistic shows the growth of the real Gross domestic product ( GDP ) in Cyprus from 2014 to 2018 , with projections up until 2024 . GDP is the total value of all goods and services produced in a country in a Year . It is considered to be a very important indicator of the economic strength of a country and a change in it is a sign of economic growth .

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

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

generated_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitleSubject[0] templateTitleSubject[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic presents the NFL Thursday Night Viewers in the NFL from 2015 to 2019 . In 2015 , the NFL Thursday Night Viewers in the NFL amounted to 15.4 millions .

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

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

generated_template: According to templateYLabel[3] Census data , the templateTitle[2] and public relations templateTitle[3] saw slightly increasing revenues in since templateXValue[2] . At approximately 1118 templateYLabel[2] templateYLabel[3] templateYLabel[4] , the templateTitle[3] reached a record high in templateXValue[max] . The significant drops in earnings in templateXValue[min] and templateXValue[9] can be tied to the general economic situation in the country at that time .
generated: According to millions) Census data , the domestic and public relations passenger saw slightly increasing revenues in since 2016 . At approximately 1118 (in millions) , the passenger reached a record high in 2018 . The significant drops in earnings in 2004 and 2009 can be tied to the general economic situation in the country at that time .

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

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

generated_template: This statistic presents the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] amounted to approximately templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] .
generated: This statistic presents the Revenue of Bloomin Brands from 2009 to 2018 . The Revenue of Bloomin Brands amounted to approximately 4.06 billion U.S. dollars in 2018 .

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

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] amounted to templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Market size of sensor components in the global from 2015 to 2030 . In 2020 , the Market size of sensor components global amounted to 15.0 billion U.S. dollars .

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

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

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] templateTitle[6] amounted to about templateYValue[max] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of oat products 2000 in the U.S. from 2000 to 2018 . According to the report , the U.S. Per capita consumption of oat products 2000 amounted to about 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: This statistic gives information on the templateTitleSubject[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateXValue[7] destination and accommodation templateTitle[5] in templateTitleDate[0] , based on templateYLabel[0] of templateYLabel[1] . During this period of time , it was found that templateYValue[max] percent of all templateYLabel[1] were directed to templateTitle[1] leader templateXValue[0] . The templateXLabel[0] website 's templateTitleDate[0] revenue amounted to 1.48 billion templateTitleSubject[0] dollars .
generated: This statistic gives information on the U.S. UHNW U.S. UHNW of super Toronto destination and accommodation population in 2014 , based on Number of Ultra-High-Net-Worth . During this period of time , it was found that 8655 percent of all Ultra-High-Net-Worth were directed to U.S. leader New York . The State website 's 2014 revenue amounted to 1.48 billion U.S. UHNW dollars .

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 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 `` March 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 successful music tours North teams with the most North America Division I America from 2019 to 2019 . The Rolling Stones has won the championship a record 177.8 times . North America tours North championship - additional information North America successful music Division I North tournament , also known as `` March Madness '' , is the most important competition for collegiate North , which is the second most followed tours sport in the U.S .

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

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

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

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

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

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

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

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

generated_template: In templateXValue[max] , the templateTitle[3] of templateTitle[0] and calves in the templateTitleSubject[0] templateTitleSubject[0] was approximately templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] . This represented a significant increase over the previous templateXLabel[0] , when the highest templateYLabel[0] of templateTitle[0] was templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Unsurprisingly and facts on recreational boating
generated: In 2016 , the U.S. of Plywood and calves in the U.S. was approximately 616 million cubic feet . This represented a significant increase over the previous Year , when the highest Production of Plywood was 616 million cubic feet . Unsurprisingly 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 timeline illustrates data on the templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] templateTitle[1] shoppers as of the fourth templateXLabel[0] of templateTitleDate[1] . During that period of time , templateTitleSubject[0] had templateTitle[1] templateYLabel[0] of templateYValue[max] templateYLabel[2] worldwide , up from templateYValue[4] templateYLabel[2] in the corresponding templateXLabel[0] of the previous year . This represents a 32.8 percent year-on-year growth .
generated: This timeline illustrates data on the Users of millions of United Kingdom mobile shoppers as of the fourth Quarter of 2016 . During that period of time , United Kingdom had mobile Users of 42.77 millions worldwide , up from 39.56 millions in the corresponding Quarter of the previous year . This represents a 32.8 percent year-on-year growth .

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

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

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

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

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

generated_template: In templateXValue[1] , the annual templateYLabel[0] of visitor exports is expected to reach templateYValue[max] templateYLabel[1] templateYLabel[2] . The spending of templateTitle[0] tourists in the country slightly decreased in templateXValue[3] , after the terrorist attacks which took place in Paris and Nice respectively in templateXValue[4] and templateXValue[3] . However , in templateXValue[2] , templateTitleSubject[0] had the fifth largest templateTitle[0] tourism templateTitle[3] worldwide .
generated: In 2018 , the annual Spending of visitor exports is expected to reach 131.4 billion euros . The spending of Domestic tourists in the country slightly decreased in 2016 , after the terrorist attacks which took place in Paris and Nice respectively in 2015 and 2016 . However , in 2017 , France had the fifth largest Domestic tourism France worldwide .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] in templateXValue[max] was at templateYValue[max] templateTitle[4] templateYLabel[5] for templateTitleSubject[0] households .
generated: This statistic shows the Death rate of the U.S. drug overdose U.S. from 1950 to 2017 . The Death rate in 2017 was at 20.1 overdose population for U.S. households .

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

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

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

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

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

generated_template: This statistic illustrates templateTitleSubject[0] 's templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] from the second templateXLabel[0] of templateTitleDate[0] to the second templateXLabel[0] of templateTitleDate[1] . As of the last reported templateXLabel[0] , templateYValue[0] percent of the group 's templateTitle[1] visits converted to purchases .
generated: This statistic illustrates Alibaba 's mobile share Percentage mobile from the second Quarter of 2016 to the second Quarter of 2016 . As of the last reported Quarter , 75 percent of the group 's mobile visits converted to purchases .

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

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

generated_template: Since the first half of templateXValue[17] , household templateTitle[0] templateTitle[1] in templateTitleSubject[0] have seen an overall increase , rising to templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( kWh ) in the first half of templateXValue[0] . This was the highest price during the reporting period . templateTitle[1] dropped noticeably between templateXValue[9] and the first half of templateXValue[7] , with templateTitle[3] paying less than templateYValue[min] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .
generated: Since the first half of 2010 S2 , household Electricity prices in Latvia have seen an overall increase , rising to 16.5 Euro cents per kilowatt-hour ( kWh ) in the first half of 2019 S1 . This was the highest price during the reporting period . prices dropped noticeably between 2014 S2 and the first half of 2015 S2 , with households paying less than 10.48 Euro cents per kWh .

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

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

generated_template: Since the first half of templateXValue[17] , household templateTitle[0] templateTitle[1] in templateTitleSubject[0] have seen an overall increase , rising to templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( kWh ) in the first half of templateXValue[0] . This was the highest price during the reporting period . templateTitle[1] dropped noticeably between templateXValue[9] and the first half of templateXValue[7] , with templateTitle[3] paying less than templateYValue[min] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh .
generated: Since the first half of 2010 S2 , household Electricity prices in Luxembourg have seen an overall increase , rising to 17.98 Euro cents per kilowatt-hour ( kWh ) in the first half of 2019 S1 . This was the highest price during the reporting period . prices dropped noticeably between 2014 S2 and the first half of 2015 S2 , with households paying less than 16.15 Euro cents per kWh .

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

gold: In 2018 , 233 fatalities were recorded on Swiss roads . Between 2006 and 2018 , traffic related deaths declined by over one third , with the lowest number seen in 2016 at 216 such incidences . Switzerland was one of the safest countries in Europe for road users .
gold_template: In templateXValue[max] , templateYValue[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[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 233 reported road deaths in Switzerland 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 217:
titleEntities: {'Subject': ['Walmart'], 'Date': ['2006', '2019']}
title: Walmart 's operating income worldwide 2006 to 2019
X_Axis['Fiscal', 'year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Operating', 'income', 'in', 'billion', 'U.S.', 'dollars']: ['21.96', '20.44', '22.76', '24.11', '27.15', '26.87', '27.73', '26.49', '25.51', '23.97', '22.77', '21.92', '20.5', '18.69']

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

generated_template: The timeline shows templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateXValue[min] to templateXValue[max] . In templateXValue[1] , templateTitleSubject[0] templateTitle[1] global templateTitle[2] templateTitle[3] amounted to about templateYValue[1] templateYLabel[0] templateYLabel[1] dollars.WalmartWal-Mart was founded in 1962 by Sam Walton when he and his brother James `` Bud '' Walton opened the first Wal-Mart Discount City in Rogers , Arkansas . Since then , Wal-Mart has grown to become the largest publicly-owned retail company in the world .
generated: The timeline shows Walmart 's operating income worldwide 2006 to 2019 . In 2018 , Walmart 's global operating income amounted to about 20.44 Operating income dollars.WalmartWal-Mart was founded in 1962 by Sam Walton when he and his brother James `` Bud '' Walton opened the first Wal-Mart Discount City in Rogers , Arkansas . Since then , Wal-Mart has grown to become the largest publicly-owned retail company in the world .

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: The statistic shows the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] due to earthquakes templateTitle[4] templateXValue[min] to templateXValue[max] . Around templateYValue[0] people died worldwide in templateXValue[max] as a result of earthquakes . Earthquakes are typically caused by the movement of the earth crusts .
generated: The statistic shows the BNP Paribas Return equity due to earthquakes 2003 to 2018 . Around 8.2 people died worldwide in 2018 as a result of earthquakes . Earthquakes are typically caused by the movement of the earth crusts .

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: 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 Slovakia from 1994 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Slovakia amounted to about 2.53 percent compared to the previous Year .

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

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

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

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

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

generated_template: This statistic presents the 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] templateTitle[2] a templateTitle[3] templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Average price acrylic a fill nail in the salons from 2006 to 2019 . In 2019 , the Average price acrylic fill nail amounted to 30.57 U.S. dollars .

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

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

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

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

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

generated_template: This statistic provides information on the templateYLabel[0] of the templateTitle[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] as of templateTitleSubject[0] templateTitle[5] , templateTitle[6] templateTitle[7] . As of that period , templateXValue[0] templateXValue[0] had the highest active templateYLabel[3] usage rate with templateYValue[max] percent of the templateYLabel[1] accessing the social network .
generated: This statistic provides information on the Value of the FIFA million U.S. dollars as of Latin American valued , Latin American . As of that period , Brazil had the highest active dollars usage rate with 981.0 percent of the million accessing the social network .

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: The statistic presents the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] Corp. generated a templateYLabel[0] of templateYValue[min] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] templateTitleSubject[0] is the world 's largest cosmetics and beauty company , concentrating on hair color , skin care , sun protection , make-up , perfumes , and hair care .
generated: The statistic presents the Revenue of Vinci Group revenue in the 2009 2018 2009 to 2018 . In 2018 , Vinci Group revenue Corp. generated a Revenue of 30.74 billion euros . Vinci Group revenue is the world 's largest cosmetics and beauty company , concentrating on hair color , skin care , sun protection , make-up , perfumes , and hair care .

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: In templateXValue[1] , the annual templateYLabel[0] of visitor exports is expected to reach templateYValue[max] templateYLabel[1] templateYLabel[2] . The spending of templateTitle[0] tourists in the country slightly decreased in templateXValue[3] , after the terrorist attacks which took place in Paris and Nice respectively in templateXValue[4] and templateXValue[3] . However , in templateXValue[2] , templateTitleSubject[0] had the fifth largest templateTitle[0] tourism templateTitle[3] worldwide .
generated: In 2018 , the annual Spending of visitor exports is expected to reach 26.4 billion euros . The spending of International tourists in the country slightly decreased in 2016 , after the terrorist attacks which took place in Paris and Nice respectively in 2015 and 2016 . However , in 2017 , Portugal had the fifth largest International tourism Portugal worldwide .

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

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

generated_template: This statistic 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', '2019']}
title: Distribution of Reddit.com traffic 2019 , by country
X_Axis['Country']: ['United_States', 'United_Kingdom', 'Canada', 'Australia', 'Germany']
Y_Axis['Share', 'of', 'traffic']: ['49.57', '7.79', '7.75', '4.28', '3.27']

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

generated_template: This statistic represents the regional templateTitle[0] of templateTitleSubject[0] templateYLabel[1] in the last three months . As of October templateTitleDate[0] , the templateXValue[0] templateXValue[0] accounted for templateYValue[max] percent of desktop templateYLabel[1] to the visual blogging site .
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 .

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

gold: The statistic depicts the revenue of the Green Bay Packers , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Green Bay Packers was 456 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] Packers , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] Packers was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

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

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

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

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] dollars.The templateTitleSubject[0] templateTitleSubject[0] are owned by 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 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 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 233:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2008', '2018']}
title: Gross domestic product ( GDP ) at current prices in Denmark 2008 to 2018
X_Axis['Year']: ['2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['GDP', 'in', 'million', 'Danish', 'kroner']: ['1801470', '1722143', '1810926', '1846854', '1895002', '1929677', '1981165', '2036356', '2107808', '2175106', '2245954']

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

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] of templateTitle[2] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[0] templateYLabel[2] templateTitle[2] had been in templateTitleSubject[0] . The Syrian refugee crisis The war in templateTitleSubject[0] – additional information According to the graph at hand , absolute unemployment in templateTitleSubject[0] has gradually risen over the past decade .
generated: This statistic shows the domestic GDP of product million in Denmark from 2008 to 2018 . In 2018 , approximately 1801470 Danish product had been in Denmark . The Syrian refugee crisis The war in Denmark – additional information According to the graph at hand , absolute unemployment in Denmark has gradually risen over the past decade .

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

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

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

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

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

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

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

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

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

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

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

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] in templateTitleDate[0] , based on sales value . In that year , templateYValue[max] percent of the templateYLabel[1] templateYLabel[2] templateTitle[2] a templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] were templateTitle[1] .
generated: The statistic shows the Share of time spent watching worldwide by in Netflix in 2017 , based on sales value . In that year , 70 percent of the time spent watching a worldwide by device in Netflix were content .

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 presents the templateYLabel[0] of people in the templateTitle[2] who have read at least one templateTitle[0] in the past 12 months as of January templateTitleDate[0] , broken down templateTitle[4] templateTitle[5] . During a survey , templateYValue[max] percent of templateYLabel[1] aged 18 to 29 stated they had read at least one templateTitle[0] in the previous 12 months . The popularity of this medium is reflected in the growing templateTitle[0] sales revenue .
generated: This statistic presents the Share of people in the U.S. who have read at least one Book in the past 12 months as of January 2019 , broken down by age . During a survey , 81 percent of respondents aged 18 to 29 stated they had read at least one Book in the previous 12 months . The popularity of this medium is reflected in the growing Book sales revenue .

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

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

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

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

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

generated_template: This statistic shows the results of 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 statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] templateTitleSubject[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In its templateXValue[max] fiscal templateXLabel[0] , templateTitleSubject[0] templateTitleSubject[0] paid out a templateYLabel[0] of approximately templateYValue[0] templateYLabel[3] templateYLabel[1] templateYLabel[2] .
generated: The statistic shows the Net profit million of SAP from the fiscal Year of 2006 to the fiscal Year of 2019 . In its 2019 fiscal Year , SAP paid out a Net of approximately 3321 euros profit million .

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

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

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , 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 , 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: During the year templateTitleDate[0] , the most templateYLabel[1] templateTitle[2] game on the physical market was templateXValue[0] templateXValue[0] for the PS4 , accounting for about templateYValue[max] thousand templateYLabel[0] templateYLabel[1] . In second place came templateXValue[1] templateXValue[1] templateXValue[1] templateXValue[1] , with sales amounting to roughly templateYValue[1] templateYLabel[0] . templateTitle[2] game market in templateTitleSubject[0] In 2016 , the French gaming market has known a peak , with its value amounting to 2.3 billion euros , before decreasing to 1.6 billion euros in templateTitleDate[0] .
generated: During the year 2018 , the most units video game on the physical market was FIFA 19 for the PS4 , accounting for about 1353.4 thousand Number units . In second place came Red Dead Redemption 2 , with sales amounting to roughly 1011.0 Number . video game market in France In 2016 , the French gaming market has known a peak , with its value amounting to 2.3 billion euros , before decreasing to 1.6 billion euros in 2018 .

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

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

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

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

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

generated_template: This timeline shows templateTitle[0] , templateTitle[1] , and templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , templateYLabel[1] templateTitle[0] , templateTitle[1] , and templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[max] templateYLabel[0] templateYLabel[1] dollars.Additional information on the templateTitle[1] and gifts market in the USThe statistic above shows how templateTitle[4] in the templateTitle[0] , templateTitle[1] and templateTitle[2] sector dropped following the market crash in 2008 , as did retail in the templateTitle[5] overall . A comparison of templateTitle[0] purchases in 2015 between millennials and consumers aged 35 and over shows that younger people are choosing travel and entertainment over more traditional gifts than their older counterparts .
generated: This timeline shows Luggage , leather , and goods store sales in the U.S. from 1992 to 2017 . In 2017 , U.S. Luggage , leather , and goods store sales amounted to about 34.14 Billion U.S. dollars.Additional information on the leather and gifts market in the USThe statistic above shows how sales in the Luggage , leather and goods sector dropped following the market crash in 2008 , as did retail in the U.S. overall . A comparison of Luggage purchases in 2015 between millennials and consumers aged 35 and over shows that younger people are choosing travel and entertainment over more traditional gifts than their older counterparts .

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

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

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

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

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

generated_template: The annual templateTitle[0] of templateTitleSubject[0] templateTitle[2] templateYLabel[0] for one templateYLabel[4] of templateTitle[4] in templateXValue[max] was templateYValue[0] templateYLabel[1] templateYLabel[2] . The global templateTitle[4] industry templateTitle[4] is a soft , white , malleable metal that has the highest electrical and thermal conductivity of all metals . templateTitle[4] is most commonly used to make coins , jewelry , and silverware .
generated: The annual Trade of Trade volume Export for one dollars of 1950 in 2018 was 19453.36 volume billion . The global 1950 industry 1950 is a soft , white , malleable metal that has the highest electrical and thermal conductivity of all metals . 1950 is most commonly used to make coins , jewelry , and silverware .

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 templateYLabel[0] of templateYLabel[1] templateYLabel[2] against ships templateTitle[3] from templateXValue[min] to templateXValue[max] . In the most recent fiscal templateXLabel[0] , the templateTitle[1] templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to about templateYValue[0] templateYLabel[3] .
generated: The statistic shows the Spending of million U.S. against ships 3M from 2010 to 2019 . In the most recent fiscal Year , the development Spending of million U.S. in Research amounted to about 1911 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: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In the templateXLabel[0] templateXValue[max] , about templateYValue[min] templateYLabel[1] were templateYLabel[2] templateYLabel[3] templateYLabel[4] of childbearing age . Despite a decreasing templateTitle[0] templateTitle[1] , the total population of templateTitleSubject[0] is expected to increase over the next few years .
generated: The statistic shows the Fertility rate in Nepal from 2007 to 2017 . In the Year 2017 , about 1.97 children were born per woman of childbearing age . Despite a decreasing Fertility rate , the total population of Nepal is expected to increase over the next few years .

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

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

generated_template: This statistic 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] 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[max] 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 March templateTitleDate[0] . The largest templateXLabel[0] templateXLabel[1] in the templateTitleSubject[0] , templateXValue[0] , employed templateYValue[max] templateYLabel[1] at the end of their fiscal year in June 2018 .

generated_template: This statistic illustrates the templateYLabel[0] of templateTitle[2] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . The highest templateYLabel[0] of templateTitle[2] was valued at templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in June templateTitleDate[0] . As of templateTitleDate[0] , Rory Mcllroy was the highest paid professional golfer in the world , followed templateTitle[4] templateXValue[15] Palmer and Phil Mickelson .
generated: This statistic illustrates the Number of leading in the U.S. ( U.S. ) in 2019 , 2019 Accounting . The highest Number of leading was valued at 73855 professionals in June 2019 . As of 2019 , Rory Mcllroy was the highest paid professional golfer in the world , followed firms Plante Moran Palmer and Phil Mickelson .

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] 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 templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[6] in templateTitle[4] to templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent of the templateTitle[5] templateTitle[6] templateTitle[7] .
generated: The statistic shows India debt National debt from 2014 to 2018 in gross to domestic product GDP ( GDP ) , with projections up until 2024 . In 2018 , the National debt in India amounted to about 68.05 percent of the domestic product GDP .

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] 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 Number kidnappings of grouped kidnappings in country Terrorism country in . 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 Somalia had 2527 sex kidnappings on average .

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: 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: 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 .

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[max] 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 81.18 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] templateXValue[0] and Europe . In templateTitleDate[0] , there were templateYValue[max] templateTitle[1] templateTitle[2] in the templateXValue[0] 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 presents a templateTitle[0] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . Through templateXValue[max] , the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] is expected to increase by templateYValue[max] percent . templateTitleSubject[0] templateTitle[2] templateTitle[4] It is expected that the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] will experience a decreasing rate of 1.3 percent from 2019 to templateXValue[min] .
generated: This statistic presents a Growth of the platforms worldwide 2008 2012 in the crowdfunding from 2008 to 2012 . Through 2012 , the platforms worldwide 2008 2012 is expected to increase by 60 percent . Growth platforms 2008 It is expected that the platforms worldwide 2008 2012 will experience a decreasing rate of 1.3 percent from 2019 to 2008 .

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

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

generated_template: The statistic shows the templateYLabel[0] of potatoes in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , approximately templateYValue[0] templateYLabel[1] templateYLabel[2] of potatoes were produced in the templateTitleSubject[0] in templateXValue[min] .
generated: The statistic shows the Production of potatoes in the Fresh from 1990 to 2018 . According to the report , approximately 401.61 million metric of potatoes were produced in the Fresh in 1990 .

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

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

generated_template: This statistic displays the templateTitle[3] templateYLabel[0] templateYLabel[1] ( as of the end of each templateXLabel[0] ) of the templateTitle[0] to the templateTitleSubject[0] templateTitle[2] ( EUR/PLN ) , according to data from the European Central Bank , from January templateTitleDate[0] to January templateTitleDate[1] . The concrete , or point in time , measure shows the templateYLabel[0] templateYLabel[1] as of the last day of each templateXLabel[0] , which is different to the standardized measure ; calculation of average based on observations throughout the period in question.Monthly change After seeing a height of templateYValue[max] rubles to the templateTitle[0] in February 2016 , the templateTitle[3] templateYLabel[0] templateYLabel[1] has fluctuated . Between December 2018 and January templateTitleDate[1] , the end of templateXLabel[0] templateYLabel[0] templateYLabel[1] between the templateTitle[0] and templateTitleSubject[0] templateTitle[2] has seen a downward trend .
generated: This statistic displays the value Percentage change ( as of the end of each Month ) of the Internet to the United Kingdom sales ( EUR/PLN ) , according to data from the European Central Bank , from January 13 to January 19 . The concrete , or point in time , measure shows the Percentage change as of the last day of each Month , which is different to the standardized measure ; calculation of average based on observations throughout the period in question.Monthly change After seeing a height of 30.1 rubles to the Internet in February 2016 , the value Percentage change has fluctuated . Between December 2018 and January 19 , the end of Month Percentage change between the Internet and United Kingdom sales has seen a downward trend .

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] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] had reached about templateYValue[max] years.Demographic development in templateTitleSubject[0] – additional information templateYLabel[0] templateYLabel[1] at templateYLabel[2] refers to the average number of years a group of people born in the same templateXLabel[0] would live , assuming constant mortality rates . The country with the highest templateYLabel[0] templateYLabel[1] at templateYLabel[2] was Japan , while templateTitleSubject[0] had reached a templateYLabel[0] templateYLabel[1] above global average .
generated: The statistic shows the Life expectancy at birth in Germany from 2007 to 2017 . In 2017 , the average Life expectancy at birth in Germany had reached about 81.09 years.Demographic development in Germany – additional information Life expectancy at birth refers to the average number of years a group of people born in the same Year would live , assuming constant mortality rates . The country with the highest Life expectancy at birth was Japan , while Germany had reached a Life expectancy above global average .

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

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

generated_template: This statistic represents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] against ships templateTitle[3] from templateXValue[min] to templateXValue[max] . During the time under consideration , the templateYLabel[0] of templateYLabel[1] remained stable . The highest templateYLabel[1] occurred in templateXValue[min] , with templateYValue[max] abortions templateYLabel[2] .
generated: This statistic represents the Number of companies against ships manufacturers from 2009 to 2018 . During the time under consideration , the Number of companies remained stable . The highest companies occurred in 2009 , with 191459 abortions companies .

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

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

generated_template: The statistic shows the templateTitle[0] templateTitle[1] ( templateYLabel[0] ) of the templateTitleSubject[0] templateTitle[4] club of Major League Soccer by templateTitle[0] in templateTitle[5] . templateXValue[0] templateXValue[0] received a salary of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Player expenses ( Payroll ) of the Philadelphia Union Union club of Major League Soccer by Player in 2019 . Marco Fabian received a salary of 2274.09 thousand U.S. dollars .

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

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

generated_template: In templateXValue[max] , the templateTitle[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 2017 , the households Savings of rate 2010 in French amounted to about 14.6 rate , all types included . The French realized Savings of rate 2010 remained fairly steady throughout the years until 2014 , when it decreased to below 14.6 rate . rate 2010 The French Savings serves as an indicator for a variety of different selling prices on the 2010 market , gathering all Savings ranges of rate wines purchased in French .

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

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

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

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

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

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

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

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

generated_template: In templateTitleDate[0] , templateXValue[0] was the largest templateTitle[1] templateXLabel[0] in the templateTitle[3] templateTitle[5] templateYLabel[0] . Its templateYLabel[0] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] that year , over 3.2 templateYLabel[1] templateYLabel[2] templateYLabel[3] more than second-place templateXValue[1] templateXValue[1] . templateYLabel[2] templateTitle[1] industry The templateTitle[1] industry in the templateTitle[3] has the advantage of its proximity to Wall Street , home to the largest stock exchanges in the world .
generated: In 2018 , Fannie Mae was the largest diversified Month in the service U.S. Revenue . Its Revenue was 120.1 billion U.S. dollars that year , over 3.2 billion U.S. dollars more than second-place Freddie Mac . U.S. diversified industry The diversified industry in the service has the advantage of its proximity to Wall Street , home to the largest stock exchanges in the world .

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

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

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] as listed by the National Crime Information Center ( NCIC ) from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] decreased slightly compared to previous years , with templateYValue[min] cases .
generated: This statistic shows the Percentage of change exports trade as listed by the National Crime Information Center ( NCIC ) from 1990 to 2019 . In 2019 , the Percentage of change exports trade decreased slightly compared to previous years , with -18.2 cases .

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

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

generated_template: This statistic represents the development of the templateTitle[0] of young people between the ages of templateTitle[9] to templateTitle[10] in the templateTitle[1] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[0] templateYLabel[3] young people templateTitle[4] that templateTitle[7] templateTitle[8] lived in the templateTitle[1] .
generated: This statistic represents the development of the Consumption of young people between the ages of 2016 to 2016 in the cement 2016 2006 to 2016 . In 2016 , approximately 2395070 tons young people 2016 that 2016 lived in the cement .

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

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

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] of nurse templateTitle[2] in the templateTitle[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , about templateYValue[1] percent of the templateXValue[0] templateXValue[0] templateXValue[0] could be templateYLabel[1] . 748,841 cases of templateXValue[0] vehicle theft were recorded in the templateTitle[4] in templateXValue[1] in templateTitleDate[0] .
generated: This statistic shows the public Amount of nurse sector in the Budgeted in , United Industry . In , about 166 percent of the Social protection could be budgeted . 748,841 cases of Social protection vehicle theft were recorded in the United in Health in .

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

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

generated_template: This statistic provides information on the templateTitle[0] templateTitle[1] templateTitle[2] on templateYLabel[1] . As of April templateTitle[4] , templateYValue[max] percent of templateYLabel[1] templateYLabel[2] had between templateYValue[2] and 100 templateXLabel[1] on the microblogging site .
generated: This statistic provides information on the Video game industry on worth . As of April wealthiest , 21.9 percent of worth billion had between 5.4 and 100 (company) on the microblogging site .

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

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

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

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

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

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

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

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

generated_template: In templateXValue[max] , the templateTitleSubject[0] templateTitleSubject[0] produced approximately templateYValue[max] templateYLabel[2] templateYLabel[3] of templateYLabel[0] worldwide , an increase of 53 templateYLabel[2] templateYLabel[3] on the previous templateXLabel[0] . The company templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] has increased by about 75 percent since templateXValue[6] . Asia at the center of templateTitle[3] operations network The templateTitleSubject[0] templateTitleSubject[0] templateTitle[2] templateYLabel[0] templateYLabel[1] has risen year-on-year since templateXValue[6] .
generated: In 2018 , the 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 rate 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: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) as of October templateTitleDate[0] . Of templateYLabel[1] templateYValue[max] percent reported action or thriller .
generated: This statistic presents the Most popular mobile payment in the Sweden ( Sweden ) as of October 2017 . Of respondents 75 percent reported action or thriller .

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 third templateXLabel[0] of templateXValue[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 third Quarter of Q3 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': ['2020']}
title: Capital spending in the semiconductor industry worldwide 2000 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Spending', 'in', 'billion', 'U.S.', 'dollars']: ['94.3', '97.8', '105.9', '95.6', '67.8', '65.2', '66.1', '55.3', '59.0', '67.4', '54.0', '26.1', '43.4', '61.0', '57.5', '48.1', '47.7', '31.3', '27.5', '38.7', '61.3']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] amounted to around templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Spending billion of the industry worldwide 2000 from 2000 to 2020 . In 2017 , Capital spending billion amounted to around 95.6 U.S. dollars .

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

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

generated_template: The statistic shows the templateTitle[0] of templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[6] through the Supplemental Nutrition Assistance Program ( templateTitleSubject[0] , formerly called templateTitleSubject[0] templateTitleSubject[0] ) in templateTitleDate[0] , by templateXLabel[0] . About templateYValue[2] percent of the templateTitle[3] in templateXValue[5] is templateTitle[4] templateTitle[6] through the Supplemental Nutrition Assistance Program .
generated: The statistic shows the Number of U.S. cases by U.S. through the Supplemental Nutrition Assistance Program ( U.S. , formerly called U.S. ) in 2018 , by State . About 8438 percent of the cases in Illinois is by U.S. through the Supplemental Nutrition Assistance Program .

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

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

generated_template: This statistic displays a timeline of the templateTitle[0] of templateTitle[1] templateTitle[2] templateYLabel[1] templateTitle[5] individual templateTitle[5] day in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , individuals saw on average templateYValue[max] television commercials or templateYLabel[0] templateTitle[5] day .
generated: This statistic displays a timeline of the Carbon of dioxide emissions metric U.S. individual U.S. day in the U.S. from 2019 to 2050 . In 2035 , individuals saw on average 15.5 television commercials or Emissions U.S. day .

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

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

generated_template: This statistic 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 North America gaming Data of North America from 2011 to 2016 . In 2014 , 44 volume units of North America were sold worldwide .

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

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

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

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

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

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

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

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

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

generated_template: The statistic above presents templateYLabel[0] data of templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . templateYLabel[0] numbers peaked in 1987 when they amounted to over 62.82 templateYLabel[1] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] was templateYValue[min] templateYLabel[1] .
generated: The statistic above presents Television data of basketball tournament in the TV/television from 1980 to 2013 . Television numbers peaked in 1987 when they amounted to over 62.82 revenue . In 2013 , the NCAA Television of basketball tournament was 8.86 revenue .

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: 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 Fiji from 1999 to 2019 . In 2019 , the Unemployment rate in Fiji was at approximately 4.15 percent .

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 number of monthly templateYLabel[0] templateYLabel[2] templateYLabel[3] ( templateTitleSubject[1] ) across templateYLabel[1] 's online shopping properties from the fourth templateXLabel[0] of 2016 to the fourth templateXLabel[0] of templateTitleDate[0] . As of the last templateXLabel[0] of templateTitleDate[0] , templateYLabel[1] 's Chinese e-commerce properties had been accessed by around templateYValue[max] templateYLabel[4] templateYLabel[2] templateTitleSubject[1] .
generated: This statistic gives information on the consolidated number of monthly Number visiting members ( LinkedIn ) across mobile 's online shopping properties from the fourth Quarter of 2016 to the fourth Quarter of 2013 . As of the last Quarter of 2013 , mobile 's Chinese e-commerce properties had been accessed by around 63 millions visiting LinkedIn .

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

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

generated_template: This statistic shows the results of a global survey on templateTitleSubject[0] in God or a supreme being . The survey was conducted in 23 countries in September 2010 . templateYValue[5] percent of templateYLabel[1] in the country stated they believe in God or a higher being .
generated: This statistic shows the results of a global survey on Facebook in God or a supreme being . The survey was conducted in 23 countries in September 2010 . 70 percent of Facebook in the country stated they believe in God or a higher being .

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

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

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Arizona Diamondbacks , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the Revenue of the Arizona Diamondbacks was 275 million U.S. dollars .

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

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

generated_template: This statistic presents the templateTitle[2] of templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] templateTitleSubject[1] as of January templateTitleDate[0] . During an online survey conducted by We Are Flint , it was found that templateYValue[0] percent of internet users in the templateTitleSubject[1] templateTitleSubject[1] accessed the messenger app templateXValue[1] templateXValue[2] templateXValue[2] templateXValue[2] .
generated: This statistic presents the fast of U.S. August eating in the U.S. August as of January 2014 . During an online survey conducted by We Are Flint , it was found that 5.2 percent of internet users in the U.S. August accessed the messenger app A few times per week About once per week .

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

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

generated_template: The graph shows the templateTitle[0] of templateTitle[1] to Disney 's new online video streaming service ( templateTitleSubject[0] ) among adults in the templateTitle[3] as of February templateTitleDate[0] . The data reveals templateXValue[2] just templateYValue[min] percent of surveyed templateTitleSubject[0] adults said templateXValue[2] they were templateXValue[0] templateXValue[0] to sign up to templateTitleSubject[0] upon its launch in templateTitleDate[0] , and templateYValue[max] percent said templateXValue[2] they were templateXValue[2] at templateXValue[3] templateXValue[0] to subscribe to the streaming service .
generated: The graph shows the U.S. of social to Disney 's new online video streaming service ( U.S. ) among adults in the user as of February 2018 . The data reveals Yes one of my social media accounts is private just 7 percent of surveyed U.S. adults said Yes one of my social media accounts is private they were Yes all of my social media accounts are private to sign up to U.S. upon its launch in 2018 , and 45 percent said Yes one of my social media accounts is private they were Yes one of my social media accounts is private at No none of my social media accounts are private Yes all of my social media accounts are private to subscribe to the streaming service .

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: The timeline shows templateTitleSubject[0] templateTitleSubject[0] 's templateYLabel[0] 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 templateYLabel[2] manufacturer of escalators and elevators generated about templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[0] .
generated: The timeline shows Newmont Mining 's Revenue between the fiscal Year of 2007 and the fiscal Year of 2018 . In the fiscal Year of 2018 , the U.S. manufacturer of escalators and elevators generated about 7253 million U.S. dollars in Revenue .

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

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

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

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

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

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

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

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

generated_template: This statistic illustrates the templateTitle[5] templateTitle[0] of templateTitleSubject[0] templateYLabel[1] in the last three months . As of October templateTitleDate[0] , the templateXValue[0] templateXValue[0] accounted for templateYValue[max] percent of desktop templateYLabel[1] to the visual blogging site .
generated: This statistic illustrates the 2009 Annual of Annual in the last three months . As of October 2009 , the Rest of world accounted for 19 percent of desktop annual to the visual blogging site .

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 appliances* in the templateTitle[5] from templateXValue[min] to templateXValue[5] and forecasts up to and including templateXValue[max] . For templateXValue[2] the Appliance Magazine projects total templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] to reach templateYValue[2] templateYLabel[2] units . templateTitleSubject[0] templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[2] - additional information Total templateYLabel[0] templateYLabel[1] of templateTitle[0] kitchen and laundry templateTitle[2] in the templateTitle[5] are expected to reach templateYValue[0] templateYLabel[2] units in templateXValue[max] .

generated_template: The annual templateTitle[3] templateTitle[4] has been on decline in templateTitleSubject[0] 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] templateTitleSubject[0] and templateTitleSubject[1] has started to decrease in the last few years .
generated: The annual unit shipments has been on decline in U.S. and the U.S. since 2005 . In 2017 , this region 's unit shipments amounted to an average of 52.02 live shipments millions 1,000 population , down from almost 68.45 shipments millions 1,000 millions in 2005 . The population growth shipments in U.S. and U.S. has started to decrease in 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] 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 Sales of the U.S. luxury brand wholesale value has increased twofold over the period surveyed , growing from roughly 1.84 billion U.S. in 1990 to 12.66 billion U.S. in the Year 2018 . Despite the steady increase in Sales during the period considered , the total reported a net loss of approximately 25 billion U.S. in 2018 . Worldwide recognition Founded in 1978 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[max] percent of the country 's templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The statistic shows the Ratio of government expenditure to gross domestic product ( GDP ) in Argentina from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure in Argentina amounted to about 41.52 percent of the country 's gross domestic product .

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

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

generated_template: This statistic presents the templateTitle[2] of templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] templateTitleSubject[1] as of January templateTitleDate[0] . During an online survey conducted by We Are Flint , it was found that templateYValue[0] percent of internet users in the templateTitleSubject[1] templateTitleSubject[1] accessed the messenger app templateXValue[1] templateXValue[2] templateXValue[2] templateXValue[2] .
generated: This statistic presents the government of U.S. U.S. U.S. in the U.S. U.S. as of January 2017 . During an online survey conducted by We Are Flint , it was found that 72 percent of internet users in the U.S. U.S. accessed the messenger app Securing confidential intelligence reports Securing citizen records (ex. IRS filings) .

Example 307:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1995', '2019']}
title: U.S. nuclear refueling outage days 1995 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995']
Y_Axis['Average', 'number', 'of', 'days']: ['32', '34', '34', '34', '36', '37', '41', '46', '45', '40', '41', '38', '40', '39', '38', '42', '40', '33', '37', '44', '40', '51', '81', '66', '66']

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

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

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

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

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

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

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

generated_template: The graph shows templateYLabel[0] of templateTitle[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In the fiscal templateXLabel[0] templateXValue[max] ( spanning from October templateXValue[1] to September templateXValue[max] ) , the media company generated templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[0] . Additional info : templateYLabel[0] generated by templateTitleSubject[0] is one of the largest media conglomerates worldwide , active in such sectors as cable TV and cinema .
generated: The graph shows Expenditure of Teva expenditure from 2006 to 2019 . In the fiscal Year 2019 ( spanning from October 2018 to September 2019 ) , the media company generated 1010 million U.S. dollars Expenditure . Additional info : Expenditure generated by Teva is one of the largest media conglomerates worldwide , active in such sectors as cable TV and cinema .

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: In templateTitleDate[0] , templateTitleSubject[0] was the fifth largest European country in terms of templateTitle[1] size , with an estimate of 59 million templateYLabel[1] . The most populous templateTitle[5] templateXLabel[0] was the second largest city in the templateXValue[0] templateXValue[0] , followed by a total of over templateYValue[max] people . The As of the fourth quarter of 2019 , templateTitleSubject[0] had 152 templateYLabel[1] monetizable daily active templateTitle[4] worldwide .
generated: In 2014 , Top was the fifth largest European country in terms of countries size , with an estimate of 59 million employees . The most populous employment Country was the second largest city in the China , followed by a total of over 2301534 people . The As of the fourth quarter of 2019 , Top had 152 employees monetizable daily active industry worldwide .

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

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

generated_template: This statistic represents the templateTitle[3] templateTitle[4] templateYLabel[0] of the British automotive company templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] from financial templateXLabel[0] templateXValue[min] to financial templateXLabel[0] templateXValue[max] in templateYLabel[1] British pounds . Between templateXValue[min] and templateXValue[1] , the company made a templateTitle[4] templateYLabel[0] higher than templateYValue[7] templateYLabel[1] British pounds in every templateXLabel[0] . However , the most recent financial templateXLabel[0] suggested a more difficult business climate , as the company recorded its first templateTitle[4] loss in this period of consideration , at 3.6 templateYLabel[1] British pounds .
generated: This statistic represents the seat miles ASMs of the British automotive company Southwest Airlines from financial Year 2011 to financial Year 2019 in billions British pounds . Between 2011 and 2018 , the company made a miles ASMs higher than 128.14 billions British pounds in every Year . However , the most recent financial Year suggested a more difficult business climate , as the company recorded its first miles loss in this period of consideration , at 3.6 billions British pounds .

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 templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] , a decline from templateYValue[4] percent in templateXValue[min] . The templateTitleSubject[0] templateTitleSubject[0] are one of the biggest online markets worldwide and in templateXValue[max] , there were templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: In 2018 , the Number recalls of U.S. was 232 recalls , a decline from 75 percent in 2001 . The U.S. are one of the biggest online markets worldwide and in 2018 , there were 52 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] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Sudan from 1999 to 2019 . In 2019 , the Unemployment rate in Sudan was at approximately 13 percent .

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

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[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']}
title: Price of selected acquisitions by Google 2017
X_Axis['Company']: ['Motorola_Mobility_(2012)', 'Nest_Labs_(2014)', 'DoubleClick_(2008)', 'YouTube_(2006)', 'Part_of_HTC_mobile_division_and_licenses_(2017)', 'Waze_(2013)', 'AdMob_(2009)', 'ITA_Software_(2012)', 'Postini_(2007)', 'Apigee_(2016)', 'DeepMind_(2014)', 'Skybox_Imaging_(2014)', 'Admeld_(2011)', 'Bebop_(2015)', 'Wildfire_(2012)', 'Slide_(2010)', 'Widevine_Technologies_(2010)', 'Zagat_(2011)', 'On2_Technologies_(2010)', 'Channel_Intelligence_(2013)', 'Divide_(2014)', 'dMarc_Broadcasting_(2006)', 'Applied_Semantics_(2003)', 'Meebo_(2012)', 'FeedBurner_(2007)', 'Invite_Media_(2010)', 'Global_IP_Solutions_(2010)', 'Android_(2012)']
Y_Axis['Price', 'in', 'million', 'U.S.', 'dollars']: ['12500.0', '3200.0', '3100.0', '1650.0', '1100.0', '1100.0', '750.0', '700.0', '625.0', '625.0', '500.0', '500.0', '391.08', '380.2', '350.0', '182.0', '160.0', '151.0', '130.0', '125.0', '120.0', '102.0', '102.0', '100.0', '100.0', '80.0', '68.2', '50.0']

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

generated_template: The statistic shows the average states of templateTitle[0] in the templateTitleSubject[0] templateTitleSubject[0] ( UK ) templateTitle[7] in templateTitleDate[0] , by templateYLabel[0] annual templateYLabel[1] . templateXValue[0] templateXValue[0] received a salary of templateYValue[max] templateYLabel[2] templateTitle[4] .
generated: The statistic shows the average states of Price in the Google ( UK ) 2017 in 2017 , by Price annual million . Motorola Mobility (2012) received a salary of 12500.0 U.S. Google .

Example 319:
titleEntities: {'Subject': ['Canada'], 'Date': ['2010', '2019']}
title: Ice hockey players in Canada 2010 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11']
Y_Axis['Number', 'of', 'players']: ['621026', '637000', '631295', '639500', '721504', '721504', '625152', '617107', '572411']

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

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

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

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

generated_template: The statistic shows 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': ['100', '2019']}
title: Number of female directors in FTSE 100 companies UK 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'female', 'directors']: ['292', '264', '259', '244', '233', '205', '169', '141']

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

generated_template: This statistic presents the templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that there were approximately templateYValue[max] templateYLabel[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] .
generated: This statistic presents the Number of Number female in UK from 2012 to 2019 . In 2019 , it is estimated that there were approximately 292 directors Number female in UK .

Example 322:
titleEntities: {'Subject': ['Louis Cardinals'], 'Date': ['2002', '2019']}
title: Franchise value of the St. Louis Cardinals 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['2100', '1900', '1800', '1600', '1400', '820', '716', '591', '518', '488', '486', '484', '460', '429', '370', '314', '308', '271']

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

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] templateTitleSubject[0] 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 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 Jerry Jones who bought the Franchise for 150 million U.S. dollars in 1989 .

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

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

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

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

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

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

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

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

generated_template: The statistic shows information on the monthly templateYLabel[0] of templateTitle[2] templateTitle[3] templateYLabel[1] of templateTitleSubject[0] templateTitleSubject[0] on templateTitle[6] worldwide as of January templateTitleDate[1] . In January templateTitleDate[1] , templateTitleSubject[0] templateTitleSubject[0] reached templateYValue[0] thousand templateTitle[3] templateYLabel[1] on templateTitle[6] . In the measured period , the game had its record high templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] in April templateTitleDate[0] , with over templateYValue[max] thousand gamers playing at the same time .
generated: The statistic shows information on the monthly Percentage of growth year-on-year growth of Waitrose on 2015 worldwide as of January 2019 . In January 2019 , Waitrose reached -1.9 thousand year-on-year growth on 2015 . In the measured period , the game had its record high growth year-on-year Great Percentage in April 2015 , with over -1.9 thousand gamers playing at the same time .

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

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

generated_template: This statistic shows the templateTitle[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 Louisiana from 2000 to 2018 . In 2018 , 18.6 percent of Louisiana 's population lived below the poverty line .

Example 327:
titleEntities: {'Subject': ['June'], 'Date': ['2019', '2019']}
title: Business climate index of June 2019
X_Axis['Months', 'from', 'June', '2018', 'to', 'June', '2019']: ["Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18"]
Y_Axis['Index', 'points', '(50', '=', 'neutral)']: ['56.9', '56.5', '58.4', '61.9', '64.5', '64.7', '63.8', '63.2', '53.7', '52.8', '53.3', '50.2', '49.6']

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

generated_template: This statistic illustrates the templateTitle[0] number of templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] from December 2018 to December 2019 . The data are seasonally adjusted . There were about templateYValue[min] templateYLabel[2] templateYLabel[0] templateYLabel[1] on the last business day of December 2019 .
generated: This statistic illustrates the Business number of Index points in the June from December 2018 to December 2019 . The data are seasonally adjusted . There were about 49.6 (50 Index points on the last business day of December 2019 .

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

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

generated_template: 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] 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] December 2017 to December templateXLabel[3] . The templateYLabel[0] is composed of several different indices , including an assessment of one 's personal financial situation . In December templateXLabel[3] , Brazil 's templateTitle[0] templateTitle[1] was at templateYValue[0] templateYLabel[1] .

generated_template: This statistic illustrates the templateTitle[0] number of templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] from December 2018 to December 2019 . The data are seasonally adjusted . There were about templateYValue[min] templateYLabel[2] templateYLabel[0] templateYLabel[1] on the last business day of December 2019 .
generated: This statistic illustrates the Consumer number of Index points in the June from December 2018 to December 2019 . The data are seasonally adjusted . There were about 114.3 (2001 Index points on the last business day of December 2019 .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateXValue[0] templateXValue[0] was the templateTitle[1] templateYLabel[0] templateYLabel[1] of templateTitle[2] in the templateTitle[1] , with templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Average usual of weekly in 2019 , worked Industry . In 2019 , the Forestry fishing mining quarrying oil and gas was the average usual of weekly in the average , with 45.0 weekly hours .

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

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

generated_template: This statistic illustrates the templateYLabel[0] of templateTitle[1] , templateYLabel[2] and templateYLabel[3] in the United Kingdom ( templateTitleSubject[0] ) from templateTitleDate[0] to templateTitleDate[1] . There were templateYValue[min] of these establishments , compared to almost 61,000 in templateXValue[min] .
generated: This statistic illustrates the Global of market , share and share in the United Kingdom ( U.S. ) from 2004 to 2008 . There were 24.3 of these establishments , compared to almost 61,000 in 2004 .

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 illustrates the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] templateTitle[6] . The templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] was templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitleDate[0] .
generated: This statistic illustrates the Import volume of crude oil imported in Q1 2018 . The Import volume of crude oil imported was 12298989 tons in 2018 .

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 templateTitle[0] templateYLabel[0] templateYLabel[1] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , some templateYValue[0] templateYLabel[2] templateYLabel[3] of templateYLabel[0] were consumed in the templateTitle[1] . This figure decreased to less than templateYValue[0] templateYLabel[2] templateYLabel[3] in templateXValue[max] .
generated: This statistic shows the Return equity in the average from 2009 to 2019 . In 2019 , some 3.6 equity of Return were consumed in the average . This figure decreased to less than 3.6 equity in 2019 .

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

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

generated_template: This statistic shows the total templateTitleSubject[0] templateYLabel[0] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , 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 total United Kingdom AIR magazine from 2006 to 2016 . In 2015 , the AIR for FourFourTwo magazine excluding pharmaceuticals in the Readership had an thousands of 554 ( 2010 thousands 631 ) . In 2016 , the thousands stood at 483 .

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

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

generated_template: As of templateTitleDate[1] , the templateTitleSubject[0] templateTitleSubject[0] men 's templateTitleSubject[0] templateTitleSubject[0] was ranked 96th on the templateYLabel[0] templateYLabel[1] templateYLabel[2] and had an average templateYLabel[3] of 133 since the templateYLabel[1] templateYLabel[2] creation . The best templateYLabel[3] the country held was in templateXValue[2] with an 83rd templateYLabel[3] , whereas the lowest templateYLabel[2] was in templateXValue[13] when the templateTitleSubject[0] templateTitleSubject[0] was placed 186th . The templateTitleSubject[0] templateTitleSubject[0] women 's templateTitleSubject[0] was ranked 113th in templateXValue[max] .
generated: As of 2019 , the Manchester Metrolink men 's Manchester Metrolink was ranked 96th on the Passenger journeys millions and had an average millions of 133 since the journeys millions creation . The best millions the country held was in 2016/17 with an 83rd millions , whereas the lowest millions was in 2005/06 when the Manchester Metrolink was placed 186th . The Manchester Metrolink women 's Manchester Metrolink was ranked 113th in 2018/19 .

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

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

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

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

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

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

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

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

generated_template: The statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] 100,000 population from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] stood at templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] , down from templateYValue[1] templateYLabel[2] a templateXLabel[0] earlier .
generated: The statistic shows the Portugal wine produced Thousands hectoliters 100,000 population from 2006 to 2018 . In 2018 , the Portugal wine produced Thousands hectoliters stood at 7.54 hectoliters , down from 6.7 hectoliters a Year earlier .

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 the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] 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 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 May templateTitleDate[0] , templateTitle[5] templateTitle[6] of templateYLabel[1] . templateXValue[0] had templateYValue[max] templateYLabel[1] , which made it the largest platform in terms of templateYLabel[1] as of May templateTitleDate[0] .

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

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

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

generated_template: 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[0] 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 per of Value of the value and Head sheep into the U.S. from 2001 to 2017 . In 2017 , Value of such products came to a total of about 203 head U.S. dollars . U.S. Value of domestic sheep – additional information Electrical devices used within a value are part of a billion-dollar home appliance industry , which is projected to grow in the coming years .

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

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

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

Example 343:
titleEntities: {'Subject': ['Portugal'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Portugal 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['27920.05', '26788.05', '25744.8', '24727.89', '23731.13', '23030.79', '23437.39', '21482.86', '19986.36', '19252.01', '22109.32', '21625.48', '20588.87', '23217.34', '22580.68', '23122.56', '24933.17', '22811.57', '19837.97', '18815.44', '18064.47', '15799.89', '12922.15', '11737.17', '11533.83', '12490.93', '12220.18', '11597.7', '12187.56', '11788.47', '9978.59', '9548.58', '10864.55', '9027.18', '7958.02', '5978.16', '5533.16', '4724.91', '3774.95', '2716.91', '2596.33']

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

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

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

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

generated_template: This statistic presents templateTitle[0] 's templateTitle[1] average revenue per user ( templateYLabel[0] ) . As of the fourth templateXLabel[0] of templateXValue[0] , the photo app company 's templateYLabel[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] per templateXLabel[0] , up from templateYValue[1] templateYLabel[1] templateYLabel[2] in the preceding period .
generated: This statistic presents Information 's Technology average revenue per user ( Revenue ) . As of the fourth Quarter of Q3 2019 , the photo app company 's Revenue amounted to 18.0 billion euros per Quarter , up from 12.83 billion euros in the preceding period .

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

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

generated_template: This statistic illustrates the templateTitle[1] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[0] to the second templateXLabel[0] of templateTitleDate[1] . In the second templateXLabel[0] of templateXValue[0] , templateTitleSubject[0] 's templateTitle[1] templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic illustrates the office Rent growth of U.S. from the first Quarter of 2015 to the second Quarter of 2016 . In the second Quarter of Q4 2016 , U.S. 's office Rent growth amounted to 1 growth .

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

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

generated_template: This statistic shows the templateYLabel[1] of disposable income spent on templateTitle[1] consumed in templateTitleDate[0] , broken down by templateTitle[5] templateTitle[6] . U.S. consumers spent templateYValue[min] percent of their disposable income on templateTitle[1] at templateTitle[2] , compared to a templateTitle[1] templateYLabel[0] templateYLabel[1] of templateYValue[1] percent in templateXValue[1] .
generated: This statistic shows the Twitter of disposable income spent on user consumed in 2018 , broken down by 2018 . U.S. consumers spent 2.6 percent of their disposable income on user at share , compared to a user Share Twitter of 14.6 percent in Japan .

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

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

generated_template: This statistic illustrates the distribution of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . That year , the templateXValue[0] templateXValue[0] accounted for more than one third of the total templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide .
generated: This statistic illustrates the distribution of India market share in 2018 , India Airline . That year , the Indigo accounted for more than one third of the total market share worldwide .

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: The statistic shows a ranking of the tennis templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[1] at the templateYLabel[0] templateYLabel[3] spot in the templateTitleSubject[0] world tennis templateTitle[7] since the inauguration of the ranking in 1973 , when templateXValue[14] templateXValue[14] became the first No . templateYLabel[3] on August 23 , 1973 . templateXValue[6] templateXValue[6] has been ranked No .
generated: The statistic shows a ranking of the tennis Share ethnic the minorities ethnic at the Share minorities spot in the China world tennis regions since the inauguration of the ranking in 1973 , when Guangxi became the first No . minorities on August 23 , 1973 . Gansu has been ranked No .

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

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

generated_template: This statistic illustrates the number of templateTitleSubject[0] templateYLabel[0] farm projects templateYLabel[2] templateYLabel[3] in leading templateYLabel[0] power countries as of October templateTitleDate[0] . There were templateYValue[1] templateYLabel[0] farm projects templateYLabel[2] templateYLabel[3] in templateXValue[2] . Higher templateYLabel[0] speeds that occur templateTitleSubject[0] in comparison to land , allows for a higher supply of electricity from templateTitleSubject[0] templateYLabel[0] energy .
generated: This statistic illustrates the number of Canada Number farm projects admitted in leading Number power countries as of October 2016 . There were 3934 Number farm projects admitted in Iraq . Higher Number speeds that occur Canada in comparison to land , allows for a higher supply of electricity from Canada Number energy .

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: First released people play templateTitleSubject[0] ? PlayerUnknown 's Battlegrounds ( templateTitleSubject[0] ) , which is available on templateTitle[1] and templateTitle[2] , crossed the templateYValue[max] templateYLabel[2] templateTitle[3] templateTitle[4] mark in templateXValue[0] templateXValue[0] - an impressive figure considering it was released little over a year earlier . The Battle Royale game developed by Bluehole was made available to the public in March templateXValue[1] . One of the biggest games on Steam While it is available on both PlayStation and Xbox One , templateTitleSubject[0] 's origins lie in templateTitle[2] and , more specifically , on Steam .
generated: First released people play United Kingdom ? PlayerUnknown 's Battlegrounds ( United Kingdom ) , which is available on food and waste , crossed the 59400 tonnes from major mark in Tesco - an impressive figure considering it was released little over a year earlier . The Battle Royale game developed by Bluehole was made available to the public in March Sainsbury's . One of the biggest games on Steam While it is available on both PlayStation and Xbox One , United Kingdom 's origins lie in waste and , more specifically , on Steam .

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[max] 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 53764 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 illustrates the regional templateTitle[0] of templateTitleSubject[0] templateYLabel[1] in the last three months . As of October templateTitleDate[0] , the templateXValue[0] templateXValue[0] accounted for templateYValue[max] percent of desktop templateYLabel[1] to the visual blogging site during this period of time .
generated: This statistic illustrates the regional Michelin of Michelin employees in the last three months . As of October 2018 , the Europe accounted for 70599 percent of desktop employees to the visual blogging site during this period of time .

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

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

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

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

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

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

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the social network 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: This statistic shows the average Production level of Great Britain levels from 2013 to 2018 . In 2018 , the social network reported Production level to the value of almost 1.15 million bricks from its cold chain Great Britain worldwide .

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

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

generated_template: The graph shows the templateYLabel[0] templateYLabel[1] for templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] percent of the templateYLabel[1] were at templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] .
generated: The graph shows the Percentage population for headcount ratio 3.20 in Ecuador from 2005 to 2017 . In 2017 , about 8.7 percent of the population were at headcount ratio 3.20 U.S. in Ecuador .

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

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

generated_template: templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] almost reached templateYValue[0] percent in templateXValue[max] , the lowest value since templateXValue[7] . The regions with the highest templateYLabel[0] rates were situated in the South . Sicily , Calabria , and Campania registered rates above 18 percent , a large difference when compared to the Northern regions .
generated: U.S. 's Percentage total almost reached 22 percent in 2050 , the lowest value since 1990 . The regions with the highest Percentage rates were situated in the South . Sicily , Calabria , and Campania registered rates above 18 percent , a large difference when compared to the Northern regions .

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

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

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

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

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

generated_template: The templateTitle[2] rate in the templateTitleSubject[0] 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']}
title: U.S. consumer business cyber security budget share 2017
X_Axis['Response']: ['3_to_4', '4_to_5', '5_to_6', '6_to_8', '8_to_10', 'More_than_10']
Y_Axis['Share', 'of', 'respondents']: ['14', '20', '21', '21', '10', '14']

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

generated_template: 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 2017 survey among Americans on the U.S. of consumer business . The survey shows that 10 percent of Americans consider themselves experts at consumer business . 21 percent of Americans stated that they have 6 to 8 More than 10 to use business .

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

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

generated_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitle[6] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[0] .
generated: This graph depicts the total Regular season Home attendance of the Denver Broncos 2006 franchise of the National Football League from 2006 to 2019 . In 2019 , the Regular season Home attendance of the franchise was 607497 .

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

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

generated_template: The 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 about 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: The 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 about 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[7] 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 4.83 millions Inhabitants .

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

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

generated_template: This statistic gives information on the templateTitleSubject[0] templateTitle[1] templateTitle[2] of templateTitle[3] templateXValue[7] destination and accommodation templateTitle[5] in November templateTitleDate[0] , based on templateYLabel[0] of templateYLabel[1] . During this period of time , it was found that templateYValue[max] percent of all templateYLabel[1] were directed to templateTitle[1] leader templateXValue[0] . The templateXValue[7] website 's templateTitleDate[0] revenue amounted to 1.48 billion templateTitleSubject[0] dollars .
generated: This statistic gives information on the Netherlands brands Netherlands of 2018 Sony destination and accommodation by in November 2018 , based on Buzz of score . During this period of time , it was found that 47.7 percent of all score were directed to brands leader Samsung . The Sony website 's 2018 revenue amounted to 1.48 billion Netherlands dollars .

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 distribution of templateXValue[1] sources used for templateYLabel[0] templateTitle[2] in templateTitleSubject[0] in March 2011 .
generated: This statistic shows the distribution of 100001 to 200000 sources used for Total sales in U.S. in March 2011 .

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

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

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] Although templateTitle[0] templateTitle[1] increased on a yearly basis from 2004 to templateXValue[min] , templateTitle[1] growth has slowly decreased annually as of 2011 , despite remaining positive .
generated: This statistic shows the Total population of Russia from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Russia amounted to approximately 146.8 millions Inhabitants . population of Russia Although Total population increased on a yearly basis from 2004 to 2014 , population growth has slowly decreased annually as of 2011 , despite remaining positive .

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

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

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

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

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

generated_template: 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: This statistic shows the Unemployment rate in Italy from 2004 to 2019 . In 2019 , the Unemployment rate in Italy was at 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: This statistic illustrates the templateTitle[0] templateYLabel[2] of the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] volume from templateTitle[6] exporters in templateTitleDate[0] . In that year , templateXValue[1] accounted for a templateYLabel[2] of templateYValue[1] percent of templateYLabel[0] templateTitle[4] to the templateTitle[2] .
generated: This statistic illustrates the Surface square of the Nordic Surface area volume from 2017 exporters in 2017 . In that year , Greenland accounted for a square of 410450 percent of Surface 2017 to the Nordic .

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': ['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] 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[max] templateYLabel[3] in templateXValue[1] .
generated: The timeline shows the Per capita consumption of fresh carrots 2000 in the U.S. from 2000 to 2018 . According to the report , the U.S. Per capita consumption of fresh carrots 2000 amounted to about 9.4 pounds in 2017 .

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

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

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

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

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

generated_template: This 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: This statistic shows Gross domestic product ( GDP ) in Syria from 1984 to 2010 . Gross domestic product ( GDP ) denotes the aggregate value of all services and goods produced within a country in any given Year . GDP is an important indicator of a country 's economic power .

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

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

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

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

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

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

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

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

generated_template: templateTitleSubject[0] templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] of templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] , a record increase to the previous templateXLabel[0] . Still , the company 's templateYLabel[0] templateYLabel[1] of over templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] templateTitleSubject[0] is one of the largest oil and gas companies .
generated: Southwest Airlines 's Net income of 2300 million U.S. dollars in 2019 , a record increase to the previous Year . Still , the company 's Net income of over 3357 million U.S. dollars . Southwest Airlines is one of the largest oil and gas companies .

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 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 births in Belgium peaked in 2013 , when almost 129173 thousand couples got divorced . Since then the divorce Number decreased until 2018 , when it again increased and amounted to almost 117800 thousand births . A similar trend can be seen in the neighbor country Norway , where the Number of births decreased for several years but increased again in 2017 .

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: The Icelandic templateTitle[4] templateTitle[5] templateTitle[6] , which is controlled by the templateTitle[5] Association of templateTitleSubject[0] , made its highest templateYLabel[3] in the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[23] . In this templateXLabel[0] , they participated as the smallest nation ever in the UEFA European Championship . The templateTitle[6] made it to the quarter finals but lost them to France .
generated: The Icelandic ' national football , which is controlled by the national Association of Faroe Islands , made its highest position in the FIFA World Ranking in 2016 . In this Year , they participated as the smallest nation ever in the UEFA European Championship . The football made it to the quarter finals but lost them to France .

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: In templateXValue[max] , approximately templateYValue[0] templateYLabel[3] templateTitle[1] templateTitle[2] templateYLabel[2] were templateYLabel[1] in the templateTitleSubject[0] , down templateTitle[6] just under templateYValue[1] templateYLabel[3] a templateXLabel[0] earlier . templateTitle[1] templateTitle[3] templateTitle[4] have dropped enormously in the last decade , and dropped below one templateYLabel[3] in templateXValue[4] after a successful few years of growth .
generated: In 2018 , approximately 20.0 hectoliters wine Germany hectoliters were million in the Germany , down 2018 just under 19.7 hectoliters a Year earlier . wine 2000 2018 have dropped enormously in the last decade , and dropped below one hectoliters in 2014 after a successful few years of growth .

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

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

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

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

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

generated_template: This statistic illustrates a templateTitle[0] of the templateTitle[2] and templateTitle[3] templateTitle[4] templateYLabel[1] templateTitle[6] in the templateTitle[1] templateYLabel[2] templateXValue[min] to templateXValue[max] . templateYLabel[2] templateXValue[2] to templateXValue[1] , the templateTitle[2] and templateTitle[3] templateTitle[4] templateYLabel[1] templateTitle[6] is expected to increase by templateYValue[max] percent . Appreciation of the templateTitleSubject[0] dollar is likely to reduce costs and increase production in the chemical industry .
generated: This statistic illustrates a Share of the population and Thailand 2015 population 2035 in the aging older 2015 to 2035 . older 2025 to 2030 , the population and Thailand 2015 population 2035 is expected to increase by 22.8 percent . Appreciation of the Thailand dollar is likely to reduce costs and increase production in the chemical industry .

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: This timeline shows the templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitle[0] from templateXValue[last] to templateXValue[1] and provides a forecast for templateXValue[0] . The templateTitleSubject[0] templateYLabel[0] of templateTitle[1] templateTitle[2] was estimated to increase to around templateYValue[0] templateYLabel[2] templateYLabel[3] at templateYLabel[4] templateYLabel[5] templateYLabel[6] in templateXValue[0] .
generated: This timeline shows the books published Number in the Number from 2012 to 2003 and provides a forecast for 2002 . The U.S. Number of books published was estimated to increase to around 25102 books / at editions in 2002 .

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

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

generated_template: 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 Most viewed in YouTube as of 2019 . In 2019 , around 6.55 views people lived in Luis Fonsi - Despacito ft. Daddy Yankee , making it the Most city in YouTube .

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

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] in templateTitle[3] templateTitle[2] templateTitle[5] in templateTitle[6] templateTitleSubject[0] templateTitle[8] in templateTitleDate[0] . In templateXValue[0] , Switzerland the templateYLabel[0] cost of a medium cappuccino ( or similar drink ) was at about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the GDP million of Domestic in Product Domestic by in industry Canada 2019 in 2019 . In Real estate and rental and leasing , Switzerland the GDP cost of a medium cappuccino ( or similar drink ) was at about 254294 chained 2012 Canadian .

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

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

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[6] in templateTitle[4] to templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to about templateYValue[7] percent of the templateTitle[5] templateTitle[6] templateTitle[7] . An eye on templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] ranks first among countries with the highest templateYLabel[1] levels in the world , far surpassing the templateYLabel[1] levels of Greece - which ranks number two - whose financial crisis has been in the spotlight recently .
generated: The statistic shows Japan balance Budget balance from 2014 to 2018 in 2024 to 2024 ( trillion ) , with projections up until 2024 . In 2018 , the Budget balance of Japan amounted to about -17.27 percent of the 2024 . An eye on Japan balance Budget balance Japan balance Budget balance ranks first among countries with the highest balance levels in the world , far surpassing the balance levels of Greece - which ranks number two - whose financial crisis has been in the spotlight recently .

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

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

generated_template: This statistic shows the trade templateYLabel[0] for templateTitle[2] sponsorship from templateXValue[min] to templateXValue[max] . In the templateXLabel[0] templateXValue[3] , the templateTitleSubject[0] templateTitle[2] sponsorship templateYLabel[0] amounted to templateYValue[3] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the trade Spending for golf sponsorship from 2010 to 2016 . In the Year 2013 , the Global golf sponsorship Spending amounted to 1.6 billion U.S. dollars .

Example 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 statistic above presents templateYLabel[0] data of templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . templateYLabel[0] numbers peaked in 1987 when they amounted to over 62.82 templateYLabel[1] . In templateXValue[max] , the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] was templateYValue[min] templateYLabel[1] .
generated: The statistic above presents Expenditures data of schools average in the expenditure from 1980 to 2016 . Expenditures numbers peaked in 1987 when they amounted to over 62.82 per . In 2016 , the U.S. Expenditures of schools average was 2272 per .

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

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

generated_template: This statistic shows the templateTitle[0] templateTitle[2] of templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] templateTitle[3] of templateTitle[2] templateTitle[3] was valued at templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . The best templateYLabel[0] of templateTitle[2] templateTitle[3] was templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the U.S. trade of trade goods in the U.S. from 1987 to 2019 . In 2019 , trade goods of trade goods was valued at 1645.53 billion U.S. dollars . The best Exports of trade goods was 254.12 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: The timeline shows the templateYLabel[0] of the templateTitleSubject[0] 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 worldwide .
generated: The timeline shows the Revenue of the UEFA European Championships from 2016 France to 2016 France , in million euros . In 2016 France , approximately 40.9 million euros of UEFA were produced worldwide .

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

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

generated_template: In the UK , more templateXValue[1] than templateXValue[0] purchased clothing and templateTitle[1] templateTitle[2] over the internet in templateTitleDate[0] . According to an annual survey conducted and released templateTitle[8] the UK 's Office for National Statistics , templateYValue[1] percent of individuals purchased clothing items templateTitle[3] within the last 12 months , and more prevalently in the younger age groups across both genders . UK shops templateTitle[3] for clothing and furniture For British consumers , clothing and templateTitle[1] templateTitle[2] are the most popular category to shop templateTitle[3] .
generated: In the UK , more 2014 than 2015 purchased clothing and book reading over the internet in 2005 . According to an annual survey conducted and released UK the UK 's Office for National Statistics , 28.6 percent of individuals purchased clothing items by within the last 12 months , and more prevalently in the younger age groups across both genders . UK shops by for clothing and furniture For British consumers , clothing and book reading are the most popular category to shop by .

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: templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitleSubject[0] templateTitleSubject[0] reached a record high of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] and has risen by roughly a third since templateXValue[min] . templateTitle[0] templateTitle[1] templateYLabel[0] increased consistently from templateXValue[9] to templateXValue[2] , after over a decade of fluctuation from templateXValue[min] to templateXValue[10] . High demand for templateTitle[0] templateTitle[1] templateTitle[0] templateTitle[1] is a colorless , odorless fossil fuel that comes from underground rock formations .
generated: Expenditure of Consumer expenditure in the U.S. reached a record high of 5.52 billion U.S. dollars in 2013 and has risen by roughly a third since 1999 . Consumer expenditure increased consistently from 2008 to 2001 , after over a decade of fluctuation from 1999 to 2009 . High demand for Consumer expenditure Consumer expenditure is a colorless , odorless fossil fuel that comes from underground rock formations .

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: The statistic shows the forecasted templateYLabel[0] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] , a comparison between templateXValue[0] and templateXValue[last] . In templateXValue[last] , the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] is expected to have a cargo templateYLabel[0] of around templateYValue[max] templateYLabel[1] templateYLabel[2] ( twenty-foot equivalent units ) .
generated: The statistic shows the forecasted Retail of the from smart wearable devices , a comparison between 2019 and 2014 . In 2014 , the from smart wearable devices is expected to have a cargo Retail of around 53.2 revenue billion ( twenty-foot equivalent units ) .

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: The graph depicts the all-time templateTitle[8] of men 's templateTitle[1] templateTitle[0] templateTitle[2] at the templateYLabel[2] templateTitleSubject[0] . After the completion of the FIFA templateYLabel[2] templateTitleSubject[0] templateTitleDate[1] in Russiam the German templateTitle[0] team is ranked second with a total of templateYValue[1] templateYLabel[0] .
generated: The graph depicts the all-time region of men 's China Population 2018 at the inhabitants China . After the completion of the FIFA inhabitants China 2018 in Russiam the German Population team is ranked second with a total of 100.47 Population .

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: In templateTitleDate[0] , it was estimated that around templateYValue[max] templateYLabel[4] 10,000 templateYLabel[1] in the templateXValue[0] templateXValue[0] had templateYLabel[3] spectrum disorder , one of the templateTitle[2] prevalence templateTitle[3] in the world . templateYLabel[3] spectrum disorder ( ASD ) includes a variety of disorders . The `` spectrum '' denotes the range of symptoms as well as their severity .
generated: In 2016 , it was estimated that around 3434.02 dollars 10,000 value in the China mainland had U.S. spectrum disorder , one of the production prevalence value in the world . U.S. spectrum disorder ( ASD ) includes a variety of disorders . The `` spectrum '' denotes the range of symptoms as well as their severity .

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: The timeline shows templateTitleSubject[0] templateTitleSubject[0] templateTitle[2] total templateYLabel[0] worldwide between templateXValue[min] and templateXValue[max] . The templateTitleSubject[0] templateTitleSubject[0] Group is a multinational exploration , development , production , and processing corporation . It is headquartered in London , UK .
generated: The timeline shows Google sites total Revenue worldwide between 2001 and 2019 . The Google Group is a multinational exploration , development , production , and processing corporation . It is headquartered in London , UK .

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

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

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

Example 401:
titleEntities: {'Subject': ['United States'], 'Date': ['2017']}
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 timeline shows a templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitleSubject[0] in the templateTitle[7] between templateXValue[min] and templateXValue[max] . It is expected that templateTitleSubject[0] will generate around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[9] .
generated: This timeline shows a Revenue engineering United States market size United States in the 2017 between 2006 and 2017 . It is expected that United States will generate around 423.76 billion U.S. dollars in 2015 .

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 a templateTitleSubject[0] forecast for the global templateYLabel[0] of templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . It is expected that the templateYLabel[0] of templateTitle[2] templateTitle[3] will reach templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[7] . Until templateXValue[max] , the templateYLabel[0] is expected to increase up to templateYValue[max] templateYLabel[2] .
generated: This statistic shows a Rome forecast for the global Occupancy of rate Rome from 2011 to 2019 . It is expected that the Occupancy of rate Rome will reach 66 rate in 2018 . Until 2019 , the Occupancy is expected to increase up to 71 rate .

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

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

generated_template: This statistic shows the distribution of templateXLabel[0] sources used for templateYLabel[0] templateTitle[2] in templateTitleSubject[0] in March 2011 .
generated: This statistic shows the distribution of Industry sources used for Number employment in United Kingdom in March 2011 .

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

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

generated_template: As of the fourth templateXLabel[0] of templateTitleDate[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 . This represents a 0.4 percent growth in available apps compared to the previous Quarter . The Google does not only offer Android apps but also apps specifically optimized for its range of Kindle Fire tablets as well as the Fire TV and Fire Phone .

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

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

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew 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 grew 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[max] 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 63451 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 depicts the total templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] templateTitleSubject[0] is a health care company headquartered in Minnetonka , Minnesota . The total templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] the templateTitleSubject[0] templateTitleSubject[0] , as of December 31 , templateXValue[max] , was about templateYValue[max] templateTitle[1] worldwide .
generated: This statistic depicts the total Costs of Dutch Shell 's Royal Dutch Shell from 2010 to 2018 . The Royal Dutch Shell is a health care company headquartered in Minnetonka , Minnesota . The total Costs of million Shell 's the Royal Dutch Shell , as of December 31 , 2018 , was about 5278 Dutch worldwide .

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[7] , 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] . templateTitle[1] of templateTitleSubject[0] Although templateTitle[0] templateTitle[1] increased on a yearly basis from 2004 to templateXValue[min] , templateTitle[1] growth has slowly decreased annually as of 2011 , despite remaining positive .
generated: This statistic shows the Total population of Pakistan from 2014 to 2017 , with projections up until 2024 . In 2017 , the Total population of Pakistan amounted to approximately 197.26 millions Inhabitants . population of Pakistan Although Total population increased on a yearly basis from 2004 to 2014 , population growth has slowly decreased annually as of 2011 , despite remaining positive .

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

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

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

generated_template: 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 Belgium from 2008 to 2018 . In 2018 , the Unemployment rate in Belgium was at 6 percent .

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

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

generated_template: 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 2017 survey among Americans on the U.S. of user reasons . The survey shows that 7 percent of Americans consider themselves experts at user reasons . 61 percent of Americans stated that they have I have a schedule that makes it hard to meet interesting people in other ways None of the above to use reasons .

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

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

generated_template: This statistic shows the templateTitleSubject[0] templateYValue[1] templateTitle[3] templateTitle[4] based on templateTitle[1] templateYLabel[0] in templateTitleDate[0] . In that year , the templateXValue[0] templateXValue[0] was the largest templateTitle[1] consumer templateTitle[4] , using a total of almost templateYValue[max] templateYLabel[2] templateYLabel[3] of templateTitle[1] . templateTitle[1] templateYLabel[0] templateTitle[4] templateTitle[1] is a heavy metal that occurs in many rocks as well as in sea water .
generated: This statistic shows the Share 22.15 market value based on global Market in 2012 . In that year , the United States was the largest global consumer value , using a total of almost 26.71 share of global . global Market value global is a heavy metal that occurs in many rocks as well as in sea water .

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

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

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

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

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

generated_template: The statistic represents templateTitleSubject[0] 's expenditure on templateTitle[1] and templateTitle[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 Germany-based multinational engineering and electronics company incurred approximately templateYValue[0] templateYLabel[1] templateYLabel[2] ( or about 6.8 templateYLabel[1] U.S. dollars ) in templateTitle[1] and templateTitle[2] templateYLabel[0] .
generated: The statistic represents European 's expenditure on ATM and numbers from the fiscal Year of 2005 to the fiscal Year of 2018 . In the fiscal Year of 2018 , the Germany-based multinational engineering and electronics company incurred approximately 406532 number ATMs ( or about 6.8 number U.S. dollars ) in ATM and numbers Total .

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 .
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 .

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

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

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

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

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

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

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

generated_template: This statistic depicts the templateTitleSubject[0] annual templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[5] templateXValue[min] through 2025* . In templateXValue[7] , the templateTitleSubject[0] templateYLabel[0] templateTitle[2] templateTitle[3] stood at templateYValue[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] templateTitleSubject[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , templateYLabel[0] in templateTitleSubject[0] 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: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The season runs from July 1 to June 30 of the following templateXLabel[0] . The templateYLabel[0] of the templateXValue[0] season was templateYValue[0] templateYLabel[1] templateYLabel[2] .
generated: The statistic shows the Revenue of the European from the 2006/07 season to the 2019/20 season . The season runs from July 1 to June 30 of the following Year . The Revenue of the 2019/20 season was 17.95 billion euros .

Example 423:
titleEntities: {'Subject': ['Jordan'], 'Date': ['2015']}
title: Median age of the population in Jordan 2015
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2015', '2010', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960', '1955', '1950']
Y_Axis['Median', 'age', 'in', 'years']: ['34.9', '33.1', '31.2', '29.3', '27.5', '25.6', '23.8', '22.1', '21.3', '20.6', '19.6', '19.1', '16.8', '16.3', '15.5', '16.4', '17.1', '17.4', '18.0', '17.6', '17.2']

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

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

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

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

generated_template: This statistic illustrates the templateTitle[2] templateYLabel[0] of templateTitle[0] templateTitle[1] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , companies detected and reported templateYValue[1] templateYLabel[2] IT templateTitle[1] breaches . This templateYLabel[0] increased by 38 percent in the following templateXLabel[0] .
generated: This statistic illustrates the travel Value of Total contribution billion from 2012 to 2028 . In 2018 , companies detected and reported 258.1 Saudi IT contribution breaches . This Value increased by 38 percent in the following Year .

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

Example 426:
titleEntities: {'Subject': ['Norway'], 'Date': ['2024']}
title: Norway 's budget balance in relation to GDP 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'balance', 'in', 'relation', 'to', 'GDP']: ['8.6', '8.21', '7.84', '7.75', '7.82', '7.57', '7.25', '4.92', '4.04', '6.07', '8.77']

gold: The statistic shows Norway 's budget balance in relation to GDP between 2014 and 2018 , with projections up until 2024 . A positive value indicates a budget surplus , a negative value indicates a deficit . In 2018 , Norway 's surplus amounted to around 7.25 percent of GDP .
gold_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a deficit . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] surplus amounted to around templateYValue[6] percent of templateYLabel[3] .

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a trade deficit . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] trade deficit amounted to around 7.23 percent of templateYLabel[3] .
generated: The statistic shows Norway 's Budget balance in relation to GDP between 2014 and 2018 , with projections up until 2024 . A positive value indicates a Budget surplus , a negative value indicates a trade deficit . In 2018 , Norway 's trade deficit amounted to around 7.23 percent of GDP .

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

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

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] with consultative status with ECOSOC from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] global organizations had consultative status with ECOSOC . Non-governmental organizations As shown in the statistic above , the templateYLabel[0] of non-governmental organizations , also known as templateYLabel[1] , has significantly increased over the last decades .
generated: The statistic shows the Number of suicides with consultative status with ECOSOC from 1990 to 2010 . In 2010 , 302 global organizations had consultative status with ECOSOC . Non-governmental organizations As shown in the statistic above , the Number of non-governmental organizations , also known as suicides , has significantly increased over the last decades .

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 Virgin templateTitleSubject[0] company from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the health templateTitle[1] chain generated a templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . In templateXValue[1] , the UK based company operated 270 clubs .
generated: The statistic depicts the Revenue of the Virgin Malaysia company from 2012 to 2014 . In 2014 , the health online chain generated a Revenue of 66.5 million U.S. dollars . In 2013 , the UK based company operated 270 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 number of templateTitleSubject[0] templateTitleSubject[0] templateYLabel[2] as of the fourth templateXLabel[0] of templateTitleDate[0] , templateTitle[5] templateTitle[6] . During that period , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] percent in the first templateXLabel[0] of 2012 .
generated: This statistic illustrates the number of LINE users as of the fourth Quarter of 2014 , 2014 2016 . During that period , LINE 's Number monthly amounted to 217.0 percent in the first Quarter of 2012 .

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: 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 Liberia from 1999 to 2019 . In 2019 , the Unemployment rate in Liberia was at approximately 2.03 percent .

Example 431:
titleEntities: {'Subject': ['Zimbabwe'], 'Date': ['2019']}
title: Youth unemployment rate in Zimbabwe in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['8.13', '8.16', '8.18', '8.56', '8.79', '8.86', '8.96', '8.86', '8.47', '8.15', '8.06', '7.39', '6.96', '6.85', '7.21', '7.41', '8.54', '9.5', '10.11', '11.02', '11.95']

gold: The statistic shows the youth unemployment rate in Zimbabwe from 1999 and 2019 . The data are ILO estimates . In 2019 , the estimated youth unemployment rate in Zimbabwe was at 8.13 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . The data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[0] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[0] percent .
generated: The statistic shows the Youth unemployment rate in Zimbabwe from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated Youth unemployment rate in Zimbabwe was at 8.13 percent .

Example 432:
titleEntities: {'Subject': ['El Pais', 'Spain'], 'Date': ['1999', '2018']}
title: El Pais ( Spain ) : circulation 1999 to 2018
X_Axis['Month']: ['July_2017-June_2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Circulation']: ['163759', '194005', '221390', '259775', '292227', '324814', '365118', '370080', '391816', '431034', '435083', '432204', '453602', '469183', '440226', '435299', '433617', '436302', '435433']

gold: This timeline shows the average circulation of the Spanish daily El Pais from 1999 to 2018 . In the period between July 2017 and June 2018 , the Spanish newspaper sold on average 163.8 thousand copies daily .
gold_template: This timeline shows the average templateYLabel[0] of the Spanish daily templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In the period between templateXValue[0] 2017 and June templateXValue[0] , the Spanish newspaper sold on average templateYValue[min] thousand copies daily .

generated_template: In templateXValue[0] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] was templateYValue[0] templateYLabel[2] 1,000 templateYLabel[3] templateYLabel[4] , the same it had a relatively increase of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The United Kingdom had the highest templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] .
generated: In July 2017-June 2018 , the Circulation of the El Pais was 163759 Circulation 1,000 Circulation , the same it had a relatively increase of 469183 Circulation . The United Kingdom had the highest Circulation in the El Pais .

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 largest templateTitle[2] ( CPA ) templateTitle[3] in the templateTitle[1] in templateTitleDate[0] , in terms of templateYLabel[1] templateYLabel[0] . According to the rankings , templateXValue[0] templateXValue[7] was the largest templateTitle[2] templateXLabel[0] in the templateTitle[1] with templateYLabel[1] templateYLabel[0] of templateYValue[max] templateYLabel[2] templateYLabel[1] templateYLabel[4] . templateTitle[0] templateTitle[2] templateTitle[3] - additional information templateXValue[0] was the templateTitle[0] CPA templateXLabel[0] in the templateTitle[1] in templateTitleDate[0] with a templateYLabel[0] of templateYValue[max] templateYLabel[2] templateYLabel[1] templateYLabel[4] in the templateXLabel[2] templateXLabel[3] ending May , templateTitleDate[0] .
generated: This statistic shows the largest own ( CPA ) based in the cars in 2016 , in terms of Percentage . According to the rankings , Tesla Model S MkI Citroen C5 MkII was the largest own Car in the cars with Percentage of 97.19 Percentage . Best own based - additional information Tesla Model S MkI was the Best CPA Car in the cars in 2016 with a Percentage of 97.19 Percentage in the Model ending May , 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 templateTitleSubject[0] of templateTitleSubject[0] generated templateYLabel[0] templateYLabel[1] worth approximately templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Number illegal of the EU of EU from 2009 to 2018 . In 2018 , the EU of EU generated Number illegal worth approximately 1822.18 entries thousands .

Example 435:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016', '2021']}
title: Self-paced e-learning industry revenue in the U.S. 2016 to 2021
X_Axis['Year']: ['2021', '2020', '2019', '2018', '2017', '2016']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['15.86', '17.35', '18.57', '19.53', '20.33', '20.85']

gold: This statistic presents the self-paced e-learning market revenue in the United States from 2016 to 2021 . The self-paced online learning market , in the U.S. , reached $ 20.85 billion in revenues in 2016 . The figure is estimated to decrease to $ 15.86 billion by 2021 .
gold_template: This statistic presents the templateTitle[0] templateTitle[1] market templateYLabel[0] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateTitle[0] online learning market , in the templateYLabel[2] , reached $ templateYValue[max] templateYLabel[1] in revenues in templateXValue[min] . The figure is estimated to decrease to $ templateYValue[min] templateYLabel[1] by templateXValue[max] .

generated_template: The graph presents data on the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] sales templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . The source estimates that the templateTitleSubject[0] VR templateTitle[4] market size in templateXValue[4] will be worth templateYValue[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] . This number is expected to grow to templateYValue[max] templateYLabel[1] by the end of templateXValue[max] .
generated: The graph presents data on the e-learning industry revenue U.S. sales Revenue worldwide from 2016 to 2021 . The source estimates that the U.S. VR U.S. market size in 2017 will be worth 20.33 billion U.S. dollars . This number is expected to grow to 20.85 billion by the end of 2021 .

Example 436:
titleEntities: {'Subject': ['North America'], 'Date': ['2018']}
title: Leading cinema circuits in North America in 2018 , by number of screens
X_Axis['Month']: ['AMC_Theatres', 'Regal_Entertainment_Group', 'Cinemark_USA_Inc.', 'Cineplex_Entertainment_LP', 'Marcus_Theatres_Corp.', 'Harkins_Theatres', 'Southern_Theatres_LLC', 'B_&_B_Theatres', 'National_Amusements_Inc.', 'Malco_Theatres_Inc.']
Y_Axis['Number', 'of', 'screens']: ['8218', '7350', '4544', '1683', '895', '515', '499', '400', '392', '353']

gold: The graph shows leading cinema circuits in North America as of July 2018 , ranked by number of screens . AMC Theatres ranked first with 8,218 screens . Total attendance at AMC Theatres worldwide reached record levels in 2017 , with over 346 million attendees .
gold_template: The graph shows templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] as of July templateTitleDate[0] , ranked templateTitle[6] templateYLabel[0] of templateYLabel[1] . templateXValue[0] ranked first with templateYValue[max] templateYLabel[1] . Total attendance at templateXValue[0] worldwide reached record levels in 2017 , with over 346 million attendees .

generated_template: This statistic represents the templateYLabel[0] of templateTitle[2] templateYLabel[1] made in templateTitleSubject[0] as of March templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] templateTitle[7] . In that year , there were approximately templateYValue[max] templateYLabel[2] templateTitle[2] templateYLabel[1] in templateTitleSubject[0] . , this was one of the previous year .
generated: This statistic represents the Number of circuits screens made in North America as of March 2018 , sorted 2018 by number . In that year , there were approximately 8218 screens circuits screens in North America . , this was one of the previous year .

Example 437:
titleEntities: {'Subject': ['North America'], 'Date': ['2017']}
title: Reasons for cutting the cord in North America 2017
X_Axis['Response']: ['Price_-_too_expensive', 'I_use_an_internet_streaming_service_such_as_Netflix_Hulu_Amazon_Video_etc.', 'I_use_an_antenna_to_get_the_basic_channels_on_my_TV', 'I_like_to_binge_watch_an_entire_season_of_a_TV_series_through_my_streaming_service', 'I_moved/relocated_and_I_do_not_plan_to_sign-up_for_cable/satellite_service_again', 'The_bulk_of_my_TV_viewing_was_the_original_series_on_streaming_services', "I_share_a_friend/family_member's_login_to_watch_shows_on_their_cable/satellite_provider's_app"]
Y_Axis['Share', 'of', 'respondents']: ['86.7', '39.7', '23', '15.9', '13', '7.7', '0.9']

gold: The graph shows reasons for cutting the cord named by respondents from North America in the fourth quarter of 2017 . During a a survey , it was found that 86.7 percent of respondents cut off their cable or satellite service because it was too expensive .
gold_template: The graph templateXValue[last] templateTitle[0] templateXValue[4] templateTitle[2] the templateTitle[3] named by templateYLabel[1] from templateTitleSubject[0] in the fourth quarter of templateTitleDate[0] . During a survey , it templateXValue[5] found that templateYValue[max] percent of templateYLabel[1] cut off templateXValue[last] cable or satellite templateXValue[1] because it templateXValue[5] templateXValue[0] .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among templateTitleSubject[0] templateTitle[1] aged 18 to 60 about their stance on templateXValue[0] on templateTitle[4] . They were asked how they would manage to templateXValue[0] templateXValue[0] on templateTitle[4] templateTitle[5] templateTitle[6] a templateXValue[0] with others . templateYValue[max] percent of the templateTitle[1] stated templateXValue[1] they would templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] templateXValue[0] is in the templateXValue[0] .
generated: This statistic shows the results of a 2017 survey among North America for aged 18 to 60 about their stance on Price - too expensive on North . They were asked how they would manage to Price - too expensive on North America 2017 a Price - too expensive with others . 86.7 percent of the for stated I use an internet streaming service such as Netflix Hulu Amazon Video etc. they would Price - too expensive is in the Price - too expensive .

Example 438:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Dietary supplement usage in U.S. adults by gender 2018
X_Axis['Sex']: ['Female', 'Male']
Y_Axis['Percentage', 'of', 'U.S.', 'adults']: ['77', '73']

gold: This statistic indicates the percentage of U.S. adults that take dietary supplements , distributed by gender . The statistic is based on a survey conducted in August 2018 . Among U.S. adult males , some 73 percent reported taking dietary supplements .
gold_template: This statistic indicates the templateYLabel[0] of templateYLabel[1] templateYLabel[2] that take templateTitle[0] supplements , distributed templateTitle[5] templateTitle[6] . The statistic is based on a survey conducted in August templateTitleDate[0] . Among templateYLabel[1] adult males , some templateYValue[min] percent reported taking templateTitle[0] supplements .

generated_template: This statistic shows the templateTitle[6] distribution of visitors to Pinterest.com , divided templateTitle[5] visitors from the templateTitle[1] and from the United Kingom as of September 2013 . templateTitleSubject[0] 's usership in the templateTitle[1] is predominantly templateXValue[0] as 67 percent of all templateYLabel[1] are women .
generated: This statistic shows the gender distribution of visitors to Pinterest.com , divided by visitors from the supplement and from the United Kingom as of September 2013 . U.S. 's usership in the supplement is predominantly Female as 67 percent of all U.S. are women .

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] templateTitle[1] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] amounted to templateYValue[max] 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 Bruins from the 2005/06 season to the 2018/19 season . The Revenue of the Boston Bruins Bruins amounted to 228 million U.S. dollars in the 2018/19 season .

Example 440:
titleEntities: {'Subject': ['Rwanda'], 'Date': ['2018']}
title: Urbanization in Rwanda 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['17.21', '17.13', '17.06', '17', '16.97', '16.95', '16.94', '16.94', '16.93', '16.93', '16.93']

gold: This statistic shows the degree of urbanization in Rwanda from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 17.21 percent of Rwanda 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

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

Example 441:
titleEntities: {'Subject': ['Production'], 'Date': ['2013', '2020']}
title: Production of pork worldwide 2013 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Thousand', 'tons', 'carcass', 'weight']: ['96.38', '106.0', '112.94', '112.07', '111.39', '112.01', '110.65', '108.85']

gold: This statistic depicts the production of pork worldwide from 2013 to 2020 . The net production of pork worldwide amounted to about 113 million tons carcass weight in 2018 , and forecasted to decrease to 96.4 million metric tones by 2020 .
gold_template: This statistic depicts the templateTitleSubject[0] of templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . The net templateTitleSubject[0] of templateTitle[1] templateTitle[2] amounted to about templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[2] , and forecasted to decrease to templateYValue[min] templateYLabel[0] metric tones by templateXValue[max] .

generated_template: This statistic illustrates the 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] increased , reaching around templateYValue[2] templateYLabel[1] Turkish liras in templateXValue[2] .
generated: This statistic illustrates the 2013 2020 of Production and pork to 2020 in Production from 2013 to 2019 , with a forecast for 2020 . Over this period , the 2020 of the Production and pork industry to 2020 in Production increased , reaching around 112.94 tons Turkish liras in 2018 .

Example 442:
titleEntities: {'Subject': ['UEFA Champions League'], 'Date': ['2005', '2018']}
title: UEFA Champions League total performance and bonus payments to clubs 2005 to 2018
X_Axis['Year']: ['2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Bonus', 'payments', 'in', 'million', 'euros']: ['1412.6', '1396.13', '1349.43', '1033.43', '904.6', '910.0', '754.1', '786.3', '757.5', '583.4', '585.6', '584.9', '437.1']

gold: The statistic shows the total amount of bonus payments to the participating clubs in the UEFA Champions League from the 2005/06 season to the 2017/18 season . In the 2017/18 season , the total bonus payments to the participating clubs amounted to 1,412.6 million euros .
gold_template: The statistic shows the templateTitle[3] amount of templateYLabel[0] templateYLabel[1] to the participating templateTitle[7] in the templateTitleSubject[0] League from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[0] season , the templateTitle[3] templateYLabel[0] templateYLabel[1] to the participating templateTitle[7] amounted to templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: The annual templateTitle[3] templateTitle[4] has been on decline in templateTitleSubject[0] 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] templateTitleSubject[0] and templateTitleSubject[1] has started to decrease in the last few years .
generated: The annual total performance has been on decline in UEFA Champions League and the UEFA Champions League since 2017/18 . In 2017/18 , this region 's total performance amounted to an average of 437.1 live payments million 1,000 population , down from almost 584.9 payments million 1,000 euros in 2017/18 . The population growth performance in UEFA Champions League and UEFA Champions League has started to decrease in the last few years .

Example 443:
titleEntities: {'Subject': ['Syngenta'], 'Date': ['2009', '2018']}
title: Syngenta 's R & D expenditure worldwide 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Expenditure', 'in', 'million', 'U.S.', 'dollars']: ['-1300', '-1273', '-1299', '1362', '1430', '1376', '1257', '1191', '1032', '952']

gold: The statistic shows Syngenta AG 's expenditure on research and development ( R & D ) worldwide from 2009 to 2018 . Syngenta is one of the largest global agribusiness companies . It is based in Basel , Switzerland , and notably markets seeds and pesticides .
gold_template: The statistic shows templateTitleSubject[0] AG templateTitle[1] templateYLabel[0] on research and development ( templateTitle[2] templateTitle[3] templateTitle[4] ) templateTitle[6] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] is one of the largest global agribusiness companies . It is based in Basel , Switzerland , and notably markets seeds and pesticides .

generated_template: templateTitleSubject[0] is an international pharmaceutical company based out of Germany . As of templateXValue[max] , the company had a total of templateYValue[0] templateYLabel[1] globally . This is a significant increase from the previous years .
generated: Syngenta is an international pharmaceutical company based out of Germany . As of 2018 , the company had a total of -1300 million globally . This is a significant increase from the previous years .

Example 444:
titleEntities: {'Subject': ['The Cheesecake Factory'], 'Date': ['2018']}
title: The Cheesecake Factory 's number of establishments 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'restaurants']: ['201', '199', '194', '188', '177', '168', '162', '156', '149', '160']

gold: This statistic shows the number of The Cheesecake Factory restaurants from 2009 to 2018 . In 2018 , U.S.-based restaurant chain The Cheesecake Factory had 201 restaurants , up from 199 the previous year .
gold_template: This statistic shows the templateYLabel[0] of The templateTitleSubject[0] restaurants from templateXValue[min] to templateXValue[max] . In templateXValue[max] , U.S.-based restaurant chain The templateTitleSubject[0] had templateYValue[max] templateYLabel[1] , up from templateYValue[1] the previous templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] templateYLabel[1] amounted to templateYValue[min] templateYLabel[2] in templateXValue[max] . templateTitleSubject[0] templateTitleSubject[0] is the world 's largest cosmetics and beauty company , concentrating on hair color , skin care , sun protection , make-up , perfumes , and hair care .
generated: This statistic shows the Number of restaurants The Cheesecake Factory from 2009 to 2018 . The Number of The Cheesecake Factory restaurants amounted to 149 restaurants in 2018 . The Cheesecake Factory is the world 's largest cosmetics and beauty company , concentrating on hair color , skin care , sun protection , make-up , perfumes , and hair care .

Example 445:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2017']}
title: Life expectancy at birth in Vietnam 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Life', 'expectancy', 'at', 'birth', 'in', 'years']: ['75.24', '75.17', '75.11', '75.06', '75.01', '74.96', '74.9', '74.84', '74.75', '74.63', '74.47']

gold: This statistic shows the life expectancy at birth in Vietnam from 2007 to 2017 . In 2017 , the average life expectancy at birth in Vietnam was 75.24 years .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] was templateYValue[max] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] had reached about templateYValue[max] years.Demographic development in templateTitleSubject[0] – additional information templateYLabel[0] templateYLabel[1] at templateYLabel[2] refers to the average number of years a group of people born in the same templateXLabel[0] would live , assuming constant mortality rates . The country with the highest templateYLabel[0] templateYLabel[1] at templateYLabel[2] was Japan , while templateTitleSubject[0] had reached a templateYLabel[0] templateYLabel[1] above global average .
generated: The statistic shows the Life expectancy at birth in Vietnam from 2007 to 2017 . In 2017 , the average Life expectancy at birth in Vietnam had reached about 75.24 years.Demographic development in Vietnam – additional information Life expectancy at birth refers to the average number of years a group of people born in the same Year would live , assuming constant mortality rates . The country with the highest Life expectancy at birth was Japan , while Vietnam had reached a Life expectancy above global average .

Example 446:
titleEntities: {'Subject': ['Major League Soccer'], 'Date': ['2019']}
title: Major League Soccer teams ranked by operating income 2019
X_Axis['Team', 'Name']: ['Atlanta_United', 'LA_Galaxy', 'Portland_Timbers', 'Real_Salt_Lake', 'Seattle_Sounders', 'D.C._United', 'Sporting_Kansas_City', 'Orlando_City_SC', 'New_England_Revolution', 'Philadelphia_Union', 'Los_Angeles_FC', 'Vancouver_Whitecaps', 'Colorado_Rapids', 'San_Jose_Earthquakes', 'New_York_Red_Bulls', 'Houston_Dynamo', 'FC_Dallas', 'Columbus_Crew', 'Minnesota_United', 'Montreal_Impact', 'Chicago_Fire', 'New_York_City_FC', 'Toronto_FC']
Y_Axis['Operating', 'income/loss', 'in', 'million', 'U.S.', 'dollars']: ['7', '5', '4', '2', '1', '1', '1', '-1', '-2', '-5', '-5', '-5', '-5', '-5', '-6', '-6', '-7', '-8', '-8', '-12', '-16', '-16', '-19']

gold: The statistic shows a ranking of Major League Soccer teams according to their operating income/loss . Atlanta United had an operating income of seven million U.S. dollars in the 2019 MLS season .
gold_template: The statistic shows a ranking of templateTitleSubject[0] Soccer templateTitle[3] according to their templateYLabel[0] templateYLabel[1] . templateXValue[0] had an templateYLabel[0] templateTitle[7] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] in the templateTitleDate[0] MLS season .

generated_template: The statistic shows a breakdown of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] as of templateTitleSubject[0] templateTitle[9] , broken down by the type of templateXLabel[0] templateXLabel[1] templateTitle[7] . The most frequently templateTitle[7] OS was templateXValue[0] templateXValue[6] ( 64 bit ) at templateYValue[1] percent .
generated: The statistic shows a breakdown of Major League Soccer income/loss as of Major League Soccer 2019 , broken down by the type of Team Name income . The most frequently income OS was Atlanta United Sporting Kansas City ( 64 bit ) at 5 percent .

Example 447:
titleEntities: {'Subject': ['Eritrea'], 'Date': ['2024']}
title: Total population of Eritrea 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['6.68', '6.58', '6.48', '6.38', '6.27', '6.16', '6.05', '5.93', '5.82', '5.7', '5.58']

gold: This statistic shows the total population of Eritrea from 2014 to 2024 . All figures are estimates . In 2018 , the total population of Eritrea was estimated to amount to approximately 6.05 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . All figures are estimates . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was estimated to amount to approximately templateYValue[6] templateYLabel[1] templateYLabel[0] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to around templateYValue[7] templateYLabel[1] people . templateTitle[1] of templateTitleSubject[0] Although templateTitle[0] templateTitle[1] increased on a yearly basis from 2004 to templateXValue[min] , templateTitle[1] growth has slowly decreased annually as of 2011 , despite remaining positive .
generated: The statistic shows the Total population of Eritrea from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Eritrea amounted to around 5.93 millions people . population of Eritrea Although Total population increased on a yearly basis from 2004 to 2014 , population growth has slowly decreased annually as of 2011 , despite remaining positive .

Example 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 displays a templateTitle[0] of the templateTitle[2] and templateTitle[3] templateTitle[4] templateYLabel[1] templateTitle[6] in the templateTitle[1] templateYLabel[2] templateXValue[min] to templateXValue[max] . templateYLabel[2] templateXValue[2] to templateXValue[1] , the templateTitle[2] and templateTitle[3] templateTitle[4] templateYLabel[1] templateTitle[6] is expected to increase by templateYValue[max] percent . Appreciation of the templateTitleSubject[0] dollar is likely to reduce costs and increase production in the chemical industry .
generated: This statistic displays a Europe of the distribution and golfers 2020 average age in the forecasted increase 20 or younger to 20 or younger . increase 40 to 50 years to 20 to 40 years , the distribution and golfers 2020 average age is expected to increase by 42 percent . Appreciation of the Europe dollar is likely to reduce costs and increase production in the chemical industry .

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 illustrates the distribution of templateTitleSubject[0] templateYLabel[0] templateYLabel[1] in templateTitleSubject[1] in templateTitleDate[0] . templateXValue[0] had the most templateYLabel[0] customer with a total of templateYValue[max] templateYLabel[2] . templateXValue[1] came in second closely followed templateTitle[6] the UK .
generated: This statistic illustrates the distribution of European Estimated number in European in 2015 . Global had the most Estimated customer with a total of 328.0 people . Europe came in second closely followed European the UK .

Example 450:
titleEntities: {'Subject': ['Forecast'], 'Date': ['2010', '2025']}
title: Forecast of rare earth oxide holmium oxide price globally 2010 to 2025
X_Axis['Year']: ['2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022', '2023', '2024', '2025']
Y_Axis['Price', 'in', 'U.S.', 'dollars', 'per', 'kilogram']: ['41', '303', '107', '66', '63', '55', '51', '49', '46', '38', '20', '20', '20', '21', '21', '22']

gold: This statistic displays the price development of rare earth oxide holmium oxide globally from 2009 to 2025 . It expected that the price of holmium oxide will reach some 49 U.S. dollars per kilogram in 2017 .
gold_template: This statistic displays the templateYLabel[0] development of templateTitle[1] templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[3] templateTitle[7] from 2009 to templateXValue[max] . It expected that the templateYLabel[0] of templateTitle[4] templateTitle[3] will reach some templateYValue[7] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[7] .

generated_template: This statistic shows 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 January templateTitleDate[0] . During the measured period , the largest templateTitle[4] templateTitle[5] presence of the brand was on templateXValue[0] with templateYValue[max] templateYLabel[2] templateYLabel[1] , as opposed to its templateYValue[min] templateYLabel[2] follower base on templateXValue[last] .

generated_template: This statistic gives information on the templateTitle[1] of templateTitleSubject[0] templateYLabel[2] worldwide as of January templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[max] percent of templateTitleSubject[0] templateTitle[3] were templateXValue[0] and templateYValue[min] percent were templateXValue[last] .
generated: This statistic gives information on the followers of Michael Kors millions worldwide as of January 2020 , sorted media 2020 . During the survey period , 17.91 percent of Michael Kors Kors were Facebook and 3.5 percent were Twitter .

Example 452:
titleEntities: {'Subject': ['Foursquare'], 'Date': ['10', '14']}
title: Number of registered members on Foursquare 2010 to 2014
X_Axis['Month']: ["Dec_'10", "Jan_'11", "Mar_'12", "May_'12", "Jan_'13", "Jan_'14", "May_'14", "Oct_'14"]
Y_Axis['Number', 'of', 'registered', 'members', 'in', 'millions']: ['5', '6', '15', '20', '30', '45', '50', '55']

gold: This statistic gives information on the number of registered members on Foursquare between December 2010 and October 2014 . As of that month , the social check-in app community had accumulated over 55 million members worldwide .
gold_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] on templateTitleSubject[0] between December templateTitleDate[0] and October templateTitleDate[1] . As of that templateXLabel[0] , the social check-in app community had accumulated over templateYValue[max] templateYLabel[3] templateYLabel[2] worldwide .

generated_template: This statistic shows the distribution of templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] as of templateTitleDate[0] , broken down by sector . In templateTitleDate[0] , approximately templateYValue[max] percent of the templateTitleSubject[0] 's templateYLabel[1] for templateTitle[1] was attributable to the templateXValue[0] transportation sector .
generated: This statistic shows the distribution of registered in the Foursquare as of 10 , broken down by sector . In 10 , approximately 55 percent of the Foursquare 's registered for registered was attributable to the Dec '10 transportation sector .

Example 453:
titleEntities: {'Subject': ['United States'], 'Date': ['1998', '2018']}
title: Natural gas production - United States 1998 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1998']
Y_Axis['Production', 'in', 'billion', 'cubic', 'meters']: ['831.8', '745.8', '727.4', '740.3', '704.7', '655.7', '649.1', '617.4', '575.2', '557.6', '546.1', '521.9', '524.0', '511.1', '526.4', '540.8', '536.0', '555.5', '543.2', '538.7']

gold: Production of natural gas in the United States has been increasing for the past decade and amounted to 831.8 billion cubic meters in 2018 . An increase in production corresponded with rising demand for natural gas in the United States , particularly after the 2008 Recession . Natural gas becomes competitive Since the early 2000s , the price of coal had been going up , and increased more rapidly following the 2008 Recession , which affected the cost of crude oil to an even greater degree .
gold_template: templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitleSubject[0] has been increasing for the past decade and amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . An increase in templateYLabel[0] corresponded with rising demand for templateTitle[0] templateTitle[1] in the templateTitleSubject[0] , particularly after the templateXValue[10] Recession . templateTitle[0] templateTitle[1] becomes competitive Since the early 2000s , the price of coal had been going up , and increased more rapidly following the templateXValue[10] Recession , which affected the cost of crude oil to an even greater degree .

generated_template: The statistic represents the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] templateYLabel[1] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , the German automotive supplier employed some 243,200 people . templateTitleSubject[0] templateTitle[2] key products include automotive electronics components and tires ; the company is ranked among the world templateTitle[2] largest tire manufacturers .
generated: The statistic represents the Production of United States billion from the fiscal Year of 1998 to the fiscal Year of 2018 . In the fiscal Year of 2018 , the German automotive supplier employed some 243,200 people . United States production key products include automotive electronics components and tires ; the company is ranked among the world production largest tire manufacturers .

Example 454:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Dating website or app usage among U.S. online users 2019
X_Axis['Response']: ["Yes_I'm_doing_so_currently", "Yes_I've_done_so_in_the_past", 'No_never', "Don't_know"]
Y_Axis['Share', 'of', 'respondents']: ['7', '24', '65', '3']

gold: This statistic presents the percentage of adult online users in the United States who have used a dating website or app as of January 2019 . According to the findings , only seven percent of respondents stated that they were currently using a dating website or app , while in comparison 65 percent of respondents reported to have never used a dating app or website before .
gold_template: This statistic presents the percentage of adult templateTitle[6] templateTitle[7] in the templateTitle[5] who have used a templateTitle[0] templateTitle[1] or templateTitle[2] as of January templateTitleDate[0] . According to the findings , only templateYValue[0] percent of templateYLabel[1] stated that they were templateXValue[0] using a templateTitle[0] templateTitle[1] or templateTitle[2] , while in comparison templateYValue[max] percent of templateYLabel[1] reported to have templateXValue[2] used a templateTitle[0] templateTitle[2] or templateTitle[1] before .

generated_template: This statistic shows the results of a 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] templateTitleSubject[0] templateTitle[6] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[0] .
generated: This graph depicts the total Regular season Home attendance of the Atlanta Falcons 2006 franchise of the National Football League from 2006 to 2019 . In 2019 , the Regular season Home attendance of the franchise was 572811 .

Example 456:
titleEntities: {'Subject': ['Inflation'], 'Date': ['2019']}
title: Inflation rate of the main industrialized and emerging countries 2019
X_Axis['Country']: ['Russia', 'Brazil', 'India', 'China', 'USA', 'United_Kingdom', 'Germany', 'France', 'Japan']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['4.68', '3.79', '3.44', '2.31', '1.82', '1.81', '1.49', '1.17', '0.99']

gold: This statistic shows the inflation rate of the main industrialized and emerging countries in 2019 . In 2019 , the inflation rate in China was estimated to amount to approximately 2.31 percent compared to the previous year .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] and templateTitle[4] templateTitle[5] in templateTitleDate[0] . In templateTitleDate[0] , the templateYLabel[0] templateYLabel[1] in templateXValue[3] was estimated to amount to approximately templateYValue[3] percent templateYLabel[2] to the templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the estimated templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] templateTitle[1] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] was estimated to be worth templateYValue[1] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the estimated Inflation rate in the Inflation rate in 2019 , emerging Country . In 2019 , the Inflation rate main was estimated to be worth 3.79 percent compared to the previous Country .

Example 457:
titleEntities: {'Subject': ['U.S.'], 'Date': ['14', '20']}
title: Monthly car loan rates in the U.S. 2017 to 2020
X_Axis['Month']: ['Jan_20', 'Dec_19', 'Nov_19', 'Oct_19', 'Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16', 'Jun_16', 'May_16', 'Apr_16', 'Mar_16', 'Feb_16', 'Jan_16', 'Dec_15', 'Nov_15', 'Oct_15', 'Sep_15', 'Aug_15', 'Jul_15', 'Jun_15', 'May_15', 'Apr_15', 'Mar_15', 'Feb_15', 'Jan_15', 'Dec_14', 'Nov_14', 'Oct_14', 'Sep_14', 'Aug_14', 'Jul_14', 'Jun_14', 'May_14', 'Apr_14', 'Mar_14', 'Feb_14', 'Jan_14']
Y_Axis['Interest', 'rate']: ['4.56', '4.61', '4.59', '4.61', '4.61', '4.63', '4.66', '4.74', '4.72', '4.77', '4.77', '4.78', '4.77', '4.96', '4.93', '4.93', '4.79', '4.8', '4.83', '4.82', '4.64', '4.59', '4.52', '4.53', '4.51', '4.43', '4.29', '4.3', '4.29', '4.25', '4.51', '4.44', '4.38', '4.35', '4.38', '4.36', '4.35', '4.32', '4.27', '4.26', '4.23', '4.18', '4.17', '4.17', '4.25', '4.28', '4.33', '4.29', '4.33', '4.4', '4.33', '4.3', '4.32', '4.38', '4.37', '4.39', '4.37', '4.37', '4.31', '4.07', '4.07', '4.12', '4.06', '4.04', '4.02', '4.03', '4.03', '4.13', '4.13', '4.18', '4.23', '4.21', '4.25']

gold: This statistic presents the average interest rate on 60-month new car loans in the United States from January 2014 to January 2020 . Car loan interest rates amounted to 4.56 percent as of January 30 , 2020 . The smaller the car loan interest rates , the cheaper the loan is .
gold_template: This statistic presents the average templateYLabel[0] templateYLabel[1] on 60-month new templateTitle[1] loans in the templateTitle[4] from January 2014 to January templateTitleDate[1] . templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[3] amounted to templateYValue[0] percent as of January 30 , templateTitleDate[1] . The smaller the templateTitle[1] templateTitle[2] templateYLabel[0] templateTitle[3] , the cheaper the templateTitle[2] is .

generated_template: The templateYLabel[1] of the DJIA templateYLabel[0] amounted to templateYValue[0] on January 31 , templateTitleDate[1] . templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] – additional information The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] is a price-weighted templateTitleSubject[0] of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ , including Goldman Sachs , IBM and Walt Disney . This templateYLabel[0] is considered to be a barometer of the state of the American economy .
generated: The rate of the DJIA Interest amounted to 4.56 on January 31 , 20 . U.S. Interest – additional information The U.S. Interest is a price-weighted U.S. of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ , including Goldman Sachs , IBM and Walt Disney . This Interest is considered to be a barometer of the state of the American economy .

Example 458:
titleEntities: {'Subject': ['Europe'], 'Date': ['2016']}
title: Number of natural mineral waters in Europe 2016 , by country
X_Axis['Country']: ['Germany', 'Italy', 'Hungary', 'Spain', 'Poland', 'France', 'Romania', 'United_Kingdom', 'Greece', 'Austria', 'Belgium', 'Bulgaria', 'Portugal', 'Slovakia', 'Lithuania', 'Netherlands', 'Czech_Republic', 'Denmark', 'Sweden', 'Slovenia', 'Latvia', 'Estonia', 'Croatia', 'Ireland', 'Finland']
Y_Axis['Litres', 'consumed', 'per', 'capita']: ['821', '322', '214', '165', '119', '90', '69', '67', '44', '33', '27', '22', '21', '20', '17', '13', '11', '11', '11', '9', '5', '4', '4', '2', '1']

gold: This statistic represents the number of natural mineral waters in Europe in 2016 . Germany had the highest number of natural mineral waters with 821 certified natural mineral water sources .
gold_template: This statistic represents the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateTitleDate[0] . templateXValue[0] had the highest templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] with templateYValue[max] certified templateTitle[1] templateTitle[2] water sources .

generated_template: This statistic shows the templateYLabel[2] templateYLabel[3] templateTitle[2] of templateTitle[0] templateTitle[1] in templateTitleSubject[0] in templateTitleDate[0] . In this year , templateXValue[0] was the leading market for the templateTitle[2] of templateTitle[0] templateTitle[1] with templateYValue[max] liters of templateTitle[0] templateTitle[1] templateYLabel[1] templateYLabel[2] person and templateXValue[1] was the second largest consumer of templateTitle[0] templateTitle[1] with templateYValue[1] liters templateYLabel[1] templateYLabel[2] person . templateTitle[0] templateTitle[1] in the European Union is predominantly made up of the natural mineral templateTitle[1] category.Germany is the market with the largest amount of different mineral templateTitle[1] brands .
generated: This statistic shows the per capita mineral of Number natural in Europe in 2016 . In this year , Germany was the leading market for the mineral of Number natural with 821 liters of Number natural consumed per person and Italy was the second largest consumer of Number natural with 322 liters consumed per person . Number natural in the European Union is predominantly made up of the natural mineral natural category.Germany is the market with the largest amount of different mineral natural brands .

Example 459:
titleEntities: {'Subject': ['Luxury'], 'Date': ['2016']}
title: Luxury destinations with the largest growth in travel worldwide 2016
X_Axis['Country']: ['Kenya', 'Iceland', 'Saint_Martin', 'China', 'Ecuador', 'Japan', 'South_Africa', 'Tanzania', 'Croatia', 'Jamaica']
Y_Axis['Year-over-year', 'travel', 'growth']: ['59', '56', '39', '35', '34', '32', '28', '27', '25', '23']

gold: This statistic shows the luxury travel destinations with the largest growth in travel worldwide as of August 2016 . Luxury travel to Kenya grew by 59 percent in 2016 compared with the previous year . In 2017 , the Caribbean was the leading international destination for a spring vacation according to U.S. respondents .
gold_template: This statistic shows the templateTitleSubject[0] templateYLabel[1] templateTitle[1] templateTitle[2] the templateTitle[3] templateYLabel[2] in templateYLabel[1] templateTitle[6] as of August templateTitleDate[0] . templateTitleSubject[0] templateYLabel[1] to templateXValue[0] grew by templateYValue[max] percent in templateTitleDate[0] compared templateTitle[2] the previous year . In 2017 , the Caribbean was the leading international destination for a spring vacation according to U.S. respondents .

generated_template: This statistic shows the templateYValue[2] templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] in the world templateTitle[5] templateTitleDate[0] to templateTitleDate[1] . Over the past decade , templateXValue[0] has demonstrated the templateTitle[2] economic templateYLabel[3] rate templateTitle[1] templateYLabel[1] templateYLabel[0] templateYLabel[2] templateYLabel[3] sitting as high as templateYValue[max] templateYLabel[4] . The overall quarterly templateYLabel[2] templateYLabel[3] in the country can be found here .
generated: This statistic shows the 39 Luxury destinations the largest growth travel in the world worldwide 2016 to 2016 . Over the past decade , Kenya has demonstrated the largest economic growth rate destinations travel Year-over-year growth sitting as high as 59 growth . The overall quarterly growth in the country can be found here .

Example 460:
titleEntities: {'Subject': ['Europe'], 'Date': ['2012', '2016']}
title: Forecast for the number of new hotel rooms opening in Europe from 2012 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'hotel', 'rooms']: ['34060', '34451', '39178', '30982', '37818']

gold: This statistic shows a forecast for the number of new hotel rooms opening in Europe from 2012 to 2016 . In 2013 , 30,982 new hotel rooms opened in the European hotel market . It was forecasted that 34,060 new hotel rooms would open in 2016 .
gold_template: This statistic shows a templateTitle[0] templateTitle[1] the templateYLabel[0] of templateTitle[3] templateYLabel[1] templateYLabel[2] templateTitle[6] in templateTitleSubject[0] templateTitle[8] templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateYValue[min] templateTitle[3] templateYLabel[1] templateYLabel[2] opened in the European templateYLabel[1] market . It was forecasted that templateYValue[0] templateTitle[3] templateYLabel[1] templateYLabel[2] would open in templateXValue[max] .

generated_template: This statistic illustrates the templateYLabel[0] of templateTitleSubject[0] of templateTitleSubject[0] ( LoL ) monthly active templateYLabel[2] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , LoL had templateYValue[max] templateYLabel[3] templateTitleSubject[0] , up from templateYValue[1] templateYLabel[3] in templateXValue[min] . Being one of the most prominent eSports games , in templateXValue[1] LoL championship finals attracted 36 templateYLabel[3] viewers worldwide .
generated: This statistic illustrates the Number of Europe of Europe ( LoL ) monthly active rooms worldwide from 2012 to 2016 . In 2016 , LoL had 39178 rooms Europe , up from 34451 rooms in 2012 . Being one of the most prominent eSports games , in 2015 LoL championship finals attracted 36 rooms viewers worldwide .

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 around 466 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 the Switzerland from 2014 to 2018 , with projections up until 2024 . In 2018 , the National debt in the Switzerland was around 466 billion U.S. dollars . For comparison , the Greek debt amounted to approximately 375 billion U.S. dollars that same Year .

Example 463:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2017', '2023']}
title: Mexico : mobile phone internet user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['59', '58', '56', '53', '50', '47', '43']

gold: The statistic shows the mobile phone internet user penetration in Mexico from 2017 to 2023 . In 2017 , 43 percent of the population users accessed the internet through their mobile device . This figure is projected to grow to 59percent in 2023 .
gold_template: The statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the templateYLabel[1] users accessed the templateTitle[3] through their templateTitle[1] device . This figure is projected to grow to 59percent in templateXValue[max] .

generated_template: This statistic provides information on templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In 2016 , 55.65 percent of the Brazilian templateYLabel[1] were accessing the templateTitle[1] . These figures are projected to grow to templateYValue[max] percent in templateXValue[max] .
generated: This statistic provides information on mobile phone internet in Mexico from 2017 to 2023 . In 2016 , 55.65 percent of the Brazilian population were accessing the mobile . These figures are projected to grow to 59 percent in 2023 .

Example 464:
titleEntities: {'Subject': ['Stryker'], 'Date': ['2011', '2019']}
title: Stryker 's annual research , development and engineering expenses 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Expenses', 'in', 'million', 'U.S.', 'dollars']: ['971', '862', '787', '715', '625', '614', '536', '471', '462']

gold: The statistic shows the annual research , development and engineering expenses of Stryker from 2011 to 2019 . Stryker 's research , development and engineering expenses have gradually increased since 2011 , reaching 971 million U.S. dollars in 2019 . The Stryker Corporation is a U.S. medical technology company headquartered in Kalamazoo , Michigan .
gold_template: The statistic shows the templateTitle[2] templateTitle[3] , templateTitle[4] and templateTitle[5] templateYLabel[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] templateTitle[1] templateTitle[3] , templateTitle[4] and templateTitle[5] templateYLabel[0] have gradually increased since templateXValue[min] , reaching templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[max] . The templateTitleSubject[0] Corporation is a templateYLabel[2] medical technology company headquartered in Kalamazoo , Michigan .

generated_template: This statistic presents the templateTitle[3] templateTitle[4] templateYLabel[0] of the British automotive company templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] from financial templateXLabel[0] templateXValue[min] to financial templateXLabel[0] templateXValue[max] in templateYLabel[1] British pounds . Between templateXValue[min] and templateXValue[1] , the company made a templateTitle[4] templateYLabel[0] higher than templateYValue[max] templateYLabel[1] British pounds in every templateXLabel[0] . However , the most recent financial templateXLabel[0] suggested a more difficult business climate , as the company recorded its first templateTitle[4] loss in this period of consideration , at 3.6 templateYLabel[1] British pounds .
generated: This statistic presents the research development Expenses of the British automotive company Stryker from financial Year 2011 to financial Year 2019 in million British pounds . Between 2011 and 2018 , the company made a development Expenses higher than 971 million British pounds in every Year . However , the most recent financial Year suggested a more difficult business climate , as the company recorded its first development loss in this period of consideration , at 3.6 million British pounds .

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 illustrates the number of templateTitleSubject[0] templateYLabel[0] farm projects templateYLabel[2] templateYLabel[3] in leading templateYLabel[0] power countries as of October templateTitleDate[0] . There were templateYValue[1] templateYLabel[0] farm projects templateYLabel[2] templateYLabel[3] in templateXValue[2] . Higher templateYLabel[0] speeds that occur templateTitleSubject[0] in comparison to land , allows for a higher supply of electricity from templateTitleSubject[0] templateYLabel[0] energy .
generated: This statistic illustrates the number of European Union Volume farm projects tons in leading Volume power countries as of October . There were 1500 Volume farm projects tons in Greece . Higher Volume speeds that occur European Union in comparison to land , allows for a higher supply of electricity from European Union Volume energy .

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 templateTitleSubject[0] templateTitle[1] research templateTitle[3] development templateTitle[5] as a templateYLabel[0] of its total templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company invested over templateYValue[0] percent of its templateYLabel[1] into research and development . templateTitleSubject[0] plc is one of the top 10 pharmaceutical companies worldwide .
generated: This statistic shows China size research China development 2018 as a Number of its total persons from 1990 to 2018 . In 2018 , the company invested over 3.03 percent of its persons into research and development . China plc is one of the top 10 pharmaceutical companies worldwide .

Example 467:
titleEntities: {'Subject': ['Phoenix Suns'], 'Date': ['2001', '2019']}
title: Phoenix Suns ' revenue 2001 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['246', '235', '218', '173', '154', '145', '137', '121', '136', '147', '148', '148', '145', '132', '132', '111', '109', '107']

gold: The statistic shows the revenue of the Phoenix Suns franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise amounted to 246 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Phoenix Suns franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 246 million U.S. dollars .

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: The statistic shows the list of templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] of templateYLabel[2] caused templateTitle[5] terrorist activities in templateTitleDate[0] . The templateTitle[2] templateTitle[3] of templateYLabel[2] from templateYLabel[1] in templateTitleDate[0] occurred in templateXValue[0] , accounting for templateYValue[max] percent of all templateYLabel[2] . In that year alone , terrorist incidents worldwide resulted in approximately 15,952 fatalities .
generated: The statistic shows the list of Stuxnet percentage the infected hosts of hosts caused country terrorist activities in . The infected hosts of hosts from infected in occurred in Iran , accounting for 58.31 percent of all hosts . In that year alone , terrorist incidents worldwide resulted in approximately 15,952 fatalities .

Example 470:
titleEntities: {'Subject': ['U.S. January TV'], 'Date': ['2020', '2020']}
title: Leading trailers in the U.S. January 2020 , by weekly TV ad spend
X_Axis['Year']: ['1917', 'Dolittle', 'Bad_Boys_for_Life', 'Like_a_Boss', 'Just_Mercy']
Y_Axis['Spending', 'in', 'million', 'U.S.', 'dollars']: ['10.41', '5.77', '4.18', '3.9', '3.72']

gold: The leading movie commercial in the United States based on weekly television advertising spending for the week ending January 5 , 2020 was for war drama film ' 1917 ' _ , with a 10.41 million U.S. dollar spend by studio Universal Pictures . Universal also spent 5.77 million U.S. dollars on TV promotion of 'Dolittle ' _ .
gold_template: The templateTitle[0] movie commercial in the templateTitle[2] based on templateTitle[6] television advertising templateYLabel[0] templateXValue[2] the week ending templateTitleSubject[0] 5 , templateTitle[4] was templateXValue[2] war drama film ' templateXValue[0] ' _ , with a templateYValue[max] templateYLabel[1] templateYLabel[2] dollar templateTitle[9] templateTitle[5] studio Universal Pictures . Universal also spent templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] on templateTitleSubject[0] promotion of 'Dolittle ' _ .

generated_template: This statistic displays a templateTitle[0] of the templateTitle[2] and templateTitle[3] templateTitle[4] templateYLabel[1] templateTitle[6] in the templateTitle[1] templateYLabel[2] templateXValue[min] to templateXValue[max] . templateYLabel[2] templateXValue[2] to templateXValue[1] , the templateTitle[2] and templateTitle[3] templateTitle[4] templateYLabel[1] templateTitle[6] is expected to increase by templateYValue[max] percent . Appreciation of the templateTitleSubject[0] dollar is likely to reduce costs and increase production in the chemical industry .
generated: This statistic displays a Leading of the U.S. and January 2020 million weekly in the trailers U.S. 1917 to 1917 . U.S. Bad Boys for Life to Dolittle , the U.S. and January 2020 million weekly is expected to increase by 10.41 percent . Appreciation of the U.S. January TV dollar is likely to reduce costs and increase production in the chemical industry .

Example 471:
titleEntities: {'Subject': ['LinkedIn'], 'Date': ['2020']}
title: LinkedIn : distribution of global audiences 2020 , by gender
X_Axis['Sex']: ['Female', 'Male']
Y_Axis['Percentage', 'of', 'users']: ['43', '57']

gold: This statistic gives information on the distribution of LinkedIn users worldwide as of January 2020 , sorted by gender . During the survey period , 43 percent of LinkedIn audiences were female and 57 percent were male .
gold_template: This statistic gives information on the templateTitle[1] of templateTitleSubject[0] templateYLabel[1] worldwide as of January templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[min] percent of templateTitleSubject[0] templateTitle[3] were templateXValue[0] and templateYValue[max] percent were templateXValue[last] .

generated_template: This statistic gives information on the templateTitle[1] of templateTitleSubject[0] templateYLabel[1] worldwide as of January templateTitleDate[0] , sorted templateTitle[5] templateTitle[6] . During the survey period , templateYValue[max] percent of templateTitleSubject[0] templateTitle[3] were templateXValue[0] and templateYValue[min] percent were templateXValue[last] .
generated: This statistic gives information on the distribution of LinkedIn users worldwide as of January 2020 , sorted by gender . During the survey period , 57 percent of LinkedIn audiences were Female and 43 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: This statistic illustrates the templateYLabel[0] of templateYLabel[1] of the templateTitle[3] templateTitle[2] in templateTitleSubject[1] , the United Kingdom ( templateTitleSubject[0] ) from templateXValue[last] to templateXValue[0] . In templateXValue[0] , about templateYValue[0] percent of the templateTitle[3] templateTitle[2] in templateTitleSubject[0] .
generated: This statistic illustrates the Broadcasting of payments of the broadcasting total in Premier League , the United Kingdom ( Premier League ) from 2010/11 to 2018/19 . In 2018/19 , about 2456.01 percent of the broadcasting total in Premier League .

Example 473:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Canada : reported arson rate 2000 to 2018
X_Axis['Year']: ['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['Rate', 'of', 'arson', 'per', '100,000', 'residents']: ['44.5', '46.27', '41.36', '43.12', '40.22', '40.48', '40.54', '39.57', '40.22', '39.86', '35.98', '30.29', '31.96', '25.41', '24.06', '25.12', '23.67', '23.4', '21.59']

gold: This statistic shows the reported arson rate in Canada from 2000 to 2018 . There were about 21.59 arsons per 100,000 residents in Canada in 2018 .
gold_template: This statistic shows the templateTitle[1] templateYLabel[1] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . There were about templateYValue[min] arsons templateYLabel[2] 100,000 templateYLabel[4] in templateTitleSubject[0] in templateXValue[max] .

generated_template: 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 depicts the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] templateYLabel[1] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In its templateXValue[max] fiscal templateXLabel[0] , the Switzerland-based electronics company employed templateYValue[0] people worldwide .
generated: This statistic depicts the Number of Marathon Oil employees from the fiscal Year of 2010 to the fiscal Year of 2018 . In its 2018 fiscal Year , the Switzerland-based electronics company employed 2400 people worldwide .

Example 475:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Most popular social networks of U.S. teens 2016
X_Axis['Platform']: ['YouTube', 'Gmail', 'Snapchat', 'Instagram', 'Facebook', 'Kik_Messenger', 'Skype', 'Twitter', 'Vine', 'Tumblr']
Y_Axis['Percentage', 'of', 'teenagers']: ['91', '75', '66', '65', '61', '52', '43', '40', '31', '24']

gold: This statistic provides information about the most popular websites visited by teenagers in the United States as of June 2016 . During the survey period , video sharing platform YouTube was most popular among U.S. teens with a 91 percent usage rate . Snapchat was ranked third with 66 percent reporting that they accessed the photo sharing app .
gold_template: This statistic provides information about the templateTitle[0] templateTitle[1] websites visited by templateYLabel[1] in the templateTitle[4] as of June templateTitleDate[0] . During the survey period , video sharing templateXLabel[0] templateXValue[0] was templateTitle[0] templateTitle[1] among templateTitleSubject[0] templateTitle[5] with a templateYValue[max] percent usage rate . templateXValue[2] was ranked third with templateYValue[2] percent reporting that they accessed the photo sharing app .

generated_template: This statistic gives information on the most popular multi-platform templateTitle[1] templateTitle[2] in the templateTitle[3] as of October templateTitleDate[0] . During that month , templateXValue[0] was ranked first with almost templateYValue[max] monthly active templateTitle[3] users . templateXValue[0] templateXValue[2] was ranked third in terms of templateTitleSubject[0] but came first in regards of mobile-only users .
generated: This statistic gives information on the most popular multi-platform popular social in the networks as of October 2016 . During that month , YouTube was ranked first with almost 91 monthly active networks users . YouTube Snapchat was ranked third in terms of U.S. but came first in regards of mobile-only users .

Example 476:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Percentage of population volunteering in the U.S. in 2015 , by age
X_Axis['Year']: ['16_to_24_years', '25_to_34_years', '35_to_44_years', '45_to_54_years', '55_to_64_years', '65_years_and_over']
Y_Axis['Percentage', 'of', 'population', 'volunteering']: ['21.8', '22.3', '28.9', '28', '25.1', '23.5']

gold: This statistic displays the percentage of population volunteering in the U.S. in 2015 , by age . In 2015 , 21.8 percent of Americans 16 to 24 years old volunteered at least once during the year .
gold_template: This statistic displays the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . In templateTitleDate[0] , templateYValue[min] percent of Americans templateXValue[0] to templateXValue[0] old volunteered at least once during the templateXLabel[0] .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per day in the templateTitleSubject[0] templateTitleSubject[0] between templateTitle[6] and templateTitle[7] , templateTitle[8] templateTitle[9] . In this templateYValue[min] templateXLabel[0] period , individuals aged between 11 and 18 templateXValue[0] old templateTitle[3] templateYValue[max] templateYLabel[1] of templateTitle[1] templateTitle[2] per day .
generated: This statistic shows the Percentage of population volunteering U.S. per day in the U.S. between age and age , age . In this 21.8 Year period , individuals aged between 11 and 18 16 to 24 years old U.S. 28.9 population of population volunteering per day .

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: The statistic shows the templateYLabel[0] volume of oranges in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , templateTitleSubject[0] templateYLabel[0] of oranges amounted to approximately templateYValue[min] templateYLabel[1] templateYLabel[2] in templateXValue[max] , down from templateYValue[1] templateYLabel[1] templateYLabel[2] the previous templateXLabel[0] .
generated: The statistic shows the Production volume of oranges in the U.S. from 2000 to 2018 . According to the report , U.S. Production of oranges amounted to approximately 181.4 thousand tons in 2018 , down from 437.6 thousand tons the previous Year .

Example 478:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Leading baby wipes vendors in the U.S. 2016 , based on sales
X_Axis['Company']: ['Private_label', 'Kimberly_Clark_Corp.', 'Procter_&_Gamble', 'Seventh_Generation', 'The_Honest_Co.', 'Johnson_&_Johnson', 'Nice-Pak_Products', 'Paper_Partners', 'Kas_Direct', 'Irish_Breeze']
Y_Axis['Million', 'U.S.', 'dollars']: ['494.4', '416.2', '276.6', '9.6', '8.6', '7.1', '6.3', '6.1', '5.7', '4.4']

gold: The statistic shows the leading baby wipes vendors in the United States in 2016 , based on sales . In that year , Kimberly Clark was the second largest U.S. baby wipes vendor with sales of 416.2 million U.S. dollars . Total sales of U.S. baby wipes vendors amounted to about 1.25 billion U.S. dollars in 2016 .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[4] in templateTitleDate[0] , templateTitle[6] on templateTitle[7] . In that year , templateXValue[1] was the second largest templateYLabel[1] templateTitle[1] templateTitle[2] vendor with templateTitle[7] of templateYValue[1] templateYLabel[0] templateYLabel[1] templateYLabel[2] . Total templateTitle[7] of templateYLabel[1] templateTitle[1] templateTitle[2] templateTitle[3] amounted to about 1.25 templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleDate[0] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the richest YouTube 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] templateYLabel[1] of about templateYValue[max] 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 Million U.S. of the richest YouTube baby wipes vendors in the U.S. in 2016 . In that year , Private label was the Leading men 's baby wipes Company in the U.S. with Million U.S. of about 494.4 dollars . Additional information on the baby wipes market baby wipes is used by men in order to facilitate baby , creating a more comfortable process and a better finish .

Example 479:
titleEntities: {'Subject': ['Los Angeles Rams'], 'Date': ['2006', '2019']}
title: Regular season home attendance of the Los Angeles Rams 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Home', 'attendance']: ['498605', '579439', '507136', '665318', '419220', '456146', '455657', '396925', '451153', '423376', '441896', '479840', '514352', '522608']

gold: This graph depicts the total regular season home attendance of the St. Louis / Los Angeles Rams franchise of the National Football League from 2006 to 2019 . In 2019 , the regular season home attendance of the franchise was 498,605 . The franchise moved from St. Louis to Los Angeles before the 2016 season .
gold_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the St. Louis / templateTitleSubject[0] Rams franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[0] . The franchise moved from St. Louis to templateTitleSubject[0] before the templateXValue[3] templateTitle[1] .

generated_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitle[6] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[0] .
generated: This graph depicts the total Regular season Home attendance of the Los Angeles Rams Rams franchise of the National Football League from 2006 to 2019 . In 2019 , the Regular season Home attendance of the franchise was 498605 .

Example 480:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2017']}
title: Estimated U.S. clothing and clothing accessories per capita sales 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'sales', 'in', 'U.S.', 'dollars']: ['795', '804', '797', '786', '774', '763', '734', '689', '667', '709', '734', '714', '680', '650', '616', '599', '587', '594']

gold: The timeline shows the estimated clothing and clothing accessories per capita sales in the United States from 2000 to 2017 . In 2017 , clothing and clothing accessories sales amounted to 795 U.S. dollars per capita .
gold_template: The timeline shows the templateTitle[0] templateTitle[2] and templateTitle[2] templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] and templateTitle[2] templateTitle[4] templateYLabel[2] amounted to templateYValue[0] templateYLabel[3] templateYLabel[4] templateYLabel[0] templateYLabel[1] .

generated_template: The timeline shows the templateTitle[0] templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[1] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] templateYLabel[2] amounted to about templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[0] templateYLabel[1] .
generated: The timeline shows the Estimated clothing Per capita sales in the U.S. capita 2000 to 2017 . In 2017 , clothing sales amounted to about 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 templateTitle[0] of templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[6] through the Supplemental Nutrition Assistance Program ( templateTitleSubject[0] , formerly called templateTitleSubject[0] templateTitleSubject[0] ) in templateTitleDate[0] , by templateXLabel[0] . About templateYValue[2] percent of the templateTitle[3] in templateXValue[5] is templateTitle[4] templateTitle[6] through the Supplemental Nutrition Assistance Program .
generated: The statistic shows the Number of U.S. 2018 state through the Supplemental Nutrition Assistance Program ( U.S. , formerly called U.S. ) in 2018 , by State . About 55551 percent of the U.S. in Michigan is 2018 state through the Supplemental Nutrition Assistance Program .

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 female fashion templateTitle[3] based in the country on how old they were when they first started working in the fashion industry . templateYValue[max] percent of templateYLabel[1] stated they were between templateXValue[1] and templateXValue[1] templateXValue[0] old when they started working as a model .
generated: This statistic shows the results of a survey among female fashion professional based in the country on how old they were when they first started working in the fashion industry . 84 percent of respondents stated they were between March 2014 and March 2014 March 2015 old when they started working as a model .

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: The statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] is forecast to amount to templateYValue[1] templateYLabel[3] templateYLabel[4] templateYLabel[5] . The global templateTitle[1] market includes telecom services , templateYLabel[1] outsourcing and hardware maintenance , templateYLabel[1] consulting and systems integration services , software , communications equipment , and computer equipment .
generated: The statistic shows the Total sulfur demand from 2014 to 2020 . In 2019 , the Forecast Total demand is forecast to amount to 67.7 metric tons . The global sulfur market includes telecom services , demand outsourcing and hardware maintenance , demand consulting and systems integration services , software , communications equipment , and computer equipment .

Example 484:
titleEntities: {'Subject': ['Smartphone'], 'Date': ['2016', '2021']}
title: Smartphone users worldwide 2016 to 2021
X_Axis['Year']: ['2021', '2020', '2019', '2018', '2017', '2016']
Y_Axis['Smartphone', 'users', 'in', 'billions']: ['3.8', '3.5', '3.2', '2.9', '2.7', '2.5']

gold: How many people have smartphones worldwide ? The number of smartphone users worldwide today surpasses three billion and is forecast to further grow by several hundred million in the next few years . China , India , and the United States are the countries with the highest number of smartphone users , with each country easily surpassing the 100 million user mark . Smartphone unit sales levelling off In the past five years , about 1.4 billion smartphones were sold worldwide annually , reflecting stagnation in the smartphone market during the last few years .
gold_template: How many people have smartphones templateTitle[2] ? The number of templateYLabel[0] templateYLabel[1] templateTitle[2] today surpasses templateYValue[2] templateYLabel[2] and is forecast to further grow by several hundred templateYLabel[2] in the next few years . China , India , and the country are the countries with the highest number of templateYLabel[0] templateYLabel[1] , with each country easily surpassing the 100 templateYLabel[2] user mark . templateYLabel[0] unit sales levelling off In the past five years , about 1.4 templateYLabel[2] smartphones were sold templateTitle[2] annually , reflecting stagnation in the templateYLabel[0] market during the last few years .

generated_template: The 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: The statistic shows the Smartphone users worldwide 2016 users 2021 from 2016 to 2021 . Smartphone users worldwide 2016 was forecasted to increase by 3.2 percent in 2018 and a further 3.2 percent in 2019 users worldwide - additional information users in the 21st century is no longer limited to a small geographical area , as internet communication and almost universal access to worldwide and accommodation allow for easy national and transnational movement between companies , their partners , customers , suppliers or distributors . Although multinational corporations and users worldwide have a long history , they have reached unprecedented levels in the modern era and seem to be growing every Year .

Example 485:
titleEntities: {'Subject': ['Instagram Stories'], 'Date': ['16', '19']}
title: Daily active users of Instagram Stories 2019
X_Axis['Month']: ["Jan_'19", "Jun_'18", "Oct_'17", "Jun_'17", "Apr_'17", "Jan_'17", "Oct_'16"]
Y_Axis['Number', 'of', 'DAU', 'in', 'millions']: ['500', '400', '300', '250', '200', '150', '100']

gold: In January 2019 , photo sharing platform Instagram reported 500 million daily active Stories users worldwide , up from 400 million global DAU in June 2018 . Stories is a feature of the app allowing users post photo and video sequences that disappear 24 hours after being posted . Instagram usageInstagram has over one billion monthly active users and is one of the most popular social networks worldwide .
gold_template: In January templateTitle[5] , photo sharing platform templateTitleSubject[0] reported templateYValue[max] templateYLabel[2] templateTitle[0] templateTitle[1] templateTitleSubject[0] templateTitle[2] worldwide , up from templateYValue[1] templateYLabel[2] global templateYLabel[1] in June 2018 . templateTitleSubject[0] is a feature of the app allowing templateTitle[2] post photo and video sequences that disappear 24 hours after being posted . templateTitleSubject[0] usageInstagram has over templateYValue[max] templateYLabel[2] monthly templateTitle[1] templateTitle[2] and is one of the most popular social networks worldwide .

generated_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] of PlayerUnknown 's Battlegrounds ( templateTitleSubject[0] ) on templateTitle[4] templateTitle[1] from templateXValue[last] to December templateTitleDate[0] . According to the estimates , the battle royale game developed by Bluehole attracted templateYValue[max] templateYLabel[2] templateYLabel[1] on templateTitle[4] in August templateTitleDate[0] .
generated: This statistic gives information on the Number of DAU of PlayerUnknown 's Battlegrounds ( Instagram Stories ) on Stories active from Oct '16 to December 16 . According to the estimates , the battle royale game developed by Bluehole attracted 500 millions DAU on Stories in August 16 .

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 the templateTitleSubject[0] 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] 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] 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 Iran from 2014 to 2018 , with projections up until 2024 . In 2018 , the National debt in the Iran was about 142.95 billion U.S. dollars . Reduction of the National debt and recovery of the economy in the UK The debt of the Iran , which amounted to around 1,600 billion pounds in 2014 - more than it has ever been - is projected to keep rising .

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: The statistic shows a breakdown of templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[1] as of templateTitleSubject[0] templateTitle[9] , broken down by the type of templateXLabel[0] templateXLabel[1] templateTitle[7] . The most frequently templateTitle[7] OS was templateXValue[0] templateXValue[6] ( 64 bit ) at templateYValue[1] percent .
generated: The statistic shows a breakdown of Latin America & per as of Latin America country , broken down by the type of Country by . The most frequently by OS was El Salvador Guatemala ( 64 bit ) at 57.0 percent .

Example 488:
titleEntities: {'Subject': ['Moscow'], 'Date': ['2019']}
title: Prime office rental prices in Moscow Q1 2015-Q2 2019
X_Axis['Quarter']: ["Q1_'15", "Q2_'15", "Q3_'15", "Q4_'15", "Q1_'16", "Q2_'16", "Q3_'16", "Q4_'16", "Q1_'17", "Q2_'17", "Q3_'17", "Q4_'17", "Q2_'18", "Q3_'18", "Q1_'19", "Q2_'19"]
Y_Axis['Cost', 'per', 'square', 'meter', 'in', 'euros']: ['760', '697', '692', '670', '670', '720', '613', '760', '726', '684', '669', '654', '693', '693', '703', '704']

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

generated_template: This statistic illustrates and estimate of templateTitleSubject[0] 's templateYLabel[1] from the first templateXLabel[0] of templateTitleDate[0] to the fourth templateXLabel[0] of templateTitle[5] . 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 illustrates 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: The timeline shows the current templateTitle[0] templateTitle[1] templateTitle[2] networking templateTitle[4] in templateTitleSubject[0] templateTitle[6] , based on user figures . As of May templateTitleDate[0] , Facebook was the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateXValue[13] with a templateYLabel[0] templateYLabel[1] of templateYValue[max] percent .
generated: The timeline shows the current Average U.S. brand networking rate in U.S. media , based on user figures . As of May 2017 , Facebook was the Average U.S. brand response in Nonprofit with a Average response of 18 percent .

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[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 volume of the thousand metric in the U.S. ( U.S. ) from 2013/14 to 2017/18 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: The statistic illustrates the templateYLabel[0] of templateTitle[6] templateTitle[7] templateTitle[8] individuals in the biggest metropolitan templateTitle[3] of the templateTitle[0] in templateTitleDate[0] . In templateTitleDate[0] , the metropolitan area of templateXValue[0] templateXValue[0] had about templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic illustrates the Damage of U.S. 1900 2016 individuals in the biggest metropolitan most of the Earthquakes in 1900 . In 1900 , the metropolitan area of January 17 1994 Los Angeles had about 30000 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: In templateXValue[max] , templateTitleSubject[0] had a templateTitle[0] templateTitle[1] of about templateYValue[max] people templateYLabel[1] templateYLabel[2] templateYLabel[3] . The country has one of the lowest templateTitle[0] densities in the world , as the total templateTitle[0] is very small in relation to the dimensions of the land . templateTitleSubject[0] has a relatively stable templateTitle[0] size , consistently with a growth of around one percent compared to the previous templateXLabel[0] .
generated: In 2018 , Italy had a Population density of about 206.67 people per square kilometer . The country has one of the lowest Population densities in the world , as the total Population is very small in relation to the dimensions of the land . Italy has a relatively stable Population size , consistently with a growth of around one percent compared to the previous Year .

Example 494:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: U.S. consumers ' purchase location of shampoos and conditioners 2014
X_Axis['Response']: ['"Big_Box"_retail_store_(e.g._Walmart_Target)', 'Grocery_store/Supermarket', 'Pharmacy_(e.g._CVS_Walgreens)', 'Online_(Net)', 'Online_mass_merchandiser_(e.g._Amazon_drugstore.com)', "Department_Stores_(e.g._Macy's_Nordstrom)", 'In-person_at_a_specialty_beauty_products_merchant_(e.g._Sephora)', 'Online_specialty_beauty_products_merchant_(e.g._Sephora_Ultra)', 'In-person/not_in_a_store_(e.g._Avon_Mary_Kay)', 'Online_through_a_"sampling"_membership_program_(e.g._Ipsy_Birchbox)', "Online_through_a_specific_brand's_website_(e.g._Clairol_CoverGirl)", 'Somewhere_else']
Y_Axis['Share', 'of', 'respondents']: ['62', '36', '31', '12', '8', '5', '4', '3', '2', '1', '1', '10']

gold: This statistic presents the results of a survey among U.S. adult consumers . The survey was fielded online by Harris Interactive in June 2014 , asking the respondents where they usually purchase their shampoo and/or conditioners . Some 12 percent of U.S. adults indicated that they buy their shampoo/conditioner online .
gold_template: This statistic presents the results of a survey among templateTitleSubject[0] adult templateTitle[1] . The survey was fielded templateXValue[3] by Harris Interactive in June templateTitleDate[0] , asking the templateYLabel[1] where they usually templateTitle[3] their shampoo and/or templateTitle[6] . Some templateYValue[3] percent of templateTitleSubject[0] adults indicated that they buy their shampoo/conditioner templateXValue[3] .

generated_template: This statistic presents data on the psychological templateTitleSubject[0] of women templateXValue[5] templateXValue[0] templateXValue[0] or templateXValue[0] worldwide . During the July templateTitleDate[0] survey period , templateYValue[max] percent of survey templateYLabel[1] who have experienced templateXValue[0] templateXValue[0] stated that they felt a templateXValue[0] of templateXValue[0] in their templateXValue[0] to templateXValue[0] to templateXValue[0] or templateXValue[0] templateXValue[0] . A total of templateYValue[1] percent of templateXValue[0] templateXValue[0] templateTitle[5] also reported to templateXValue[1] templateXValue[1] templateXValue[1] to templateXValue[1] templateXValue[1] .
generated: This statistic presents data on the psychological U.S. of women Department Stores (e.g. Macy's Nordstrom) "Big Box" retail store (e.g. Walmart Target) or "Big Box" retail store (e.g. Walmart Target) worldwide . During the July 2014 survey period , 62 percent of survey respondents who have experienced "Big Box" retail store (e.g. Walmart Target) stated that they felt a "Big Box" retail store (e.g. Walmart Target) of "Big Box" retail store (e.g. Walmart Target) in their "Big Box" retail store (e.g. Walmart Target) to "Big Box" retail store (e.g. Walmart Target) to "Big Box" retail store (e.g. Walmart Target) or "Big Box" retail store (e.g. Walmart Target) . A total of 36 percent of "Big Box" retail store (e.g. Walmart Target) shampoos also reported to Grocery store/Supermarket to Grocery store/Supermarket .

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 templateYLabel[0] of templateTitle[0] templateYLabel[1] templateYLabel[2] in templateTitle[4] templateTitle[5] in templateTitleSubject[0] templateTitle[7] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In that year , there were approximately templateYValue[2] templateYLabel[3] templateYLabel[1] templateYLabel[2] in templateTitle[6] .
generated: This statistic shows the Production of Leading thousand pounds in production 2019 in U.S. 2019 in 2019 , 2019 State . In that year , there were approximately 102630 pounds thousand pounds in 2019 .

Example 496:
titleEntities: {'Subject': ['Twitter'], 'Date': ['2016']}
title: Leading eSports pro players on Twitter worldwide 2016 , by number of followers
X_Axis['Month']: ['Søren_Bjerg_(Bjergsen)', 'Enrique_Cedeño_Martínez_(xPeke)', 'Yiliang_Peng_(Doublelift)', 'Jason_Tran_(WildTurtle)', 'Danil_Ishutin_(Dendi)', 'Hai_Du_Lam_(Hai)', 'Henrik_Hansen_(Froggen)', 'Martin_Larsson_(Rekkles)', 'Bora_Kim_(Yell0wStaR)', 'Zachary_Scuderi_(Sneaky)']
Y_Axis['Number', 'of', 'Twitter', 'followers', 'in', 'thousands']: ['604', '596', '393', '376', '334', '297', '270', '258', '256', '244']

gold: The graph shows the leading eSports professional players on Twitter worldwide as of January 2016 , ranked by the number of fans . As of the measured period , Søren Bjerg , a player from Denmark also known as Bjergsen , was the most famous on Twitter , with 604 thousand followers . He was followed by Enrique Martínez , aka xPeke , who gathered 596 thousand followers on Twitter .
gold_template: The graph shows the templateTitle[0] templateTitle[1] professional templateTitle[3] on templateYLabel[1] templateTitle[5] as of January templateTitleDate[0] , ranked templateTitle[7] the templateYLabel[0] of fans . As of the measured period , templateXValue[0] , a player from Denmark also known as Bjergsen , was the most famous on templateYLabel[1] , with templateYValue[max] thousand templateYLabel[2] . He was followed templateTitle[7] templateXValue[1] , aka xPeke , who gathered templateYValue[1] thousand templateYLabel[2] on templateYLabel[1] .

generated_template: As of June templateTitleDate[0] , Amazon 's templateYLabel[1] templateTitle[1] was templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] , making the company the most valuable U.S.-based templateTitle[4] company by templateYLabel[1] templateTitle[1] . Second-ranked templateXValue[1] had a templateYLabel[1] templateYLabel[2] of templateYValue[1] templateYLabel[3] templateYLabel[4] templateYLabel[5] . templateTitle[4] templateTitle[5] generate their revenue through various means .
generated: As of June 2016 , Amazon 's Twitter eSports was 604 thousands , making the company the most valuable U.S.-based Twitter company by Twitter eSports . Second-ranked Enrique Cedeño Martínez (xPeke) had a Twitter followers of 596 thousands . Twitter worldwide generate their revenue through various means .

Example 497:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2009', '2019']}
title: Population in Sweden 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Inhabitants', 'in', 'millions']: ['10.33', '10.23', '10.12', '10.0', '9.85', '9.75', '9.64', '9.56', '9.48', '9.42', '9.34']

gold: This statistic shows the total population in Sweden from 2009 to 2019 . The number of inhabitants in Sweden has increased by nearly one million in this time period . In 2009 , there were approximately 9.34 million inhabitants in Sweden and by the end of 2019 the Swedish population reached 10.33 million people .
gold_template: This statistic shows the total templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The number of templateYLabel[0] in templateTitleSubject[0] has increased by nearly one templateYLabel[1] in this time period . In templateXValue[min] , there were approximately templateYValue[min] templateYLabel[1] templateYLabel[0] in templateTitleSubject[0] and by the end of templateXValue[max] the Swedish templateTitle[0] reached templateYValue[max] templateYLabel[1] people .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was at approximately templateYValue[max] templateYLabel[1] templateYLabel[0] . The Syrian refugee crisis The war in templateTitleSubject[0] has been escalating since 2011 , which has made it difficult to estimate the current templateTitle[1] of the country .
generated: This statistic shows the Population Sweden of Sweden from 2009 to 2019 . In 2019 , the Population Sweden of Sweden was at approximately 10.33 millions Inhabitants . The Syrian refugee crisis The war in Sweden has been escalating since 2011 , which has made it difficult to estimate the current Sweden of the country .

Example 498:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018', '2027']}
title: Projected increase of the national defense budget in Canada fiscal years 2018 to 2027
X_Axis['Year']: ['2026-2027', '2025-2026', '2024-2025', '2023-2024', '2022-2023', '2021-2022', '2020-2021', '2019-2020', '2018-2019', '2017-2018']
Y_Axis['Annual', 'funding', 'increase', 'in', 'million', 'Canadian', 'dollars']: ['2300', '2020', '1751', '1495', '1250', '1016', '793', '580', '377', '184']

gold: This statistic shows the projected increase over baseline spending for the national defense budget of Canada between the fiscal years 2018 to 2027 . In fiscal year 2027 , the defense budget for Canada is projected to be 2.3 billion Canadian dollars greater than 2026 budget .
gold_template: This statistic shows the templateTitle[0] templateYLabel[2] over baseline spending for the templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleSubject[0] between the templateTitle[6] templateTitle[7] templateTitle[8] to templateTitleDate[0] . In templateTitle[6] templateXLabel[0] templateTitleDate[0] , the templateTitle[3] templateTitle[4] for templateTitleSubject[0] is templateTitle[0] to be templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] greater than 2026 templateTitle[4] .

generated_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] on templateTitle[3] and templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateYLabel[0] was at around templateYValue[0] templateYLabel[1] templateYLabel[2] of templateTitle[1] . In templateXValue[max] , the templateYLabel[0] is projected to be around templateYValue[0] templateYLabel[1] templateYLabel[2] .
generated: This statistic shows Canada increase Annual on defense and budget from 2026-2027 to 2026-2027 . In 2026-2027 , the Annual was at around 2300 funding increase of increase . In 2026-2027 , the Annual is projected to be around 2300 funding increase .

Example 499:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2015', '2020']}
title: Purchasing power change in the Netherlands 2015 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Year', 'on', 'year', 'percentage', 'change']: ['1.3', '1.6', '0.3', '0.3', '2.6', '1']

gold: In 2018 , the purchasing power in the Netherlands increased for the fourth year in a row . Purchasing power growth slowed down considerably though in 2017 and 2018 in comparison to 2015 and especially 2016 . In the next two years , the purchasing power was forecast to increase further .
gold_template: In templateXValue[2] , the templateTitle[0] templateTitle[1] in the templateTitleSubject[0] increased for the fourth templateXLabel[0] in a row . templateTitle[0] templateTitle[1] growth slowed down considerably though in templateXValue[3] and templateXValue[2] in comparison to templateXValue[min] and especially templateXValue[4] . In the next templateYValue[1] years , the templateTitle[0] templateTitle[1] was forecast to increase further .

generated_template: This statistic shows the projected templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateTitle[0] templateTitle[1] in templateTitleSubject[0] was estimated at templateYValue[2] percent .
generated: This statistic shows the projected Purchasing power in Netherlands from 2015 to 2020 . In 2015 , Purchasing power in Netherlands was estimated at 0.3 percent .

Example 500:
titleEntities: {'Subject': ['General Motors'], 'Date': ['2014']}
title: General Motors - passenger cars produced worldwide 1999 to 2014
X_Axis['Year']: ['2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Passenger', 'cars', 'produced', '(in', 'millions)']: ['6.64', '6.73', '6.61', '6.87', '6.27', '5.0', '6.02', '6.26', '5.78', '5.66', '4.5', '4.68', '4.9', '4.66', '5.27', '5.34']

gold: The timeline shows the passenger car production of General Motors worldwide from 1999 to 2014 . In 2013 , GM produced 6.7 million passenger cars worldwide . The U.S. automaker is world 's fourth largest manufacturer of passenger cars in terms of production .
gold_template: The timeline shows the templateYLabel[0] car production of templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[1] , GM templateYLabel[2] templateYValue[1] million templateYLabel[0] templateYLabel[1] templateTitle[5] . The U.S. automaker is world 's fourth largest manufacturer of templateYLabel[0] templateYLabel[1] in terms of production .

generated_template: The timeline shows the templateYLabel[0] car production of templateTitleSubject[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateYLabel[2] around templateYValue[max] million templateYLabel[0] templateYLabel[1] templateTitle[4] . templateTitleSubject[0] is ranked among the 15 largest automakers templateTitle[4] .
generated: The timeline shows the Passenger car production of General Motors produced from 1999 to 2014 . In 2014 , General Motors produced around 6.87 million Passenger cars produced . General Motors is ranked among the 15 largest automakers produced .

Example 501:
titleEntities: {'Subject': ['ACSI'], 'Date': ['2019']}
title: ACSI - U.S. customer satisfaction with social media 2019
X_Axis['Platform']: ['Pinterest', 'YouTube', 'Wikipedia', 'Instagram', 'Snapchat', 'Twitter', 'LinkedIn', 'Tumblr', 'Facebook']
Y_Axis['ACSI', 'score', '(100-point', 'scale)']: ['80', '78', '74', '72', '71', '69', '69', '64', '63']

gold: This graph shows the American Customer Satisfaction Index ( ACSI ) of customer satisfaction with social media websites in 2019 . Overall , Pinterest scored the highest level of customer satisfaction with 80 index points . Facebook was ranked last with an index score rating of 63 / 100 index points .
gold_template: This graph shows the American templateTitle[2] templateTitle[3] Index ( templateYLabel[0] ) of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] websites in templateTitleDate[0] . Overall , templateXValue[0] scored the highest level of templateTitle[2] templateTitle[3] templateTitle[4] templateYValue[max] index points . templateXValue[last] was ranked last templateTitle[4] an index templateYLabel[1] rating of templateYValue[min] / 100 index points .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[2] templateYLabel[3] internet users in the templateTitle[0] who were using selected templateTitle[3] networks as of February templateTitleDate[0] . During that period of time , templateYValue[4] percent of templateYLabel[1] templateYLabel[4] aged between 50 and 64 years used the photo sharing app templateXValue[4] .
generated: This statistic shows the ACSI of (100-point scale) internet users in the ACSI who were using selected satisfaction networks as of February 2019 . During that period of time , 71 percent of score scale) aged between 50 and 64 years used the photo sharing app Snapchat .

Example 502:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. mobile device owner monthly app download rate 2018 , by age group
X_Axis['Response']: ['Teens_(13-17)', 'Millennials_(18-34)', 'Gen_X_(35-54)', 'Boomers_(55-64)']
Y_Axis['Share', 'of', 'respondents']: ['83', '74', '59', '39']

gold: This statistic gives information on the percentage of mobile device owners in the United States who download apps at least once a month or more as of April 2018 , sorted by age group . During the survey period , it was found that 74 percent of responding Millennial app users downloaded apps to their mobile device on a monthly basis .
gold_template: This statistic gives information on the percentage of templateTitle[1] templateTitle[2] owners in the templateTitle[0] who templateTitle[6] apps at least once a month or more as of April templateTitleDate[0] , sorted templateTitle[9] templateTitle[10] templateTitle[11] . During the survey period , it was found that templateYValue[1] percent of responding Millennial templateTitle[5] users downloaded apps to their templateTitle[1] templateTitle[2] on a templateTitle[4] basis .

generated_template: This statistic shows the results of a survey among female templateTitleSubject[0] high school students templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . According to the source , templateYValue[max] percent of female students in the templateTitle[1] were templateTitle[5] templateTitle[6] as of 2013 .
generated: This statistic shows the results of a survey among female U.S. high school students owner monthly app download . According to the source , 83 percent of female students in the mobile were app download as of 2013 .

Example 503:
titleEntities: {'Subject': ['Boeing'], 'Date': ['2004', '2019']}
title: Boeing 737 - orders 2004 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Number', 'of', 'aircraft']: ['69', '837', '865', '701', '666', '1196', '1208', '1184', '625', '508', '197', '488', '850', '733', '574', '152']

gold: In 2019 , Boeing received gross orders for 69 units of its 737 narrow-body jet airliner series , but net orders after cancellations and changes ( under the ASC 606 revenue recognition standard ) came to a devastating negative 183 units . The slump in net orders came as a result of the jet maker 's 737 MAX crisis . Boeing delivered some 18 units of its 737 aircraft to Delta Air Lines in 2019 .
gold_template: In templateXValue[max] , templateTitleSubject[0] received gross templateTitle[2] for templateYValue[min] units of its templateTitle[1] narrow-body jet airliner series , but net templateTitle[2] after cancellations and changes ( under the ASC 606 revenue recognition standard ) came to a devastating negative 183 units . The slump in net templateTitle[2] came as a result of the jet maker 's templateTitle[1] MAX crisis . templateTitleSubject[0] delivered some 18 units of its templateTitle[1] templateYLabel[1] to Delta Air Lines in templateXValue[max] .

generated_template: In templateXValue[max] , approximately templateYValue[0] templateYLabel[2] templateYLabel[3] of templateYLabel[0] were consumed by templateTitleSubject[0] templateTitle[1] , compared to a high of templateYValue[max] templateYLabel[2] templateYLabel[3] in templateXValue[12] . While the templateYLabel[1] of airline templateYLabel[0] in the templateTitle[0] has increased in recent years , it is yet to surpass the levels seen prior to the templateXValue[11] recession . Commercial templateTitle[1] The above figures include all commercial air carriers based in the templateTitleSubject[0] who carry cargo and/or passengers on domestic or international flights , and with annual revenue of over 20 templateYLabel[2] templateTitleSubject[0] dollars .
generated: In 2019 , approximately 69 aircraft of Number were consumed by Boeing 737 , compared to a high of 1208 aircraft in 2007 . While the aircraft of airline Number in the Boeing has increased in recent years , it is yet to surpass the levels seen prior to the 2008 recession . Commercial 737 The above figures include all commercial air carriers based in the Boeing who carry cargo and/or passengers on domestic or international flights , and with annual revenue of over 20 aircraft Boeing dollars .

Example 504:
titleEntities: {'Subject': ['Leading'], 'Date': ['2017']}
title: Leading countries worldwide based on coffee area harvested 2017
X_Axis['Country']: ['Brazil', 'Indonesia', 'Côte_d’Ivoire', 'Colombia', 'Ethiopia', 'Mexico', 'Viet_Nam', 'Honduras', 'India', 'Peru']
Y_Axis['Area', 'in', 'thousand', 'hectares']: ['1800.4', '1253.8', '925.44', '798.36', '694.33', '638.6', '605.18', '505.12', '449.36', '423.55']

gold: This statistic illustrates the global leading 10 countries based on coffee area harvested in 2017 . In that year , Mexico harvested an area of 638.6 thousand hectares of green coffee and was ranked sixth among coffee-growing countries worldwide .
gold_template: This statistic illustrates the global templateTitleSubject[0] 10 templateTitle[1] templateTitle[3] on templateTitle[4] templateYLabel[0] templateTitle[6] in templateTitleDate[0] . In that year , templateXValue[5] templateTitle[6] an templateYLabel[0] of templateYValue[5] thousand templateYLabel[2] of green templateTitle[4] and was ranked sixth among coffee-growing templateTitle[1] templateTitle[2] .

generated_template: This statistic shows the templateYValue[2] templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] in the world templateTitle[5] templateTitleDate[0] to templateTitleDate[1] . Over the past decade , templateXValue[0] has demonstrated the templateTitle[2] economic templateYLabel[3] rate templateTitle[1] templateYLabel[1] templateYLabel[0] templateYLabel[2] templateYLabel[3] sitting as high as templateYValue[max] templateYLabel[4] . The overall quarterly templateYLabel[2] templateYLabel[3] in the country can be found here .
generated: This statistic shows the 925.44 Leading countries the worldwide based coffee in the world area 2017 to 2017 . Over the past decade , Brazil has demonstrated the worldwide economic hectares rate countries thousand Area hectares sitting as high as 1800.4 hectares . The overall quarterly hectares in the country can be found here .

Example 505:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2011', '2019']}
title: Facebook : worldwide quarterly revenue 2011 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14", "Q3_'14", "Q2_'14", "Q1_'14", "Q4_'13", "Q3_'13", "Q2_'13", "Q1_'13", "Q4_'12", "Q3_'12", "Q2_'12", "Q1_'12", "Q4_'11"]
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['21082', '17652', '16886', '15077', '16914', '13727', '13231', '11966', '12972', '10328', '9321', '8032', '8809', '7011', '6436', '5382', '5841', '4501', '4042', '3543', '3851', '3203', '2910', '2502', '2585', '2016', '1813', '1458', '1585', '1262', '1184', '1058', '1131']

gold: In the fourth quarter of 2019 , social network Facebook 's total revenues amounted to 21.08 billion U.S. dollars , the majority of which were generated through advertising . The company announced over seven million active advertisers on Facebook during the third quarter of 2019 . During that fiscal period , the company 's net income was 7.35 billion U.S. dollars .
gold_template: In the fourth templateXLabel[0] of templateTitleDate[1] , social network templateTitleSubject[0] 's total revenues amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , the majority of which were generated through advertising . The company announced over seven templateYLabel[1] active advertisers on templateTitleSubject[0] during the third templateXLabel[0] of templateTitleDate[1] . During that fiscal period , the company 's net income was 7.35 templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitle[5] templateTitle[6] templateYLabel[0] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the final templateXLabel[0] of templateXValue[0] , up templateTitle[8] 263.5 templateYLabel[1] in the corresponding templateXLabel[0] of templateXValue[4] . The templateTitleSubject[0] ' subscription templateYLabel[0] generally grows steadily over the course of each year . However , for the most part , the company sees a small templateYLabel[0] decrease in the third templateXLabel[0] , which has been an ongoing and consistent trend for the company over the last few years .
generated: The Facebook 2019 Revenue amounted to 21082 million U.S. dollars in the final Quarter of Q4 '19 , up 2019 263.5 million in the corresponding Quarter of Q4 '18 . The Facebook ' subscription Revenue generally grows steadily over the course of each year . However , for the most part , the company sees a small Revenue decrease in the third Quarter , which has been an ongoing and consistent trend for the company over the last few years .

Example 506:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Payment type preference when shopping at fast food restaurants in the U.S. 2018
X_Axis['Response']: ['Credit_card', 'Debit_card', 'Cash']
Y_Axis['Share', 'of', 'respondents']: ['18', '44', '32']

gold: This statistic shows the preferred form of payment when shopping at fast food restaurants among consumers in the United States in 2018 . In the study it was found that 32 percent of consumers preferred to use cash when making purchases at fast food restaurants .
gold_template: This statistic shows the preferred form of templateTitle[0] templateTitle[3] templateTitle[4] at templateTitle[5] templateTitle[6] templateTitle[7] among consumers in the templateTitle[8] in templateTitleDate[0] . In the study it was found that templateYValue[2] percent of consumers preferred to use templateXValue[last] templateTitle[3] making purchases at templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: 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] templateXValue[0] the templateXValue[0] 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] templateXValue[1] templateXValue[1] for an templateTitleSubject[0] templateXValue[0] .
generated: The statistic illustrates the answers to the following survey question : `` The preference Credit card fast will probably cost a thousand euros . Cash you willing to pay that ? '' As of 2018 , roughly 20 percent of the respondents said to Credit card the Credit card from U.S. when it is released , even if it food them a thousand euros . However , more than half of the respondents said the price is Debit card for an U.S. Credit card .

Example 507:
titleEntities: {'Subject': ['Easter U.S.'], 'Date': ['2019']}
title: Planned Easter expenditure per capita in the U.S. by item 2019
X_Axis['Month']: ['Food', 'Clothing', 'Gifts', 'Candy', 'Flowers', 'Decorations', 'Greeting_cards', 'Other']
Y_Axis['Average', 'expenditure', 'in', 'U.S.', 'dollars']: ['47.97$', '27.29$', '24.01$', '20.78$', '10.79$', '8.73$', '6.52$', '5.15$']

gold: This statistic shows the results of a survey among people in the United States on the amount of money they are planning to spend on the following items for the 2019 Easter holidays . Respondents stated that they are planning to spend an average of 20.78 U.S. dollars on candy for the upcoming Easter holidays .
gold_template: This statistic shows the results of a survey among people in the templateTitle[5] on the amount of money they are planning to spend on the following items for the templateTitleDate[0] templateTitleSubject[0] holidays . Respondents stated that they are planning to spend an templateYLabel[0] of 20.78 templateYLabel[2] templateYLabel[3] on templateXValue[3] for the upcoming templateTitleSubject[0] holidays .

generated_template: How many people play templateTitleSubject[0] ? PlayerUnknown 's Battlegrounds ( templateTitleSubject[0] ) , which is available on templateTitle[1] and templateTitle[2] , crossed the templateYValue[max] templateYLabel[2] templateTitle[3] templateTitle[4] mark in templateXValue[0] templateXValue[0] - an impressive figure considering it was released little over a year earlier . The Battle Royale game developed by Bluehole was made available to the public in March templateXValue[1] . One of the biggest games on Steam While it is available on both PlayStation and Xbox One , templateTitleSubject[0] 's origins lie in templateTitle[2] and , more specifically , on Steam .
generated: How many people play Easter U.S. ? PlayerUnknown 's Battlegrounds ( Easter U.S. ) , which is available on Easter and expenditure , crossed the 47.97$ U.S. per capita mark in Food - an impressive figure considering it was released little over a year earlier . The Battle Royale game developed by Bluehole was made available to the public in March Clothing . One of the biggest games on Steam While it is available on both PlayStation and Xbox One , Easter U.S. 's origins lie in expenditure and , more specifically , on Steam .

Example 508:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2017', '2023']}
title: Argentina : internet user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['94', '93', '91', '88', '85', '82', '77']

gold: This statistic provides information on internet user penetration in Argentina from 2017 to 2023 . In 2017 , 77 percent of the population in Argentina were accessing the internet . This figure is projected to grow to 94 percent by 2023 .
gold_template: This statistic provides information on templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[min] percent of the templateYLabel[1] in templateTitleSubject[0] were accessing the templateTitle[1] . This figure is projected to grow to templateYValue[max] percent by templateXValue[max] .

generated_template: This statistic gives information on templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In 2016 , 55.65 percent of the Brazilian templateYLabel[1] were accessing the templateTitle[1] . These figures are projected to grow to templateYValue[max] percent in templateXValue[max] .
generated: This statistic gives information on internet user penetration in Argentina from 2017 to 2023 . In 2016 , 55.65 percent of the Brazilian population were accessing the internet . These figures are projected to grow to 94 percent in 2023 .

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: 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 Belgium 's Migration and balance from the fiscal Year of 2010 to the fiscal Year of 2018 . In the fiscal Year of 2018 , the company incurred around 50180 balance in Migration and 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 January templateXValue[max] , over 733 thousand people were estimated to be working in print or software templateTitle[2] companies , down from the templateYValue[1] thousand people recorded in January of the previous templateXLabel[0] .

generated_template: 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 January templateXValue[max] , 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 January 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: In templateTitleSubject[0] , the total templateYLabel[0] of reported criminal offences fluctuated in recent years . In templateXValue[max] , the templateYLabel[0] stood at close to 499 thousand crimes , and among them there were templateYValue[1] templateYLabel[1] reported . According to the Danish Penal Code , the penalty for homicide ranges from a minimum of a five-year prison sentence to imprisonment for life .
generated: In Switzerland , the total Employed of reported criminal offences fluctuated in recent years . In 2020 , the Employed stood at close to 499 thousand crimes , and among them there were 4.97 persons reported . According to the Danish Penal Code , the penalty for homicide ranges from a minimum of a five-year prison sentence to imprisonment for life .

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 templateYLabel[0] templateTitle[4] of templateYLabel[1] templateYLabel[2] worldwide in templateTitleDate[0] , broken down templateTitle[7] templateXLabel[0] . According to the report , the templateXValue[0] templateXValue[0] accounted for approximately templateYValue[1] percent of templateTitleSubject[0] templateTitle[4] templateYLabel[0] templateYLabel[1] that year .
generated: This statistic shows the National relation of 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 illustrates the compound annual growth rate of the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] between templateYLabel[2] and templateYLabel[3] , templateTitle[9] templateXLabel[0] . During that period , the compound annual growth rate of templateXValue[last] American templateTitle[1] templateTitle[4] templateTitle[2] was estimated at templateYValue[min] percent . Global templateTitle[1] MarketTraveling has increased to a great extent with the rise in urbanization and leisure activities .
generated: This statistic illustrates the compound annual growth rate of the transplants United Kingdom UK 2018/19 between transplants and transplants , 2018/19 Country . During that period , the compound annual growth rate of Northern Ireland American transplants UK United was estimated at 34 percent . Global transplants MarketTraveling has increased to a great extent with the rise in urbanization and leisure activities .

Example 515:
titleEntities: {'Subject': ['Countries'], 'Date': ['2017']}
title: Countries with the highest infant mortality rate 2017
X_Axis['Country']: ['Afghanistan', 'Somalia', 'Central_African_Republic', 'Guinea-Bissau', 'Chad', 'Niger', 'Burkina_Faso', 'Nigeria', 'Mali', 'Sierra_Leone', 'Democratic_Republic_of_Congo', 'Angola', 'Mozambique', 'Equatorial_Guinea', 'South_Sudan', 'Zambia', 'Gambia', 'Comoros', 'Burundi', 'Uganda']
Y_Axis['Child', 'deaths', 'in', 'the', 'first', 'year', 'of', 'life', 'per', '1,000', 'live', 'births']: ['110.6', '94.8', '86.3', '85.7', '85.4', '81.1', '72.2', '69.8', '69.5', '68.4', '68.2', '67.6', '65.9', '65.2', '62.8', '61.1', '60.2', '60.0', '58.8', '56.1']

gold: This statistic shows the 20 countries* with the highest infant mortality rate in 2017 . An estimated 110.6 infants per 1,000 live births died in the first year of life in Afghanistan in 2017 . Infant and child mortality Infant mortality usually refers to the death of children younger than one year .
gold_template: This statistic shows the 20 countries* templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleDate[0] . An estimated templateYValue[max] infants templateYLabel[5] 1,000 templateYLabel[7] templateYLabel[8] died in the templateYLabel[2] templateYLabel[3] of templateYLabel[4] in templateXValue[0] in templateTitleDate[0] . templateTitle[3] and templateYLabel[0] templateTitle[4] templateTitle[3] templateTitle[4] usually refers to the death of children younger than one templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateYLabel[4] since the founding of the Foundation in 1901 until templateTitleDate[0] , templateTitle[4] templateTitle[5] of the respective winner . Between 1901 and templateTitleDate[0] , templateYValue[max] Americans have been awarded with the templateYLabel[1] templateYLabel[2] in templateYLabel[4] .
generated: The statistic shows the Child of deaths first year in life since the founding of the Foundation in 1901 until 2017 , rate 2017 of the respective winner . Between 1901 and 2017 , 110.6 Americans have been awarded with the deaths first in life .

Example 516:
titleEntities: {'Subject': ['Hollywood'], 'Date': ['2016']}
title: Stereotyping of ethnic minorities in Hollywood movies 2016
X_Axis['Response']: ['Do_a_good_job_of_portraying_racial_minorities', 'Give_into_stereotypes_when_portraying_racial_minorities', 'Not_sure']
Y_Axis['Share', 'of', 'respondents']: ['38', '37', '24']

gold: The survey shows result of survey on stereotyping of racial minorities in Hollywood movies in the United States as of February 2016 . Durign the survey , 38 of respondents stated Hollywood movies did a good job of potraying racial minorities .
gold_template: The survey shows result of survey on templateTitle[0] of templateXValue[0] in templateTitleSubject[0] templateTitle[4] in the country as of February templateTitleDate[0] . Durign the survey , templateYValue[max] of templateYLabel[1] stated templateTitleSubject[0] templateTitle[4] did a templateXValue[0] of potraying templateXValue[0] .

generated_template: 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] templateXValue[0] the templateXValue[0] 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] templateXValue[1] templateXValue[1] for an templateTitleSubject[0] templateXValue[0] .
generated: The statistic illustrates the answers to the following survey question : `` The minorities Do a good job of portraying racial minorities 2016 will probably cost a thousand euros . Not sure you willing to pay that ? '' As of 2016 , roughly 20 percent of the respondents said to Do a good job of portraying racial minorities the Do a good job of portraying racial minorities from Hollywood when it is released , even if it 2016 them a thousand euros . However , more than half of the respondents said the price is Give into stereotypes when portraying racial minorities for an Hollywood 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] 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 Russia 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 Russia amounted to 1.42 children 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 presents the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] of templateTitleSubject[0] generated templateYLabel[0] templateYLabel[1] worth approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic presents the Operating income of the King of King from 2010 to 2018 . In 2018 , the King of King generated Operating income worth approximately 750.0 million U.S. 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: Of all the players to appear in the templateTitleSubject[0] competition since templateTitleDate[0] , Porto Goalkeeper templateXValue[0] templateXValue[0] has the highest number of templateTitle[4] , taking the pitch templateYValue[max] times . templateTitle[3] of these templateTitle[4] have been for Real Madrid . templateXValue[0] templateXValue[0] is followed templateTitle[5] templateXValue[1] templateXValue[1] who made templateYValue[1] templateTitle[4] in the templateTitleSubject[0] .
generated: Of all the players to appear in the U.S. competition since 2017 , Porto Goalkeeper United States  has the highest number of enrolled , taking the pitch 1565168 times . children of these enrolled have been for Real Madrid . United States  is followed pre-K California who made 241859 enrolled in the U.S. .

Example 520:
titleEntities: {'Subject': ['UK'], 'Date': ['2019']}
title: UK : real estate prime office rent prices in selected cities Q3 2019
X_Axis['City']: ['Reading', 'Manchester', 'Bristol', 'Edinburgh', 'Birmingham', 'Glasgow', 'Leeds', 'Cardiff', 'Newcastle']
Y_Axis['Price', 'per', 'square', 'meter', 'in', 'euros']: ['468', '444', '438', '425', '413', '389', '365', '304', '298']

gold: This statistic displays the most expensive cities for prime office rents in the United Kingdom ( UK ) as of September 2019 , excluding London . As of September 2019 , it can be seen that Reading was the most expensive location within the UK for prime office rents outside of London , with an average price reaching 468 euros per square meter per year . This was followed by Manchester , Bristol and Edinburgh .
gold_template: This statistic displays the most expensive templateTitle[8] for templateTitle[3] templateTitle[4] rents in the United Kingdom ( templateTitleSubject[0] ) as of September templateTitleDate[0] , excluding London . As of September templateTitleDate[0] , it can be seen that templateXValue[0] was the most expensive location within the templateTitleSubject[0] for templateTitle[3] templateTitle[4] rents outside of London , with an average templateYLabel[0] reaching templateYValue[max] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[1] year . This was followed by templateXValue[1] , templateXValue[2] and templateXValue[3] .

generated_template: This statistic illustrates the templateYLabel[0] of templateTitle[2] templateYLabel[1] made templateTitle[0] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitle[7] templateTitle[8] , templateTitle[9] templateTitle[2] templateTitle[10] . During this month , internet templateYLabel[1] accounted for templateYValue[3] percent of templateXValue[3] templateXLabel[0] templateYLabel[1] .
generated: This statistic illustrates the Price of estate per made UK in the UK ( UK ) in selected cities , Q3 estate 2019 . During this month , internet per accounted for 425 percent of Edinburgh City per .

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: The templateTitle[0] engine templateTitle[1] of templateTitle[2] registered in templateTitleSubject[0] 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 Number engine drive-in of cinema registered in U.S. increased between 1995 and 2019 . By 2019 , sites with a volume of 593 Number centimeters had become the norm , an increase of nearly 40 percent since the beginning of the reporting period . Most common engine drive-in As of 2019 , most registered cinema had an engine drive-in either between 51 and 125 Number centimeters or greater than 1,000 Number centimeters .

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: While the standard image of the nuclear family with two parents and 2.5 children has persisted in the American imagination , the templateYLabel[0] of templateYLabel[1] in the templateTitle[3] has steadily been decreasing since templateXValue[min] , with about templateYValue[min] templateYLabel[2] babies born in templateXValue[max] . In templateXValue[min] , this figure was templateYValue[28] templateYLabel[2] . Birth and replacement rates A country 's birth rate is defined as the templateYLabel[0] of live templateYLabel[1] per 1,000 inhabitants , and it is this particularly important templateYLabel[0] that has been decreasing over the past few decades .
generated: While the standard image of the nuclear family with two parents and 2.5 children has persisted in the American imagination , the Births of per in the U.S. has steadily been decreasing since 1990 , with about 14.8 1,000 babies born in 2018 . In 1990 , this figure was 26.7 1,000 . Birth and replacement rates A country 's birth rate is defined as the Births of live per 1,000 inhabitants , and it is this particularly important Births that has been decreasing over the past few decades .

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: In templateXValue[max] , templateYValue[max] percent of templateYLabel[1] were committed templateTitle[1] templateTitle[2] in the templateTitle[3] . While this figure has remained relatively steady since templateXValue[min] , it has been increasing since templateXValue[2] . Firearms in the templateTitle[3] Firearms in the templateTitle[3] have become a large part of American culture .
generated: In 2018 , 76 percent of households were committed internet access in the Greece . While this figure has remained relatively steady since 2007 , it has been increasing since 2016 . Firearms in the Greece Firearms in the Greece have become a large part of American culture .

Example 524:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2011', '2018']}
title: Number of enrolled university students in South Korea 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Enrolled', 'university', 'students', 'in', 'millions']: ['2.03', '2.05', '2.08', '2.11', '2.13', '2.13', '2.1', '2.07']

gold: This statistic illustrates the number of students enrolled in universities in South Korea from 2011 to 2018 . In 2018 , there were approximately 2.03 million students enrolled in universities in South Korea .
gold_template: This statistic illustrates the templateTitle[0] of templateYLabel[2] templateYLabel[0] in universities in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[min] templateYLabel[3] templateYLabel[2] templateYLabel[0] in universities in templateTitleSubject[0] .

generated_template: In templateXValue[max] , the templateTitle[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 2018 , the enrolled of students South in South Korea amounted to about 2.03 university students millions , all types included . The Number realized Enrolled of students South remained fairly steady throughout the years until 2015 , when it decreased to below 2.03 university students millions . students South The Number Enrolled serves as an indicator for a variety of different selling prices on the South market , gathering all Enrolled ranges of students wines purchased in South Korea .

Example 525:
titleEntities: {'Subject': ['Worlds'], 'Date': ['2018']}
title: Worlds ' most dangerous cities , by murder rate 2018
X_Axis['City']: ['Tijuana_-_Mexico', 'Acapulco_-_Mexico', 'Caracas_-_Venezuela', 'Ciudad_Victoria_-_Mexico', 'Ciudad_Juarez_-_Mexico', 'Irapuato_-_Mexico', 'Ciudad_Guayana_-_Venezuela', 'Natal_-_Brazil', 'Fortaleza_-_Brazil', 'Ciudad_Bolivar_-_Venezuela', 'Cape_Town_-_South_Africa', 'Belem_-_Brazil', 'Cancun_-_Mexico', 'Feira_de_Santana_-_Brazil', 'St._Louis_Missouri_-_U.S.', 'Culiacan_-_Mexico', 'Barquisimeto_-_Venezuela', 'Uruapan_-_Mexico', 'Kingston_-_Jamaica', 'Ciudad_Obregón_-_Mexico', 'Maceio_-_Brazil', 'Vitoria_da_Conquista_-_Brazil', 'Baltimore_Maryland_-_U.S.', 'San_Salvador_-_El_Salvador', 'Aracaju_-_Brazil', 'Coatzacoalcos_-_Mexico', 'Palmira_-_Colombia', 'Maturin_-_Venezuela', 'Salvador_-_Brazil', 'Macapa_-_Brazil', 'Cali_-_Colombia', 'Celaya_-_Mexico', 'San_Pedro_Sula_-_Honduras', 'Ensenada_-_Mexico', 'Campos_dos_Goytacazes_-_Brazil', 'Tepic_-_Mexico', 'Manaus_-_Brazil', 'Guatemala_City_-_Guatemala', 'Recife_-_Brazil', 'Distrito_Central_-_Honduras', 'San_Juan_-_Puerto_Rico', 'Valencia_-_Venezuela', 'Reynosa_-_Mexico', 'João_Pessoa_-_Brazil', 'Nelson_Mandela_Bay_-_South_Africa', 'Detroit_Michigan_-_U.S.', 'Durban_-_South_Africa', 'Teresina_-_Brazil', 'Chihuahua_-_Mexico', 'New_Orleans_Louisiana_-_U.S.']
Y_Axis['Murder', 'rate', 'per', '100,000', 'inhabitants']: ['138.26', '110.5', '99.98', '86.01', '85.56', '81.44', '78.3', '74.67', '69.15', '69.09', '66.36', '65.31', '64.46', '63.29', '60.59', '60.52', '56.67', '54.52', '54.12', '52.09', '51.46', '50.75', '50.52', '50.32', '48.77', '48.35', '47.97', '47.24', '47.23', '47.2', '47.03', '46.99', '46.67', '46.6', '46.28', '44.89', '44.0', '43.73', '43.72', '43.3', '42.4', '42.36', '41.48', '41.36', '39.16', '38.78', '38.51', '37.61', '37.5', '36.87']

gold: This statistic ranks the 50 most dangerous cities of 2018 , by murder rate per 100,000 inhabitants . Tijuana 's murder rate was 138.26 for every 100,000 people living in the city . The world 's most dangerous cities The Citizens ' Council for Public Security and Criminal Justice published a ranking of the world 's most dangerous cities in 2018 , ranking cities according to the number of murders per 100,000 inhabitants that year .
gold_template: This statistic ranks the templateYValue[23] templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleDate[0] , templateTitle[5] templateYLabel[0] templateYLabel[1] templateYLabel[2] 100,000 templateYLabel[4] . templateXValue[0] 's templateYLabel[0] templateYLabel[1] was templateYValue[max] for every 100,000 people living in the templateXValue[37] . The world 's templateTitle[2] templateTitle[3] templateTitle[4] The Citizens templateTitle[1] Council for Public Security and Criminal Justice published a ranking of the world 's templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleDate[0] , ranking templateTitle[4] according to the number of murders templateYLabel[2] 100,000 templateYLabel[4] that year .

generated_template: The statistic shows the templateTitle[0] of templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[6] through the Supplemental Nutrition Assistance Program ( templateTitleSubject[0] , formerly called templateTitleSubject[0] templateTitleSubject[0] ) in templateTitleDate[0] , by templateXLabel[0] . About templateYValue[2] percent of the templateTitle[3] in templateXValue[5] is templateTitle[4] templateTitle[6] through the Supplemental Nutrition Assistance Program .
generated: The statistic shows the Worlds of Worlds dangerous cities murder through the Supplemental Nutrition Assistance Program ( Worlds , formerly called Worlds ) in 2018 , by City . About 99.98 percent of the dangerous in Irapuato - Mexico is cities murder through the Supplemental Nutrition Assistance Program .

Example 526:
titleEntities: {'Subject': ['Leading'], 'Date': ['2018']}
title: Leading internet traffic categories worldwide 2018
X_Axis['Category']: ['Video', 'Web', 'Gaming', 'Social_media', 'Content_marketplaces', 'File_sharing', 'Audio_streaming']
Y_Axis['Share', 'of', 'downstream', 'internet', 'traffic']: ['57.7', '17', '7.8', '5.1', '4.6', '2.8', '1']

gold: This statistic presents the distribution of global downstream internet traffic as of October 2018 , by category . During the measured period , video accounted for over half of downstream internet traffic volume . Within that category , Netflix was by far the market leader in terms of global video traffic .
gold_template: This statistic presents the distribution of global templateYLabel[1] templateYLabel[2] templateYLabel[3] as of October templateTitleDate[0] , by templateXLabel[0] . During the measured period , templateXValue[0] accounted for over half of templateYLabel[1] templateYLabel[2] templateYLabel[3] volume . Within that templateXLabel[0] , Netflix was by far the market leader in terms of global templateXValue[0] templateYLabel[3] .

generated_template: This statistic illustrates the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] of templateTitle[4] in the templateTitle[5] in templateTitleDate[0] templateTitle[7] templateXLabel[0] and ethnicity . In templateTitleDate[0] , templateYValue[max] percent of templateTitleSubject[0] templateYLabel[1] aged between 25 and 24 years played a templateTitle[3] templateTitle[4] .
generated: This statistic illustrates the Leading internet traffic Share of worldwide in the 2018 in 2018 Category and ethnicity . In 2018 , 57.7 percent of Leading downstream aged between 25 and 24 years played a categories worldwide .

Example 527:
titleEntities: {'Subject': ['Golf-Association'], 'Date': ['2012', '2013']}
title: Golf-Association executives ' compensation 2012 to 2013
X_Axis['Month']: ['Tim_Finchem_(PGA_Tour_commissioner_&_CEO)_2013', 'Joe_Steranka_(Former_of_America_CEO)_2013', 'Dick_Rugge_(Former_USGA_senior_director_equipment_standards)_2012', 'Tom_Wade_(PGA_Tour_global_commercial_officer)_2013', 'Charles_Zink_(PGA_Tour_co-chief_operating_officer)_2013', 'Ed_Moorhouse_(PGA_Tour_co-chief_operating_officer)_2013', 'Ron_Price_(PGA_Tour_executive_VP_CFO)_2013', 'David_Pillsbury_(PGA_Tour_executive_VP_championship_managment_&_tournament_business_affairs)_2013', 'Mike_Whan_(LPGA_Tour_commissioner)_2012', 'Ty_Votaw_(PGA_Tour_executive_VP_&_chief_of_global_communications)_2013', 'Mike_Davis_(USGA_executive_director)_2012', 'Joseph_Monahan_(PGA_Tour_executive_VP_&_chief_marketing_officer)_2013', 'David_Fay_(Former_USGA_executive_director)_2012', 'Michael_Butz_(USGA_senior_managing_director_Open_championships_&_association_relations)', 'Joe_Louis_Barrow_Jr._(World_Golf_Foundation_executive_VP_The_First_Tee_CEO)_2013', 'Bill_Calfee_(PGA_Tour_president_Web.com_Tour)_2013', 'Darrell_Crall_(PGA_of_America_COO)_2013', 'Kerry_Haigh_(PGA_of_America_chief_championships_officer)_2013', 'Rick_Anderson_(PGA_Tour_executive_VP_television_and_digital)_2013', 'James_Pazder_(PGA_Tour_executive_VP_&_chief_of_operations)_2013', 'Mike_Stevens_(PGA_Tour_president_Champions_Tour)_2013', 'Stephen_Mona_(World_Golf_Foundation_CEO)_2013', 'Mark_Russell_(PGA_Tour_VP_rules_and_competitions)_2013', 'Stephen_Hamblin_(American_Junior_Golf_Assosiation_executive_director)_2012', 'Joseph_Beditz_(National_Golf_Foundation_president/CEO)_2012']
Y_Axis['Compensations', 'in', 'million', 'U.S.', 'dollars']: ['4.58', '2.59', '1.8', '1.17', '1.16', '1.13', '1.06', '0.97', '0.89', '0.79', '0.77', '0.73', '0.65', '0.64', '0.62', '0.55', '0.54', '0.54', '0.54', '0.51', '0.49', '0.45', '0.45', '0.43', '0.25']

gold: The graph depicts the earnings of 25 golf association executives in 2012 and 2013 . Tim Finchem , PGA Tour commissioner and CEO , tops the earnings with an amount of 4.58 million U.S. dollars .
gold_template: The graph depicts the earnings of 25 templateXValue[14] templateXValue[13] templateTitle[1] in templateXValue[2] and templateXValue[0] . templateXValue[0] , PGA templateXValue[0] and CEO , tops the earnings with an amount of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic illustrates the templateYLabel[0] of templateTitle[2] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) in templateTitleDate[0] and templateTitleDate[1] . The templateYLabel[0] of templateTitle[2] templateTitleSubject[0] templateTitle[4] templateTitleSubject[0] was valued at approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in June templateTitleDate[1] .
generated: This statistic illustrates the Compensations of ' in the Golf-Association ( Golf-Association ) in 2012 and 2013 . The Compensations of ' Golf-Association 2012 Golf-Association was valued at approximately 4.58 million U.S. dollars in June 2013 .

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 number of metal and metal products that are produced from templateTitleSubject[0] templateTitle[1] and other low-grade residues worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitle[2] templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] templateTitle[1] amounted to templateYValue[3] percent .
generated: This statistic shows the number of metal and metal products that are produced from Denmark population and other low-grade residues worldwide from 2019 to 2029 . In 2023 , the growth Number inhabitants for Denmark population amounted to 6.02 percent .

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[0] templateTitle[2] templateYLabel[0] of different templateTitle[4] in the templateTitle[5] in templateTitleDate[0] templateTitle[7] templateXLabel[0] and ethnicity . In templateTitleDate[0] , templateYValue[max] percent of templateTitleSubject[0] templateYLabel[1] aged between 18 and 24 years played a templateTitle[3] templateTitle[4] .
generated: This statistic displays the Piracy attempted Number of different worldwide in the by in 2019 Country and ethnicity . In 2019 , 35 percent of Piracy incidents aged between 18 and 24 years played a attacks worldwide .

Example 530:
titleEntities: {'Subject': ['Frozen'], 'Date': ['2006', '2018']}
title: Frozen yogurt production in the U.S. 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Production', 'in', 'million', 'gallons']: ['50.2', '62.5', '66.93', '74.23', '66.76', '74.48', '74.0', '62.7', '50.1', '46.0', '78.6', '74.7', '66.0']

gold: This statistic shows the frozen yogurt production in the United States from 2006 to 2018 . In 2018 , about 50.2 million gallons of frozen yogurt were produced . Frozen yogurt is a frozen , low-calorie dessert , which is often served in a large variety of flavors .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] templateYLabel[1] templateYLabel[2] of templateTitle[0] templateTitle[1] were produced . templateTitle[0] templateTitle[1] is a templateTitle[0] , low-calorie dessert , which is often served in a large variety of flavors .

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateTitle[2] 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 templateYLabel[2] manufacturer of escalators and elevators generated about templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitle[5] .
generated: The statistic shows Frozen yogurt production from the fiscal Year of 2006 to the fiscal Year of 2018 . In the fiscal Year of 2018 , the gallons manufacturer of escalators and elevators generated about 50.2 million gallons in 2018 .

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: In the templateYLabel[1] many templateTitle[0] pay a portion of templateTitle[2] templateTitle[3] templateYLabel[0] for employees . As of templateXValue[min] the total annual medical templateYLabel[0] for employees was 11.7 thousand templateYLabel[1] templateYLabel[2] . That cost is expected to increase to templateYValue[max] thousand templateYLabel[1] templateYLabel[2] by templateXValue[max] .
generated: In the rate many Welsh pay a portion of elections turnout for employees . As of 1999 the total annual medical Turnout for employees was 11.7 thousand rate . That cost is expected to increase to 46.4 thousand rate by 2011 .

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[16] 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 41.7 percent in 2002 . In 2018 , 84 percent of the Chilean population acceded to users .

Example 534:
titleEntities: {'Subject': ['GDP'], 'Date': ['2017']}
title: U.S. exports , as a percentage of GDP 1990 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Exports', 'as', 'a', 'percentage', 'of', 'GDP']: ['12.06', '11.85', '12.43', '13.53', '13.54', '13.53', '13.53', '12.32', '11.01', '12.51', '11.5', '10.65', '10', '9.63', '9.04', '9.13', '9.67', '10.66', '10.27', '10.48', '11.08', '10.71', '10.61', '9.86', '9.52', '9.68', '9.64', '9.23']

gold: In 2017 , exports of goods and services from the United States made up just over 12 percent of its gross domestic product ( GDP ) . This is an increase from 9.23 percent of the GDP of the United States in 1990 . Trade and foreign relations The United States ' GDP is the largest in the world , clocking in at around 18.57 trillion U.S. dollars in 2018. International trade is a huge boon to the U.S. economy , both financially and regarding foreign relations .
gold_template: In templateXValue[max] , templateYLabel[0] of goods and services from the templateTitle[0] made up just over templateYValue[0] percent of its gross domestic product ( templateYLabel[2] ) . This is an increase from templateYValue[27] percent of the templateYLabel[2] of the templateTitle[0] in templateXValue[min] . Trade and foreign relations The templateTitle[0] ' templateYLabel[2] is the largest in the world , clocking in at around 18.57 trillion templateTitle[0] dollars in 2018. International trade is a huge boon to the templateTitle[0] economy , both financially and regarding foreign relations .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitleSubject[0] templateTitle[3] templateTitle[4] were living below the templateYLabel[0] line in the templateTitle[5] . templateYLabel[0] is the state of one who lacks a certain amount of material possessions or money .
generated: This statistic shows the Exports percentage of GDP 1990 in the GDP from 1990 to 2017 . In 2017 , 12.06 percent of GDP 1990 were living below the Exports line in the 2017 . Exports is the state of one who lacks a certain amount of material possessions or money .

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 illustrates the annual templateYLabel[0] templateYLabel[1] templateYLabel[2] abstractions in selected countries in templateTitleDate[0] , or the latest year available . templateXValue[1] reported templateYLabel[2] templateYLabel[3] levels of templateYValue[1] templateYLabel[4] templateYLabel[5] templateYLabel[0] templateYLabel[1] . templateYLabel[2] templateYLabel[3] templateYLabel[0] person About 70 percent of the planet 's surface is covered with templateYLabel[2] .
generated: This statistic illustrates the annual Number millionaire households abstractions in selected countries in 2014 , or the latest year available . France reported households levels of 1334066 households Number millionaire . households Number person About 70 percent of the planet 's surface is covered with households .

Example 536:
titleEntities: {'Subject': ['CVS Health'], 'Date': ['2012', '2025']}
title: CVS Health 's share of retail prescriptions filled in the U.S. 2012 to 2025
X_Axis['Year']: ['2025', '2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Share', 'of', 'retail', 'prescriptions', 'filled']: ['46.55', '44.55', '42.55', '40.55', '38.55', '36.55', '34.55', '33.55', '32.72', '30.1', '23.45', '21.63', '20.99', '17.25']

gold: This statistic depicts CVS Caremark 's share of retail prescriptions filled in the United States from 2012 to 2025 . The CVS Caremark Corporation is a U.S. drug retailing company , with three major segments : CVS/pharmacy , Caremark Pharmacy Services and MinuteClinic . CVS Caremark is headquartered in Woonsocket , Rhode Island .
gold_template: This statistic depicts templateTitleSubject[0] Caremark templateTitle[2] templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[7] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] Caremark Corporation is a templateTitle[7] drug retailing company , with three major segments : CVS/pharmacy , Caremark Pharmacy Services and MinuteClinic . templateTitleSubject[0] Caremark is headquartered in Woonsocket , Rhode Island .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[7] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in the templateTitleSubject[0] templateTitleSubject[0] is estimated to be approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Health Share retail of the share retail prescriptions filled 2012 to 2025 . In 2018 , the Health Share retail in the CVS Health is estimated to be approximately 46.55 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: This statistic illustrates the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] was approximately templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . The templateTitle[2] templateTitle[3] is an informal network of banks , brokers , dealers and financial institutions which are linked electronically .
generated: This statistic illustrates the Profit of advertising spending 2011 2018 in the 's from 2011 to 2018 . In 2018 , the Profit of the advertising spending 2011 2018 in the 's was approximately 1376 million British pounds . The advertising spending is an informal network of banks , brokers , dealers and financial institutions which are linked electronically .

Example 539:
titleEntities: {'Subject': ['Births'], 'Date': ['2018', '2018']}
title: Births - number by age of mother 2018
X_Axis['Year']: ['15_to_19_years', '20_to_24_years', '25_to_29_years', '30_to_34_years', '35_to_39_years', '40_to_44_years', '45_to_50_years']
Y_Axis['Number', 'of', 'children', 'born', 'in', 'thousands']: ['469', '3268', '9668', '15269', '19902', '20145', '25038']

gold: This statistic displays the total number of births in the United States as of June 2018 , by age of mother . In 2018 , women aged between 15 and 19 years gave birth to 469,000 children in the United States .
gold_template: This statistic displays the total templateYLabel[0] of templateTitleSubject[0] in the country as of June templateTitleDate[0] , templateTitle[2] templateTitle[3] of templateTitle[4] . In templateTitleDate[0] , women aged between templateXValue[0] and templateXValue[0] gave birth to templateYValue[min] templateYLabel[1] in the country .

generated_template: This statistic shows the predicted templateTitle[4] of templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) from templateTitleDate[0] to templateTitleDate[1] in British pounds ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] . The templateTitle[4] of templateTitle[3] is expected to increase to templateYValue[0] British pounds in templateXValue[0] .
generated: This statistic shows the predicted mother of age in the Births ( Births ) from 2018 to 2018 in British pounds ( Number ) children born . The mother of age is expected to increase to 469 British pounds in 15 to 19 years .

Example 540:
titleEntities: {'Subject': ['Orlando Magic'], 'Date': ['2001', '2019']}
title: Orlando Magic 's revenue 2001 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['244', '223', '211', '166', '143', '143', '139', '126', '140', '108', '107', '100', '92', '89', '82', '78', '80', '82']

gold: The statistic shows the revenue of the Orlando Magic franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise amounted to 244 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the Orlando Magic franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 244 million U.S. dollars .

Example 541:
titleEntities: {'Subject': ['Kickstarter'], 'Date': ['2019']}
title: Distribution of Kickstarter funding amounts raised 2019
X_Axis['Money', 'raised', '(in', 'U.S.', 'dollars)']: ['Less_than_1000', '1000_to_9999', '10000_to_19999', '20000_to_99999', '100K_to_999999', 'More_than_1M+']
Y_Axis['Number', 'of', 'projects']: ['21945', '92970', '24579', '24804', '6063', '385']

gold: The statistic shows the number of successfully funded projects on the crowdfunding platform Kickstarter as of October 2 , 2018 . It shows the number of total successfully funded projects by funds raised . As of that time , the number of successfully funded projects at Kickstarter which raised more than one million U.S. dollars amounted to 385 projects .
gold_template: The statistic shows the templateYLabel[0] of successfully funded templateYLabel[1] on the crowdfunding platform templateTitleSubject[0] as of October 2 , 2018 . It shows the templateYLabel[0] of total successfully funded templateYLabel[1] by funds templateXLabel[1] . As of that time , the templateYLabel[0] of successfully funded templateYLabel[1] at templateTitleSubject[0] which templateXLabel[1] templateXValue[last] templateXValue[0] one million templateXLabel[3] dollars amounted to templateYValue[min] templateYLabel[1] .

generated_template: This statistic shows the templateYLabel[0] of sentenced templateYLabel[1] under templateTitle[2] jurisdiction in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateTitle[5] . As of August 17 , templateTitleDate[0] , templateYValue[3] templateYLabel[1] in the templateXValue[0] templateXValue[0] were from templateXValue[last] .
generated: This statistic shows the Number of sentenced projects under funding jurisdiction in the Kickstarter in 2019 , raised 2019 . As of August 17 , 2019 , 24804 projects in the Less than 1000 were from More than 1M+ .

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: The statistic represents the total templateYLabel[0] templateYLabel[1] for templateTitle[0] templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . For templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateYLabel[3] templateYLabel[4] dollars .
generated: The statistic represents the total Consumption kilowatt for Household electricity consumption from 2000 to 2016 . For 2016 , the Household electricity consumption kilowatt amounted to 348.3 per capita dollars .

Example 544:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2019']}
title: Facebook : number of followers of popular luxury brands 2019
X_Axis['Designer', 'Brand']: ['Louis_Vuitton', 'Chanel', 'Gucci', 'Michael_Kors', 'Burberry', 'Dior', 'Dolce_&_Gabbana', 'Ralph_Lauren', 'Armani', 'Coach', 'Prada', 'Versace', 'Jimmy_Choo', 'Christian_Louboutin', 'Hermès']
Y_Axis['Followers', 'in', 'millions']: ['23.28', '21.96', '18.2', '17.94', '17.31', '16.65', '11.74', '9.16', '8.63', '7.36', '6.6', '5.37', '3.71', '3.35', '3.13']

gold: This statistic provides information on the leading luxury brands with the most followers on Facebook as of May 2019 , ranked by number of followers . According to the findings , the luxury brand Louis Vuitton had recorded in a total of 23.28 million likes on Facebook , and ranking second was Chanel with 21.96 million page likes .
gold_template: This statistic provides information on the leading templateTitle[4] templateTitle[5] with the most templateYLabel[0] on templateTitleSubject[0] as of May templateTitleDate[0] , ranked by templateTitle[1] of templateYLabel[0] . According to the findings , the templateTitle[4] templateXLabel[1] templateXValue[0] had recorded in a total of templateYValue[max] templateYLabel[1] likes on templateTitleSubject[0] , and ranking second was templateXValue[1] with templateYValue[1] templateYLabel[1] page likes .

generated_template: This statistic presents a ranking of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] from RuPaul 's templateTitle[2] Race on templateTitleSubject[0] as of March templateTitleDate[0] , sorted by the templateYLabel[0] of global templateYLabel[1] . According to the source , California-native Adora templateXValue[0] was the templateTitle[0] templateTitle[1] queen with templateYValue[max] templateYLabel[2] templateTitleSubject[0] templateYLabel[1] . Adora had also participated in the seventh season of American idol under her real name , Daniel Anthony Noriega .
generated: This statistic presents a ranking of the Facebook number followers popular from RuPaul 's followers Race on Facebook as of March 2019 , sorted by the Followers of global millions . According to the source , California-native Adora Louis Vuitton was the Facebook number queen with 23.28 millions Facebook millions . Adora had also participated in the seventh season of American idol under her real name , Daniel Anthony Noriega .

Example 545:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2017']}
title: Fairtrade food and drink sales revenue in the United Kingdom 1999 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Sales', 'in', 'million', 'GBP']: ['1720', '1608', '1572', '1612', '1710', '1553', '1253', '1064', '749', '635', '458', '285', '195', '141', '92', '63', '51', '33', '22']

gold: This statistic illustrates the sales of Fairtrade food and drink products in the United Kingdom ( UK ) from 1999 to 2017 . In 2005 , 195 million British pounds was spent on Fairtrade food and drink products . Sales rose during the period under consideration to approximately 1.72 billion British pounds in sales in 2017 .
gold_template: This statistic illustrates the templateYLabel[0] of templateTitle[0] templateTitle[1] and templateTitle[2] products in the templateTitleSubject[0] ( UK ) from templateXValue[min] to templateXValue[max] . In templateXValue[12] , templateYValue[12] templateYLabel[1] British pounds was spent on templateTitle[0] templateTitle[1] and templateTitle[2] products . templateYLabel[0] rose during the period under consideration to approximately templateYValue[max] templateYLabel[1] British pounds in templateYLabel[0] in templateXValue[max] .

generated_template: 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 Sales of sales worldwide from 1999 to 2017 . In 2017 , the total Sales from sales amounted to 635 million GBP .

Example 546:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Leading prescriptions dispensed in the U.S. diabetes market 2014
X_Axis['Medicine']: ['Metformin_HCI', 'Glimepiride', 'Metformin_ER_(G)', 'Glipizide', 'Lantus_(long-acting_insulin)', 'Lantus_SoloStar_(long-acting_insulin)', 'Januvia_(sitagliptin)', 'Glipizide_ER', 'Glyburide', 'Pioglitazone']
Y_Axis['Rx', 'dispensed', 'in', 'million', 'units']: ['59.2', '12.7', '12.5', '10.4', '9.6', '9.5', '8.8', '7.1', '6.5', '5.5']

gold: The statistic shows the leading prescriptions dispensed in the U.S. diabetes market in 2014 . In that year , Metformin HCI was the leading diabetes prescription dispensed in the United States at 59.2 million units .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] templateTitle[4] templateTitle[5] in templateTitleDate[0] . In that year , templateXValue[0] was the templateTitle[0] templateTitle[4] prescription templateYLabel[1] in the templateTitle[3] at templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] templateTitle[3] mudslides in the period from 1900 to 2016* . The templateXLabel[0] in templateXValue[0] in 1983 templateTitle[2] an templateYLabel[0] loss of almost templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Rx dispensed U.S. mudslides in the period from 1900 to 2016* . The Medicine in Metformin HCI in 1983 dispensed an Rx loss of almost 59.2 million units .

Example 547:
titleEntities: {'Subject': ['Europe'], 'Date': ['2018']}
title: Direct investment position of the U.S. in Europe 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Direct', 'investments', 'in', 'trillion', 'U.S.', 'dollars']: ['3.61', '3.55', '3.31', '3.08', '2.9', '2.6', '2.45', '2.25', '2.03', '1.99', '1.84', '1.68', '1.4', '1.21', '1.18', '0.98', '0.86', '0.77', '0.69']

gold: This statistic shows the direct investment position of the United States in Europe from 2000 to 2018 , on a historical-cost basis . In 2018 , the U.S. investments made in Europe were valued at approximately 3.61 trillion U.S. dollars . The total direct position of the United States abroad amounted to 5.95 trillion U.S. dollars in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitleSubject[0] were valued at approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The total templateYLabel[0] templateTitle[2] of the templateTitle[3] abroad amounted to 5.95 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: In templateXValue[max] , foreign templateYLabel[0] templateTitle[1] ( FDI ) from the templateTitle[3] to other countries amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Foreign templateYLabel[0] templateTitle[1] reflects the ownership of business from templateYValue[17] country in another country . It differs from a traditional templateTitle[1] in companies located templateTitle[4] by the ownership factor in case of FDI .
generated: In 2018 , foreign Direct investment ( FDI ) from the U.S. to other countries amounted to 3.61 trillion U.S. dollars . Foreign Direct investment reflects the ownership of business from 0.77 country in another country . It differs from a traditional investment in companies located Europe by the ownership factor in case of FDI .

Example 548:
titleEntities: {'Subject': ['Carolina Panthers', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Carolina Panthers ( NFL ) 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['2400', '2300', '2300', '2075', '1560', '1250', '1057', '1048', '1002', '1037', '1049', '1040', '956', '936', '878', '760', '642', '609']

gold: This graph depicts the franchise value of the Carolina Panthers from the National Football League from 2002 to 2019 . In 2019 , the franchise value came to 2.4 billion U.S. dollars.The Carolina Panthers are owned by David Tepper , who bought the franchise for about 2.3 billion U.S. dollars in 2018 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateYLabel[2] templateYLabel[3] dollars.The templateTitleSubject[0] are owned by David Tepper , who bought the templateYLabel[0] for about templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[1] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to around templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] are owned by 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 Carolina Panthers from 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 Robert Wood Johnson IV , who bought the Franchise for 635 million U.S. dollars in 2000 .

Example 549:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019', '2050']}
title: U.S. production of energy from biomass forecast 2019 to 2050
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2019']
Y_Axis['Production', 'in', 'quadrillion', 'Btu']: ['5.54', '5.39', '5.27', '5.2', '5.13', '4.96', '4.74', '4.82']

gold: This statistic gives outlook figures on the production of biomass energy between 2019 and 2050 . In 2050 , U.S. biomass energy production is forecast to increase to around 5.54 quadrillion British thermal units .
gold_template: This statistic gives outlook figures on the templateYLabel[0] of templateTitle[4] templateTitle[2] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[4] templateTitle[2] templateYLabel[0] is templateTitle[5] to increase to around templateYValue[max] templateYLabel[1] British thermal units .

generated_template: This statistic shows the estimated templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , in templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[min] , the templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] is estimated to be around templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the estimated Production quadrillion of U.S. production energy in the U.S. from 2019 to 2050 , in Btu . In 2019 , the Production quadrillion of production energy is estimated to be around 4.74 Btu .

Example 550:
titleEntities: {'Subject': ['China'], 'Date': ['2008', '2015']}
title: Market value of honey in China based on sale price 2008 to 2015
X_Axis['Year']: ['2015', '2011', '2010', '2009', '2008']
Y_Axis['Market', 'value', 'in', 'million', 'U.S.', 'dollars']: ['553.6', '419.6', '380.8', '348.2', '328.7']

gold: The statistic shows the market value of honey in China between 2008 and 2010 , including a forecast for 2015 , based on sales price .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] in templateTitleSubject[0] between templateXValue[min] and templateXValue[2] , including a forecast for templateXValue[max] , templateTitle[4] on sales templateTitle[6] .

generated_template: The statistic illustrates 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 illustrates the China based million rate of the China Market value from 2008 to 2015 . The Market value is expected to grow by 380.8 percent from 2009 to 2010 .

Example 551:
titleEntities: {'Subject': ['Photo'], 'Date': ['2013']}
title: Photo sharing sites : daily upload market share 2013
X_Axis['Platform']: ['Snapchat', 'Facebook', 'Instagram', 'Flickr']
Y_Axis['Share', 'of', 'uploads']: ['49', '43', '7', '1']

gold: This statistic presents the four most popular photo sharing sites as of November 2013 , sorted by share of daily photo uploads . During that month , Instagram accounted for seven percent of daily photo uploads .
gold_template: This statistic presents the four most popular templateTitleSubject[0] templateTitle[1] templateTitle[2] as of November templateTitleDate[0] , sorted by templateYLabel[0] of templateTitle[3] templateTitleSubject[0] templateYLabel[1] . During that month , templateXValue[2] accounted for templateYValue[2] percent of templateTitle[3] templateTitleSubject[0] templateYLabel[1] .

generated_template: The statistic illustrates the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] in templateTitleDate[0] . templateYValue[max] percent of the respondents with an annual templateXLabel[0] templateXLabel[1] of greater than 100,000 templateTitleSubject[0] dollars templateYLabel[0] a templateYLabel[1] .
generated: The statistic illustrates the Photo of sharing sites daily upload in the market in 2013 . 49 percent of the respondents with an annual Platform of greater than 100,000 Photo dollars Share a uploads .

Example 552:
titleEntities: {'Subject': ['Nokia'], 'Date': ['2019']}
title: Nokia 's net sales 1999 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Net', 'sales', 'in', 'billion', 'euros']: ['23.32', '22.56', '23.15', '23.64', '12.5', '11.76', '12.71', '30.18', '38.66', '42.45', '40.98', '50.71', '51.06', '41.12', '34.19', '29.37', '29.53', '30.02', '31.19', '30.38', '19.77']

gold: In 2018 , Nokia had 22.5 billion euros in net sales , which is a small decrease from the year before . In the past decade , the company has made several directional shifts from handheld devices to network infrastructure in order to keep up with the quickly evolving electronics and telecommunications industry . Focus on B2B markets and products After selling its mobile and devices segment to Microsoft in 2014 , Nokia has focused on its network infrastructure business .
gold_template: In templateXValue[1] , templateTitleSubject[0] had 22.5 templateYLabel[2] templateYLabel[3] in templateYLabel[0] templateYLabel[1] , which is a small decrease from the templateXLabel[0] before . In the past decade , the company has made several directional shifts from handheld devices to network infrastructure in order to keep up with the quickly evolving electronics and telecommunications industry . Focus on B2B markets and products After selling its mobile and devices segment to Microsoft in templateXValue[5] , templateTitleSubject[0] has focused on its network infrastructure business .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of this templateTitle[5] came to around templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateYLabel[3] templateTitle[4] templateTitle[5] The templateTitle[4] templateTitle[5] in the templateTitle[3] is a huge sector – especially when oil and gas extraction is included .
generated: This statistic shows the Net sales of the sales 1999 2019 from 1999 to 2019 . In 2019 , the Net sales of this 2019 came to around 23.32 billion euros . euros 1999 2019 The 1999 2019 in the sales is a huge sector – especially when oil and gas extraction is included .

Example 553:
titleEntities: {'Subject': ['Europe'], 'Date': ['2017', '2017']}
title: Leading food and beverage retailers of Europe 2017 , based on revenue
X_Axis['Company', '(Country', 'of', 'origin)']: ['Schwarz_Unternehmenstreuhand_KG_(Germany)', 'Aldi_Einkauf_GmbH_&_Co._oHG_(Germany)', 'Tesco_PLC_(UK)', 'Ahold_Delhaize_(formerly_Koninklijke_Ahold_N.V._and_Delhaize_Group_SA_[Netherlands])', 'Auchan_Holding_SA_(France)', 'Edeka_Group_(Germany)', 'Rewe_Combine_(Germany)', 'Casino_Guichard-Perrachon_S.A._(France)', 'Centres_Distributeurs_E._Leclerc_(France)_', 'Metro_AG_(Germany)', 'The_IKEA_Group_(INGKA_Holding_B.V.)_(Netherlands)', 'J_Sainsbury_plc_(UK)', 'LVMH_Moët_Hennessy-Louis_Vuitton_S.A._(France)', 'ITM_Developpement_International_(Intermarche;_France)_', 'Inditex_S.A._(Spain)', 'Migros-Genossenschafts_Bund_(Switzerland)_', 'Ceconomy_AG_(Germany)', 'Mercadona_SA_(Spain)', 'Coop_Group_(Switzerland)_', 'Wm_Morrison_Supermarkets_PLC_(UK)']
Y_Axis['Billion', 'U.S.', 'dollars']: ['111.77', '98.29', '73.96', '72.31', '58.61', '57.48', '49.71', '42.6', '41.54', '40.96', '37.43', '36.6', '33.29', '31.85', '28.89', '24.53', '24.43', '23.68', '22.52', '22.43']

gold: In 2018 , the German based Schwarz Gruppe was the leading food and beverage retailer from Europe and generated 111.77 billion U.S. dollars in revenue . The second largest retailer was also German . Aldi Einkauf GmbH & Ko .
gold_template: In 2018 , the German templateTitle[6] templateXValue[0] Gruppe was the templateTitle[0] templateTitle[1] and templateTitle[2] retailer from templateTitleSubject[0] and generated templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitle[7] . The second largest retailer was also German . templateXValue[1] GmbH templateXValue[1] Ko .

generated_template: The statistic shows a ranking of the templateTitle[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[0] ) in templateTitleDate[0] . In templateTitleDate[0] , templateXValue[13] ranked 18th templateTitle[1] an estimated templateYLabel[0] of about templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] , making it the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitleDate[0] .
generated: The statistic shows a ranking of the Leading food the beverage retailers Europe 2017 ( Billion ) in 2017 . In 2017 , ITM Developpement International (Intermarche; France)  ranked 18th food an estimated Billion of about 98.29 U.S. dollars , making it the beverage retailers Europe 2017 .

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: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of this templateTitle[5] came to around templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateYLabel[3] templateTitle[4] templateTitle[5] The templateTitle[4] templateTitle[5] in the templateTitle[3] is a huge sector – especially when oil and gas extraction is included .
generated: This statistic shows the Number children of the numbers U.S. 1970 from 1970 to 2018 . In 2018 , the Number children of this 1970 came to around 8.74 enrolled (in millions) . (in U.S. 1970 The U.S. 1970 in the numbers is a huge sector – especially when oil and gas extraction is included .

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'], '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] templateTitle[1] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] amounted to templateYValue[max] 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 illustrates the annual templateYLabel[0] templateYLabel[1] templateYLabel[2] abstractions in selected countries in templateTitleDate[0] , or the latest year available . templateXValue[1] reported templateYLabel[2] templateYLabel[3] levels of templateYValue[1] templateYLabel[4] templateYLabel[5] templateYLabel[0] templateYLabel[1] . templateYLabel[2] templateYLabel[3] templateYLabel[0] person About 70 percent of the planet 's surface is covered with templateYLabel[2] .
generated: This statistic illustrates the annual Average wealth per abstractions in selected countries in 2014 , or the latest year available . Switzerland reported per adult levels of 394917 adult Average wealth . per adult Average person About 70 percent of the planet 's surface is covered with per .

Example 559:
titleEntities: {'Subject': ['Hays'], 'Date': ['2007', '2019']}
title: Revenue of Hays worldwide 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'million', 'GBP']: ['1129.7', '1072.8', '954.6', '810.3', '764.2', '724.9', '719.0', '734.0', '672.1', '557.7', '670.8', '786.8', '633.6']

gold: This statistic shows the revenue of Hays worldwide from 2007 to 2019 . In 2019 , the UK-based recruitment specialist Hays generated over 1.1 billion British pounds in revenue worldwide , up from one billion the previous year .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the UK-based recruitment specialist templateTitleSubject[0] generated over templateYValue[max] templateYLabel[1] British pounds in templateYLabel[0] templateTitle[2] , up from templateYValue[max] templateYLabel[1] the previous templateXLabel[0] .

generated_template: British online travel retailer templateTitleSubject[0] accounted for a templateYLabel[0] templateYLabel[1] of approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] , down from the previous templateXLabel[0] 's total of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . A global player in the hotel industry With nearly seven thousand hotels around the world , Marriot templateTitleSubject[0] was one of the largest hotel chains templateTitle[4] in templateXValue[max] . It also ranked ahead of other large hotel chains such as Hilton templateTitle[4] , Hyatt Hotel , and Host Hotels & Resorts in terms of revenue in templateXValue[1] .
generated: British online travel retailer Hays accounted for a Revenue million of approximately 1129.7 GBP in 2019 , down from the previous Year 's total of 1129.7 GBP . A global player in the hotel industry With nearly seven thousand hotels around the world , Marriot Hays was one of the largest hotel chains 2019 in 2019 . It also ranked ahead of other large hotel chains such as Hilton 2019 , Hyatt Hotel , and Host Hotels & Resorts in terms of revenue in 2018 .

Example 560:
titleEntities: {'Subject': ['Engie'], 'Date': ['2018']}
title: Engie - revenue by region 2018
X_Axis['Country']: ['France', 'Other_EU_countries', 'Belgium', 'Asia_Middle_East_and_Oceania', 'South_America', 'North_America', 'Other_European_countries', 'Africa']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['24.98', '15.45', '5.96', '4.94', '4.2', '3.87', '0.82', '0.39']

gold: This statistic represents Engie 's revenue in the fiscal year of 2018 , by region . The French multinational energy company generated a revenue of around six billion euros in its Belgium segment . The company was formed by the merger of Gaz de France and Suez to GDF Suez and officially changed its name to Engie in April 2015 .
gold_template: This statistic represents templateTitleSubject[0] 's templateYLabel[0] in the fiscal year of templateTitleDate[0] , templateTitle[2] templateTitle[3] . The French multinational energy company generated a templateYLabel[0] of around templateYValue[2] templateYLabel[1] templateYLabel[2] in its templateXValue[2] segment . The company was formed templateTitle[2] the merger of Gaz de templateXValue[0] and Suez to GDF Suez and officially changed its name to templateTitleSubject[0] in April 2015 .

generated_template: This statistic shows the projected templateYLabel[0] starting salaries templateTitle[3] templateTitleSubject[0] templateTitle[5] templateTitle[6] graduates in templateTitleDate[0] in the templateXValue[0] templateXValue[0] , templateTitle[8] . In templateTitleDate[0] , templateTitleSubject[0] templateTitle[5] templateTitle[6] graduates earning templateXValue[0] degrees are projected to have an templateYLabel[0] starting templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the projected Revenue starting salaries region Engie 2018 graduates in 2018 in the France , 2018 . In 2018 , Engie 2018 graduates earning France degrees are projected to have an Revenue starting billion of 24.98 euros .

Example 561:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1982', '2020']}
title: Mass shootings in the U.S. 1982 to 2020
X_Axis['Year']: ['1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020']
Y_Axis['Total', 'number', 'of', 'mass', 'shootings']: ['1', '0', '2', '0', '1', '1', '1', '2', '1', '3', '2', '4', '1', '1', '1', '2', '3', '5', '1', '1', '0', '1', '1', '2', '3', '4', '3', '4', '1', '3', '7', '5', '4', '7', '6', '11', '12', '10', '1']

gold: As of February 26 , there was one mass shootings in the United States in 2020 . This is compared to one mass shooting in 1982 , one in 2000 , and 12 mass shootings in 2018 . School shootings The United States sees the most school shootings in the world .
gold_template: As of February 26 , there was templateYValue[0] templateYLabel[2] templateYLabel[3] in the templateTitle[2] in templateXValue[max] . This is compared to templateYValue[0] templateYLabel[2] shooting in templateXValue[min] , templateYValue[0] in templateXValue[18] , and templateYValue[max] templateYLabel[2] templateYLabel[3] in templateXValue[36] . School templateYLabel[3] The templateTitle[2] sees the most school templateYLabel[3] in the world .

generated_template: Since templateXValue[min] the templateTitleSubject[0] templateTitle[2] templateTitle[3] for twins has risen significantly . As of templateXValue[max] the templateTitle[2] templateTitle[3] for twins in the templateTitleSubject[0] was templateYValue[0] templateYLabel[2] 1,000 templateYLabel[4] templateYLabel[1] . It is believed that an increase in the templateYLabel[0] templateTitle[2] templateTitle[3] may be associated with an increased use of vitro fertilization and other assisted reproductive technologies in recent years .
generated: Since 1982 the U.S. 1982 for twins has risen significantly . As of 2020 the U.S. 1982 for twins in the U.S. was 1 mass 1,000 shootings number . It is believed that an increase in the Total U.S. 1982 may be associated with an increased use of vitro fertilization and other assisted reproductive technologies in recent years .

Example 562:
titleEntities: {'Subject': ['Cineplex'], 'Date': ['2010', '2018']}
title: Attendance at Cineplex cinemas 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Attendance', 'in', 'millions']: ['69.27', '70.4', '74.6', '77.0', '73.6', '72.7', '71.2', '66.1', '67.0']

gold: The timeline presents the attendance figures at Cineplex from 2010 to 2018 . In 2018 , 69.27 million people attended movies at the Canadian movie theater chain , down from 70.4 million visitors a year earlier .
gold_template: The timeline presents the templateYLabel[0] figures at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] templateYLabel[1] people attended movies at the Canadian movie theater chain , down from templateYValue[1] templateYLabel[1] visitors a templateXLabel[0] earlier .

generated_template: This statistic represents 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 Cineplex 's Attendance and millions from the fiscal Year of 2010 to the fiscal Year of 2018 . In the fiscal Year of 2018 , the company incurred around 69.27 millions in Attendance and millions .

Example 563:
titleEntities: {'Subject': ['Angola'], 'Date': ['2019']}
title: Unemployment rate in Angola 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['7.25', '7.25', '7.14', '7.28', '7.28', '7.43', '7.45', '7.36', '7.36', '9.09', '10.61', '12.04', '14.63', '17.67', '20.53', '23.64', '23.93', '23.9', '23.12', '22.89', '20.9']

gold: This statistic shows the unemployment rate in Angola from 1999 to 2019 . In 2019 , the unemployment rate in Angola was 7.25 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[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 templateYValue[0] percent .
generated: This statistic shows the Unemployment rate in Angola from 1999 to 2019 . In 2019 , the Unemployment rate in Angola was 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] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In the templateXValue[0] season , the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] had a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Jersey sponsorship of the Aston Villa from 2015/16 (Intuit Quickbooks) to 2015/16 (Intuit Quickbooks) . In the 2015/16 (Intuit Quickbooks) season , the Aston Villa had a Jersey sponsorship of 8 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 shows the templateTitleDate[0] ranking of the top templateYValue[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[2] by online buyers in templateTitleSubject[0] . templateXValue[0] and templateXValue[1] were ranked first and second with templateYValue[max] percent and templateYValue[1] percent respectively . The search of lodging has consolidated as the main purpose for Spanish users .
generated: This statistic shows the 2014 ranking of the top 13 vandalism against businesses unreported by online buyers in England . Trivial/no loss and Lack of police engagement were ranked first and second with 43 percent and 40 percent respectively . The search of lodging has consolidated as the main purpose for Spanish users .

Example 566:
titleEntities: {'Subject': ['England', 'Wales'], 'Date': ['2005', '2019']}
title: Road deaths involving police pursuit in England and Wales from 2005 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05']
Y_Axis['Fatailities']: ['30', '17', '28', '13', '7', '10', '27', '12', '13', '19', '22', '17', '19', '32', '23']

gold: This statistic shows the number of road traffic fatalities related to police pursuits in England and Wales from 2004/05 to 2018/19 . During the period concerned , the number of road traffic fatalities related to police pursuits fluctuated , peaking in 2005/06 at 32 deaths .
gold_template: This statistic shows the number of templateTitle[0] traffic fatalities related to templateTitle[3] pursuits in templateTitleSubject[0] and templateTitleSubject[1] templateTitle[7] templateXValue[last] to templateXValue[0] . During the period concerned , the number of templateTitle[0] traffic fatalities related to templateTitle[3] pursuits fluctuated , peaking in templateXValue[13] at templateYValue[max] templateTitle[1] .

generated_template: This statistic shows the number of templateTitle[0] templateTitle[2] by templateTitle[1] in templateTitleSubject[0] and templateTitleSubject[1] from templateXValue[last] to templateXValue[0] . During this period , the number of templateTitle[0] templateTitle[2] by templateTitle[1] fluctuated , peaking in templateXValue[2] at templateYValue[max] templateTitle[2] . By templateXValue[0] it fell down to templateYValue[0] templateTitle[2] .
generated: This statistic shows the number of Road involving by deaths in England and Wales from 2004/05 to 2018/19 . During this period , the number of Road involving by deaths fluctuated , peaking in 2016/17 at 32 involving . By 2018/19 it fell down to 30 involving .

Example 567:
titleEntities: {'Subject': ['Burger King'], 'Date': ['2011', '2014']}
title: Burger King 's EBITDA margin worldwide 2011 to 2014
X_Axis['Year']: ['2014', '2013', '2012', '2011']
Y_Axis['EBITDA', 'margin']: ['16.71', '51.3', '27', '21.3']

gold: This statistic shows Burger King 's EBITDA margin worldwide from 2011 to 2014 . Between 2012 and 2013 fast food chain Burger King 's earnings before interest , taxes , depreciation and amortization increased by 51.3 percent .
gold_template: This statistic shows templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] templateTitle[5] from templateXValue[min] to templateXValue[max] . Between templateXValue[2] and templateXValue[1] fast food chain templateTitleSubject[0] 's earnings before interest , taxes , depreciation and amortization increased by templateYValue[max] percent .

generated_template: The graph shows data on templateTitle[3] templateTitle[4] of Suzanne Collins 's trilogy 'The templateTitleSubject[0] templateTitleSubject[0] ' in the country from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[2] sold templateYValue[0] templateYLabel[3] copies .
generated: The graph shows data on EBITDA margin of Suzanne Collins 's trilogy 'The Burger King ' in the country from 2011 to 2014 . In 2011 , the 's sold 16.71 margin copies .

Example 568:
titleEntities: {'Subject': ['NBA'], 'Date': ['2018/19', '2018/19']}
title: Gate receipts of the Golden State Warriors ( NBA ) 2018/19
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11']
Y_Axis['Gate', 'receipts', 'in', 'million', 'U.S.', 'dollars']: ['178', '164', '143', '134', '77', '55', '50', '31', '41']

gold: The statistic depicts the gate receipts/ticket sales of the Golden State Warriors , franchise of the National Basketball Association , from 2010/11 to 2018/19 . In the 2018/19 season , the gate receipts of the Golden State Warriors were at 178 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] receipts/ticket sales of the templateTitleSubject[0] Warriors , franchise of the National Basketball Association , from 2010/11 to templateTitle[6] . In the templateTitle[6] season , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Warriors were at templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] games from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[0] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[min] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Gate receipts million of the NBA games from the 10/11 season to the 18/19 season . In the 18/19 season , the Gate receipts million was at 31 U.S. dollars .

Example 569:
titleEntities: {'Subject': ['Subaru', 'United Kingdom', 'UK'], 'Date': ['2019']}
title: Subaru car sales in the United Kingdom ( UK ) 2016 to 2019
X_Axis['Month']: ['Dec_19', 'Nov_19', 'Oct_19', 'Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16']
Y_Axis['Units', 'sold']: ['708', '157', '88', '526', '52', '103', '244', '138', '101', '632', '74', '174', '193', '145', '135', '829', '100', '112', '265', '231', '153', '761', '62', '155', '246', '216', '99', '510', '44', '152', '202', '155', '123', '706', '48', '178', '330', '219', '256', '762', '69', '148']

gold: This statistic shows the monthly amount of cars sold by Subaru in the United Kingdom ( UK ) between July 2016 and December 2019 . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December 2019 , 708 new Subaru cars had been sold .
gold_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between July templateTitleDate[0] and December templateTitleDate[1] . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In December templateTitleDate[1] , templateYValue[0] new templateTitleSubject[0] cars had been templateYLabel[1] .

generated_template: This statistic shows the number of cars templateYLabel[1] by templateTitleSubject[0] in the templateTitleSubject[1] templateTitleSubject[1] ( templateTitleSubject[2] ) between July templateTitleDate[0] and December templateTitleDate[1] . templateTitleSubject[0] is a British company and subsidiary of the Malaysian automotive company Proton since the bankruptcy of its former owner Romano Artioli in 1996 . templateTitle[2] of the British brand peaked at templateYValue[max] templateYLabel[0] in August templateTitleDate[0] .
generated: This statistic shows the number of cars sold by Subaru in the United Kingdom ( UK ) between July 2019 and December 2019 . Subaru is a British company and subsidiary of the Malaysian automotive company Proton since the bankruptcy of its former owner Romano Artioli in 1996 . sales of the British brand peaked at 829 Units in August 2019 .

Example 570:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2008', '2018']}
title: Number of butcher shops and meat retailers in the United Kingdom ( UK ) 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'enterprises']: ['5690', '5864', '5929', '5874', '5940', '6056', '6134', '6220', '6283', '6399', '6633']

gold: Between 2008 and 2018 , the number of stores that specialize in the sales of meat has been shrinking In the United Kingdom . During this period , the number of meat specialty stores has seen a decrease of almost 943 . Of these stores , approximately 1.75 thousand generated a turnover between one hundred thousand and 250 thousand British pounds in 2018 .
gold_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] of stores that specialize in the sales of templateTitle[3] has been shrinking In the templateTitleSubject[0] . During this period , the templateYLabel[0] of templateTitle[3] specialty stores has seen a decrease of almost 943 . Of these stores , approximately 1.75 thousand generated a turnover between one hundred thousand and 250 thousand British pounds in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the percentage of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[max] percent of the total templateYLabel[2] in templateTitleSubject[0] lived in cities .
generated: This statistic shows the Number of enterprises in United Kingdom from 2008 to 2018 and details the percentage of the entire enterprises , living in enterprises areas . In 2018 , 6633 percent of the total enterprises in United Kingdom lived in cities .

Example 571:
titleEntities: {'Subject': ['Spain'], 'Date': ['2018']}
title: Average annual wages in Spain 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Average', 'annual', 'wages', 'in', 'euros']: ['27946', '28171', '28738', '28902', '28405', '28400', '28336', '29166', '29585', '30101', '28198', '27101', '26751', '26853', '26697', '26976', '27049', '26851', '26856']

gold: This statistic shows the average annual wages in Spain from 2000 to 2018 . Over this 18-year period , annual wages in Spain have fluctuated greatly , peaking at approximately 30 thousand euros in 2009 and decreasing to approximately 28 thousand euros yearly in 2012 . The average annual wage stood at approximately 28 thousand euros in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Over this 18-year period , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] have fluctuated greatly , peaking at approximately templateYValue[8] thousand templateYLabel[3] in templateXValue[9] and decreasing to approximately templateYValue[0] thousand templateYLabel[3] yearly in templateXValue[6] . The templateYLabel[0] templateYLabel[1] wage stood at approximately templateYValue[0] thousand templateYLabel[3] in templateXValue[max] .

generated_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] wage in templateTitleSubject[0] was stable , remaining between templateYValue[4] thousand and templateYValue[0] thousand templateYLabel[3] . In templateXValue[max] , male managers represented the group of people with the highest earnings . Their gross salary amounted to 102 thousand templateYLabel[3] a templateXLabel[0] .
generated: Between 2000 and 2018 , the Average wage in Spain was stable , remaining between 28405 thousand and 27946 thousand euros . In 2018 , male managers represented the group of people with the highest earnings . Their gross salary amounted to 102 thousand euros a Year .

Example 572:
titleEntities: {'Subject': ['Countries'], 'Date': []}
title: Countries ranked by number of ice hockey players 2018/19
X_Axis['Country']: ['Canada', 'United_States', 'Czech_Republic', 'Russia', 'Finland', 'Sweden', 'Switzerland', 'France', 'Germany', 'Japan', 'Slovakia', 'Norway', 'Great_Britain', 'Austria', 'Hungary', 'Latvia', 'Kazakhstan', 'Ukraine', 'Italy', 'Belarus']
Y_Axis['Number', 'of', 'players']: ['621026', '567908', '121613', '112236', '64641', '55431', '27867', '21667', '21340', '18837', '11394', '10353', '8162', '7670', '7106', '7000', '6915', '5384', '5210', '4580']

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

generated_template: The statistic shows a ranking of the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[0] ) at templateYLabel[5] templateYLabel[6] in templateTitleDate[0] . templateXValue[0] templateXValue[0] is the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateXLabel[0] templateTitle[4] , with a templateYLabel[0] of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Top templateTitle[2] templateTitle[3] – additional information With templateTitle[2] an essential part of people 's lives , it is no surprise to find templateTitle[2] companies among the templateTitleSubject[0] templateTitle[1] templateTitle[3] templateTitle[4] .
generated: The statistic shows a ranking of the 20 Countries ranked the by number ice hockey ( Number ) at players in . Canada is the Countries ranked by Country ice , with a Number of 621026 players . Top by number – additional information With by an essential part of people 's lives , it is no surprise to find by companies among the Countries ranked number ice .

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 .
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 .

Example 574:
titleEntities: {'Subject': ['Vegetables'], 'Date': ['2000', '2018']}
title: Vegetables : global production volume 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Production', 'volume', 'in', 'million', 'metric', 'tons']: ['1088.9', '1094.34', '1075.2', '1051.52', '1030.32', '997.84', '978.52', '954.89', '921.52', '900.66', '876.15', '843.23', '809.33', '779.82', '760.29', '750.86', '721.42', '700.09', '682.43']

gold: This statistic depicts the total production volume of vegetables ( including melons ) worldwide from 1990 to 2018 . In 2014 , some 1169.45 million metric tons of vegetables and melons were produced worldwide .
gold_template: This statistic depicts the total templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] ( including melons ) worldwide from 1990 to templateXValue[max] . In templateXValue[4] , some 1169.45 templateYLabel[2] templateYLabel[3] templateYLabel[4] of templateTitleSubject[0] and melons were produced worldwide .

generated_template: The templateTitleSubject[0] templateTitleSubject[0] is home to some templateYValue[max] templateYLabel[2] passenger templateYLabel[1] . After slowly increasing over the past years , figures reached their peak in templateXValue[max] , with templateTitleSubject[1] roads busier than ever before . The only noticeable decline – apart from templateXValue[4] when the source changed – was seen in templateXValue[9] during the recession .
generated: The Vegetables is home to some 1094.34 million passenger volume . After slowly increasing over the past years , figures reached their peak in 2018 , with Vegetables roads busier than ever before . The only noticeable decline – apart from 2014 when the source changed – was seen in 2009 during the recession .

Example 575:
titleEntities: {'Subject': ['Latvia'], 'Date': ['2006', '2018']}
title: Number of arrivals in tourist accommodation Latvia 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'arrivals', 'in', 'millions']: ['2.8', '2.58', '2.3', '2.14', '2.1', '1.84', '1.64', '1.58', '1.31', '1.11', '1.56', '1.49', '1.33']

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Latvia from 2006 to 2018 . Since 2009 there has been an increasing trend in arrivals . In 2018 , the number of arrivals ( including both foreign and domestic ) at accommodation in Latvia amounted to approximately 2.8 million .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Since templateXValue[9] there has been an increasing trend in templateYLabel[1] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( including both foreign and domestic ) at templateTitle[3] in templateTitleSubject[0] amounted to approximately templateYValue[max] templateYLabel[2] .

generated_template: templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] have been steadily rising each templateXLabel[0] since the start of the decade . In templateXValue[max] , a record templateYValue[max] templateYLabel[2] tourists ( including residents and non-residents ) arrived at templateTitle[3] in templateTitleSubject[0] . Tourism in templateTitleSubject[0] In line with trends across Europe , tourism in templateTitleSubject[0] has experienced a boost in the last ten years .
generated: tourist arrivals at accommodation establishments in Latvia have been steadily rising each Year since the start of the decade . In 2018 , a record 2.8 millions tourists ( including residents and non-residents ) arrived at accommodation in Latvia . Tourism in Latvia In line with trends across Europe , tourism in Latvia has experienced a boost in the last ten years .

Example 576:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013']}
title: Age distribution of mobile gamers in the U.S. 2013
X_Axis['Year']: ['18-24', '25-34', '35-44', '45-54', '55-64', '65+']
Y_Axis['Percentage', 'of', 'respondents']: ['7', '17', '19', '22', '24', '11']

gold: This statistic gives information on the age distribution of mobile gamers in the United States as of May 2013 . During the survey period , it was found that 17 percent of mobile games were 25 to 34 years old . The average age of a mobile gamer was 46.5 years .
gold_template: This statistic gives information on the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] in the templateTitle[4] as of May templateTitleDate[0] . During the survey period , it was found that templateYValue[1] percent of templateTitle[2] games were 25 to 34 years old . The average templateTitle[0] of a templateTitle[2] gamer was 46.5 years .

generated_template: This statistic displays the templateTitle[7] distribution of templateTitle[1] at templateTitleSubject[0] in the templateTitleSubject[1] templateTitleSubject[1] as of templateTitleDate[0] . According to their annual report , templateYValue[max] percent of templateTitleSubject[0] templateTitle[1] are in the templateTitle[7] templateTitle[8] 45 - 54 . templateTitleSubject[0] is a British retailer selling clothing , furniture and other household items in a department store format .
generated: This statistic displays the 2013 distribution of distribution at U.S. in the U.S. as of 2013 . According to their annual report , 24 percent of U.S. distribution are in the 2013 45 - 54 . U.S. is a British retailer selling clothing , furniture and other household items in a department store format .

Example 577:
titleEntities: {'Subject': ['NASA'], 'Date': ['2014', '2024']}
title: NASA - budget 2014 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'in', 'billion', 'U.S.', 'dollars']: ['21.87', '21.66', '21.44', '21.23', '21.02', '21.5', '20.74', '19.65', '19.29', '18.01', '17.65']

gold: This graph show NASA 's projected budget from 2014 to 2024 . NASA 's budget is projected to be at around 21 billion U.S. dollars in 2020 . The National Aeronautics and Space Administration ( NASA ) is the U.S. agency responsible for aeronautics and aerospace research .
gold_template: This graph show templateTitleSubject[0] 's projected templateYLabel[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] 's templateYLabel[0] is projected to be at around templateYValue[2] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[4] . The National Aeronautics and Space Administration ( templateTitleSubject[0] ) is the templateYLabel[2] agency responsible for aeronautics and aerospace research .

generated_template: The statistic shows the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] ' ( UAE ) templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , The templateTitleSubject[0] templateTitleSubject[0] ' 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 ' ( UAE ) NASA budget 2014 2024 from 2014 to 2018 , with projections up until 2024 . In 2018 , The NASA ' Budget 2024 rate amounted to approximately 17.65 percent U.S. to the dollars Year .

Example 578:
titleEntities: {'Subject': ['U.S. June'], 'Date': ['2014', '2014']}
title: Frequency of making online restaurant reservations in the U.S. as of June 2014
X_Axis['Response']: ['Yes_many_times', 'Yes_once_or_twice', 'Never']
Y_Axis['Share', 'of', 'respondents']: ['9.6', '37.6', '52.8']

gold: This statistic shows the frequency with which consumers made online reservations when dining out in restaurants in the United States as of June 2014 . During the survey , 37.6 percent of respondents said they had made online reservations once or twice .
gold_template: This statistic shows the templateTitleSubject[0] with which consumers made templateTitle[2] templateTitle[4] when dining out in restaurants in the templateTitle[5] as of templateTitle[6] templateTitle[7] . During the survey , templateYValue[1] percent of templateYLabel[1] said they had made templateTitle[2] templateTitle[4] templateXValue[1] or templateXValue[1] .

generated_template: templateXValue[1] case of templateTitle[3] ( COVID-19 ) was confirmed in templateTitleSubject[0] as of February 27 , templateTitleDate[0] . Nevertheless , more than 60 percent of the society believes that templateTitleSubject[0] is not prepared templateTitle[2] a possible virus templateTitle[4] .
generated: Yes once or twice case of restaurant ( COVID-19 ) was confirmed in U.S. June as of February 27 , 2014 . Nevertheless , more than 60 percent of the society believes that U.S. June is not prepared online a possible virus reservations .

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: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) templateTitle[5] in templateTitleDate[0] , templateTitle[7] total templateYLabel[1] . In the survey period , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitleSubject[0] is expected to reach templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Number players of the Overwatch ( Overwatch ) 2018 in 2018 , 2018 total players . In the survey period , the Number players of Overwatch is expected to reach 40 millions .

Example 580:
titleEntities: {'Subject': ['Chinese'], 'Date': ['2008/09', '2018/19']}
title: Number of Chinese students in the U.S. 2008/09 - 2018/19
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09']
Y_Axis['Number', 'of', 'students']: ['369548', '363341', '350755', '328547', '304040', '274439', '235597', '194029', '157558', '127628', '98235']

gold: Colleges and universities in the United States are becoming an increasingly popular study destination for Chinese students , with over 369 thousand choosing to take courses there in the 2018/19 academic year . This made China the leading source of international students in the U.S. education market . The education exodus Business and management courses led the field in terms of what Chinese students were studying in the United States , followed closely by engineering , mathematics and computer science programs .
gold_template: Colleges and universities in the templateTitle[3] are becoming an increasingly popular study destination for templateTitleSubject[0] templateYLabel[1] , with over 369 thousand choosing to take courses there in the templateXValue[0] academic templateXLabel[0] . This made China the leading source of international templateYLabel[1] in the templateTitle[3] education market . The education exodus Business and management courses led the field in terms of what templateTitleSubject[0] templateYLabel[1] were studying in the templateTitle[3] , followed closely by engineering , mathematics and computer science programs .

generated_template: This statistic shows the approximate templateYLabel[0] of templateTitle[0] enrollments in templateTitle[3] across the templateTitle[2] from the academic templateXLabel[0] templateXValue[last] to templateXValue[0] . In templateXValue[0] , around templateYValue[min] templateYLabel[2] templateYLabel[1] enrolled for templateTitle[0] degrees in the templateTitle[2] .
generated: This statistic shows the approximate Number of Number enrollments in U.S. across the students from the academic Year 2008/09 to 2018/19 . In 2018/19 , around 98235 students enrolled for Number degrees in the students .

Example 581:
titleEntities: {'Subject': ['Vale'], 'Date': ['2009', '2018']}
title: Vale 's employee number 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'employees']: ['70270', '73596', '73062', '74098', '76531', '83286', '85305', '79646', '70785', '60036']

gold: This statistic shows mining company Vale 's number of employees worldwide from 2009 to 2018 . In 2018 , the company employed some 70,300 people . Vale S.A. , formerly called by the full name Companhia Vale do Rio Doce , is a Brazilian multinational metals and mining company , also specializing in logistic operations .
gold_template: This statistic shows mining company templateTitleSubject[0] templateTitle[1] templateYLabel[0] of templateYLabel[1] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company employed some 70,300 people . templateTitleSubject[0] S.A. , formerly called by the full name Companhia templateTitleSubject[0] do Rio Doce , is a Brazilian multinational metals and mining company , also specializing in logistic operations .

generated_template: templateTitleSubject[0] is an international pharmaceutical company based out of Germany . As of templateXValue[max] , the company had a total of templateYValue[0] templateYLabel[1] globally . This is a significant increase from the previous years .
generated: Vale is an international pharmaceutical company based out of Germany . As of 2018 , the company had a total of 70270 employees globally . This is a significant increase from the previous years .

Example 582:
titleEntities: {'Subject': ['Canada'], 'Date': ['2018']}
title: Production of copper in Canada by province 2018
X_Axis['Month']: ['Newfoundland_and_Labrador', 'Prince_Edward_Island', 'Nova_Scotia', 'New_Brunswick', 'Quebec', 'Ontario', 'Manitoba', 'Saskatchewan', 'Alberta', 'British_Columbia', 'Yukon', 'Northwest_Territories', 'Nunavut']
Y_Axis['Production', 'in', 'metric', 'tons']: ['27456', '0', '0', '487', '35912', '135297', '33608', '0', '0', '293468', '9282', '0', '0']

gold: This statistic displays preliminary estimates of the copper production in Canada , distributed by province , in 2018 . During that year , Quebec produced some 35,912 metric tons of this mineral . Copper is often used as part of coin currency , as well as electrical equipment due to its heat and electrical conduction properties .
gold_template: This statistic displays preliminary estimates of the templateTitle[1] templateYLabel[0] in templateTitleSubject[0] , distributed templateTitle[3] templateTitle[4] , in templateTitleDate[0] . During that year , templateXValue[4] produced some templateYValue[4] templateYLabel[1] templateYLabel[2] of this mineral . templateTitle[1] is often used as part of coin currency , as well as electrical equipment due to its heat and electrical conduction properties .

generated_template: In January templateTitleDate[0] , the templateYLabel[0] for one templateYLabel[4] of templateTitle[0] templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[0] ) templateTitle[4] templateTitle[5] stood at some templateYValue[0] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] is also known as `` templateTitleSubject[0] light sweet '' , and is a grade of templateTitle[4] templateTitle[5] often used as a benchmark for templateTitle[5] pricing . templateYLabel[0] of templateTitle[0] templateTitleSubject[0] templateTitleSubject[0] templateTitle[4] templateTitle[5] , templateTitle[7] templateTitleSubject[0] is also known as `` templateTitleSubject[0] light sweet '' and is a grade of templateTitle[4] templateTitle[5] .
generated: In January 2018 , the Production for one tons of Production Canada ( Canada ) province 2018 stood at some 27456 metric tons . Canada is also known as `` Canada light sweet '' , and is a grade of province 2018 often used as a benchmark for 2018 pricing . Production of Production Canada province 2018 , 2018 Canada is also known as `` Canada light sweet '' and is a grade of province 2018 .

Example 583:
titleEntities: {'Subject': ['Number'], 'Date': ['2014']}
title: Number of crowdfunding platforms worldwide 2014 , by region
X_Axis['Country']: ['Europe', 'North_America', 'Asia', 'South_America', 'Oceania', 'Africa']
Y_Axis['Number', 'of', 'CFPs']: ['600', '375', '169', '50', '37', '19']

gold: The statistic shows the number of crowdfunding platforms worldwide in 2014 , by region . In that year , there were 375 crowdfunding platforms in North America . Crowdfunding is a way of collecting money from various individuals interested in a given project .
gold_template: The statistic shows the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . In that year , there were templateYValue[1] templateTitle[1] templateTitle[2] in templateXValue[1] . templateTitle[1] is a way of collecting money from various individuals interested in a given project .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] the templateXValue[1] templateTitleSubject[0] in templateTitleDate[0] . In templateTitleDate[0] , the templateXValue[1] templateTitleSubject[0] 's templateTitle[0] templateTitle[1] partner was templateXValue[0] , accounting templateTitle[3] templateYValue[max] percent of all templateYLabel[2] .
generated: This statistic shows the Number crowdfunding platforms worldwide the North America Number in 2014 . In 2014 , the North America Number 's Number crowdfunding partner was Europe , accounting worldwide 600 percent of all CFPs .

Example 584:
titleEntities: {'Subject': ['Manitoba', 'Canada'], 'Date': ['2018']}
title: GDP of Manitoba , Canada 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['GDP', 'in', 'million', 'chained', '2012', 'Canadian', 'dollars']: ['62862.5', '61932.6', '59966.8', '59082.5', '58276.3', '57169.9', '55676.4', '54057.9', '52841.8', '51522.1', '51668.8', '50017.3', '48918.3', '47127.8', '45727.8', '44494.6', '44031.4', '43301.5', '42734.1']

gold: This statistic shows the gross domestic product ( GDP ) of Manitoba from 2000 to 2018 . In 2018 , Manitoba 's GDP was 62.86 billion chained 2012 Canadian dollars .
gold_template: This statistic shows the gross domestic product ( templateYLabel[0] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateXValue[6] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the 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 April templateTitleDate[0] . During the survey period , templateYValue[1] percent of responding templateTitle[3] templateTitle[4] shoppers stated that their usual templateTitle[3] templateTitle[4] templateTitle[1] amounted to templateXValue[1] to 25 templateTitleSubject[0] dollars .

generated_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitleSubject[0] templateTitle[4] to templateTitle[5] templateTitle[6] templateXValue[0] in templateTitleDate[0] . The survey revealed templateTitle[1] templateYValue[4] percent of the templateYLabel[1] said templateTitle[1] the templateXValue[4] influences their templateTitle[6] templateXValue[0] purchases.Luggage industry in the templateTitle[3] To understand the templateXValue[0] industry , one must understand how the airline industry impacts templateXValue[0] demands . With airlines continually increasing their baggage fees , the majority of travellers are adjusting all aspects of their travel , including considerations when it comes to purchasing luggage.Consumers are n't going for the lightweight templateXValue[0] without checking out some additional templateXValue[2] .
generated: This statistic presents the Average order value U.S. food to orders U.S. 0$ no expenses in 2017 . The survey revealed order 14 percent of the respondents said order the Up to 100$ influences their U.S. 0$ no expenses purchases.Luggage industry in the online To understand the 0$ no expenses industry , one must understand how the airline industry impacts 0$ no expenses demands . With airlines continually increasing their baggage fees , the majority of travellers are adjusting all aspects of their travel , including considerations when it comes to purchasing luggage.Consumers are n't going for the lightweight 0$ no expenses without checking out some additional Up to 50$ .

Example 586:
titleEntities: {'Subject': ['New England Patriots'], 'Date': []}
title: Career touchdown leaders - New England Patriots 1960 to 2020
X_Axis['Month']: ['Rob_Gronkowski', 'Stanley_Morgan', 'Ben_Coates', 'Randy_Moss', 'Sam_Cunningham', 'Jim_Nance', 'Tony_Collins', 'Gino_Cappelletti', 'Irving_Fryar', 'Larry_Garron', 'Julian_Edelman', 'Jim_Colclough', 'Corey_Dillon', 'Curtis_Martin', 'Wes_Welker', 'Steve_Grogan', 'Troy_Brown', 'LeGarrette_Blount', 'Kevin_Faulk', 'James_White']
Y_Axis['Touchdowns', 'scored']: ['80', '68', '50', '50', '49', '46', '44', '42', '42', '42', '41', '39', '39', '37', '37', '36', '35', '35', '33', '32']

gold: The statistic shows New England Patriots players with the most touchdowns scored in franchise history . Rob Gronkowski is the career touchdown leader of the New England Patriots with 80 touchdowns .
gold_template: The statistic shows templateTitleSubject[0] Patriots players with the most templateYLabel[0] templateYLabel[1] in franchise history . templateXValue[0] is the templateTitle[0] templateTitle[1] leader of the templateTitleSubject[0] Patriots with templateYValue[max] templateYLabel[0] .

generated_template: The statistic shows templateTitleSubject[0] templateTitle[4] players with the most templateYLabel[0] templateYLabel[1] in franchise history . templateXValue[0] templateXValue[0] is the templateTitle[0] templateTitle[1] leader of the templateTitleSubject[0] templateTitle[4] with templateYValue[max] templateYLabel[0] .
generated: The statistic shows New England Patriots England players with the most Touchdowns scored in franchise history . Rob Gronkowski is the Career touchdown leader of the New England Patriots England with 80 Touchdowns .

Example 587:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010/11', '2018/19']}
title: Total U.S. domestic raisin consumption 2010/11 - 2018/19
X_Axis['Year']: ['2018/2019', '2017/2018', '2016/2017', '2015/2016', '2014/2015', '2013/2014', '2012/2013', '2011/2012', '2010/2011']
Y_Axis['Domestic', 'consumption', 'in', 'metric', 'tons']: ['205564', '205000', '220909', '235136', '238039', '215636', '205122', '215579', '208646']

gold: This statistic shows the total United States domestic raisin consumption from 2010/2011 to 2017/2018 , and provides a projection for 2018/2019 . In crop year 2015/2016 , the domestic raisin consumption in the United States amounted to 235,136 metric tons .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] templateTitle[3] templateYLabel[1] from templateXValue[last] to templateXValue[1] , and provides a projection for templateXValue[0] . In crop templateXLabel[0] templateXValue[3] , the templateYLabel[0] templateTitle[3] templateYLabel[1] in the templateTitle[1] amounted to templateYValue[3] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic illustrates the templateYLabel[0] of templateYLabel[1] of the templateTitle[3] templateTitle[2] in templateTitleSubject[1] , the United Kingdom ( templateTitleSubject[0] ) from templateTitleDate[0] to templateXValue[0] . In templateXValue[2] , the templateTitle[2] templateYLabel[0] of templateYLabel[1] is expected to be templateYValue[0] templateYLabel[2] .
generated: This statistic illustrates the Domestic of consumption of the raisin domestic in U.S. , the United Kingdom ( U.S. ) from 2010/11 to 2018/2019 . In 2016/2017 , the domestic of consumption is expected to be 205564 metric .

Example 588:
titleEntities: {'Subject': ['Texas'], 'Date': ['2018']}
title: Per capita real GDP of Texas 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'real', 'GDP', 'in', 'chained', '2012', 'U.S.', 'dollars']: ['59674', '58125', '57280', '58077', '56457', '55573', '54097', '52397', '51568', '51264', '52481', '53470', '51811', '49732', '49241', '47583', '48031', '47932', '47664']

gold: This statistic shows the per capita real Gross Domestic Product of Texas from 2000 to 2018 . In 2018 , the per capita real GDP of Texas stood at 59,674 chained 2012 U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[max] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] templateTitleSubject[0] stood at templateYValue[max] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .
generated: This statistic shows the Per capita real Gross Domestic Product of Texas from 2000 to 2018 . In 2018 , the Per capita real GDP of Texas stood at 59674 chained 2012 U.S. dollars .

Example 589:
titleEntities: {'Subject': ['Disneyland Paris'], 'Date': ['2006', '2016']}
title: Disneyland Paris visitors spending per day 2006 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Average', 'spend', 'in', 'euros', '(excluding', 'VAT)']: ['54.0', '53.7', '50.7', '48.1', '46.4', '46.2', '45.3']

gold: This statistic displays daily expenditure per person at Disneyland Paris theme parks in France between 2006 and 2016 . Visitors spending includes entrance fee , catering , merchandise and other services in the theme parks . In the fiscal year 2016 , the average spending dipped to 54 euros ( before VAT ) .
gold_template: This statistic displays daily expenditure templateTitle[4] person at templateTitleSubject[0] theme parks in France between templateTitle[6] and templateXValue[max] . templateTitle[2] templateTitle[3] includes entrance fee , catering , merchandise and other services in the theme parks . In the fiscal templateXLabel[0] templateXValue[max] , the templateYLabel[0] templateTitle[3] dipped to templateYValue[max] templateYLabel[2] ( before VAT ) .

generated_template: This statistic presents the templateTitle[2] templateYLabel[0] of cyber templateTitle[1] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , companies detected and reported templateYValue[1] templateYLabel[2] IT templateTitle[1] breaches . This templateYLabel[0] increased by 38 percent in the following templateXLabel[0] .
generated: This statistic presents the visitors Average of cyber Paris spend from 2010 to 2016 . In 2014 , companies detected and reported 53.7 euros IT Paris breaches . This Average increased by 38 percent in the following Year .

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: This statistic illustrates the templateTitle[0] templateTitle[1] templateTitle[2] networking templateTitle[4] in selected templateTitleSubject[0] templateTitle[6] , based on user figures . As of May templateTitleDate[0] , Facebook was the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateXValue[13] with templateYValue[13] million templateYLabel[1] templateYLabel[2] .
generated: This statistic illustrates the U.S. winter heating networking prices in selected U.S. 2019/20 , based on user figures . As of May , Facebook was the U.S. winter heating oil in 2006/07 with 2.42 million U.S. dollars .

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] in the templateTitle[3] templateTitle[4] templateXValue[last] average templateYLabel[0] templateYLabel[1] in templateTitle[9] . YouGov 's brand templateYLabel[0] templateTitle[8] results show that templateXValue[0] templateTitle[4] highest with a score of templateYValue[max] , followed templateXValue[last] templateXValue[1] templateXValue[1] at templateYValue[1] .
generated: This statistic shows Share U.S. nickel in the imports by Canada average Share nickel in 2018 . YouGov 's brand Share 2018 results show that Other by highest with a score of 41 , followed Canada Finland at 8 .

Example 592:
titleEntities: {'Subject': ['Under Armour'], 'Date': ['2009', '2019']}
title: Global revenue growth of Under Armour 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'growth']: ['1', '4', '3', '22', '28', '32', '27', '25', '38', '24', '18']

gold: This statistic depicts the growth of Under Armour 's revenue worldwide from 2009 to 2019 . In 2019 , Under Armour 's net revenue increased by one percent . Under Armour is an American sporting goods manufacturer , based in Baltimore , Maryland .
gold_template: This statistic depicts the templateYLabel[1] of templateTitleSubject[0] 's templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's net templateYLabel[0] increased by templateYValue[min] percent . templateTitleSubject[0] is an American sporting goods manufacturer , based in Baltimore , Maryland .

generated_template: The templateYLabel[0] of templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] of templateTitleSubject[0] ( NAR ) has steadily increased since templateXValue[7] , reaching a membership of templateYValue[max] templateYLabel[2] in templateXValue[max] , an all-time high . This trend also reflects the recovery of the property market after the financial crisis of 2007 - templateXValue[min] , as the volume of home sales began to climb from templateXValue[8] . The NAR is a North American trade templateTitleSubject[0] for real estate workers formed in 1908 and currently based in Chicago , Illinois .
generated: The Revenue of growth of the Under Armour of Under Armour ( NAR ) has steadily increased since 2012 , reaching a membership of 38 growth in 2019 , an all-time high . This trend also reflects the recovery of the property market after the financial crisis of 2007 - 2009 , as the volume of home sales began to climb from 2011 . The NAR is a North American trade Under Armour for real estate workers formed in 1908 and currently based in Chicago , Illinois .

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] templateTitleSubject[0] templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Cincinnati Bengals , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the Revenue of the Cincinnati Bengals was 380 million U.S. dollars .

Example 595:
titleEntities: {'Subject': ['India'], 'Date': ['2019']}
title: Highest grossing domestic movies India 2019
X_Axis['Movie', 'Name']: ['War', 'Kabir_Singh', 'Uri-_The_Surgical_Strike', 'Bharat', 'Mission_Mangal', 'Kesari', 'Total_Dhamaal', 'Saaho', 'Chhichhore', 'Super_30']
Y_Axis['Box', 'office', 'gross', 'in', 'billion', 'Indian', 'rupees']: ['2.92', '2.76', '2.44', '1.97', '1.93', '1.52', '1.5', '1.49', '1.47', '1.47']

gold: The Bollywood movie 'War ' was the highest grossing domestic movie produced in India in 2019 with an all India net collection of almost three billion Indian rupees . This was followed by 'Kabir Singh ' at around 2.8 billion rupees worth box office collection that year .
gold_template: The Bollywood templateXLabel[0] 'War ' was the templateTitle[0] templateTitle[1] templateTitle[2] templateXLabel[0] produced in templateTitleSubject[0] in templateTitleDate[0] with an all templateTitleSubject[0] net collection of almost templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] . This was followed by 'Kabir templateXValue[1] ' at around templateYValue[1] templateYLabel[3] templateYLabel[5] worth templateYLabel[0] templateYLabel[1] collection that year .

generated_template: The number one templateTitle[3] templateTitle[4] drug , as of templateTitleDate[0] , was templateXValue[0] or alipogene tiparvovec with a templateYLabel[0] templateYLabel[1] of around templateYValue[max] million templateYLabel[4] templateYLabel[5] templateYLabel[2] templateYLabel[3] . templateXValue[1] was ranked the second templateTitle[4] drug , used in the long-term treatment of urea cycle disorders . templateXValue[1] is marketed by Horizon Pharma , a former U.S.-based but now Irish-based pharmaceutical company .
generated: The number one movies India drug , as of 2019 , was War or alipogene tiparvovec with a Box office of around 2.92 million Indian rupees gross billion . Kabir Singh was ranked the second India drug , used in the long-term treatment of urea cycle disorders . Kabir Singh is marketed by Horizon Pharma , a former U.S.-based but now Irish-based pharmaceutical company .

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 illustrates the distribution of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . That year , the templateXValue[0] templateXValue[0] accounted for more than one third of the total templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide .
generated: This statistic illustrates the distribution of European amount euros in 2015 , presents Country . That year , the United Kingdom accounted for more than one third of the total amount euros worldwide .

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: templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] have been steadily rising each templateXLabel[0] since the start of the decade . In templateXValue[max] , a record templateYValue[max] templateYLabel[2] tourists ( including residents and non-residents ) arrived at templateTitle[3] in templateTitleSubject[0] . Tourism in templateTitleSubject[0] In line with trends across Europe , tourism in templateTitleSubject[0] has experienced a boost in the last ten years .
generated: Sporting profit at Goods establishments in Sporting Goods have been steadily rising each Year since the start of the decade . In 2018 , a record 2489 million tourists ( including residents and non-residents ) arrived at Goods in Sporting Goods . Tourism in Sporting Goods In line with trends across Europe , tourism in Sporting Goods has experienced a boost in the last ten years .

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 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: This statistic shows the median Household income in Michigan from 1990 to 2018 . In 2018 , the median Household income in Michigan amounted to 60449 U.S. dollars .

Example 601:
titleEntities: {'Subject': ['NFL'], 'Date': ['2006', '2019']}
title: Average Fan Cost Index of NFL teams 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Fan', 'Cost', 'Index', 'in', 'U.S.', 'dollars']: ['540.52', '536.04', '502.84', '480.89', '479.11', '459.73', '443.93', '427.42', '420.54', '412.64', '396.36', '367.31', '346.16']

gold: The statistic shows the average Fan Cost Index in the National Football League from 2006 to 2019 . The average Fan Cost Index was at 540.52 U.S. dollars in 2019 .
gold_template: The statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the National Football League from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[max] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: This statistic shows the templateTitle[2] and net income of biotechnology company templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] Inc. is a Massachusetts-based biotech company , specialized in drugs for neurological disorders , autoimmune disorders and cancer . In templateXValue[max] , templateTitleSubject[0] generated revenues worth around templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Cost and net income of biotechnology company NFL from 2006 to 2019 . NFL Inc. is a Massachusetts-based biotech company , specialized in drugs for neurological disorders , autoimmune disorders and cancer . In 2019 , NFL generated revenues worth around 540.52 Cost Index U.S. .

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', 'CIS January'], 'Date': []}
title: Weekend box office revenue in Russia and CIS January 2020 , by film
X_Axis['Month']: ['Kholop', 'Perfect_Man', 'Spies_in_Disguise', 'Bad_Boys_for_Life', 'Invasion', 'Marafon_Zhelaniy', 'Soyuz_Spaseniya', 'The_Grudge', 'Jumanji:_The_Next_Level', 'Richard_Jewell']
Y_Axis['Revenue', 'in', 'thousand', 'U.S.', 'dollars']: ['12530.82', '6603.13', '5899.09', '5092.75', '3106.25', '1927.94', '1856.38', '1376.52', '1098.84', '828.02']

gold: Over three weekends of January 2020 , the Russian comedy film `` Kholop , '' translated as `` Serf , '' had the largest aggregate gross box office in Armenia , Belarus , Kazakhstan , Moldova , and Russia , measuring at approximately 12.5 million U.S. dollars , which made it the leading movie of the month by revenue . The romantic comedy `` Perfect Man , '' where the main character was played by a popular Russian singer Egor Kreed , ranked second with the box office of over 6.6 million U.S. dollars .
gold_template: Over three weekends of templateTitle[6] templateTitle[7] , the Russian comedy templateTitle[9] `` templateXValue[0] , '' translated as `` Serf , '' had the largest aggregate gross templateTitle[1] templateTitle[2] in Armenia , Belarus , Kazakhstan , Moldova , and templateTitleSubject[0] , measuring at approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , which made it the leading movie of the templateXLabel[0] templateTitle[8] templateYLabel[0] . The romantic comedy `` templateXValue[1] , '' where the main character was played templateTitle[8] a popular Russian singer Egor Kreed , ranked second with the templateTitle[1] templateTitle[2] of over templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic presents the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] as of December templateTitleDate[0] , ranked templateTitle[5] templateTitle[6] templateYLabel[0] . According to the estimates , Johan Sundstein , a templateXLabel[0] from Denmark , also known as templateXValue[0] , earned templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] throughout his recorded templateTitle[1] gaming career . templateTitle[1] gaming – additional information Video gaming is no longer a pastime of purported home-dwellers or arcade-loitering individuals ; the professionalization of this activity has now validated it as a career choice for talented templateTitle[2] to earn templateYLabel[1] and sign lucrative sponsorship deals .
generated: The statistic presents the Russia box office revenue as of December , ranked CIS January Revenue . According to the estimates , Johan Sundstein , a Month from Denmark , also known as Kholop , earned 12530.82 thousand U.S. dollars throughout his recorded box gaming career . box gaming – additional information Video gaming is no longer a pastime of purported home-dwellers or arcade-loitering individuals ; the professionalization of this activity has now validated it as a career choice for talented office to earn thousand and sign lucrative sponsorship deals .

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 templateTitleSubject[0] templateYLabel[0] of the templateTitle[3] of the USA in templateTitleDate[0] , templateTitle[5] templateXLabel[0] of residence . The templateXLabel[0] with the highest templateTitleSubject[0] templateYLabel[0] of its templateTitle[3] was templateXValue[19] at templateYValue[max] templateYLabel[1] . templateXValue[45] had the lowest templateTitleSubject[0] templateYLabel[0] ( templateYValue[min] templateYLabel[1] ) .
generated: This statistic shows the U.S. COPD of the 2017 of the USA in 2017 , state of residence . The State with the highest U.S. COPD of its 2017 was Maine at 13.8 prevalence . Utah had the lowest U.S. COPD ( 3.4 prevalence ) .

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] of templateTitleSubject[0] ( UK ) templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . templateYValue[4] templateYLabel[1] were recorded in templateXValue[min] , with numbers rising during the templateTitle[3] in templateXValue[min] .
generated: This statistic shows the Number of households of Mexico ( UK ) households 2005 from 2005 to 2017 . 24.8 households were recorded in 2005 , with numbers rising during the 2005 in 2005 .

Example 606:
titleEntities: {'Subject': ['Amazon'], 'Date': ['2017', '2019']}
title: U.S. Amazon Prime Day conversion rate 2017 to 2019
X_Axis['Year']: ['2019', '2018', '2017']
Y_Axis['Conversion', 'rate']: ['13.5', '11.6', '11.2']

gold: During the Amazon Prime Day shopping event in July 2019 , the desktop conversion rate amounted to 13.5 percent , which represented a 16 percent growth from the previous year . Prime Day does not only drive conversion on Amazon but also on other retail platforms .
gold_template: During the templateTitleSubject[0] Day shopping event in July templateXValue[max] , the desktop templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] percent , which represented a 16 percent growth from the previous templateXLabel[0] . templateTitleSubject[0] does not only drive templateYLabel[0] on templateTitleSubject[0] but also on other retail platforms .

generated_template: The statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] ( personal emergency response system ) templateYLabel[0] templateYLabel[1] in templateXValue[min] , and provides a forecast for templateXValue[1] and templateXValue[max] . In templateXValue[min] , the templateTitleSubject[0] templateYLabel[0] size for such templateTitle[1] systems stood at some templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Until templateXValue[max] , the templateYLabel[0] is expected to increase to some templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Amazon Prime Day ( personal emergency response system ) Conversion rate in 2017 , and provides a forecast for 2018 and 2019 . In 2017 , the Amazon Conversion size for such Amazon systems stood at some 11.2 rate . Until 2019 , the Conversion is expected to increase to some 13.5 rate .

Example 607:
titleEntities: {'Subject': ['Saudi Arabia'], 'Date': ['1984']}
title: Inflation in Saudi Arabia since 1984
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2.09', '2.09', '2.12', '2.09', '2.23', '-1.05', '2.48', '-0.85', '2.03', '1.27', '2.2', '3.52', '2.88', '3.76', '3.78', '4.18', '6.08', '5.06', '1.9', '0.55', '0.27', '0.55', '0.14', '-1.22', '-1.08', '-2.11', '-0.39', '-0.26', '0.26', '5.25', '1.26', '1.28', '-0.98', '3.79', '-1.01', '1.17', '-0.35', '-2.39', '-3.12', '-2.31', '-0.68']

gold: The statistic shows the inflation rate in Saudi Arabia from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate amounted to 2.48 percent compared to the previous year . Oil production in Saudi Arabia Saudi Arabia 's economy relies heavily on production and export of oil and petroleum .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] amounted to templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . Oil production in templateTitleSubject[0] Saudi templateTitleSubject[0] 's economy relies heavily on production and export of oil and petroleum .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] had amounted to about templateYValue[1] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Saudi Arabia from 1984 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Saudi Arabia had amounted to about 2.09 percent compared to the previous Year .

Example 608:
titleEntities: {'Subject': ['Americans'], 'Date': ['2018']}
title: Americans ' moral stance towards pornography in 2018
X_Axis['Response']: ['Morally_acceptable', 'Morally_wrong', 'Depends', 'No_opinion']
Y_Axis['Share', 'of', 'respondents']: ['43', '55', '1', '1']

gold: This statistic shows the moral stance of Americans regarding pornography in 2018 . During the survey , 43 percent of respondents stated they think pornography is morally acceptable , while 1 percent stated it depends on the situation .
gold_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitleSubject[0] regarding templateTitle[5] in templateTitleDate[0] . During the survey , templateYValue[0] percent of templateYLabel[1] stated they think templateTitle[5] is templateXValue[0] , while templateYValue[min] percent stated it templateXValue[2] on the situation .

generated_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitleSubject[0] regarding templateTitle[5] or templateTitle[6] templateTitle[7] in templateTitleDate[0] . During this survey , templateYValue[max] percent of templateYLabel[1] stated they think templateTitle[5] or templateTitle[6] templateTitle[7] are templateXValue[0] templateXValue[0] , while templateYValue[min] percent said it templateXValue[2] on the situation .
generated: This statistic shows the moral stance of Americans regarding pornography or 2018 in 2018 . During this survey , 55 percent of respondents stated they think pornography or 2018 are Morally acceptable , while 1 percent said it Depends on the situation .

Example 609:
titleEntities: {'Subject': ['BSI'], 'Date': ['2019']}
title: Top 10 strongest nation brands by BSI score 2019
X_Axis['Country']: ['Singapore', 'Switzerland', 'Netherlands', 'Germany', 'Luxembourg', 'United_Arab_Emirates', 'Finland', 'Japan', 'United_States', 'Denmark']
Y_Axis['Brand', 'Strength', 'Index', 'Score']: ['90.5', '89.9', '89.6', '88.2', '86.9', '86.6', '86.4', '85.8', '85.7', '85.6']

gold: The statistic depicts the top ten strongest nation brands of 2019 as measured by the Brand Strength Index ( BSI ) . In 2019 , Singapore received the highest BSI score of any nation with a score of 90.5 .
gold_template: The statistic depicts the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleDate[0] as measured templateTitle[5] the templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitleSubject[0] ) . In templateTitleDate[0] , templateXValue[0] received the highest templateTitleSubject[0] templateYLabel[3] of any templateTitle[3] with a templateYLabel[3] of templateYValue[max] .

generated_template: This statistic shows the templateYLabel[1] of disposable income spent on templateTitle[1] consumed in templateTitleDate[0] , broken down by templateTitle[5] templateTitle[6] . U.S. consumers spent templateYValue[min] percent of their disposable income on templateTitle[1] at templateTitle[2] , compared to a templateTitle[1] templateYLabel[0] templateYLabel[1] of templateYValue[1] percent in templateXValue[1] .
generated: This statistic shows the Strength of disposable income spent on 10 consumed in 2019 , broken down by BSI . U.S. consumers spent 85.6 percent of their disposable income on 10 at strongest , compared to a 10 Brand Strength of 89.9 percent in Switzerland .

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 March templateTitleDate[0] , ordered templateTitle[5] templateXValue[last] templateXLabel[1] . At that time , there were templateYValue[2] templateXValue[2] templateTitle[1] in templateTitleSubject[0] . In templateXValue[last] , there were almost 4,000 templateYLabel[1] in the state .

generated_template: This statistic depicts the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] as of March templateTitleDate[0] , ordered templateTitle[5] templateXValue[last] templateXLabel[1] . At that time , there were templateYValue[2] templateXValue[2] templateTitle[1] in templateTitleSubject[0] . There are approximately 21,400 templateXValue[last] templateYLabel[1] templateTitle[1] in the state .
generated: This statistic depicts the Number of active physicians in Utah as of March 2019 , ordered by Total specialty area . At that time , there were 439 Anesthesiologists active in Utah . There are approximately 21,400 Total specialty physicians active in the state .

Example 612:
titleEntities: {'Subject': ['Europe'], 'Date': ['2017']}
title: General practitioners practicing in Europe in 2017 , by country
X_Axis['Country']: ['France', 'Germany', 'United_Kingdom', 'Italy', 'Spain', 'Portugal', 'Netherlands', 'Belgium', 'Poland', 'Austria', 'Ireland', 'Greece', 'Slovenia', 'Estonia', 'Luxembourg']
Y_Axis['Number', 'of', 'employees']: ['60214', '58170', '49824', '43731', '35378', '24248', '14641', '12992', '8418', '6637', '3942', '3647', '1237', '937', '534']

gold: In 2017 , there were over 60 thousand general practitioners ( GP ) practicing in France , the highest number recorded in Europe , followed by Germany with approximately 58.1 thousand GPs and the United Kingdom with almost 49.8 thousand . These three countries having the highest number of GPs goes in direct correlation with their population sizes being the highest in Europe . Visiting the doctor In 2018 , a survey of French individuals revealed that 83 percent of respondents consult with a physician at least once a year .
gold_template: In templateTitleDate[0] , there were over templateYValue[max] thousand templateTitle[0] templateTitle[1] ( GP ) templateTitle[2] in templateXValue[0] , the highest templateYLabel[0] recorded in templateTitleSubject[0] , followed templateTitle[5] templateXValue[1] with approximately 58.1 thousand GPs and the templateXValue[2] with almost templateYValue[2] thousand . These three countries having the highest templateYLabel[0] of GPs goes in direct correlation with their population sizes being the highest in templateTitleSubject[0] . Visiting the doctor In 2018 , a survey of French individuals revealed that 83 percent of respondents consult with a physician at least once a year .

generated_template: This statistic illustrates the templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] as of January templateTitleDate[0] , templateTitle[5] templateXLabel[0] . As of that year , the templateXValue[0] templateXValue[0] accounted for templateYValue[max] people .
generated: This statistic illustrates the Number of Europe employees as of January 2017 , by Country . As of that year , the France accounted for 60214 people .

Example 613:
titleEntities: {'Subject': ['Norway'], 'Date': ['2019']}
title: Leading companies in Norway 2019 , by number of employees
X_Axis['Month']: ['Helse_Sør-Øst_RHF', 'Telenor_ASA', 'Aker_ASA', 'Equinor_ASA_/_Statoil', 'Posten_Norge_AS', 'Orkla_ASA', 'Yara_International_ASA', 'Aker_Solutions_ASA', 'Tallyman_AS', 'Norges_Statsbaner_AS', 'Norsk_Hydro_ASA', 'Marine_Harvest_ASA', 'Strawberry_Holding_AS', 'Nordic_Choice_Hospitality_Group_AS', 'Kongsberg_Automotive_ASA', 'DNB_ASA', 'Hfn_Group_AS', 'Evry_ASA', 'Hospitality_Invest_AS', 'Nokas_AS']
Y_Axis['Number', 'of', 'employees']: ['60368', '31000', '20753', '20245', '18327', '18154', '14736', '14300', '13760', '13006', '12911', '12717', '10412', '10320', '9791', '9561', '9172', '9100', '9001', '8273']

gold: This statistic shows the 20 biggest companies in Norway as of March 2019 , by number of employees . Helse Sør-Øst RHF was ranked first with over 60 thousand employees , while Telenor ASA was ranked second with 31 thousand employees .
gold_template: This statistic shows the 20 biggest templateTitle[1] in templateTitleSubject[0] as of March templateTitleDate[0] , templateTitle[4] templateYLabel[0] of templateYLabel[1] . templateXValue[0] RHF was ranked first with over templateYValue[max] thousand templateYLabel[1] , while templateXValue[1] was ranked second with templateYValue[1] thousand templateYLabel[1] .

generated_template: Ranked as the templateTitle[0] employer among the Stockholm-based templateTitle[1] in templateTitleDate[0] was the security templateXValue[14] templateXValue[0] templateXValue[0] , the employer of over templateYValue[max] thousand people . templateXValue[1] templateXValue[1] templateXValue[1] 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] 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 and Latin 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 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 and Latin 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': ['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: 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: cartridge spending on the National Football League ( North America ) and its teams has increased annually since 2007 , reaching 23886 million U.S. dollars worldwide during the 2015 season . Nike is one of the league 's main sponsors , providing uniform and apparel to all 32 North America teams since 2009 – the two parties agreed a long-term extension to their rights partnership in 2015 . Why are cartridge 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 North America .

Example 616:
titleEntities: {'Subject': ['United Kingdom', 'HMRC'], 'Date': ['2019']}
title: Inheritance tax : United Kingdom HMRC tax receipts 2000 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05', '2003/04', '2002/03', '2001/02', '2000/01']
Y_Axis['Inheritance', 'tax', 'receipts', 'in', 'billion', 'GBP']: ['5.36', '5.2', '4.8', '4.7', '3.8', '3.4', '3.1', '2.9', '2.7', '2.4', '2.8', '3.8', '3.5', '3.3', '2.9', '2.5', '2.4', '2.4', '2.2']

gold: This statistic shows the total United Kingdom ( UK ) HMRC inheritance tax receipts from fiscal year 2000/01 to fiscal year 2018/19 . Despite a dip in 2008/09 and 2009/10 the overall trend was one of increase . The peak was in 2018/19 at 5.36 billion British pounds ( GBP ) .
gold_template: This statistic shows the total templateTitleSubject[0] ( UK ) templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] templateXValue[0] . Despite a dip in templateXValue[10] and templateXValue[9] the overall trend was one of increase . The peak was in templateXValue[0] at templateYValue[max] templateYLabel[3] British pounds ( templateYLabel[4] ) .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , there were templateYValue[0] new templateYLabel[1] to templateTitleSubject[0] .
generated: This statistic shows the Inheritance of tax in United Kingdom from 2019 to 2019 . In 2019 , there were 5.36 new tax to United Kingdom .

Example 617:
titleEntities: {'Subject': ['LVMH Group'], 'Date': ['2008', '2019']}
title: LVMH Group 's R & D expenditure worldwide 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['R&D', 'expenditure', 'in', 'million', 'euros']: ['140', '130', '130', '111', '97', '79', '71', '68', '63', '46', '45', '43']

gold: This statistic highlights the trend in research and development ( R & D ) expenditure of the LVMH Group worldwide from 2008 to 2019 . In 2019 , LVMH Group 's global R & D expenditure amounted to about 140 million euros .
gold_template: This statistic highlights the trend in research and development ( templateTitleSubject[0] templateTitle[4] templateTitle[5] ) templateYLabel[1] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's global templateTitleSubject[0] templateTitle[4] templateTitle[5] templateYLabel[1] amounted to about templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: The templateTitleSubject[0] templateTitleSubject[0] of templateTitleSubject[0] ( RBS ) is part of The templateTitleSubject[0] templateTitleSubject[0] of templateTitleSubject[0] Group plc with Natwest and Ulster templateTitleSubject[0] and consists of 67 thousand employees over 187 branches across the United Kingdom . As of templateXValue[1] , RBS had over 830 templateYLabel[2] euros in templateYLabel[1] , the fourth highest in the United Kingdom . History of RBS Founded in Edinburgh in 1727 , the templateTitleSubject[0] templateTitleSubject[0] of templateTitleSubject[0] is an amalgamation of hundreds of past banks .
generated: The LVMH Group of LVMH Group ( RBS ) is part of The LVMH Group of LVMH Group Group plc with Natwest and Ulster LVMH Group and consists of 67 thousand employees over 187 branches across the United Kingdom . As of 2018 , RBS had over 830 million euros in expenditure , the fourth highest in the United Kingdom . History of RBS Founded in Edinburgh in 1727 , the LVMH Group of LVMH Group is an amalgamation of hundreds of past banks .

Example 618:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018', '2018']}
title: Murder in the U.S. : number of offenders by age 2018
X_Axis['Age', 'of', 'offender', 'in', 'years']: ['Infant_(<1)', '1_to_4', '5_to_8', '9_to_12', '13_to_16', '17_to_19', '20_to_24', '25_to_29', '30_to_34', '35_to_39', '40_to_44', '45_to_49', '50_to_54', '55_to_59', '60_to_64', '65_to_69', '70_to_74', '75+', 'Unknown']
Y_Axis['Number', 'of', 'offenders']: ['0', '1', '1', '8', '496', '1479', '2254', '1998', '1440', '1161', '651', '495', '439', '346', '186', '106', '82', '93', '5099']

gold: 2,254 murderers in the United States in 2018 were individuals between the ages of 20 and 24 . In the same year , the youngest murder offender was between the ages of one and four , and there were 93 murder offenders over the age of 75 . Murder rate in the United States Despite some feeling that violent crime in the United States is on the rise , perhaps due to sensationalized media coverage , the murder and nonnegligent manslaughter rate has declined steeply since 1990 .
gold_template: templateYValue[6] murderers in the templateTitle[1] in templateTitle[6] were individuals between the ages of templateXValue[6] and templateXValue[6] . In the same year , the youngest templateTitle[0] templateXLabel[1] was between the ages of templateXValue[1] and templateXValue[1] , and there were templateYValue[17] templateTitle[0] templateYLabel[1] over the templateXLabel[0] of 75 . templateTitle[0] rate in the templateTitle[1] Despite some feeling that violent crime in the templateTitle[1] is on the rise , perhaps due to sensationalized media coverage , the templateTitle[0] and nonnegligent manslaughter rate has declined steeply since 1990 .

generated_template: This statistic shows the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[4] templateXLabel[0] . In templateTitleDate[0] , the templateYLabel[0] of templateTitle[1] templateYLabel[1] in templateTitleSubject[0] was templateYValue[3] percent .
generated: This statistic shows the Number of U.S. offenders in U.S. in 2018 , by Age . In 2018 , the Number of U.S. offenders in U.S. was 8 percent .

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 statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[min] percent .
generated: 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 at approximately 4.82 percent .

Example 620:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2008', '2018']}
title: Number of students in upper secondary education in Denmark 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'students']: ['148551', '150608', '149788', '148144', '147760', '148051', '144791', '140259', '132619', '122837', '118217']

gold: The statistic shows the number of students in upper secondary education in Denmark from 2008 to 2018 . The number increased from about 118 thousand upper secondary education students in 2008 to about 149 thousand students in 2018 .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] increased from about templateYValue[min] thousand templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] in templateXValue[min] to about templateYValue[0] thousand templateYLabel[1] in templateXValue[max] .

generated_template: In the fall semester templateXValue[max] , templateYValue[0] templateYLabel[1] were templateTitle[1] in universities and other higher education institutions in templateTitleSubject[0] . Since 2000 , the templateYLabel[0] of individuals in templateTitleSubject[0] with an upper secondary education increased , while the individuals without decreased . In templateXValue[max] , 21.7 percent of the female population had an upper secondary education degree of three years and 24.9 percent of the male population .
generated: In the fall semester 2018 , 148551 students were students in universities and other higher education institutions in Denmark . Since 2000 , the Number of individuals in Denmark with an upper secondary education increased , while the individuals without decreased . In 2018 , 21.7 percent of the female population had an upper secondary education degree of three years and 24.9 percent of the male population .

Example 621:
titleEntities: {'Subject': ['UK'], 'Date': ['14', '19']}
title: Monthly hours of sunlight in UK 2014 to 2019
X_Axis['Month']: ["Dec_'19", "Nov_'19", "Oct_'19", "Sep_'19", "Aug_'19", "Jul_'19", "Jun_'19", "May_'19", "Apr_'19", "Mar_'19", "Feb_'19", "Jan_'19", "Dec_'18", "Nov_'18", "Oct_'18", "Sep_'18", "Aug_'18", "Jul_'18", "Jun_'18", "May_'18", "Apr_'18", "Mar_'18", "Feb_'18", "Jan_'18", "Dec_'17", "Nov_'17", "Oct_'17", "Sep_'17", "Aug_'17", "Jul_'17", "Jun_'17", "May_'17", "Apr_'17", "Mar_'17", "Feb_'17", "Jan_'17", "Dec_'16", "Nov_'16", "Oct_'16", "Sep_'16", "Aug_'16", "Jul_'16", "Jun_'16", "May_'16", "Apr_'16", "Mar_'16", "Feb_'16", "Jan_'16", "Dec_'15", "Nov_'15", "Oct_'15", "Sep_'15", "Aug_'15", "Jul_'15", "Jun_'15", "May_'15", "Apr_'15", "Mar_'15", "Feb_'15", "Jan_'15", "Dec_'14", "Nov_'14", "Oct_'14", "Sep_'14", "Aug_'14", "Jul_'14", "Jun_'14", "May_'14", "Apr_'14", "Mar_'14", "Feb_'14", "Jan_'14"]
Y_Axis['Number', 'of', 'hours']: ['46.2', '48.1', '87.9', '144.0', '173.6', '173.2', '160.8', '188.5', '168.9', '115.6', '100.6', '47.9', '37.6', '63.0', '113.2', '134.1', '147.4', '237.6', '239.9', '246.0', '132.6', '85.0', '95.6', '48.7', '45.3', '71.1', '72.7', '109.0', '155.5', '168.7', '155.7', '208.3', '158.0', '119.7', '55.0', '55.1', '40.7', '74.7', '105.3', '119.9', '181.7', '156.4', '136.5', '209.6', '160.8', '117.3', '84.9', '37.1', '29.2', '35.6', '91.2', '157.8', '148.8', '160.6', '189.7', '174.4', '212.9', '121.9', '76.0', '58.5', '57.1', '51.9', '82.8', '123.3', '171.0', '223.0', '178.4', '149.6', '144.9', '126.7', '75.0', '42.8']

gold: In the period of consideration , the total monthly hours of sunlight in the UK followed a similar pattern each year . The most notable change occurred in 2018 , when the hours of sunlight shot up in May , June and July to 246 , 240 and 238 hours respectively . Unsurprisingly it was the end of each year when sunlight hours were lowest .
gold_template: In the period of consideration , the total templateTitle[0] templateYLabel[1] of templateTitle[2] in the templateTitleSubject[0] followed a similar pattern each year . The most notable change occurred in 2018 , when the templateYLabel[1] of templateTitle[2] shot up in templateXValue[7] , June and July to templateYValue[max] , templateYValue[18] and templateYValue[17] templateYLabel[1] respectively . Unsurprisingly it was the end of each year when templateTitle[2] templateYLabel[1] were lowest .

generated_template: This statistic displays the templateTitle[3] templateYLabel[0] templateYLabel[1] ( as of the end of each templateXLabel[0] ) of the templateTitle[0] to the templateTitleSubject[0] templateTitle[2] ( EUR/PLN ) , according to data from the European Central Bank , from January templateTitleDate[0] to January templateTitleDate[1] . The concrete , or point in time , measure shows the templateYLabel[0] templateYLabel[1] as of the last day of each templateXLabel[0] , which is different to the standardized measure ; calculation of average based on observations throughout the period in question.Monthly change After seeing a height of templateYValue[max] rubles to the templateTitle[0] in February 2016 , the templateTitle[3] templateYLabel[0] templateYLabel[1] has fluctuated . Between December 2018 and January templateTitleDate[1] , the end of templateXLabel[0] templateYLabel[0] templateYLabel[1] between the templateTitle[0] and templateTitleSubject[0] templateTitle[2] has seen a downward trend .
generated: This statistic displays the UK Number hours ( as of the end of each Month ) of the Monthly to the UK sunlight ( EUR/PLN ) , according to data from the European Central Bank , from January 14 to January 19 . The concrete , or point in time , measure shows the Number hours as of the last day of each Month , which is different to the standardized measure ; calculation of average based on observations throughout the period in question.Monthly change After seeing a height of 246.0 rubles to the Monthly in February 2016 , the UK Number hours has fluctuated . Between December 2018 and January 19 , the end of Month Number hours between the Monthly and UK sunlight has seen a downward trend .

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: The statistic shows a ranking of the tennis templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateYLabel[0] templateYLabel[1] as of templateTitleDate[0] . The forecast templateTitle[4] templateTitle[5] templateTitle[6] in the templateXValue[0] templateXValue[0] , a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows a ranking of the tennis Countries lowest the access electricity 2017 Access rate as of 2017 . The forecast 2017 in the Burundi , a Access rate of 88.8 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] , it is projected that more than templateYValue[max] templateYLabel[2] templateTitle[2] templateTitle[3] would be shipped in the templateTitle[4] alone . templateTitle[2] television templateYLabel[1] have remained relatively stable over the measured period , with total shipment figures peaking around the fourth templateXLabel[0] or holiday season each year . From a worldwide perspective , hundreds of templateYLabel[2] of individual templateTitle[3] are sold each year .
generated: In the fourth Quarter of 2019 , it is projected that more than 70682 machines United would be shipped in the Kingdom alone . machines television cash have remained relatively stable over the measured period , with total shipment figures peaking around the fourth Quarter or holiday season each year . From a worldwide perspective , hundreds of machines of individual United are sold each year .

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 presents the templateTitle[1] templateTitle[2] templateTitle[4] templateYLabel[0] of templateTitleSubject[0] profiles from August to December templateTitleDate[0] . In the last measured templateXLabel[0] , templateTitleSubject[0] templateTitle[2] profiles gained an average of templateYValue[max] percent more followers than in the previous templateXLabel[0] .
generated: This statistic presents the operating margin companies Operating of CPG profiles from August to December 2016 . In the last measured Company , CPG margin profiles gained an average of 21.9 percent more followers than in the previous Company .

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': ['1896', '2016']}
title: Number of sports at the Summer Olympic Games 1896 to 2016
X_Axis['Year']: ['2016', '2012', '2008', '2004', '2000', '1996', '1992', '1988', '1984', '1980', '1976', '1972', '1968', '1964', '1960', '1956', '1952', '1948', '1936', '1932', '1928', '1924', '1920', '1912', '1908', '1906', '1904', '1900', '1896']
Y_Axis['Number', 'of', 'sports', 'played']: ['35', '32', '34', '34', '34', '31', '29', '27', '26', '23', '23', '23', '20', '21', '19', '18', '19', '20', '24', '18', '17', '20', '25', '17', '24', '13', '18', '20', '9']

gold: The statistic illustrates the number of sports at the Summer Olympic Games between 1896 and 2016 . In 1900 , 20 sporting events took place at the Summer Olympic Games .
gold_template: The statistic illustrates the templateYLabel[0] of templateYLabel[1] at the templateTitleSubject[0] Games between templateXValue[min] and templateXValue[max] . In templateXValue[27] , templateYValue[12] sporting events took place at the templateTitleSubject[0] Games .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitleSubject[0] templateTitle[3] templateTitle[4] were living below the templateYLabel[0] line in the templateTitle[5] . templateYLabel[0] is the state of one who lacks a certain amount of material possessions or money .
generated: This statistic shows the Number sports of Summer Olympic Games Olympic Games in the Summer Olympic Games from 1896 to 2016 . In 2016 , 35 percent of Summer Olympic Games Olympic Games were living below the Number line in the 1896 . Number is the state of one who lacks a certain amount of material possessions or money .

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: The statistic represents the total templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In the most recent measured templateXLabel[0] , the templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] was templateYValue[max] .
generated: The statistic represents the total Number of Canada 2000 2019 in the 2019 from 2001 to 2019 . In the most recent measured Year , the Number of Canada 2000 2019 in the 2019 was 383579 .

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: 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[1] , the templateTitle[2] templateTitle[3] templateTitle[4] grew by an estimated templateYValue[max] percent compared to the previous sales templateXLabel[0] . templateTitle[2] templateTitle[3] templateTitle[4] Since the early twentieth century , the production of templateTitle[3] has been controlled by a handful of multi-national corporations .
generated: This statistic shows the Consumption per capita of the poultry consumption Indonesia from 2006 to 2025 . In 2019 , the poultry consumption Indonesia grew by an estimated 8.39 percent compared to the previous sales Year . poultry consumption Indonesia Since the early twentieth century , the production of consumption has been controlled by a handful of multi-national corporations .

Example 630:
titleEntities: {'Subject': ['United States'], 'Date': ['2011']}
title: Geographic distance between grandparents and their grandchildren in the United States in 2011
X_Axis['Year']: ['10_miles_or_less', '11_-_50_miles', '51_-_100_miles', '101_-_150_miles', '151_-_200_miles', 'More_than_200_miles', 'Only_have_grandchildren_who_live_with_me', "Don't_know"]
Y_Axis['Percentage', 'of', 'respondents']: ['21', '17', '7', '4', '4', '43', '1', '2']

gold: This statistic shows the results of a survey among grandparents in the United States in 2011 on the geographic distance between themselves and their grandchildren . In 2011 , 43 percent of the respondents stated they live more than 200 miles away from their grandchildren , whereas 21 percent said they live 10 or less miles away from their grandchildren .
gold_template: This statistic shows the results of a survey among templateTitle[3] in the templateTitleSubject[0] in templateTitleDate[0] on the templateTitle[0] templateTitle[1] templateTitle[2] themselves and templateTitle[4] templateXValue[6] . In templateTitleDate[0] , templateYValue[max] percent of the templateYLabel[1] stated they templateXValue[6] templateXValue[5] 200 templateXValue[0] away from templateTitle[4] templateXValue[6] , whereas templateYValue[0] percent said they templateXValue[6] templateXValue[0] or templateXValue[0] away from templateTitle[4] templateXValue[6] .

generated_template: The statistic shows the results of a survey among Americans on how much templateTitle[0] they planned to spend on templateTitleSubject[0] templateTitle[4] templateTitleSubject[0] templateTitle[6] templateTitle[2] templateTitle[8] each templateXLabel[0] . Overall , templateYValue[max] percent of templateYLabel[1] in templateTitleSubject[0] templateTitleSubject[0] users were aged between 25 and 34 templateXValue[0] .
generated: The statistic shows the results of a survey among Americans on how much Geographic they planned to spend on United States their United States United between 2011 each Year . Overall , 43 percent of respondents in United States users were aged between 25 and 34 10 miles or less .

Example 631:
titleEntities: {'Subject': ['Armour', 'United States'], 'Date': ['2014']}
title: Product quality rating of Under Armour footwear United States 2014
X_Axis['Response']: ['Extremely_positive', 'Somewhat_positive', 'Neutral', 'Somewhat_negative', 'Extremely_Negative']
Y_Axis['Share', 'of', 'respondents']: ['72', '22', '5', '-', '-']

gold: This statistic shows how consumers rate the product quality of Under Armour footwear . 72 % of respondents rated Under Armour 's quality as extremely positive .
gold_template: This statistic shows how consumers rate the templateTitle[0] templateTitle[1] of templateTitle[3] templateTitleSubject[0] templateTitle[5] . templateYValue[0] % of templateYLabel[1] rated templateTitle[3] templateTitleSubject[0] 's templateTitle[1] as templateXValue[0] .

generated_template: The graph shows the templateTitle[0] of templateTitle[1] to Disney 's new online video streaming service ( templateTitleSubject[0] ) among adults in the templateTitle[3] as of February templateTitleDate[0] . The data reveals templateXValue[2] just templateYValue[min] percent of surveyed templateTitleSubject[0] adults said templateXValue[2] they were templateXValue[0] templateXValue[0] to sign up to templateTitleSubject[0] upon its launch in templateTitleDate[0] , and templateYValue[max] percent said templateXValue[2] they were templateXValue[2] at templateXValue[3] templateXValue[0] to subscribe to the streaming service .
generated: The graph shows the Product of quality to Disney 's new online video streaming service ( Armour ) among adults in the Under as of February 2014 . The data reveals Neutral just 72 percent of surveyed Armour adults said Neutral they were Extremely positive to sign up to Armour upon its launch in 2014 , and 72 percent said Neutral they were Neutral at Somewhat negative Extremely positive to subscribe to the streaming service .

Example 632:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2020']}
title: Public opinion on the most important problem facing the U.S. 2020
X_Axis['Response']: ['Dissatisfaction_with_government/Poor_leadership', 'Immigration', 'Healthcare', 'Ethics/moral/religious/family_decline', 'Unifying_the_country', 'Poverty/Hunger/Homelessness', 'Lack_of_respect_for_each_other', 'Environment/Pollution/Climate_change', 'Race_relations/Racism', 'Situation_in_Iraq/ISIS', 'Foreign_policy/Foreign_aid/Focus_overseas', 'Economy_in_general', 'Guns/Gun_control', 'Gap_between_rich_and_poor', 'Education', 'Wars/War_(nonspecific)/Fear_of_war']
Y_Axis['Share', 'of', 'respondents']: ['28', '6', '6', '5', '5', '5', '4', '4', '3', '2', '2', '2', '2', '2', '2', '2']

gold: This statistic represents American adults ' view of the most important problem facing the United States . In January 2020 , 28 percent of the participants stated that poor leadership and a general dissatisfaction with the government were the most important problems facing the U.S .
gold_template: This statistic represents American adults ' view of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] the templateTitle[6] . In January templateTitleDate[0] , templateYValue[max] percent of the participants stated that templateXValue[13] templateXValue[0] and a templateXValue[11] templateXValue[0] the government were the templateTitle[2] templateTitle[3] problems templateTitle[5] the templateTitle[6] .

generated_template: This statistic 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 important problem facing in U.S. in the third quarter of 2020 . The most used important problem Response in U.S. was Dissatisfaction with government/Poor leadership at 28 percent , closely followed by Immigration at 6 percent . On the opposite side , Wars/War (nonspecific)/Fear of war is used by four percent of respondents .

Example 633:
titleEntities: {'Subject': ['Yemen'], 'Date': ['2002', '2019']}
title: U.S. airstrikes in Yemen 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2002']
Y_Axis['Number', 'of', 'airstrikes']: ['8', '35', '125', '44', '23', '23', '26', '42', '10', '4', '2', '1']

gold: This statistic shows the number of U.S. airstrikes in Yemen from 2002 to 2019 . In 2018 , there were 35 United States airstrikes in Yemen .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , there were templateYValue[1] templateTitle[0] templateYLabel[1] in templateTitleSubject[0] .

generated_template: This statistic illustrates the templateTitle[3] templateTitle[4] templateTitle[1] templateTitle[6] templateTitle[7] and paid out in the United Kingdom ( templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . These figures include both domestic and commercial Net templateTitle[6] templateTitle[7] , with a much larger share of templateTitle[6] displayed attributable to domestic templateTitle[4] templateTitle[1] in each templateXLabel[0] under observation . It can be seen that in templateXValue[max] , there was a templateTitle[3] of over templateYValue[0] templateYLabel[2] British pounds templateTitle[7] , an overall decrease of approximately 1.2 templateYLabel[2] British pounds in templateTitle[6] in comparison to templateXValue[min] .
generated: This statistic illustrates the 2002 2019 airstrikes 2019 and paid out in the United Kingdom ( Yemen ) from 2002 to 2019 . These figures include both domestic and commercial Net 2019 , with a much larger share of 2019 displayed attributable to domestic 2019 airstrikes in each Year under observation . It can be seen that in 2019 , there was a 2002 of over 8 airstrikes British pounds 2019 , an overall decrease of approximately 1.2 airstrikes British pounds in 2019 in comparison to 2002 .

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', '2017']}
title: Gross margin on furniture in U.S. wholesale 2000 to 2017
X_Axis['Year']: ['17', '16', '15', '14', '13', '12', '11', '10', '9', '8', '7', '6', '5', '4', '3', '2', '1', '0']
Y_Axis['Gross', 'margin', 'in', 'billion', 'U.S.', 'dollars']: ['27.92', '26.54', '25.0', '23.93', '22.73', '21.11', '19.2', '18.73', '15.81', '20.32', '21.17', '21.19', '19.09', '18.25', '17.61', '16.94', '15.49', '15.97']

gold: This timeline depicts the U.S. merchant wholesalers ' gross margin on furniture and home furnishings from 2000 to 2017 . In 2017 , the gross margin on furniture and home furnishings in U.S. wholesale was about 27.92 billion U.S. dollars .
gold_template: This timeline depicts the templateYLabel[3] merchant wholesalers ' templateYLabel[0] templateYLabel[1] on templateTitle[2] and home furnishings from templateTitleDate[0] to templateTitle[6] . In templateTitle[6] , the templateYLabel[0] templateYLabel[1] on templateTitle[2] and home furnishings in templateYLabel[3] templateTitle[4] was about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] from the 2001/02 season to the templateXValue[0] season . In templateXValue[0] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] templateTitleSubject[0] are owned by Dee and Jimmy Haslam .
generated: This graph depicts the Gross margin of the U.S. from the 2001/02 season to the 17 season . In 17 , the Gross margin came to 27.92 billion U.S. dollars . The U.S. are owned by Dee and Jimmy Haslam .

Example 636:
titleEntities: {'Subject': ['Volcanic'], 'Date': ['2016']}
title: Volcanic eruptions - people affected worldwide up to 2016
X_Axis['Location', 'and', 'Date']: ['Volcanic_eruption_in_the_Philippines_(June_9_1991)', 'Volcano_eruption_in_Ecuador_(August_14_2015)', 'Volcanic_eruption_in_Nicaragua_(April_9_1992)', 'Volcano_eruption_in_Ecuador_(August_14_2006)', 'Volcano_eruption_in_Indonesia_(April_5_1982)', 'Volcano_eruption_in_Indonesia_(1969)', 'Volcanic_eruption_in_Comoros_(November_24_2005)', 'Volcanic_eruption_in_the_Philippines_(Feb._6_1993)', 'Volcanic_eruption_in_Papua_New_Guinea_(September_19_1994)', 'Volcanic_eruption_in_Indonesia_(October_24_2002)']
Y_Axis['Number', 'of', 'victims']: ['1036065', '800000', '300075', '300013', '300000', '250000', '245000', '165009', '152002', '137140']

gold: The statistic shows the number of people , who were affected by the world 's most significant volcanic eruptions from 1900 to 2016* . In 1991 , total 1,036,035 were affected due to volcanic eruption in Philippines .
gold_template: The statistic shows the templateYLabel[0] of templateTitle[2] , who were templateTitle[3] by the world 's most significant templateXValue[0] templateTitle[1] from 1900 to 2016* . In 1991 , total 1,036,035 were templateTitle[3] due to templateXValue[0] in templateXValue[0] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] the world 's templateTitle[4] templateTitle[5] templateTitle[3] 1900 to 2016* . The templateXLabel[0] in templateXValue[0] 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 victims people affected the world 's worldwide up affected 1900 to 2016* . The Location in Volcanic eruption in the Philippines (June 9 1991) in 1973 claimed 300075 lives . Natural disasters Natural disasters , such as earthquakes , volcanic eruption , tsunamis , floods , tornados or up affect people 2016 .

Example 637:
titleEntities: {'Subject': ['Black Friday'], 'Date': ['2017']}
title: U.S. consumer sentiments towards Black Friday shopping 2017
X_Axis['Response']: ['It_is_a_great_opportunity_to_buy_gifts_for_the_holidays', "It's_a_tradition", 'I_like_it_even_more_now_that_I_can_shop_online', 'It_is_the_best_opportunity_to_buy_expensive_items_at_a_discount', 'It_is_when_you_find_promotions_that_are_not_available_at_any_other_time_of_year', 'It_is_a_good_way_to_spend_quality_time_with_friends/family', 'I_will_wait_until_Cyber_Monday_to_do_most_of_my_shopping', 'Promotions_are_never_on_products_I_am_interested_in', 'Retailers_just_discount_their_worst_brands', 'None_of_these']
Y_Axis['Share', 'of', 'respondents']: ['42', '39', '37', '33', '31', '19', '18', '16', '9', '5']

gold: This statistic shows the results of a 2017 survey in which U.S. consumers were asked about their attitude towards Black Friday shopping . According to the survey , 42 percent of respondents said that Black Friday is a great opportunity to buy gifts for the holidays .
gold_template: This statistic shows the results of a templateTitleDate[0] survey in which templateTitle[0] consumers were asked about templateXValue[8] attitude templateTitle[3] templateTitleSubject[0] shopping . According to the survey , templateYValue[max] 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 the templateTitleDate[0] ranking of the top templateYValue[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[2] by online buyers in templateTitleSubject[0] . templateXValue[0] and templateXValue[1] were ranked first and second with templateYValue[max] percent and templateYValue[1] percent respectively . The search of lodging has consolidated as the main purpose for Spanish users .
generated: This statistic shows the 2017 ranking of the top 37 towards Black Friday sentiments by online buyers in Black Friday . It is a great opportunity to buy gifts for the holidays and It's a tradition were ranked first and second with 42 percent and 39 percent respectively . The search of lodging has consolidated as the main purpose for Spanish users .

Example 638:
titleEntities: {'Subject': ['YouTube'], 'Date': ['2020']}
title: All-time most viewed YouTube channel owners 2020
X_Axis['Month']: ["Ryan's_World", 'PewDiePie', 'Like_Nastya_Vlog', '✿_Kids_Diana_Show', 'DanTDM_(TheDiamondMinecart)', 'Fun_Toys_Collector_Disney', 'Vlad_and_Nikita', 'FGTeeV', 'Family_Fun_Pack', 'CookieSwirlC', 'Markiplier']
Y_Axis['All-time', 'channel', 'views', 'in', 'billions']: ['35.18', '24.44', '22.68', '17.01', '16.01', '14.86', '14.07', '13.11', '12.66', '12.42', '12.29']

gold: As of January 2020 , Ryan from Ryan 's World ( formerly known as Ryan ToysReview ) had reached almost 35.2 billion lifetime video views , making the elementary schooler the most viewed YouTube channel owner of all time . This achievement unseated former first-ranked video game commentator Felix Kjellberg , who produces videos under the name PewDiePie . Ryan has been uploading YouTube videos since March 2015 , and mainly features in videos where he is playing with and reviews toys `` for kids , by a kid '' .
gold_template: As of January templateTitle[6] , Ryan from Ryan 's templateXValue[0] ( formerly known as Ryan ToysReview ) had reached almost templateYValue[max] templateYLabel[3] lifetime video templateYLabel[2] , making the elementary schooler the templateTitle[1] templateTitle[2] templateTitleSubject[0] templateYLabel[1] owner of all time . This achievement unseated former first-ranked video game commentator Felix Kjellberg , who produces videos under the name templateXValue[1] . Ryan has been uploading templateTitleSubject[0] videos since March 2015 , and mainly features in videos where he is playing with and reviews templateXValue[5] `` for templateXValue[3] , by a kid '' .

generated_template: This statistic shows the templateYLabel[0] templateTitleSubject[0] of templateTitle[2] templateYLabel[1] in the templateTitle[4] as of templateTitleDate[0] , in order of the templateTitle[6] templateTitle[7] the templateTitle[8] templateTitle[9] . templateYValue[max] templateTitleSubject[0] templateYLabel[1] had been templateYLabel[2] to Indian workers for the templateTitle[4] as of templateTitleDate[0] .
generated: This statistic shows the All-time YouTube of viewed channel in the channel as of 2020 , in order of the 2020 the 2020 . 35.18 YouTube channel had been views to Indian workers for the channel as of 2020 .

Example 639:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2018']}
title: Brazil : most popular music genres 2018
X_Axis['Response']: ['Pop', 'Brazilian_pop', 'Sertanejo', 'Rock', 'Samba/pagode', 'Electronic/dance_music', 'Dance', 'Gospel', 'Hip_hop', 'Reggae', 'Forró', 'Funk/soul', 'Blues', 'Latin', 'Rap', 'Country', 'Metal', 'Techno/EDM', 'R&B/soul', 'Jazz', 'Heavy_metal', 'Classical/opera', 'Reggaeton', 'Easy_listening', 'Punk', 'Folk', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['55.5', '54', '50.3', '48.8', '38.1', '37.9', '35.6', '35.1', '31.8', '31', '30.2', '25.2', '24.7', '23.4', '23.2', '22.6', '17.6', '17.4', '17.3', '16.8', '14.5', '14.3', '13.2', '10.7', '10.4', '8.9', '4.8']

gold: This statistic shows the results of a Deezer survey on music listening habits among adults in Brazil as of 2018 . That year , 55.5 percent of Brazilian respondents claimed to listen to pop music , whereas 54 percent said they listened to Brazilian pop .
gold_template: This statistic shows the results of a Deezer survey on templateXValue[5] templateXValue[23] habits among adults in templateTitleSubject[0] as of templateTitleDate[0] . That year , templateYValue[max] percent of templateXValue[1] templateYLabel[1] claimed to listen to templateXValue[0] templateXValue[5] , whereas templateYValue[1] percent said they listened to templateXValue[1] templateXValue[0] .

generated_template: This statistic displays the results of a survey , conducted in December 2018 in the country , on templateTitleSubject[1] ' templateXValue[8] templateTitleSubject[0] templateTitle[2] resolutions templateTitle[5] templateTitleDate[0] . During the survey , templateYValue[2] percent of templateYLabel[1] said their templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[7] templateXValue[0] templateXValue[0] and templateXValue[0] in templateTitleDate[0] .
generated: This statistic displays the results of a survey , conducted in December 2018 in the country , on Brazil ' Hip hop Brazil popular resolutions 2018 . During the survey , 50.3 percent of respondents said their music genres 2018 in the 2018 Pop and Pop in 2018 .

Example 640:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: Per capita consumption of butter in the U.S. 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['5.8', '5.7', '5.7', '5.6', '5.5', '5.5', '5.5', '5.4', '4.9', '5.0', '5.0', '4.7', '4.7', '4.5', '4.5', '4.5', '4.4', '4.3', '4.5']

gold: This statistic shows the per capita consumption of butter in the United States from 2000 to 2018 . The U.S. per capita consumption of butter amounted to 5.8 pounds in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] amounted to templateYValue[max] templateYLabel[3] in templateXValue[max] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[0] templateYLabel[3] in templateXValue[max] .
generated: The timeline shows the Per capita consumption of U.S. 2000 in the Per from 2000 to 2018 . According to the report , the U.S. Per capita consumption of U.S. 2000 amounted to approximately 5.8 pounds in 2018 .

Example 641:
titleEntities: {'Subject': ['Daily'], 'Date': ['2018']}
title: Daily online video usage in selected countries 2018
X_Axis['Country']: ['Saudi_Arabia', 'Turkey', 'Brazil', 'New_Zealand', 'Australia', 'Mexico', 'Spain', 'Canada', 'United_States', 'South_Korea', 'France', 'Philippines', 'India', 'Germany', 'Japan', 'China', 'Indonesia', 'South_Africa', 'Nigeria']
Y_Axis['Share', 'of', 'respondents']: ['64', '64', '62', '61', '60', '56', '53', '50', '50', '44', '42', '34', '33', '32', '32', '30', '21', '19', '16']

gold: This statistic gives information on the share of internet users in selected countries who watch online videos every day as of January 2018 . During the survey , it was found that 50 percent of U.S. internet users watched online video content on a daily basis . Additionally , more than half of the internet users in Mexico watched online videos every day .
gold_template: This statistic gives information on the templateYLabel[0] of internet users in templateTitle[4] templateTitle[5] who watch templateTitle[1] videos every day as of January templateTitleDate[0] . During the survey , it was found that templateYValue[7] percent of U.S. internet users watched templateTitle[1] templateTitle[2] content on a templateTitleSubject[0] basis . Additionally , more than half of the internet users in templateXValue[5] watched templateTitle[1] videos every day .

generated_template: This statistic shows the templateYLabel[0] of 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 July templateTitleDate[0] 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 shows the Share of online worldwide who have every experienced abuse or Daily on video usage and selected countries 2018 . During the July 2018 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] of the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[max] templateYLabel[3] templateYLabel[4] templateYLabel[5] of the templateTitle[1] .
generated: The statistic shows the Youth unemployment rate of the Singapore from 2005 to 2018 . In 2018 , the Youth unemployment rate was at 10.7 rate of the unemployment .

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 templateTitleSubject[0] templateTitleSubject[0] of templateTitleSubject[0] ( RBS ) is part of The templateTitleSubject[0] templateTitleSubject[0] of templateTitleSubject[0] Group plc with Natwest and Ulster templateTitleSubject[0] and consists of 67 thousand employees over 187 branches across the United Kingdom . As of templateXValue[1] , RBS had over 830 templateYLabel[2] euros in templateYLabel[1] , the fourth highest in the United Kingdom . History of RBS Founded in Edinburgh in 1727 , the templateTitleSubject[0] templateTitleSubject[0] of templateTitleSubject[0] is an amalgamation of hundreds of past banks .
generated: The Germany of Germany ( RBS ) is part of The Germany of Germany Group plc with Natwest and Ulster Germany and consists of 67 thousand employees over 187 branches across the United Kingdom . As of 2017 , RBS had over 830 euros in billion , the fourth highest in the United Kingdom . History of RBS Founded in Edinburgh in 1727 , the Germany of Germany is an amalgamation of hundreds of past banks .

Example 646:
titleEntities: {'Subject': ['Latin American'], 'Date': ['2018']}
title: 2018 FIFA World Cup : average age of Latin American soccer teams
X_Axis['Country']: ['Costa_Rica', 'Argentina', 'Mexico', 'Panama', 'Brazil', 'Colombia', 'Uruguay', 'Peru']
Y_Axis['Average', 'age', 'in', 'years']: ['29.8', '29.6', '29.3', '28.9', '28.6', '28.4', '28.2', '27.5']

gold: The statistic presents the average age of all Latin American soccer teams participating in the 2018 FIFA World Cup in Russia . Costa Rica was the Latin American soccer team with the oldest average age ( 29.8 years ) , followed by Argentina with team players averaging 29.6 years old .
gold_template: The statistic presents the templateYLabel[0] templateYLabel[1] of all templateTitleSubject[0] soccer templateTitle[9] participating in the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in Russia . templateXValue[0] was the templateTitleSubject[0] soccer team with the oldest templateYLabel[0] templateYLabel[1] ( templateYValue[max] templateYLabel[2] ) , followed by templateXValue[1] with team players averaging templateYValue[1] templateYLabel[2] old .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] in templateTitleDate[0] , templateTitle[7] templateXLabel[0] . In that year , the templateYLabel[0] templateYLabel[1] of templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] was about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Average age of World Cup average in age in 2018 , American Country . In that year , the Average age of Cup average age in Latin American was about 29.8 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 March templateTitleDate[0] . templateXValue[0] ranked first with a templateTitle[0] templateTitle[1] templateYLabel[0] of more than templateYValue[max] templateYLabel[1] templateYLabel[2] , almost templateTitleDate[0] times as much as the templateYLabel[0] in the templateXValue[6] templateXValue[18] ( templateYValue[18] templateYLabel[2] ) . Global unit sales data from templateTitleDate[0] and 2015 shows that templateTitleSubject[0] templateTitleDate[0] was the highest selling platform worldwide in those years .

generated_template: 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: During the year templateTitleDate[0] , the total templateTitle[3] exports templateTitle[4] templateTitleSubject[0] to the templateXValue[0] templateXValue[0] amounted to about 27 templateYLabel[2] of 75 cl . templateYLabel[1] . The UK is the first templateTitle[1] market for French templateTitle[3] .
generated: During the year 2016 , the total industry exports by Manufacturing to the Mexico amounted to about 27 index of 75 cl . costs . The UK is the first costs market for French industry .

Example 649:
titleEntities: {'Subject': ['European'], 'Date': ['2028']}
title: European Union-27 : poultry meat consumption volume forecast 2015 to 2028
X_Axis['Year']: ['2028', '2027', '2026', '2025', '2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Volume', 'in', 'thousand', 'metric', 'tons']: ['12182', '12111', '12041', '11976', '11921', '11869', '11817', '11751', '11690', '11664', '11861', '11606', '11667', '11102']

gold: Forecasts up until the year 2018 show that poultry meat consumption across the European Union is expected to increase to 11.86 million metric tons . In the following decade consumption will likely slow down , with the forecast up until 2028 remaining constant . By the end of the period in consideration , consumption will amount to an estimated 12.18 million metric tons .
gold_template: Forecasts up until the templateXLabel[0] templateXValue[10] show that templateTitle[2] templateTitle[3] templateTitle[4] across the templateTitleSubject[0] Union is expected to increase to templateYValue[10] templateYLabel[1] templateYLabel[2] templateYLabel[3] . In the following decade templateTitle[4] will likely slow down , with the templateTitle[6] up until templateXValue[max] remaining constant . By the end of the period in consideration , templateTitle[4] will amount to an estimated templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: In templateXValue[max] , the templateTitle[3] of templateTitle[0] and calves in the templateTitleSubject[0] templateTitleSubject[0] was approximately templateYValue[min] templateYLabel[1] , a slight decrease from the previous templateXLabel[0] . This was the lowest templateYLabel[0] for the entire period shown in this graph . Despite a small rebound in templateXValue[4] and templateXValue[3] this constitutes a slow long-term decline of herd sizes .
generated: In 2028 , the meat of European and calves in the European was approximately 11102 thousand , a slight decrease from the previous Year . This was the lowest Volume for the entire period shown in this graph . Despite a small rebound in 2024 and 2025 this constitutes a slow long-term decline of herd sizes .

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: Canadians aged between templateXValue[1] and templateXValue[1] had the highest rate of templateTitle[4] templateTitle[5] nationally as of December templateTitle[9] . Some templateYValue[max] percent of survey templateYLabel[1] in this templateTitle[8] group stated that they consumed templateTitle[4] of any form in the templateTitle[0] three templateTitle[2] . Those aged templateXValue[last] or templateXValue[last] had the lowest levels of templateTitle[5] , with just templateYValue[min] percent of templateYLabel[1] consuming templateTitle[4] in this time .
generated: Canadians aged between 2011 and 2011 had the highest rate of use WiFi nationally as of December 2013 . Some 96 percent of survey respondents in this 2003 group stated that they consumed use of any form in the Great three Households . Those aged 2003 or 2003 had the lowest levels of WiFi , with just 1 percent of respondents consuming use in this time .

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: The statistic shows the templateTitle[0] of templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[6] through the Supplemental Nutrition Assistance Program ( templateTitleSubject[0] , formerly called templateTitleSubject[0] templateTitleSubject[0] ) in templateTitleDate[0] , by templateXLabel[0] . About templateYValue[2] percent of the templateTitle[3] in templateXValue[5] is templateTitle[4] templateTitle[6] through the Supplemental Nutrition Assistance Program .
generated: The statistic shows the U.S. of U.S. among adults state through the Supplemental Nutrition Assistance Program ( U.S. , formerly called U.S. ) in 2018 , by State . About 22.7 percent of the among in Illinois is adults state through the Supplemental Nutrition Assistance Program .

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': ['Bundesliga', 'Germany'], 'Date': ['2020']}
title: Market value of first Bundesliga football clubs in Germany in 2020
X_Axis['Club', 'Name']: ['FC_Bayern_München', 'Borussia_Dortmund', 'RasenBallsport_Leipzig', 'Bayer_04_Leverkusen', 'Borussia_Mönchengladbach', 'FC_Schalke_04', 'TSG_1899_Hoffenheim', 'Hertha_BSC', 'VfL_Wolfsburg', 'Eintracht_Frankfurt', 'SV_Werder_Bremen', '1._FSV_Mainz_05', 'SC_Freiburg', 'FC_Augsburg', '1._FC_Köln', 'Fortuna_Düsseldorf', '1._FC_Union_Berlin', 'SC_Paderborn']
Y_Axis['Market', 'value', 'in', 'million', 'euros']: ['933.15', '637.4', '594.4', '445.75', '312.0', '242.75', '238.23', '233.2', '230.95', '215.8', '189.75', '147.4', '145.4', '131.15', '102.2', '93.15', '43.05', '31.25']

gold: This statistic shows the market value of the first Bundesliga football clubs in Germany as of February 11 , 2020 . The market value of FC Bayern Munich was highest at 933.15 million euros , followed by 637.4 million euros for Borussia Dortmund and 594.4 million euros for RB Leipzig .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] football templateTitle[5] in templateTitleSubject[1] as of February 11 , templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] of templateXValue[0] Munich was highest at templateYValue[max] templateYLabel[2] templateYLabel[3] , followed by templateYValue[1] templateYLabel[2] templateYLabel[3] for templateXValue[1] and templateYValue[2] templateYLabel[2] templateYLabel[3] for RB templateXValue[2] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of health templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] in templateTitleDate[0] . templateXValue[0] templateXValue[0] templateXValue[0] had a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Market value of health first Bundesliga football in the clubs in 2020 . FC Bayern München had a Market value of 933.15 million euros .

Example 654:
titleEntities: {'Subject': ['France'], 'Date': ['2017']}
title: Distribution of women practicing nudism at the beach in France 2017 , by age
X_Axis['Year']: ['18_to_24_years', '25_to_34_years', '35_to_49_years', '50_to_59_years', '60_years_and_older']
Y_Axis['Share', 'of', 'women', 'surveyed']: ['6', '5', '9', '11', '13']

gold: This statistic indicates the share of French women who have already practiced naturism on the beach or in a nudist camp in 2017 , by age group . We can see that more than 10 percent of women aged 50 to 59 had already practiced nudism at the beach or in a naturist camp . Discover also the level of interest of the French for naturism .
gold_template: This statistic indicates the templateYLabel[0] of French templateYLabel[1] who have already practiced naturism on the templateTitle[4] or in a nudist camp in templateTitleDate[0] , templateTitle[7] templateTitle[8] group . We can see that more than 10 percent of templateYLabel[1] aged templateXValue[3] to templateXValue[3] had already practiced templateTitle[3] at the templateTitle[4] or in a naturist camp . Discover also the level of interest of the French for naturism .

generated_template: This statistic displays the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] per day in the templateTitleSubject[0] templateTitleSubject[0] between templateTitle[6] and templateTitle[7] , templateTitle[8] templateTitle[9] . In this four templateXLabel[0] period , individuals aged between 4 and 10 templateXValue[0] old templateTitle[3] templateYValue[max] templateYLabel[1] of templateTitle[1] templateTitle[2] per day .
generated: This statistic displays the Distribution of women practicing nudism per day in the France between 2017 and by , age . In this four Year period , individuals aged between 4 and 10 18 to 24 years old nudism 13 women of women practicing per day .

Example 655:
titleEntities: {'Subject': ['Dell'], 'Date': ['1996', '2019']}
title: Dell : Number of employees 1996 to 2019
X_Axis['Year']: ["'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96"]
Y_Axis['Number', 'of', 'employees', 'in', 'thousands']: ['157.0', '145.0', '138.0', '102.0', '98.3', '108.3', '111.3', '109.4', '103.3', '96.0', '78.9', '88.2', '90.5', '65.2', '55.2', '46.0', '39.1', '34.6', '40.0', '36.5', '24.4', '16.2', '10.35', '8.4']

gold: As of early 2019 , Dell 's employee count was 157 thousand . The majority , approximately 145 thousand , of these employees are full-time employees . 37 percent of Dell 's full-time employees are located in the company 's home market , the United States .
gold_template: As of early templateTitleDate[1] , templateTitleSubject[0] 's employee count was templateYValue[max] thousand . The majority , approximately templateYValue[1] thousand , of these templateYLabel[1] are full-time templateYLabel[1] . templateYValue[19] percent of templateTitleSubject[0] 's full-time templateYLabel[1] are located in the company 's home market , the country .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] of men templateTitleSubject[0] from templateTitleDate[0] - 1993 to 2014 - templateTitleDate[1] . There was a steady increase over this period , totaling 7.1 templateYLabel[1] . In 2014 - templateTitleDate[1] , the templateYLabel[0] of templateYLabel[1] at templateTitle[4] of the templateTitleSubject[0] templateTitleSubject[0] was of templateYValue[max] templateYLabel[1] .
generated: This statistic shows the Number of employees of men Dell from 1996 - 1993 to 2014 - 2019 . There was a steady increase over this period , totaling 7.1 employees . In 2014 - 2019 , the Number of employees at 2019 of the Dell was of 157.0 employees .

Example 656:
titleEntities: {'Subject': ['Bhutan'], 'Date': ['2019']}
title: Youth unemployment rate in Bhutan in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['9.69', '9.58', '9.57', '9.88', '9.97', '9', '9.25', '6.99', '8.93', '8.6', '12.46', '10.81', '11.2', '9.57', '9.41', '7.74', '5.82', '6.06', '6.26', '5.8', '4.89']

gold: The statistic shows the youth unemployment rate in Bhutan from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated youth unemployment rate in Bhutan was at 9.69 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[0] percent .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[0] percent .
generated: The statistic shows the Youth unemployment rate in Bhutan from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated Youth unemployment rate in Bhutan was at 9.69 percent .

Example 657:
titleEntities: {'Subject': ['Facebook'], 'Date': ['2017']}
title: Facebook reactions on top shared content 2017
X_Axis['Response']: ['Love', 'Haha', 'Wow', 'Sad', 'Angry']
Y_Axis['Share', 'of', 'reactions']: ['41', '28', '15', '12', '5']

gold: This statistic presents the reaction usage in top shared posts on Facebook in September 2017 . During the measured period , Love was the most popular Facebook reaction on top shared posts on the social network .
gold_template: This statistic presents the reaction usage in templateTitle[2] templateTitle[3] posts on templateTitleSubject[0] in September templateTitleDate[0] . During the measured period , templateXValue[0] was the most popular templateTitleSubject[0] reaction on templateTitle[2] templateTitle[3] posts on the social network .

generated_template: This statistic shows 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 Facebook reactions top shared in the content 2017 average Share reactions in 2017 . YouGov 's Response Share 2017 Share results show that Love 2017 highest with a score of 41 , followed 2017 Haha at 28 .

Example 658:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Household income of black families in the U.S. 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Median', 'income', 'in', '2018', 'CPI-U-RS', 'adjusted', 'U.S.', 'dollars']: ['41361', '40324', '40339', '38178', '36689', '37232', '35641', '35203', '36195', '37319', '39054', '40196', '38963', '38828', '39151', '39607', '39661', '40902', '42348', '41192', '38212', '38269', '36649', '35880', '34503', '32721', '32210', '33103', '34068']

gold: This statistic shows the household income of black families in the United States from 1990 to 2018 . The median income in 2018 was at 41,361 U.S. dollars for black households .
gold_template: This statistic shows the templateTitle[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] in templateXValue[max] was at templateYValue[0] templateYLabel[5] templateYLabel[6] for templateTitle[2] households .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitleSubject[0] templateTitle[3] templateTitle[4] were living below the templateYLabel[0] line in the templateTitle[5] . templateYLabel[0] is the state of one who lacks a certain amount of material possessions or money .
generated: This statistic shows the Median income of U.S. families U.S. in the U.S. from 1990 to 2018 . In 2018 , 41361 percent of U.S. families U.S. were living below the Median line in the 1990 . Median is the state of one who lacks a certain amount of material possessions or money .

Example 659:
titleEntities: {'Subject': ['Sales'], 'Date': ['2013']}
title: Sales of the leading toy companies worldwide 2013
X_Axis['Company']: ['Mattel', 'Lego', 'Hasbro', 'MGA_Entertainment', 'Playmobil', 'Jakks_Pacific', 'LeapFrog', 'MEGA_Bloks', 'Melissa_&_Doug']
Y_Axis['Sales', 'in', 'million', 'U.S.', 'dollars']: ['6300', '4500', '4000', '2000', '790', '700', '580', '400', '325']

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

generated_template: This statistic shows the templateYLabel[0] of the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] in templateTitleDate[0] . In that year , templateXValue[0] was the country 's top selling templateTitle[1] templateTitle[2] in the templateTitle[3] , with templateYLabel[0] of about templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Sales of the Sales leading toy companies worldwide in the 2013 in 2013 . In that year , Mattel was the country 's top selling leading toy in the companies , with Sales of about 6300 million U.S. dollars .

Example 660:
titleEntities: {'Subject': ['Denver Broncos', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Denver Broncos ( NFL ) 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['3000', '2650', '2600', '2400', '1940', '1450', '1161', '1132', '1046', '1049', '1081', '1061', '994', '975', '907', '815', '683', '604']

gold: This graph depicts the franchise value of the Denver Broncos from the National Football League from 2002 to 2019 . In 2019 , the franchise value came to three billion U.S. dollars . The Denver Broncos are owned by the Pat Bowlen Trust , who bought the franchise for 78 million U.S. dollars in 1984 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by the Pat Bowlen Trust , who bought the templateYLabel[0] for 78 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1984 .

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] are owned by Charles Johnson , who bought the templateYLabel[0] for 100 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1993 .
generated: This graph depicts the value of the Denver Broncos Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 3000 million U.S. dollars . The Denver Broncos are owned by Charles Johnson , who bought the Franchise for 100 million U.S. dollars in 1993 .

Example 661:
titleEntities: {'Subject': ['Aramark'], 'Date': ['2008', '2019']}
title: Facilities management industry - Aramark worldwide revenue 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['16227.3', '15789.6', '14604.4', '14415.8', '14329.1', '14832.9', '13945.7', '13505.4', '13082.4', '12419.1', '12138.1', '13252.1']

gold: This statistic shows the annual total worldwide revenue of Aramark from 2008 to 2019 . In 2019 , Aramark had total revenues of over 16.2 billion U.S. dollars . The Aramark Corporation is an American foodservice , facilities , and clothing provider headquartered in Philadelphia , Pennsylvania .
gold_template: This statistic shows the annual total templateTitle[4] templateYLabel[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] had total revenues of over templateYValue[max] 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 the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . Global hotel company templateTitleSubject[0] templateTitleSubject[0] International generated approximately templateYValue[max] 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 Revenue of Aramark worldwide from 2008 to 2019 . Global hotel company Aramark International generated approximately 16227.3 million U.S. dollars in Revenue in 2019 , up from 15789.6 million the previous Year .

Example 662:
titleEntities: {'Subject': ['RIM/Blackberry'], 'Date': ['2019']}
title: Revenue of RIM/Blackberry worldwide 2004 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['904', '932', '1309', '2160', '3335', '6813', '11073', '18423', '19907', '14953', '11065', '6009', '3037', '2066', '1350', '595']

gold: In its 2019 fiscal year , Canadian company BlackBerry recorded revenues of less than one billion U.S. dollars for only the second time in the past 15 years . The company , previously known as Research in Motion ( RIM ) , has transitioned from developing BlackBerry brand smartphones to enterprise software and services involving the internet of things . Transitioning to the Enterprise of Things In the past decade , BlackBerry has seen a significant decline in their revenue figures and share of the global and U.S. smartphone market .
gold_template: In its templateXValue[max] fiscal templateXLabel[0] , Canadian company BlackBerry recorded revenues of less than templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] for only the second time in the past 15 years . The company , previously known as Research in Motion ( RIM ) , has transitioned from developing BlackBerry brand smartphones to enterprise software and services involving the internet of things . Transitioning to the Enterprise of Things In the past decade , BlackBerry has seen a significant decline in their templateYLabel[0] figures and share of the global and templateYLabel[2] smartphone market .

generated_template: The timeline shows the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] franchise from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] templateTitle[3] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The timeline shows the Revenue of the RIM/Blackberry franchise from 2004 to 2019 . In 2019 , the Revenue of the RIM/Blackberry 2004 was 19907 million U.S. dollars .

Example 663:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Revenue share of various apparel decorating services in the U.S. 2014
X_Axis['Decorating', 'service']: ['Embroidery', 'Screen_printing', 'Heat_transfers', 'Vinyl_(cut)_letters/designs', 'Digitizing/artwork_services', 'Sublimation_printing', 'Emblems/patches', 'Direct-to-garment_printing', 'Rhinestones/crystals', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['45', '26', '7', '6', '5', '3', '3', '3', '1', '1']

gold: This statistic depicts the revenue share of various apparel decorating services in the United States in 2014 . The survey revealed that some 45 percent of the respondents felt that embroidery decorating services for apparel generated the most revenue .
gold_template: This statistic depicts the templateTitle[0] templateYLabel[0] of templateTitle[2] templateTitle[3] templateXLabel[0] templateXValue[4] in the templateTitle[6] in templateTitleDate[0] . The survey revealed that some templateYValue[max] percent of the templateYLabel[1] felt that templateXValue[0] templateXLabel[0] templateXValue[4] for templateTitle[3] generated the most templateTitle[0] .

generated_template: This statistic gives information on the templateTitleSubject[0] templateTitle[1] templateTitle[2] of templateXValue[0] templateXValue[0] offerings as of January templateTitleDate[0] . 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 U.S. share various of Embroidery offerings as of January 2014 . During a survey of smartphone owners , 26 percent of respondents stated they had Screen printing a Screen printing via Embroidery .

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] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] templateTitleSubject[0] stood at templateYValue[max] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .
generated: This statistic shows the Per capita real Gross Domestic Product of Florida from 2000 to 2018 . In 2018 , the Per capita real GDP of Florida stood at 45926 chained 2012 U.S. dollars .

Example 666:
titleEntities: {'Subject': ['Scotland'], 'Date': ['2014', '2039']}
title: Scotland : forecasted median age of population 2014 to 2039
X_Axis['Year']: ['2039', '2034', '2029', '2024', '2019', '2014']
Y_Axis['Age']: ['45.2', '44.3', '43.5', '42.9', '42.4', '41.9']

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

generated_template: This statistic shows templateTitleSubject[0] 's templateTitle[1] templateTitle[2] templateTitle[3] ( templateYLabel[0] ) from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] 's templateYLabel[0] increased till the end of the period by a total of over 19 templateYLabel[1] British pounds .
generated: This statistic shows Scotland 's forecasted median age ( Age ) from 2014 to 2039 . Scotland 's Age increased till the end of the period by a total of over 19 Age British pounds .

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 gives information on the templateTitleSubject[0] templateTitle[1] templateTitle[2] of templateXValue[0] templateXValue[0] offerings as of January templateTitleDate[0] . 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 U.S. company data of Training and awareness programs offerings as of January 2017 . During a survey of smartphone owners , 55 percent of respondents stated they had Expanded use of encryption a Expanded use of encryption via Training and awareness programs .

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] 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': ['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 templateYLabel[0] of templateTitleSubject[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the country from templateXValue[min] to templateXValue[max] . According to the report , the U.S. templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] amounted to about templateYValue[0] templateYLabel[4] in templateXValue[max]
generated: This statistic shows the Trillion of U.S. dollars in the country from 2000 to 2019 . According to the report , the U.S. Trillion U.S. dollars amounted to about 3.12 dollars in 2019

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: templateXValue[0] had the most automobile registrations in templateTitleDate[0] : More than templateYValue[max] million such vehicles were templateYLabel[1] in the most populous templateTitleSubject[0] federal templateXLabel[0] . templateXValue[0] also leads in terms of annual car sales with almost templateYValue[26] million cars sold in templateTitleDate[0] . Additionally , templateXValue[0] residents also purchased about 715,000 crossover and sport-utility vehicles ( CUVs and SUVs ) , some 221,270 pickups , and almost 80,000 vans and minivans in templateTitleDate[0] .
generated: California had the most automobile registrations in 2018 : More than 1739 million such vehicles were murder in the most populous U.S. federal State . California also leads in terms of annual car sales with almost 176 million cars sold in 2018 . Additionally , California residents also purchased about 715,000 crossover and sport-utility vehicles ( CUVs and SUVs ) , some 221,270 pickups , and almost 80,000 vans and minivans in 2018 .

Example 671:
titleEntities: {'Subject': ['National Basketball Association'], 'Date': ['1946', '2020']}
title: National Basketball Association all-time triple double leaders 1946 to 2020
X_Axis['Player']: ['Oscar_Robertson', 'Russell_Westbrook', 'Magic_Johnson', 'Jason_Kidd', 'LeBron_James', 'Wilt_Chamberlain', 'Larry_Bird', 'James_Harden', 'Fat_Lever', 'Nikola_Jokić', 'Bob_Cousy', 'Rajon_Rondo', 'John_Havlicek']
Y_Axis['Number', 'of', 'triple', 'doubles']: ['181', '146', '138', '107', '92', '78', '59', '45', '43', '39', '33', '32', '31']

gold: Which player has the most triple doubles ? Oscar Robertson - nicknamed ‘ The Big O ' _ , is the all-time leader in triple doubles in the National Basketball Assocation . He compiled 181 triple doubles during his playing career with the Cincinnati Royals and the Milwaukee Bucks ( 1960 to 1974 ) . The highest ranked active player is Russell Westbrook of the Oklahoma City Thunder with 144 triple doubles in second place .
gold_template: Which templateXLabel[0] has the most templateYLabel[1] templateYLabel[2] ? templateXValue[0] - nicknamed ‘ The Big O ' _ , is the templateTitle[3] leader in templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] Assocation . He compiled templateYValue[max] templateYLabel[1] templateYLabel[2] during his playing career with the Cincinnati Royals and the Milwaukee Bucks ( 1960 to 1974 ) . The highest ranked active templateXLabel[0] is templateXValue[1] of the Oklahoma City Thunder with 144 templateYLabel[1] templateYLabel[2] in second place .

generated_template: Taking full advantage of the perks of the fantasy genre , J.R.R . Tolkien not only invented a whole universe , he also did not adhere to known templateYLabel[0] structures when he wrote The templateTitleSubject[0] of The templateTitleSubject[0] . The ages of his templateTitle[2] differ wildly ; templateXValue[0] is almost 600 templateYLabel[1] old , while Elrond , the elf , is several thousand templateYLabel[1] old , and Gandalf the Grey – and later the White – is immortal ( for display reasons , both are thus not mentioned here ) .
generated: Taking full advantage of the perks of the fantasy genre , J.R.R . Tolkien not only invented a whole universe , he also did not adhere to known Number structures when he wrote The National Basketball Association of The National Basketball Association . The ages of his Association differ wildly ; Oscar Robertson is almost 600 triple old , while Elrond , the elf , is several thousand triple old , and Gandalf the Grey – and later the White – is immortal ( for display reasons , both are thus not mentioned here ) .

Example 672:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita United Kingdom 2024 ( in U.S. dollars )
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['45934.7', '44311.93', '42842.47', '41504.89', '40391.84', '41030.23', '42579.82', '39976.78', '40657.86', '44494.86', '47003.88', '42981.25', '42023.1', '41649.66', '39122.19', '38601.32', '47469.38', '50315.56', '44403.81', '41842.7', '40111.75', '34302.42', '29912.99', '27510.33', '28043.87', '28435.06', '28077.34', '26647.95', '24256.46', '23026.71', '21344.25', '19925.66', '22305.36', '21671.88', '20808.23', '17617.85', '17364.25', '14294.99', '11551.07', '9491.99', '8943.27']

gold: The statistic shows GDP per capita in the United Kingdom from 1984 to 2018 , with projections up until 2024 . In 2018 , GDP per capita in the United Kingdom was at around 42,579.82 US dollars . The same year , the total UK population amounted to about 64.6 million people .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] was at around templateYValue[6] US templateYLabel[4] . The same templateXLabel[0] , the total UK population amounted to about 64.6 million people .

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

Example 673:
titleEntities: {'Subject': ['Global'], 'Date': ['2030']}
title: Global energy commodity price index 2013 to 2030
X_Axis['Year']: ['2030', '2025', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Price', 'index', 'in', 'real', '2010', 'U.S.', 'dollars']: ['87.2', '79.1', '74.7', '73.3', '72.0', '74.3', '87.0', '68.1', '55.1', '65.0', '111.7', '120.1']

gold: This statistic shows a commodity price index of energy from 2013 through 2018 , and gives projections for 2030 . In 2018 , the index stood at around 87 real US dollars . Real 2010 US dollars reflect buying power in 2010 .
gold_template: This statistic shows a templateTitle[2] templateYLabel[0] templateYLabel[1] of templateTitle[1] from templateXValue[min] through templateXValue[6] , and gives projections for templateXValue[max] . In templateXValue[6] , the templateYLabel[1] stood at around templateYValue[0] templateYLabel[2] US templateYLabel[5] . templateYLabel[2] templateYLabel[3] US templateYLabel[5] reflect buying power in templateYLabel[3] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to around templateYValue[7] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Global Price index in Global from 2013 to 2017 , with projections up until 2030 . In 2017 , Global 's Price index amounted to around 68.1 real 2010 .

Example 674:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Most used paint brands in the U.S. 2018
X_Axis['Brand']: ['Sherwin-Williams', 'Benjamin_Moore', 'Behr_Paint_Cooperation', 'Kelly_Moore', 'Valspar', 'PPG_Pittsburgh_Paints', 'Zar_(United_Gilsonite_Labs)', 'Devoe_&_Raynolds', 'Dutch_Boy', 'Olympic', 'None_of_these']
Y_Axis['Share', 'of', 'respondents']: ['49.5', '22.4', '12.1', '2.8', '1.9', '1.9', '1.9', '0.9', '0.9', '0.9', '4.7']

gold: This statistic depicts paints used the most by U.S. construction firms in 2018 . The survey revealed that 49.5 percent of the respondents used Sherwin-Williams brand paints the most .
gold_template: This statistic depicts templateXValue[5] templateTitle[1] the templateTitle[0] by templateTitleSubject[0] construction firms in templateTitleDate[0] . The survey revealed that templateYValue[max] percent of the templateYLabel[1] templateTitle[1] templateXValue[0] templateXLabel[0] templateXValue[5] the templateTitle[0] .

generated_template: This statistic 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 templateTitle[3] forecast of templateTitleSubject[0] templateTitle[1] templateYLabel[0] between templateTitle[4] and templateTitle[5] , sorted templateTitle[6] select templateXLabel[0] . The forecast predicts that between templateTitle[4] and templateTitle[5] the templateTitleSubject[0] templateTitle[1] templateTitle[3] will grow templateTitle[6] templateYValue[5] percent , with the biggest templateYLabel[0] forecast given for templateXValue[0] with templateYValue[max] percent .
generated: This statistic shows the U.S. forecast of U.S. growth Percent between cosmetic and brands , sorted 2014 select Brand/Segment . The forecast predicts that between cosmetic and brands the U.S. growth U.S. will grow 2014 -2.4 percent , with the biggest Percent forecast given for Neutrogena/ makeup remover implements with 144 percent .

Example 676:
titleEntities: {'Subject': ['Global'], 'Date': ['2012', '2022']}
title: Global ulcerative colitis market 2012 and 2022
X_Axis['Year']: ['2012', '2022']
Y_Axis['Market', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['4.2', '6.6']

gold: This statistic displays the global ulcerative colitis market value in 2012 , and a forecast for 2022 . In 2012 , the ulcerative colitis market was valued at 4.2 billion U.S. dollars . Ulcerative colitis is a type of inflammatory bowel disease ( IBD ) which is characterized by the formation of ulcers in the colon .
gold_template: This statistic displays the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] in templateXValue[min] , and a forecast for templateXValue[max] . In templateXValue[min] , the templateTitle[1] templateTitle[2] templateYLabel[0] was valued at templateYValue[min] templateYLabel[2] templateYLabel[3] templateYLabel[4] . templateTitle[1] templateTitle[2] is a type of inflammatory bowel disease ( IBD ) which is characterized by the formation of ulcers in the colon .

generated_template: The statistic shows a growth forecast for the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] in templateXValue[max] and shows the actual projected templateYLabel[0] value of the templateTitle[1] templateTitle[2] templateYLabel[0] in templateXValue[min] . For templateXValue[max] , the templateTitleSubject[0] smart templateTitle[1] templateTitle[2] templateYLabel[0] is forecasted to reach templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The predicted compound annual growth rate between templateXValue[min] and templateXValue[max] should be 26.3 percent .
generated: The statistic shows a growth forecast for the Global ulcerative colitis Market in 2022 and shows the actual projected Market value of the ulcerative colitis Market in 2012 . For 2022 , the Global smart ulcerative colitis Market is forecasted to reach 6.6 billion U.S. dollars . The predicted compound annual growth rate between 2012 and 2022 should be 26.3 percent .

Example 677:
titleEntities: {'Subject': ['Estonia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Estonia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.8', '2.8', '2.8', '2.8', '2.9', '3.2', '4.76', '5.75', '2.63', '1.85', '2.99']

gold: The statistic shows the growth in real GDP in Estonia from 2014 to 2018 , with projections up until 2024 . In 2018 , Estonia 's real gross domestic product grew by around 4.76 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] grew by around templateYValue[max] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Estonia from 2014 to 2018 , with projections up until 2024 . In 2018 , Estonia 's real Gross domestic product grew by around 5.75 percent compared to the previous Year .

Example 678:
titleEntities: {'Subject': ['Northern Ireland', 'UK'], 'Date': ['2013', '2019']}
title: Youth unemployment rate in Northern Ireland ( UK ) 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Unemployment', 'rate']: ['7.6', '8.4', '12.6', '13.9', '19.5', '19.4', '20.4']

gold: This statistic shows the unemployment rate of young people ( aged 18 to 24 ) in Northern Ireland from 2013 to 2019 . At the start of this period the youth unemployment rate stood at over 20 percent , but by 2019 this had decreased to 7.6 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of young people ( aged 18 to 24 ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . At the start of this period the templateTitle[0] templateYLabel[0] templateYLabel[1] stood at over templateYValue[4] percent , but by templateXValue[max] this had decreased to templateYValue[min] percent .

generated_template: 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': ['12', '15']}
title: Viki : number of monthly active users 2012 to 2015
X_Axis['Month']: ["Mar_'12", "Jun_'12", "Sep_'12", "Dec_'12", "Mar_'13", "Jun_'13", "Sep_'13", "Dec_'13", "Mar_'14", "Jun_'14", "Sep_'14", "Dec_'14", "Mar_'15", "Jun_'15"]
Y_Axis['Number', 'of', 'monthly', 'active', 'users', 'in', 'millions']: ['3.8', '4.9', '7.3', '14.9', '15.9', '19.9', '23.9', '28.2', '29.3', '31.9', '35.2', '39.4', '40.1', '39.5']

gold: This statistic presents the number of monthly active Viki video platform users as of June 2015 . As of that month , the video portal had 39.5 million monthly active users worldwide . The VoD service had been acquired by Japanese e-commerce company Rakuten in September 2013 .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitleSubject[0] video platform templateYLabel[3] as of June templateTitleDate[1] . As of that templateXLabel[0] , the video portal had templateYValue[13] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] worldwide . The VoD service had been acquired by Japanese e-commerce company Rakuten in September 2013 .

generated_template: This statistic shows a timeline with the amount of templateYLabel[1] templateYLabel[2] templateTitleSubject[0] templateYLabel[3] worldwide as of December templateTitleDate[1] . As of that templateXLabel[0] , the mobile messaging app announced more than templateYValue[max] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from over templateYValue[1] templateYLabel[4] MAU in February 2016 . The service is one of the most popular mobile apps worldwide .
generated: This statistic shows a timeline with the amount of monthly active Viki users worldwide as of December 15 . As of that Month , the mobile messaging app announced more than 40.1 millions monthly active users , up from over 4.9 millions MAU in February 2016 . The service is one of the most popular mobile apps worldwide .

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] 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 Citigroup luxury brand 2011 2018 has increased twofold over the period surveyed , growing from roughly 204 direct staff in 2011 to 266 direct staff in the Year 2018 . Despite the steady increase in Number during the period considered , the Citigroup reported a net loss of approximately 25 direct staff in 2018 . Worldwide recognition Founded in 1978 in Milan , 2011 2018 is one of the leading international staff design houses .

Example 681:
titleEntities: {'Subject': ['Mattel'], 'Date': ['2006', '2019']}
title: Gross profit of toy manufacturer Mattel 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Gross', 'profit', 'in', 'million', 'U.S.', 'dollars']: ['1980.78', '1798.68', '1824.57', '2546.69', '2806.36', '3001.02', '3478.88', '3409.2', '3145.83', '2954.97', '2714.7', '2684.41', '2777.3', '2611.79']

gold: This statistic shows the gross profit of the U.S. toy manufacturer Mattel worldwide from 2006 to 2019 . In 2019 , their gross profit came to around 1.98 billion U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateTitle[2] templateTitle[3] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , their templateYLabel[0] templateYLabel[1] came to around templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The median templateYLabel[0] earnings for templateTitle[0] templateTitle[1] increased by 3.77 pounds between templateXValue[min] and templateXValue[max] , reaching templateYValue[max] pounds in that templateXLabel[0] . This has occurred due to small incremental increases in every templateXLabel[0] , with the largest such rise occurring between templateXValue[12] and templateXValue[11] at 0.52 British pounds . Minimum and living templateTitle[4] in the templateTitleSubject[1] In the templateTitleSubject[0] templateTitleSubject[0] , employers are expected to templateYLabel[1] their templateTitle[1] a minimum templateTitle[4] that is determined by how old they are .
generated: The median Gross earnings for Gross profit increased by 3.77 pounds between 2006 and 2019 , reaching 3478.88 pounds in that Year . This has occurred due to small incremental increases in every Year , with the largest such rise occurring between 2007 and 2008 at 0.52 British pounds . Minimum and living Mattel in the Mattel In the Mattel , employers are expected to profit their profit a minimum Mattel that is determined by how old they are .

Example 682:
titleEntities: {'Subject': ['March'], 'Date': ['2', '2020']}
title: COVID-19 cases worldwide as of March 2 , 2020 , by country
X_Axis['Country']: ['Total_(worldwide)', 'China', 'Republic_of_Korea', 'Italy', 'Iran_(Islamic_Republic_of)', 'Cases_on_an_international_conveyance_(Japan)', 'Japan', 'Germany', 'Singapore', 'France', 'United_States_of_America', 'Kuwait', 'Bahrain', 'Spain', 'Thailand', 'United_Kingdom', 'Australia', 'Malaysia', 'Switzerland', 'United_Arab_Emirates', 'Norway', 'Iraq', 'Canada', 'Viet_Nam', 'Sweden', 'Netherlands', 'Lebanon', 'Austria', 'Israel', 'Croatia', 'Greece', 'Oman', 'Finland', 'Mexico', 'Pakistan', 'Denmark', 'India', 'Czechia', 'Romania', 'Georgia', 'Philippines', 'Azerbaijan', 'Qatar', 'Indonesia', 'Iceland', 'Egypt', 'Brazil', 'Russian_Federation', 'Armenia', 'Ecuador', 'Dominican_Republic', 'Estonia', 'Ireland', 'Lithuania', 'Luxembourg', 'Monaco', 'Algeria', 'New_Zealand', 'Cambodia', 'North_Macedonia', 'San_Marino', 'Nepal', 'Sri_Lanka', 'Afghanistan', 'Nigeria', 'Belarus', 'Belgium']
Y_Axis['Number', 'of', 'cases']: ['88948', '80174', '4212', '1689', '978', '706', '254', '129', '106', '100', '62', '56', '47', '45', '42', '36', '27', '24', '24', '21', '19', '19', '19', '16', '14', '13', '10', '10', '7', '7', '7', '6', '6', '5', '4', '4', '3', '3', '3', '3', '3', '3', '3', '2', '2', '2', '2', '2', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1']

gold: As of March 2 , 2020 , the outbreak of the coronavirus disease ( COVID-19 ) had been confirmed in 65 countries , with the overwhelming majority of cases reported in China . The virus had infected 88,948 people worldwide , and the number of deaths had totaled 3,043 . The most severely affected countries outside of China were the Republic of Korea and Italy .
gold_template: As of templateTitleSubject[0] templateYValue[43] , templateTitle[5] , the outbreak of the coronavirus disease ( templateTitle[0] ) had been confirmed in 65 countries , with the overwhelming majority of templateXValue[5] reported in templateXValue[1] . The virus had infected templateYValue[max] people templateTitle[2] , and the templateYLabel[0] of deaths had totaled 3,043 . The most severely affected countries outside of templateXValue[1] were the templateXValue[2] of templateXValue[2] and templateXValue[3] .

generated_template: This graph shows the templateTitle[0] of templateTitle[1] templateTitle[2] of all American templateTitleSubject[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , President templateXValue[0] 's templateTitle[1] address was templateYValue[0] templateYLabel[1] long . The longest templateTitle[1] address was templateXValue[5] templateXValue[1] templateXValue[37] 's in 1841 , which amounted to templateYValue[max] templateYLabel[1] .
generated: This graph shows the COVID-19 of cases worldwide of all American March from 2 to 2020 . In 2020 , President Total (worldwide) 's cases address was 88948 cases long . The longest cases address was Cases on an international conveyance (Japan) China Czechia 's in 1841 , which amounted to 88948 cases .

Example 683:
titleEntities: {'Subject': ['Duty U.S. Navy'], 'Date': ['1995', '2018']}
title: Active Duty U.S. Navy personnel numbers from 1995 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995']
Y_Axis['Number', 'of', 'Navy', 'personnel']: ['325395', '319492', '320101', '323334', '321599', '319838', '314339', '320141', '323139', '324239', '326684', '332269', '345098', '357853', '367371', '429630']

gold: This graph shows the number of active duty U.S. Navy personnel from 1995 to 2018 . In 2018 , there were 325,395 active duty Navy members in the United States Department of Defense . In 2000 , there were 367,371 active duty members .
gold_template: This graph shows the templateYLabel[0] of templateTitleSubject[0] U.S. templateYLabel[1] templateYLabel[2] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] templateTitleSubject[0] Navy members in the templateTitle[2] Department of Defense . In templateXValue[14] , there were templateYValue[14] templateTitleSubject[0] members .

generated_template: In templateXValue[max] , approximately templateYValue[0] babies were born in templateTitleSubject[0] , a slight decrease from the templateXLabel[0] before . templateTitleSubject[0] is the country with the highest templateYLabel[0] of templateYLabel[2] in the European Union , with over 20 thousand more templateYLabel[2] more than France , the country with the next highest templateYLabel[0] of templateYLabel[1] templateYLabel[2] in Europe . Population growth in templateTitleSubject[0] has about 83 million inhabitants , making it the most populous country in Europe .
generated: In 2018 , approximately 325395 babies were born in Duty U.S. Navy , a slight decrease from the Year before . Duty U.S. Navy is the country with the highest Number of personnel in the European Union , with over 20 thousand more personnel more than France , the country with the next highest Number of Navy personnel in Europe . Population growth in Duty U.S. Navy has about 83 million inhabitants , making it the most populous country in Europe .

Example 684:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2002', '2015']}
title: Commodity prices of wheat in the United Kingdom ( UK ) 2002 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Price', 'per', 'tonne', 'in', 'GBP']: ['127.15', '143.06', '175.95', '179.26', '169.17', '123.76', '107.05', '137.87', '120.97', '78.88', '67.43', '80.3', '79.32', '65.02']

gold: This statistic shows the average price per tonne of wheat in the United Kingdom ( UK ) between 2002 and 2015 by year , according to published agricultural and commodity price figures . In 2012 , the price of wheat was 179.26 British Pound Sterling ( GBP ) per tonne .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) between templateXValue[min] and templateXValue[max] by templateXLabel[0] , according to published agricultural and templateTitle[0] templateYLabel[0] figures . In templateXValue[3] , the templateYLabel[0] of templateTitle[2] was templateYValue[max] British Pound Sterling ( templateYLabel[3] ) templateYLabel[1] templateYLabel[2] .

generated_template: The timeline depicts how many templateYLabel[0] templateYLabel[1] were templateYLabel[2] in each templateXLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYValue[3] templateYLabel[0] templateYLabel[1] were templateYLabel[2] in the templateTitle[5] , a change of minus 1 percent compared to the previous templateXLabel[0] . More statistics and facts on recreational boating
generated: The timeline depicts how many Price per were tonne in each Year from 2002 to 2015 . In 2013 , 179.26 Price per were tonne in the UK , a change of minus 1 percent compared to the previous Year . More statistics and facts on recreational boating

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: Canadians aged between templateXValue[1] and templateXValue[1] had the highest rate of templateTitle[4] templateTitle[5] nationally as of December templateTitle[9] . Some templateYValue[max] percent of survey templateYLabel[1] in this templateTitle[8] group stated that they consumed templateTitle[4] of any form in the templateTitle[0] three templateTitle[2] . Those aged templateXValue[last] or templateXValue[last] had the lowest levels of templateTitle[5] , with just templateYValue[min] percent of templateYLabel[1] consuming templateTitle[4] in this time .
generated: Canadians aged between 25 to 34 and 25 to 34 had the highest rate of charity England nationally as of December age . Some 83 percent of survey respondents in this age group stated that they consumed charity of any form in the Share three who . Those aged 75 and over or 75 and over had the lowest levels of England , with just 59 percent of respondents consuming charity in this time .

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': ['2018']}
title: Sales volume of USB flash drives in Germany 2004 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Sales', 'volume', 'in', 'millions']: ['12.88', '13.51', '16.17', '15.94', '15.46', '13.5', '15.85', '13.82', '11.78', '12.99', '13.0', '8.18', '5.0', '3.2', '2.03']

gold: USB flash drives experienced fluctuating sales numbers in recent years , with almost 12.9 million units sold in 2018 . Meanwhile , revenue generated amounted to 155 million euros in the same year , a decrease on the one before . Storage media USB flash drives revolutionized data storage for computer users everywhere when they emerged on the market , joining such well-known storage media as floppy discs , hard drives and optical storage units like CD-R and CD-RW discs .
gold_template: templateTitleSubject[0] templateTitle[3] templateTitle[4] experienced fluctuating templateYLabel[0] numbers in recent years , with almost templateYValue[0] templateYLabel[2] units sold in templateXValue[max] . Meanwhile , revenue generated amounted to 155 templateYLabel[2] euros in the same templateXLabel[0] , a decrease on the one before . Storage media templateTitleSubject[0] templateTitle[3] templateTitle[4] revolutionized data storage for computer users everywhere when they emerged on the market , joining such well-known storage media as floppy discs , hard templateTitle[4] and optical storage units like CD-R and CD-RW discs .

generated_template: The statistic represents the annual 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 about templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic represents the annual Sales volume in USB from 2004 to 2018 . In 2018 , the Sales volume in USB amounted to about 16.17 millions .

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] templateTitleSubject[0] is growing in every aspect . Over the last decade , the total templateYLabel[0] templateTitle[7] of templateTitle[3] in the templateTitleSubject[0] 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: 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 Most of followed sports . The survey shows that 3 percent of Americans consider themselves experts at followed sports . 33 percent of Americans stated that they have NHL I don't follow any of these leagues to use sports .

Example 690:
titleEntities: {'Subject': ['United States'], 'Date': ['2019']}
title: Pinterest usage reach in the United States 2019 , by household income
X_Axis['Annual', 'household', 'income']: ['Under_$30000', '$30000-$74999', '$75000+']
Y_Axis['Reach']: ['18', '27', '41']

gold: This statistic shows the share of adults in the United States who were using Pinterest as of February 2019 , sorted by income . During that period of time , 18 percent of respondents earning 30,000 U.S. dollars or less used the social networking site .
gold_template: This statistic shows the share of adults in the templateTitleSubject[0] who were using templateTitle[0] as of February templateTitleDate[0] , sorted templateTitle[6] templateXLabel[2] . During that period of time , templateYValue[min] percent of respondents earning 30,000 templateTitle[4] dollars or less used the social networking site .

generated_template: This statistic shows the results of a survey , conducted by Gallup in the templateTitle[7] in May templateTitleDate[0] , on templateTitle[2] templateTitle[3] templateTitle[4] gay and lesbian templateTitle[6] . During this survey , templateYValue[max] percent of the templateYLabel[1] were in favor of templateTitle[2] templateTitle[3] templateTitle[4] gay and lesbian templateTitle[6] in the templateTitle[7] .
generated: This statistic shows the results of a survey , conducted by Gallup in the household in May 2019 , on reach United States gay and lesbian by . During this survey , 41 percent of the Reach were in favor of reach United States gay and lesbian by in the household .

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] amounted to approximately templateYValue[6] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] Although templateTitle[0] templateTitle[1] increased on a yearly basis from 2004 to templateXValue[min] , templateTitle[1] growth has slowly decreased annually as of 2011 , despite remaining positive .
generated: 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 amounted to approximately 51.64 millions Inhabitants . population of South Korea Although Total population increased on a yearly basis from 2004 to 2014 , population growth has slowly decreased annually as of 2011 , despite remaining positive .

Example 692:
titleEntities: {'Subject': ['Market'], 'Date': ['100', '2019']}
title: Market capitalization of leading 100 banks worldwide 2016 to 2019
X_Axis['Quarter']: ["Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16"]
Y_Axis['Market', 'capitalization', 'in', 'trillion', 'Euros']: ['5.3', '5.2', '5.2', '4.8', '5.3', '5.2', '5.4', '5.6', '5.4', '5.3', '5.4', '5.2', '4.4', '4.1', '4.2']

gold: This statistic presents the market capitalization of leading 100 banks worldwide from the first quarter 2016 to the third quarter 2019 . The market cap of top 100 global banks amounted to 5.3 trillion Euros in the third quarter of 2019 .
gold_template: This statistic presents the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] from the first templateXLabel[0] templateTitleDate[0] to the third templateXLabel[0] templateTitleDate[1] . The templateYLabel[0] cap of top templateTitle[3] global templateTitle[4] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] in the third templateXLabel[0] of templateTitleDate[1] .

generated_template: This statistic presents the templateTitle[1] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[0] to the second templateXLabel[0] of templateTitleDate[1] . As of the last reported period , the templateTitle[1] templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic presents the capitalization Market capitalization of Market from the first Quarter of 100 to the second Quarter of 2019 . As of the last reported period , the capitalization Market capitalization amounted to 5.3 trillion Euros .

Example 693:
titleEntities: {'Subject': ['American'], 'Date': ['2013']}
title: Frequency of American families having dinner together at home 2013
X_Axis['Response']: ['0_to_3_nights', '4_to_5_nights', '6_to_7_nights']
Y_Axis['Share', 'of', 'respondents']: ['21', '28', '53']

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

generated_template: 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] templateXValue[0] the templateXValue[0] 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] templateXValue[1] templateXValue[1] 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 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 templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] ' ( UAE ) templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , The templateTitleSubject[0] templateTitleSubject[0] ' templateYLabel[0] templateTitle[4] rate amounted to approximately templateYValue[min] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the Iran ' ( UAE ) Iran 's national relation from 2014 to 2018 , with projections up until 2024 . In 2018 , The Iran ' Share relation rate amounted to approximately 11.82 percent GDP to the GDP Year .

Example 695:
titleEntities: {'Subject': ['Dubai', 'UAE'], 'Date': ['2006', '2026']}
title: Direct tourism contribution of Dubai to GDP of the UAE 2006 to 2026
X_Axis['Year']: ['2026', '2016', '2006']
Y_Axis['GDP', 'contribution', 'in', 'billion', 'U.S.', 'dollars']: ['20.9', '11.4', '4.0']

gold: This statistic described the direct tourism contribution of Dubai to the gross domestic product of the United Arab Emirates from 2006 to 2016 and a forecast for 2026 . The forecast of the direct tourism contribution of Dubai to the GDP of the United Arab Emirates for 2026 was approximately 20.9 billion U.S. dollars .
gold_template: This statistic described the templateTitle[0] templateTitle[1] templateYLabel[1] of templateTitleSubject[0] to the gross domestic product of the United Arab Emirates from templateXValue[min] to templateXValue[1] and a forecast for templateXValue[max] . The forecast of the templateTitle[0] templateTitle[1] templateYLabel[1] of templateTitleSubject[0] to the templateYLabel[0] of the United Arab Emirates for templateXValue[max] was approximately templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: In terms of motor vehicle templateTitle[7] volume , templateTitleSubject[0] templateTitle[1] templateYLabel[1] templateYLabel[2] reached 11.7 in templateXValue[max] . That templateXLabel[0] , templateTitleSubject[0] produced between 10 and templateYValue[min] million vehicles worldwide .
generated: In terms of motor vehicle 2026 volume , Dubai tourism contribution billion reached 11.7 in 2026 . That Year , Dubai produced between 10 and 4.0 million vehicles worldwide .

Example 696:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2018']}
title: Participants in hunting in the U.S. from 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'participants', 'in', 'millions']: ['15.69', '15.63', '15.47', '15.53', '14.85', '13.53', '14.71', '14.89', '14.01', '15.27', '13.98', '14.14', '15.1']

gold: This statistic shows the number of participants in hunting in the United States from 2006 to 2018 . In 2018 , the number of participants ( aged six years and older ) in hunting amounted to approximately 15.69 million .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] in the templateTitle[2] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] amounted to approximately templateYValue[max] templateYLabel[2] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] templateTitle[2] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] templateTitle[2] amounted to approximately templateYValue[0] templateYLabel[2] .
generated: This statistic shows the Number of participants in hunting U.S. in the from 2006 to 2018 . In 2018 , the Number of participants ( aged six years and older ) in hunting U.S. amounted to approximately 15.69 millions .

Example 697:
titleEntities: {'Subject': ['England'], 'Date': ['2017']}
title: Total household waste in England 2010 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Kilograms', 'per', 'person', 'per', 'year']: ['403', '412', '406', '413', '402', '412', '421', '425']

gold: Household waste volumes per person in England remained at a similar level between 2010 and 2017 . Although there was an overall decrease during this period , the household volumes were still over 400 kilograms per person in 2017 . The region which generated the largest volume of residual waste per household was the North East of England , where an average of 601 kilograms of waste was generated per person in 2017/2018 .
gold_template: templateTitle[1] templateTitle[2] volumes templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] remained at a similar level between templateXValue[min] and templateXValue[max] . Although there was an overall decrease during this period , the templateTitle[1] volumes were still over 400 templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[max] . The region which generated the largest volume of residual templateTitle[2] templateYLabel[1] templateTitle[1] was the North East of templateTitleSubject[0] , where an average of 601 templateYLabel[0] of templateTitle[2] was generated templateYLabel[1] templateYLabel[2] in 2017/2018 .

generated_template: In templateXValue[max] , the 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 2017 , the household Kilograms of England 2010 in England amounted to about 403 per person per , all types included . The Total realized Kilograms of England 2010 remained fairly steady throughout the years until 2014 , when it decreased to below 403 per person per . England 2010 The Total Kilograms serves as an indicator for a variety of different selling prices on the 2010 market , gathering all Kilograms ranges of England wines purchased in England .

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 presents the distribution of online consumers in the templateTitle[0] who are templateTitleSubject[0] templateTitle[2] members . During the most recent survey period in August templateTitleDate[0] , it was found that templateYValue[max] percent of responding Millennials had an templateTitleSubject[0] templateTitle[2] templateTitle[3] .
generated: This statistic presents the distribution of online consumers in the Preferred who are U.S. transportation members . During the most recent survey period in August 2015 , it was found that 63 percent of responding Millennials had an U.S. transportation when .

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 September 30 , 2019 , templateTitleSubject[0] generated a net templateYLabel[0] of templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Here you can find information about EA 's templateTitle[0] net income .

generated_template: In the fourth templateXLabel[0] of templateXValue[36] , The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] generated a total templateYLabel[0] of templateYValue[39] templateYLabel[1] templateYLabel[2] templateYLabel[3] , marking a small increase from the corresponding templateXLabel[0] of templateXValue[32] . The fourth templateXLabel[0] of the year is generally the one in which the templateTitleSubject[0] makes the most templateYLabel[0] , and at the end of templateXValue[32] the figure surpassed 500 templateYLabel[1] for the first time in years .
generated: In the fourth Quarter of Q2 '11 , The Electronic Arts generated a total Revenue of 1243 million U.S. dollars , marking a small increase from the corresponding Quarter of Q2 '12 . The fourth Quarter of the year is generally the one in which the Electronic Arts makes the most Revenue , and at the end of Q2 '12 the figure surpassed 500 million for the first time in years .

Example 703:
titleEntities: {'Subject': ['Leading'], 'Date': ['2014']}
title: Leading global travel booking sites by number of page visits 2014
X_Axis['Month']: ['Booking.com', 'TripAdvisor_Family', 'Expedia_Family', 'Hotels.com', 'Priceline.com', 'Agoda.com', 'Hotelurbano', 'Kayak.com', 'Travel.yahoo.com', 'Cheapoair.com', 'Makemytrip.com', 'Orbitz.com', 'Travelocity', 'Hotwire.com', 'Airbnb.com', 'Travelzoo.com', 'Decolar.com', 'Slyscanner.com', 'Ctrip.com', 'HomeAway.com']
Y_Axis['Number', 'of', 'site', 'visits', 'in', 'millions']: ['166.0', '159.9', '59.3', '34.5', '31.3', '30.7', '25.5', '24.4', '24.1', '20.2', '17.5', '17.2', '15.0', '13.2', '12.4', '12.2', '11.3', '9.6', '8.6', '7.4']

gold: This statistic shows the number of visits to travel booking sites worldwide in January 2014 . Booking.com had the most visits in January 2014 , with an estimated number of visits of 166 million .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[2] to templateTitle[2] templateTitle[3] templateTitle[4] worldwide in January templateTitleDate[0] . templateXValue[0] had the most templateYLabel[2] in January templateTitleDate[0] , with an estimated templateYLabel[0] of templateYLabel[2] of templateYValue[max] templateYLabel[3] .

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] in templateTitle[5] in the templateTitleSubject[0] from templateXValue[7] templateTitleDate[0] to December templateTitleDate[1] ( in templateYLabel[4] templateYLabel[2] templateYLabel[3] ) . In December templateTitleDate[1] , the templateYLabel[0] of templateYLabel[1] templateTitle[2] a templateYLabel[3] templateTitle[4] on an auction in the templateTitleSubject[0] was templateYValue[0] templateYLabel[4] .
generated: This statistic presents the Number of site travel booking sites in by in the Leading from Kayak.com 2014 to December 2014 ( in millions visits millions ) . In December 2014 , the Number of site travel a millions sites on an auction in the Leading was 166.0 millions .

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: This statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , some templateYValue[0] templateYLabel[2] templateYLabel[3] of templateYLabel[0] were consumed in the templateTitle[1] . This figure decreased to less than templateYValue[0] templateYLabel[2] templateYLabel[3] in templateXValue[max] .
generated: This statistic shows the Unemployment Share individuals in the rate from 2009 to 2019 . In 2018 , some 4.3 individuals of Share were consumed in the rate . This figure decreased to less than 4.3 individuals in 2019 .

Example 705:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2019']}
title: Mexico : most important issues facing women and girls in 2019
X_Axis['Response']: ['Sexual_harassment', 'Sexual_violence', 'Physical_violence', 'Domestic_abuse', 'Equal_pay', 'Workplace_discrimination', 'Gender_stereotyping', 'Sexualization_of_women_and_girls_in_the_media', 'Access_to_employment', 'Balancing_work_and_caring_responsibilities', 'Lack_of_women_in_leadership_roles_in_business_and_public_life', 'Abuse_on_social_media', 'Support_for_pregnant_women_and_new_mothers', 'The_amount_of_unpaid_work_that_women_do_(e.g._cooking_cleaning_childcare)', 'Lack_of_financial/economic_independence']
Y_Axis['Share', 'of', 'respondents']: ['40', '37', '32', '19', '19', '18', '16', '15', '9', '8', '7', '7', '6', '6', '5']

gold: The statistic presents the results of a survey conducted in December 2018 and January 2019 to find out about the situation of women and gender ( in ) equality across 27 countries . When asked which were the main issues that women and girls were facing in Mexico , 40 percent of respondents answered sexual harassment .
gold_template: The statistic presents the results of a survey conducted in December 2018 and January templateTitleDate[0] to find out about the situation of templateXValue[7] and templateXValue[6] ( in ) equality across 27 countries . When asked which were the main templateTitle[3] templateXValue[13] templateXValue[7] and templateXValue[7] were templateTitle[4] in templateTitleSubject[0] , templateYValue[max] percent of templateYLabel[1] answered templateXValue[0] .

generated_template: This graph shows the templateTitle[0] templateTitle[4] of templateTitle[1] templateTitleSubject[0] templateTitle[3] templateTitle[5] regularly in the templateTitle[6] as of May templateTitleDate[0] . 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 May 2019 . 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] .
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 .

Example 707:
titleEntities: {'Subject': ['Chile'], 'Date': ['2014', '2018']}
title: Chile : gender gap index 2014 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014']
Y_Axis['Index', 'score']: ['0.72', '0.7', '0.7', '0.7', '0.7']

gold: The graph presents the gender gap index in Chile from 2014 to 2018 . In 2018 , Chile scored 0.72 points , which shows a gender gap of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . In 2018 , the gender gap in the area of political empowerment in Chile amounted to 69 percent .
gold_template: The graph presents the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] scored templateYValue[max] points , which shows a templateTitle[1] templateTitle[2] of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . In templateXValue[max] , the templateTitle[1] templateTitle[2] in the area of political empowerment in templateTitleSubject[0] amounted to 69 percent .

generated_template: The graph presents the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] scored templateYValue[max] , which shows a templateTitle[1] templateTitle[2] of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . That same templateXLabel[0] , the templateTitle[1] templateTitle[2] in the area of political empowerment in templateTitleSubject[0] amounted to 72 percent .
generated: The graph presents the gender gap Index in Chile from 2014 to 2018 . In 2018 , Chile scored 0.72 , which shows a gender gap of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . That same Year , the gender gap in the area of political empowerment in Chile amounted to 72 percent .

Example 708:
titleEntities: {'Subject': ['United States'], 'Date': ['2019']}
title: Instagram usage reach in the United States 2019 , by age group
X_Axis['Age', 'group']: ['18-29', '30-49', '50-64', '65+']
Y_Axis['Share', 'of', 'respondents']: ['67', '47', '23', '8']

gold: As of February 2019 , 67 percent of U.S. adults aged between 18 and 29 years used the photo sharing app Instagram . Furthermore , it was found that 43 percent of female adults in the United States used Instagram compared to only 31 percent of adult men . Instagram usage in the United StatesInstagram is one of the most popular social networks in the United States with a 37 percent usage reach among the adult population .
gold_template: As of February templateTitleDate[0] , templateYValue[max] percent of templateTitle[4] adults aged between 18 and 29 years used the photo sharing app templateTitleSubject[0] . Furthermore , it was found that 43 percent of female adults in the templateTitleSubject[1] used templateTitleSubject[0] compared to only 31 percent of adult men . templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] StatesInstagram is one of the most popular social networks in the templateTitleSubject[1] with a 37 percent templateTitle[1] templateTitle[2] among the adult population .

generated_template: This statistic shows the results of a survey , conducted by Gallup in the templateTitle[7] in May templateTitleDate[0] , on templateTitle[2] templateTitle[3] templateTitle[4] gay and lesbian templateTitle[6] . During this survey , templateYValue[max] percent of the templateYLabel[1] were in favor of templateTitle[2] templateTitle[3] templateTitle[4] gay and lesbian templateTitle[6] in the templateTitle[7] .
generated: This statistic shows the results of a survey , conducted by Gallup in the age in May 2019 , on reach United States gay and lesbian by . During this survey , 67 percent of the respondents were in favor of reach United States gay and lesbian by in the age .

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[max] 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] amounted to approximately templateYValue[7] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] Although templateTitle[0] templateTitle[1] increased on a yearly basis from 2004 to templateXValue[min] , templateTitle[1] growth has slowly decreased annually as of 2011 , despite remaining positive .
generated: This statistic shows the Total population of Iraq from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Iraq amounted to approximately 37.14 millions Inhabitants . population of Iraq Although Total population increased on a yearly basis from 2004 to 2014 , population growth has slowly decreased annually as of 2011 , despite remaining positive .

Example 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 graph depicts the top templateYValue[14] templateTitleSubject[0] templateTitle[1] templateTitle[2] with the most templateYLabel[1] templateYLabel[2] from 1950 to templateTitle[8] . templateXValue[0] templateXValue[0] had templateYValue[max] templateYLabel[1] templateYLabel[2] throughout his career .
generated: The graph depicts the top 47055 European football clubs with the most attendance from 1950 to 2013/14 . Boussia Dortmund had 80295 attendance throughout his career .

Example 712:
titleEntities: {'Subject': ['Northern Ireland', 'UK'], 'Date': ['2000', '2019']}
title: Unemployment rate in Northern Ireland ( UK ) 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Unemployment', 'rate']: ['2.7', '3.6', '4.6', '5.7', '6.1', '6.4', '7.5', '7.4', '7.2', '7.1', '6.4', '4.4', '3.9', '4.4', '4.6', '5', '5.6', '5.9', '6', '6.2']

gold: This statistic shows the unemployment rate in Northern Ireland from 2000 to 2019 . Unemployment in Northern Ireland peaked in 2013 when there were 7.5 percent of the population unemployed , compared with just 2.7 percent in the most recent reporting year of 2019 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateYLabel[0] in templateTitleSubject[0] peaked in templateXValue[6] when there were templateYValue[max] percent of the population unemployed , compared with just templateYValue[min] percent in the most recent reporting templateXLabel[0] of templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[min] percent of the labor force in templateTitleSubject[0] was unemployed .
generated: This statistic shows the Unemployment rate in Northern Ireland from 2000 to 2019 . In 2019 , 2.7 percent of the labor force in Northern Ireland was unemployed .

Example 713:
titleEntities: {'Subject': ['Video'], 'Date': ['2022']}
title: Video analytics market revenues worldwide 2015 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Market', 'in', 'million', 'U.S.', 'dollars']: ['2997.8', '2692.7', '2347.1', '1998.4', '1665.5', '1405.1', '1137.7', '858.0']

gold: The statistic shows the size of the video analytics market worldwide , from 2015 to 2022 . In 2015 , revenues from video analytics reached 858 million U.S. dollars .
gold_template: The statistic shows the size of the templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateTitle[4] , from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateTitle[3] from templateTitleSubject[0] templateTitle[1] reached templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic displays a forecast of the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the Russian Federation from templateXValue[min] to templateXValue[max] . For templateXValue[4] , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] is estimated to reach templateYValue[4] templateYLabel[3] , with the templateYLabel[0] of templateYLabel[1] templateYLabel[2] worldwide forecast to exceed 2.5 templateYLabel[3] templateYLabel[2] by that time . By templateXValue[max] another ten templateYLabel[3] individuals are expected to own a templateYLabel[1] taking the predicted templateYLabel[0] of templateYLabel[1] templateYLabel[2] to templateYValue[max] templateYLabel[3] .
generated: This statistic displays a forecast of the Market of million U.S. in the Russian Federation from 2015 to 2022 . For 2018 , the Market of million U.S. in Video is estimated to reach 1665.5 dollars , with the Market of million U.S. worldwide forecast to exceed 2.5 dollars U.S. by that time . By 2022 another ten dollars individuals are expected to own a million taking the predicted Market of million U.S. to 2997.8 dollars .

Example 714:
titleEntities: {'Subject': ['American'], 'Date': ['2007', '2019']}
title: American Customer Satisfaction Index : full-service restaurants in the U.S. 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['ACSI', 'score']: ['81', '81', '78', '81', '82', '82', '81', '80', '82', '81', '84', '80', '81']

gold: This statistic shows the American Customer Satisfaction Index scores for full-service restaurants in the United States from 2007 to 2019 . In 2019 , the ACSI score for full-service restaurants in the U.S. was 81 .
gold_template: This statistic shows the templateTitleSubject[0] Satisfaction templateTitle[3] scores for templateTitle[4] templateTitle[5] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] for templateTitle[4] templateTitle[5] in the templateTitle[6] was templateYValue[0] .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] scores for templateTitleSubject[1] templateTitleSubject[1] templateTitle[6] in the templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] for templateTitleSubject[1] templateTitleSubject[1] templateTitle[6] in the templateTitle[7] was templateYValue[min] , down from templateYValue[1] the previous templateXLabel[0] .
generated: This statistic shows the American Customer Satisfaction Index scores for American U.S. in the 2007 from 2007 to 2019 . In 2019 , the ACSI for American U.S. in the 2007 was 78 , down from 81 the previous Year .

Example 715:
titleEntities: {'Subject': ['Eastman Chemical'], 'Date': ['2008', '2018']}
title: Eastman Chemical 's revenue 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['10151', '9549', '9008', '9648', '9527', '9350', '8102', '7178', '5842', '4396', '5936']

gold: This statistic shows the revenues of Eastman Chemical from 2007 to 2018 . United States-based Eastman Chemical Company is engaged in the manufacture and sale of chemicals , fibers , and plastics . In 2018 , the company generated approximately 10.15 billion U.S. dollars of sales revenues .
gold_template: This statistic shows the revenues of templateTitleSubject[0] from 2007 to templateXValue[max] . United States-based templateTitleSubject[0] Company is engaged in the manufacture and sale of chemicals , fibers , and plastics . In templateXValue[max] , the company generated approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] of sales revenues .

generated_template: 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', '2017']}
title: Number of stores of Sears Holdings worldwide 2009 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'stores']: ['1002', '1430', '1672', '1725', '2429', '2548', '4010', '3949', '3862']

gold: This statistic depicts the total number of stores of Sears Holdings from 2009 to 2017 . In 2017 , Sears Holdings had a total of 1,002 stores worldwide . The company was founded in 2005 in Hoffman Estates , Illinois and is the tenth largest retailer by annual revenue in the United States .
gold_template: This statistic depicts the total templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] had a total of templateYValue[min] templateYLabel[1] templateTitle[4] . The company was founded in 2005 in Hoffman Estates , Illinois and is the tenth largest retailer by annual revenue in the country .

generated_template: This statistic depicts the templateYLabel[0] of specialized templateYLabel[1] for the templateTitle[2] sale of templateTitle[1] in the templateTitleSubject[0] 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 depicts 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': ['2005', '2017']}
title: Colombia : poverty headcount ratio at 3.20 U.S. dollars a day 2005 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2005']
Y_Axis['Percentage', 'of', 'population']: ['10.8', '11.8', '11.9', '13.1', '14.2', '15.4', '16.3', '18.3', '20.5', '22.4', '23.4']

gold: In Colombia , the poverty rate has been decreasing throughout recent years . In 2017 , approximately 10.8 percent of Colombians were living on less than 3.20 U.S. dollars per day , down from 23.4 percent of the country 's population in 2005.Moreover , it was recently found that the incidence rate of poverty in Colombia is higher in families whose heads of household were women .
gold_template: In templateTitleSubject[0] , the templateTitle[1] rate has been decreasing throughout recent years . In templateXValue[max] , approximately templateYValue[min] percent of Colombians were living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] percent of the country 's templateYLabel[1] in 2005.Moreover , it was recently found that the incidence rate of templateTitle[1] in templateTitleSubject[0] is higher in families whose heads of household were women .

generated_template: templateTitle[0] computing has come a long way in the past decade , with increased power , battery life , portability and display quality all enhancing the attractiveness of a portable computer over a desktop computer . The templateTitle[1] rate of the templateTitle[0] in the templateTitleSubject[0] templateTitleSubject[0] reflects this , increasing from templateYValue[min] percent in templateXValue[min] , to templateYValue[0] percent in templateXValue[max] . Laptops templateTitle[1] highest among 35 - 54 templateXLabel[0] olds templateTitle[0] templateTitle[1] rates are above 50 percent across each of the main age groups , and highest among those aged 35 - 54 years .
generated: Colombia computing has come a long way in the past decade , with increased power , battery life , portability and display quality all enhancing the attractiveness of a portable computer over a desktop computer . The poverty rate of the Colombia in the Colombia reflects this , increasing from 10.8 percent in 2005 , to 10.8 percent in 2017 . Laptops poverty highest among 35 - 54 Year olds Colombia poverty rates are above 50 percent across each of the main age groups , and highest among those aged 35 - 54 years .

Example 718:
titleEntities: {'Subject': ['Iran'], 'Date': ['2011', '2011']}
title: Iran 's oil exports 2011
X_Axis['Country']: ['China', 'European_Union_(total)', 'Japan', 'India', 'South_Korea', 'Italy', 'Turkey', 'Spain', 'France', 'Netherlands', 'Germany', 'United_Kingdom']
Y_Axis['Oil', 'imports', 'in', 'thousand', 'barrels', 'per', 'day']: ['543', '450', '341', '328', '244', '183', '182', '137', '49', '33', '17', '11']

gold: This statistic depicts the volume of crude oil imported from Iran by its leading destination countries between January and June 2011 . The European Union imported a total of around 450,000 barrels of oil per day from Iran during that period . Iran has stopped oil exports to France , where crude oil is the second most important energy source and Britain , where crude oil production has been declining since 2002 .
gold_template: This statistic depicts the volume of crude templateYLabel[0] imported from templateTitleSubject[0] by its leading destination countries between January and June templateTitleDate[0] . The templateXValue[1] imported a total of around templateYValue[1] templateYLabel[3] of templateYLabel[0] templateYLabel[4] templateYLabel[5] from templateTitleSubject[0] during that period . templateTitleSubject[0] has stopped templateYLabel[0] templateTitle[3] to templateXValue[8] , where crude templateYLabel[0] is the second most important energy source and Britain , where crude templateYLabel[0] production has been declining since 2002 .

generated_template: templateTitleSubject[0] , the warehouse shopping club , continued to grow in templateTitleDate[0] with 782 locations worldwide at year 's end . The U.S. is still home to the vast majority of the American retailer 's templateYLabel[1] , but it has expanded into templateYValue[7] other markets . With the expansion in store count comes an increase of members , and at the end of 2018 templateTitleSubject[0] could boast over 98 million cardholders .
generated: Iran , the warehouse shopping club , continued to grow in 2011 with 782 locations worldwide at year 's end . The U.S. is still home to the vast majority of the American retailer 's imports , but it has expanded into 137 other markets . With the expansion in store count comes an increase of members , and at the end of 2018 Iran could boast over 98 million cardholders .

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: templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] of templateYValue[0] templateYLabel[2] British pounds ( templateTitleSubject[0] ) of the fourth templateXLabel[0] of templateTitle[4] , making templateTitleSubject[0] the results of the biggest social network templateTitle[5] . In the third templateXLabel[0] of 2012 , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[4] amounted to templateYValue[0] percent . This represents the first templateXLabel[0] of the year , the templateYLabel[0] templateYLabel[1] of templateYLabel[2] , reaching a value of over eight percent of the first templateXLabel[0] of 2018 .
generated: Samsung Electronics 's Operating profit of 7.16 trillion British pounds ( Samsung Electronics ) of the fourth Quarter of profit , making Samsung Electronics the results of the biggest social network 2009 . In the third Quarter of 2012 , the Operating profit trillion in the profit amounted to 7.16 percent . This represents the first Quarter of the year , the Operating profit of trillion , reaching a value of over eight percent of the first Quarter of 2018 .

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: 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 40357.8 sales smartwatches were sold in the market . Between 2013 and 2018 annual U.S. Retail grew from just 34782.0 thousand million to over 40357.8 sales 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 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 statistic shows the templateYLabel[0] of templateTitleSubject[0] 's templateTitle[1] templateTitle[2] templateYLabel[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[max] percent of the social network 's templateTitle[2] revenues were generated via templateTitle[1] , up from templateYValue[1] percent in the previous templateXLabel[0] . This translates to over 50 billion U.S. dollars in annual templateTitle[1] templateYLabel[2] revenues .
generated: The statistic shows the Number of UFC 's number events from 2012 to 2018 . In 2018 , 46 percent of the social network 's events revenues were generated via number , up from 39 percent in the previous Year . This translates to over 50 billion U.S. dollars in annual number events revenues .

Example 722:
titleEntities: {'Subject': ['United States'], 'Date': []}
title: Ratio of government expenditure to gross domestic product ( GDP ) in the United States
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Ratio', 'of', 'government', 'expenditure', 'to', 'GDP']: ['36.77', '36.75', '36.73', '36.51', '36.41', '36.19', '35.14', '35.25', '35.46', '35.15', '35.47']

gold: The statistic shows the ratio of government expenditure to gross domestic product ( GDP ) in the United States from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure amounted to 35.14 percent of the gross domestic product . See the US GDP for further information .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] amounted to templateYValue[min] percent of the templateTitle[3] templateTitle[4] templateTitle[5] . See the US templateYLabel[3] for further information .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to about templateYValue[6] percent of the country 's templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The statistic shows the Ratio of government expenditure to gross domestic product ( GDP ) in United States from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure in United States amounted to about 35.14 percent of the country 's gross domestic product .

Example 723:
titleEntities: {'Subject': ['Croatia'], 'Date': ['2006', '2018']}
title: Croatia : Number of road deaths 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'fatalities']: ['317', '331', '307', '348', '308', '368', '393', '418', '426', '548', '664', '619', '614']

gold: This statistic illustrates the number of road traffic fatalities per year in Croatia between 2006 and 2018 . In the period of consideration , road fatalities presented an overall trend of decline . The year with the lowest amount of fatalities was 2016 , with a total of 207 road traffic fatalities in Croatia .
gold_template: This statistic illustrates the templateYLabel[0] of templateTitle[2] traffic templateYLabel[1] per templateXLabel[0] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In the period of consideration , templateTitle[2] templateYLabel[1] presented an overall trend of decline . The templateXLabel[0] with the lowest amount of templateYLabel[1] was templateXValue[2] , with a total of 207 templateTitle[2] traffic templateYLabel[1] in templateTitleSubject[0] .

generated_template: There were templateYValue[0] reported templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateXValue[max] . This was a slight increase compared to the previous templateXLabel[0] , but an increase of 14 incidents compared to the low reported in templateXValue[4] . The Northern European island state is known for enforcing a strict templateTitle[1] safety policy in order to ensure the security of its residents and tourists in the country .
generated: There were 317 reported Number road in Croatia in 2018 . This was a slight increase compared to the previous Year , but an increase of 14 incidents compared to the low reported in 2014 . The Northern European island state is known for enforcing a strict Number safety policy in order to ensure the security of its residents and tourists in the country .

Example 724:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2017']}
title: Fatality rate per 100,000 drivers licensed in the U.S. 1990 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995', '1990']
Y_Axis['Fatalities', 'per', '100,000', 'licensed', 'drivers']: ['16.48', '17.05', '16.27', '15.29', '15.5', '15.95', '15.33', '15.71', '16.16', '17.96', '20.05', '21.06', '21.7', '22.0', '23.68', '26.7']

gold: The timeline shows the fatality rate per 100,000 drivers licensed to operate a motor vehicle in the United States from 1990 to 2017 . The fatality rate stood at 16.5 deaths per 100,000 licensed drivers in 2017 .
gold_template: The timeline shows the templateTitle[0] templateTitle[1] templateYLabel[1] 100,000 templateYLabel[4] templateYLabel[3] to operate a motor vehicle in the templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] stood at templateYValue[0] deaths templateYLabel[1] 100,000 templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: In templateXValue[max] , approximately templateYValue[0] babies were born in templateTitleSubject[0] , a slight decrease from the templateXLabel[0] before . templateTitleSubject[0] is the country with the highest templateYLabel[0] of templateYLabel[2] in the European Union , with over 20 thousand more templateYLabel[2] more than France , the country with the next highest templateYLabel[0] of templateYLabel[1] templateYLabel[2] in Europe . Population growth in templateTitleSubject[0] has about 83 million inhabitants , making it the most populous country in Europe .
generated: In 2017 , approximately 16.48 babies were born in U.S. , a slight decrease from the Year before . U.S. is the country with the highest Fatalities of 100,000 in the European Union , with over 20 thousand more 100,000 more than France , the country with the next highest Fatalities of per 100,000 in Europe . Population growth in U.S. has about 83 million inhabitants , making it the most populous country in Europe .

Example 725:
titleEntities: {'Subject': ['Chicago White Sox'], 'Date': ['2019']}
title: Franchise value of the Chicago White Sox 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1600', '1500', '1350', '1050', '975', '695', '692', '600', '526', '466', '450', '443', '381', '315', '262', '248', '233', '223']

gold: This graph depicts the value of the Chicago White Sox franchise of Major League Baseball from 2002 to 2019 . In 2019 , the franchise had an estimated value of 1.6 billion U.S. dollars . The Chicago White Sox are owned by Jerry Reinsdorf , who bought the franchise for 20 million U.S. dollars in 1981 .
gold_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] Sox templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Sox are owned by Jerry Reinsdorf , who bought the templateYLabel[0] for 20 templateYLabel[2] templateYLabel[3] templateYLabel[4] in 1981 .

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] are owned by 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 Franchise of Major League Baseball from 2002 to 2019 . In 2019 , the Franchise had an estimated value of 1600 million U.S. dollars . The Chicago White Sox are owned by 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: This statistic contains data on templateTitle[0] 's templateYLabel[1] templateYLabel[2] templateYLabel[3] . In the third templateXLabel[0] of templateTitleDate[0] , the social network game developer based in San Franscisco , California , reached an templateYLabel[0] of templateYValue[0] templateYLabel[4] templateYLabel[1] templateYLabel[2] templateYLabel[3] . At the same time , templateTitle[0] reported daily templateYLabel[2] user numbers reaching 20 templateYLabel[4] .
generated: This statistic contains data on Number 's Bitcoins millions . In the third Quarter of 2012 , the social network game developer based in San Franscisco , California , reached an Number of 18.13 millions Bitcoins millions . At the same time , Number reported daily millions user numbers reaching 20 millions .

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 timeline shows the templateYLabel[0] of the templateYLabel[2] templateTitle[2] templateTitle[3] templateTitle[4] in templateXValue[min] provides a forecast for templateXValue[max] . templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] in total templateYLabel[0] was generated by the templateYLabel[2] templateTitle[2] templateTitle[3] templateTitle[4] in templateXValue[min] .
generated: The timeline shows the Revenue of the U.S. network revenue 2018 in 2018 provides a forecast for 2018 . 10.58 billion U.S. dollars in total Revenue was generated by the U.S. network revenue 2018 in 2018 .

Example 728:
titleEntities: {'Subject': ['Average'], 'Date': ['2009', '2015']}
title: Average global hotel rates from 2009 to 2015
X_Axis['Year']: ['2009', '2010', '2011', '2012', '2013', '2014', '2015']
Y_Axis['Average', 'hotel', 'rate', 'in', 'U.S.', 'dollars']: ['155', '162', '174', '171', '171', '174', '179']

gold: This statistic shows average global hotel rates from 2009 to 2015 . In 2013 , the average global hotel rate was 171 U.S. dollars . This figure was forecasted to increase to 174 U.S. dollars in 2014 and again to 179 dollars in 2015 .
gold_template: This statistic shows templateYLabel[0] templateTitle[1] templateYLabel[1] templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[4] , the templateYLabel[0] templateTitle[1] templateYLabel[1] templateYLabel[2] was templateYValue[3] templateYLabel[3] templateYLabel[4] . This figure was forecasted to increase to templateYValue[2] templateYLabel[3] templateYLabel[4] in templateXValue[5] and again to templateYValue[max] templateYLabel[4] in templateXValue[max] .

generated_template: This statistic illustrates the templateTitle[2] templateYLabel[0] of cyber templateTitle[1] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , companies detected and reported templateYValue[1] templateYLabel[2] IT templateTitle[1] breaches . This templateYLabel[0] increased by 38 percent in the following templateXLabel[0] .
generated: This statistic illustrates the hotel Average of cyber global hotel from 2009 to 2015 . In 2012 , companies detected and reported 162 rate IT global breaches . This Average increased by 38 percent in the following Year .

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[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[7] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] Although templateTitle[0] templateTitle[1] increased on a yearly basis from 2004 to templateXValue[min] , templateTitle[1] growth has slowly decreased annually as of 2011 , despite remaining positive .
generated: This statistic shows the Total population of Kenya from 2014 to 2017 , with projections up until 2024 . In 2018 , the Total population of Kenya amounted to approximately 46.73 millions Inhabitants . population of Kenya Although Total population increased on a yearly basis from 2004 to 2014 , population growth has slowly decreased annually as of 2011 , despite remaining positive .

Example 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': ['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 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 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 .

Example 734:
titleEntities: {'Subject': ['NCAA'], 'Date': ['2019']}
title: NCAA division I men 's basketball attendance leaders 2019
X_Axis['State']: ['Syracuse', 'Kentucky', 'North_Carolina', 'Tennessee', 'Wisconsin', 'Louisville', 'Kansas', 'Creighton', 'Marquette', 'Nebraska', 'Arkansas', 'Indiana', 'Michigan_St.', 'Perdue', 'Iowa_St.', 'Virginia', 'Memphis', 'Maryland', 'Ohio_St.', 'NC_State', 'Arizona', 'Dayton', 'Iowa', 'Michigan', 'Illinois', 'Texas_Tech', 'BYU', 'South_Carolina', 'Cincinnati', 'New_Mexico']
Y_Axis['Average', 'attendance']: ['21992', '21695', '19715', '19034', '17170', '16601', '16236', '15980', '15611', '15341', '15278', '15206', '14797', '14467', '14099', '14087', '14065', '14009', '13922', '13897', '13744', '12957', '12869', '12505', '12456', '12098', '11958', '11472', '11256', '11107']

gold: While the players on the court might still be college students , the National Collegiate Athletic Association men 's basketball top division still draws in big crowds . The Syracuse Orange men 's basketball , which represents Syracuse University in New York , attracted the highest average attendance during the 2019 season . The team , traditionally known as the Syracuse Orangemen , had an average home audience of almost 22 thousand in 2019 .
gold_template: While the players on the court might still be college students , the National Collegiate Athletic Association templateTitle[3] templateTitle[4] templateTitle[5] top templateTitle[1] still draws in big crowds . The templateXValue[0] Orange templateTitle[3] templateTitle[4] templateTitle[5] , which represents templateXValue[0] University in templateXValue[last] York , attracted the highest templateYLabel[0] templateYLabel[1] during the templateTitleDate[0] season . The team , traditionally known as the templateXValue[0] Orangemen , had an templateYLabel[0] home audience of almost templateYValue[max] thousand in templateTitleDate[0] .

generated_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] in the country . The templateYLabel[0] templateYLabel[1] of the templateXValue[0] templateXValue[0] had the highest templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic depicts the Average attendance of the NCAA division I men in the country . The Average attendance of the Syracuse had the highest Average attendance of 21992 attendance .

Example 735:
titleEntities: {'Subject': ['New Jersey'], 'Date': ['2000', '2018']}
title: New Jersey - poverty rate 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'population']: ['9.5', '10', '10.4', '10.8', '11.1', '11.4', '10.8', '10.4', '10.3', '9.4', '8.7', '8.6', '8.7', '8.7', '8.5', '8.4', '7.5', '7.9', '7.9']

gold: This statistic shows the poverty rate in New Jersey from 2000 to 2018 . For instance , 9.5 percent of New Jersey 's population lived below the poverty line in 2018
gold_template: This statistic shows the templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . For instance , templateYValue[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[2] line in templateXValue[max]

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[0] percent of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[1] line .
generated: This statistic shows the Jersey poverty in New Jersey from 2000 to 2018 . In 2018 , about 9.5 percent of New Jersey 's population lived below the Jersey line .

Example 736:
titleEntities: {'Subject': ['Croatian'], 'Date': []}
title: Leading Croatian national team players at FIFA World Cup 2018 , by market value
X_Axis['Month']: ['Ivan_Rakitic', 'Ivan_Perisic', 'Mateo_Kovacic', 'Andrej_Kramaric', 'Marcelo_Brozovic', 'Luka_Modric', 'Sime_Vrsaljko', 'Dejan_Lovren', 'Mario_Mandzukic', 'Milan_Badelj', 'Marko_Pjaca', 'Nikola_Kalinic', 'Ante_Rebic', 'Duje_Caleta–Car', 'Domagoj_Vida', 'Lovre_Kalinic', 'Tin_Jedvaj', 'Danijel_Subasic', 'Vedran_Corluka', 'Ivan_Strinic', 'Filip_Bradaric', 'Josip_Pivaric', 'Dominik_Livakovic']
Y_Axis['Market', 'value', 'in', 'million', 'euros']: ['50.0', '40.0', '30.0', '27.0', '27.0', '25.0', '25.0', '20.0', '18.0', '15.0', '15.0', '14.0', '10.0', '10.0', '7.0', '6.5', '5.0', '4.5', '4.0', '4.0', '3.5', '2.0', '1.5']

gold: The statistic displays the leading players of the national football team of Croatia at FIFA World Cup as of June 2018 , by market value . The most valuable player was Ivan Rakitic , with a market value of 50 million euros .
gold_template: The statistic displays the templateTitle[0] templateTitle[4] of the templateTitle[2] football templateTitle[3] of Croatia at templateTitle[5] templateTitle[6] templateTitle[7] as of June templateTitle[8] , templateTitle[9] templateYLabel[0] templateYLabel[1] . The most valuable player was templateXValue[0] , with a templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the top-25 templateTitle[0] at the templateTitle[6] templateTitle[8] templateTitle[9] templateTitle[7] in templateTitleSubject[0] according to their current templateYLabel[0] / templateTitle[4] templateYLabel[1] . templateXValue[0] templateXValue[0] of Argentina is the templateTitle[2] valued player templateTitle[1] a templateTitle[4] templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] . templateYLabel[0] values of templateTitle[0] at the templateTitle[8] templateTitle[9] templateTitle[7] - additional information The market/transfer templateYLabel[1] of a football player is determined by a number of statistics , including the player 's age , salary , playing stats and recent performances .
generated: The statistic shows the top-25 Leading at the World 2018 by Cup in Croatian according to their current Market / players value . Ivan Rakitic of Argentina is the national valued player Croatian a players Market value of 50.0 million euros . Market values of Leading at the 2018 by Cup - additional information The market/transfer value of a football player is determined by a number of statistics , including the player 's age , salary , playing stats and recent performances .

Example 737:
titleEntities: {'Subject': ['Distribution'], 'Date': ['2018']}
title: Distribution of consumer transactions worldwide 2018 , by payment channel
X_Axis['Response']: ['In-store', 'Other_online', 'Buy_buttons', 'Other_mobile_transfers', 'P2P_transfer', 'Mobile_messenger_apps', 'QR_codes', 'Other_in-app_payments', 'Smart_home_device', 'Wearables_/_contactless', 'Other']
Y_Axis['Share', 'of', 'respondents']: ['41', '14', '9', '8', '7', '7', '5', '4', '2', '2', '2']

gold: This statistic presents the most popular payment methods for everyday transactions according to internet users worldwide as of June 2018 . When asked to think about they payment methods for their ten most recent transactions , it was found that seven percent were made via P2P transfer . In-store still accounted for the single largest share of everyday transactions with 41 percent .
gold_template: This statistic presents the most popular templateTitle[6] methods for everyday templateTitle[2] according to internet users templateTitle[3] as of June templateTitleDate[0] . When asked to think about they templateTitle[6] methods for their ten most recent templateTitle[2] , it was found that templateYValue[4] percent were made via templateXValue[4] . templateXValue[0] still accounted for the single largest templateYLabel[0] of everyday templateTitle[2] with templateYValue[max] percent .

generated_template: 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 2018 Distribution payment channel who access selected Distribution consumer as of the third quarter 2018 . During the survey period , it was found that 41 percent of 2018 payment channel in the by used In-store .

Example 738:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: U.S. preterm birth rate 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1990']
Y_Axis['Percentage', 'of', 'all', 'births']: ['10.02', '9.93', '9.85', '9.63', '9.57', '9.62', '9.76', '9.81', '9.98', '10.07', '10.36', '10.44', '12.8', '12.73', '11.64', '10.62']

gold: This statistic depicts the percentage of births that were preterm births in the United States from 1990 to 2018 . In 1990 , some 10.6 percent of all births in the United States were preterm births . A preterm birth means that a child was delivered after less than 37 weeks of gestation .
gold_template: This statistic depicts the templateYLabel[0] of templateYLabel[2] that were templateTitle[1] templateYLabel[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , some templateYValue[15] percent of templateYLabel[1] templateYLabel[2] in the templateTitle[0] were templateTitle[1] templateYLabel[2] . A templateTitle[1] templateTitle[2] means that a child was delivered after less than 37 weeks of gestation .

generated_template: This statistic represents the annual templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] templateTitleSubject[0] had around templateYValue[max] templateYLabel[1] worldwide . This figure is considerably lower than that of other vehicle manufacturers : General Motors .
generated: This statistic represents the annual Percentage of U.S. all from 1990 to 2018 . In 2018 , the U.S. had around 12.8 all worldwide . This figure is considerably lower than that of other vehicle manufacturers : General Motors .

Example 739:
titleEntities: {'Subject': ['PV'], 'Date': ['2018']}
title: Solar PV capacity - new installations worldwide by country 2018
X_Axis['Country']: ['China', 'India', 'US', 'Japan', 'Australia', 'Germany', 'Mexico', 'Republic_of_Korea', 'Turkey', 'Netherlands']
Y_Axis['Percentage', 'of', 'newly', 'installed', 'capacity']: ['45', '11', '11', '7', '4', '3', '3', '2', '2', '1']

gold: This statistic shows the share of new installed solar PV capacity worldwide in 2018 , by country . In 2018 , new solar PV capacity installations in China accounted for around 45 percent of the world 's total new installed grid-connected PV capacity .
gold_template: This statistic shows the share of templateTitle[3] templateYLabel[2] templateTitle[0] templateTitleSubject[0] templateYLabel[3] templateTitle[5] in templateTitleDate[0] , templateTitle[6] templateXLabel[0] . In templateTitleDate[0] , templateTitle[3] templateTitle[0] templateTitleSubject[0] templateYLabel[3] templateTitle[4] in templateXValue[0] accounted for around templateYValue[max] percent of the world 's total templateTitle[3] templateYLabel[2] grid-connected templateTitleSubject[0] templateYLabel[3] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] the templateTitle[2] templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[6] during the first quarter of templateTitle[7] . During that quarter , it was found that templateYValue[max] percent of internet templateYLabel[3] in templateXValue[0] templateXValue[0] averaged a connection speed of templateTitle[3] templateTitleSubject[0] or more , placing the templateXLabel[0] also in first place in a global ranking of highest average internet speeds . The global templateYLabel[1] templateYLabel[2] templateTitle[6] rate is 45 percent .
generated: The statistic shows the Solar PV the capacity Percentage of newly installed by during the first quarter of country . During that quarter , it was found that 45 percent of internet capacity in China averaged a connection speed of new PV or more , placing the Country also in first place in a global ranking of highest average internet speeds . The global newly installed by rate is 45 percent .

Example 740:
titleEntities: {'Subject': ['Groupon'], 'Date': ['2009', '2019']}
title: Groupon : annual net income 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['-11.7', '1.99', '26.63', '-183.32', '33.68', '-63.92', '-88.95', '-51.03', '-297.76', '-413.39', '-1.34']

gold: The statistic above shows the annual net income of Groupon from 2008 to 2019 . In 2019 , the coupon site accumulated a net loss of more than 11.6 million dollars , an decline from the previous year 's net income of two million US dollars .
gold_template: The statistic above shows the templateTitle[1] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from 2008 to templateXValue[max] . In templateXValue[max] , the coupon site accumulated a templateYLabel[0] loss of more than 11.6 templateYLabel[2] templateYLabel[4] , an decline from the previous templateXLabel[0] 's templateYLabel[0] templateYLabel[1] of templateYValue[1] templateYLabel[2] US templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitleSubject[0] amounted to approximately templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[max] .
generated: This statistic shows the Net income of Groupon from 2009 to 2019 . The Net income of Groupon amounted to approximately -11.7 million U.S. dollars in 2019 .

Example 741:
titleEntities: {'Subject': ['China'], 'Date': ['2013', '2018']}
title: China smartphone unit shipments 2013 to 2018
X_Axis['Quarter']: ['Q1_2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Shipments', 'in', 'million', 'units']: ['109.6', '454.4', '448.5', '385.3', '392.8', '359.0']

gold: The statistic shows the smartphone unit shipments in China from 2013 to Q1 2018 . In Q1 2018 , 109.6 million smartphones were shipped in China .
gold_template: The statistic shows the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In templateXValue[0] , templateYValue[min] templateYLabel[1] smartphones were shipped in templateTitleSubject[0] .

generated_template: The statistic illustrates the templateYLabel[0] templateYLabel[1] of templateTitle[1] from the first templateXLabel[0] of templateXValue[2] to the fourth templateXLabel[0] of templateXValue[0] . During the most recent templateXLabel[0] of templateXValue[0] , Mexicans paid on average templateYValue[0] templateYLabel[2] templateYLabel[3] to acquire residential property . Compared to the same period of the previous year , the nominal e-commerce templateYLabel[0] increased by more than 30 percent .
generated: The statistic illustrates the Shipments million of smartphone from the first Quarter of 2016 to the fourth Quarter of Q1 2018 . During the most recent Quarter of Q1 2018 , Mexicans paid on average 109.6 units to acquire residential property . Compared to the same period of the previous year , the nominal e-commerce Shipments increased by more than 30 percent .

Example 742:
titleEntities: {'Subject': ['Kazakhstan'], 'Date': ['2018']}
title: Urbanization in Kazakhstan 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['57.43', '57.34', '57.26', '57.19', '57.12', '57.05', '56.97', '56.9', '56.83', '56.76', '56.68']

gold: This statistic shows the degree of urbanization in Kazakhstan from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 57.43 percent of Kazakhstan 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[max] percent of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Kazakhstan from 2008 to 2018 . Urbanization means the Share of urban population in the total population of a country . In 2018 , 57.43 percent of Kazakhstan 's total population lived in urban areas and cities .

Example 743:
titleEntities: {'Subject': ['eBay'], 'Date': ['2014', '2019']}
title: eBay : quarterly classifieds revenue 2014 to 2019
X_Axis['Quarter']: ["Q4_'19", "Q3_'19", "Q2_'19", "Q1_'19", "Q4_'18", "Q3_'18", "Q2_'18", "Q1_'18", "Q4_'17", "Q3_'17", "Q2_'17", "Q1_'17", "Q4_'16", "Q3_'16", "Q2_'16", "Q1_'16", "Q4_'15", "Q3_'15", "Q2_'15", "Q1_'15", "Q4_'14"]
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['269', '265', '271', '256', '263', '254', '259', '246', '244', '235', '219', '199', '201', '197', '207', '186', '183', '178', '180', '162', '180']

gold: eBay 's classifieds revenue in the fourth quarter of 2019 amounted to 269 million U.S. dollars . This represents a three percent year-on-year change . The classifieds revenue is counted towards the company 's marketing services and other revenues segment .
gold_template: templateTitleSubject[0] 's templateTitle[2] templateYLabel[0] in the fourth templateXLabel[0] of templateTitleDate[1] amounted to templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . This represents a three percent year-on-year change . The templateTitle[2] templateYLabel[0] is counted towards the company 's marketing services and other revenues segment .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] from the first templateXLabel[0] of templateTitleDate[0] to the fourth templateXLabel[0] of templateTitleDate[1] . In the last reported templateXLabel[0] , the estimated templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] amounted to templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Revenue of the eBay from the first Quarter of 2014 to the fourth Quarter of 2019 . In the last reported Quarter , the estimated Revenue of the eBay amounted to 269 million U.S. dollars .

Example 744:
titleEntities: {'Subject': ['Countries'], 'Date': ['2017']}
title: Countries with the highest population decline rate 2017
X_Axis['Country']: ['Cook_Islands', 'Puerto_Rico', 'American_Samoa', 'Lebanon', 'Saint_Pierre_and_Miquelon', 'Latvia', 'Lithuania', 'Moldova', 'Bulgaria', 'Estonia', 'Federated_States_of_Micronesia', 'Northern_Mariana_Islands', 'Croatia', 'Serbia', 'Ukraine', 'Romania', 'Slovenia', 'Cuba', 'Montenegro', 'Virgin_Islands']
Y_Axis['Population', 'decline', 'compared', 'to', 'the', 'previous', 'year']: ['2.79', '1.74', '1.3', '1.1', '1.08', '1.08', '1.08', '1.05', '0.61', '0.57', '0.52', '0.51', '0.5', '0.46', '0.41', '0.33', '0.31', '0.29', '0.28', '0.25']

gold: This statistic shows the 20 countries with the highest population decline rate in 2017 . In the Cook Islands , the population decreased by about 2.8 percent compared to the previous year , making it the country with the highest population decline rate in 2017 . The population decline of Syria Due to the conflicts and instability in Syria in 2014 , thousands of people have fled have fled the country to cross into surrounding countries such as Turkey .
gold_template: This statistic shows the 20 templateTitleSubject[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitleDate[0] . In the templateXValue[0] , the templateYLabel[0] decreased by about templateYValue[max] percent templateYLabel[2] to the templateYLabel[3] templateYLabel[4] , making it the templateXLabel[0] templateTitle[1] the templateTitle[2] templateYLabel[0] templateYLabel[1] templateTitle[5] in templateTitleDate[0] . The templateYLabel[0] templateYLabel[1] of Syria Due to the conflicts and instability in Syria in 2014 , thousands of people have fled have fled the templateXLabel[0] to cross into surrounding templateTitleSubject[0] such as Turkey .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitleDate[0] to templateTitleDate[1] . templateXValue[2] , in Africa , was the third templateTitleSubject[0] templateTitle[1] templateTitle[2] destination with an templateYLabel[0] templateYLabel[1] rate of templateYValue[2] percent in templateYLabel[2] and templateTitle[2] 's templateYLabel[4] templateYLabel[5] to templateYLabel[6] projected templateTitle[5] templateTitleDate[0] to templateTitleDate[1] .
generated: This statistic shows the Countries highest population decline rate 2017 to 2017 . American Samoa , in Africa , was the third Countries highest population destination with an Population decline rate of 1.3 percent in compared and population 's year to year projected 2017 to 2017 .

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 March templateTitle[6] , sorted templateTitle[7] templateTitle[8] and templateTitle[9] group . During that period of time , templateYValue[max] percent of female templateTitleSubject[0] teens aged 15 to 17 years used the social networking app .

generated_template: This statistic shows the results of a survey among female templateTitleSubject[0] high school students templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] . According to the source , templateYValue[max] percent of female students in the templateTitle[1] were templateTitle[5] templateTitle[6] as of 2013 .
generated: This statistic shows the results of a survey among female U.S. Instagram high school students who use Instagram 2015 . According to the source , 64 percent of female students in the U.S. were Instagram 2015 as of 2013 .

Example 748:
titleEntities: {'Subject': ['EU-28'], 'Date': ['2017']}
title: Proportion of individuals who have tried waterpipe , shisha or hooka in EU-28 2017
X_Axis['Response']: ['Yes', 'Never', 'Spontaneous']
Y_Axis['Share', 'of', 'respondents']: ['13', '87', '0']

gold: This statistic displays the proportion of individuals who have tried water pipe , shisha or hookah in EU-28 countries in 2017 . A majority of 87 percent of respondents said they have never tried water pipe , shisha or hookah products . Additionally , the proportion of individuals who have tried oral , nasal or chewing tobacco can be found at the following .
gold_template: This statistic displays the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] water pipe , templateTitle[6] or hookah in templateTitleSubject[0] countries in templateTitleDate[0] . A majority of templateYValue[max] percent of templateYLabel[1] said they templateTitle[3] templateXValue[1] templateTitle[4] water pipe , templateTitle[6] or hookah products . Additionally , the templateTitle[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] oral , nasal or chewing tobacco can be found at the following .

generated_template: This statistic shows the results of a templateTitleDate[0] survey among adult Cubans living in templateTitleSubject[0] on their opinion of templateTitle[1] templateTitle[2] , their former President . templateYValue[max] percent of templateYLabel[1] stated they have a very or somewhat templateXValue[1] opinion of templateTitle[1] templateTitle[2] .
generated: This statistic shows the results of a 2017 survey among adult Cubans living in EU-28 on their opinion of individuals who , their former President . 87 percent of respondents stated they have a very or somewhat Never opinion of individuals who .

Example 749:
titleEntities: {'Subject': ['The'], 'Date': ['2018']}
title: The 20 worst terrorist attacks by number of fatalities 2018
X_Axis['City,', 'country', '(date),', 'organisation']: ['Ghazni_Afghanistan_(Taliban)_(8/10/2018)', 'Farah_Afghanistan_(Taliban)_(5/15/2018)', 'Darengarh_Pakistan_(Khorasan_Chapter_of_the_Islamic_State)_(7/13/2018)', 'Kabul_Afghanistan_(Taliban)_(1/27/2018)', 'Dila_District_Afghanistan_(Taliban)_(10/12/2018)', 'Kabul_Afghanistan_(Khorasan_Chapter_of_the_Islamic_State)_(4/22/2018)', 'Muhmand_Dara_District_Afghanistan_(Unknown)_(9/11/2018)', 'Day_Mirdad_District_Afghanistan_(Taliban)_(9/9/2018)', 'Maywand_District_Afghanistan_(Taliban)_(9/11/2018)', 'Farah_Afghanistan_(Taliban)_(5/12/2018)', 'Gwaska_Nigeria_(Attributed_to_"Fulani_Extremists")_(5/5/2018)', 'Kabul_Afghanistan_(Khorasan_Chapter_of_the_Islamic_State)_(11/20/2018)', 'Sari_Pul_Afghanistan_(Taliban)_(9/10/2018)', 'Chora_District_Afghanistan_(Taliban)_(8/3/2018)', 'Pur_Chaman_District_Afghanistan_(Taliban)_(6/12/2018)', 'Albu_Kamal_Syria_(ISIL)_(6/8/2018)', 'Azra_District_Afghanistan_(Taliban)_(8/6/2018)', 'Kabul_Afghanistan_(Taliban)_(12/24/2018)', 'Oshan_Afghanistan_(Taliban)_(5/11/2018)', 'Tagbara_Central_African_Republic_(Anti-Balaka_Militia)_(4/3/2018)']
Y_Axis['Number', 'of', 'fatalities']: ['466', '330', '150', '104', '77', '70', '69', '62', '61', '61', '58', '56', '56', '51', '51', '51', '50', '47', '46', '44']

gold: The statistic shows the 20 worst terrorist attacks of 2018 , by number of fatalities . The worst terrorist attack in 2018 occurred on August 10 , 2018 , was carried out by the Taliban in Ghazni , Afghanistan , and caused 466 fatalities .
gold_template: The statistic shows the templateTitleDate[0] templateTitle[1] templateTitle[2] templateTitle[3] of templateTitleDate[0] , templateTitle[4] templateYLabel[0] of templateYLabel[1] . The templateTitle[1] templateTitle[2] attack in templateTitleDate[0] occurred on August 10 , templateTitleDate[0] , was carried out templateTitle[4] the Taliban in templateXValue[0] , templateXValue[0] , and caused templateYValue[max] templateYLabel[1] .

generated_template: This statistic displays the templateYLabel[0] of templateYLabel[1] in to templateXValue[0] in templateTitleDate[0] , templateTitle[5] top twenty countries of templateTitle[7] . The majority of immigrants in templateXValue[0] that year were Swedes returning to templateXValue[0] . The remaining top five years of templateTitle[7] were templateXValue[1] , templateXValue[2] , Syria and templateXValue[4] .
generated: This statistic displays the Number of fatalities in to Ghazni Afghanistan (Taliban) (8/10/2018) in 2018 , number top twenty countries of 2018 . The majority of immigrants in Ghazni Afghanistan (Taliban) (8/10/2018) that year were Swedes returning to Ghazni Afghanistan (Taliban) (8/10/2018) . The remaining top five years of 2018 were Farah Afghanistan (Taliban) (5/15/2018) , Darengarh Pakistan (Khorasan Chapter of the Islamic State) (7/13/2018) , Syria and Dila District Afghanistan (Taliban) (10/12/2018) .

Example 750:
titleEntities: {'Subject': ['Finland'], 'Date': ['2007', '2017']}
title: Number of hospitals in Finland 2007 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'hospitals']: ['247', '262', '268', '258', '259', '263', '275', '280', '298', '320', '325']

gold: The number of hospitals in Finland was down at the lowest point of the observed period in 2017 , when there were 247 hospitals . At the beginning of the observed period , in 2007 , the number of hospitals amounted to 325 .
gold_template: The templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] was down at the lowest point of the observed period in templateXValue[max] , when there were templateYValue[min] templateYLabel[1] . At the beginning of the observed period , in templateXValue[min] , the templateYLabel[0] of templateYLabel[1] amounted to templateYValue[max] .

generated_template: The 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 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 Leading men 's men 's hair in the coloring in 2019 . In that year , Just For Men was the Leading men 's men 's Brand in the coloring with Sales of about 138.0 million U.S. dollars . Additional information on the men 's market men 's is used by men in order to facilitate men , creating a more comfortable process and a better finish .

Example 752:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018']}
title: Birth rate in Italy 2018 , by region
X_Axis['Month']: ['Trentino-South_Tyrol', 'Campania', 'Sicily', 'Calabria', 'Lombardy', 'Emilia-Romagna', 'Lazio', 'Apulia', 'Aosta_Valley', 'Veneto', 'Abruzzo', 'Piedmont', 'Tuscany', 'Marche', 'Umbria', 'Basilicata', 'Friuli-Venezia_Giulia', 'Molise', 'Liguria', 'Sardinia']
Y_Axis['Birth', 'rate', 'per', 'thousand', 'inhabitants']: ['9.0', '8.3', '8.1', '7.8', '7.5', '7.3', '7.2', '7.2', '7.2', '7.2', '6.8', '6.7', '6.7', '6.7', '6.6', '6.6', '6.4', '6.2', '5.8', '5.7']

gold: In 2018 , Trentino-South Tyrol was the region in Italy with the highest birth rate nationwide , with nine births per every 1,000 inhabitants . The following three positions of the ranking were occupied by Southern regions : Campania , Sicily , and Calabria . Indeed , South-Italy was the macro-region with the largest birth-rate in Italy .
gold_template: In templateTitleDate[0] , templateXValue[0] was the templateTitle[5] in templateTitleSubject[0] with the highest templateYLabel[0] templateYLabel[1] nationwide , with templateYValue[max] births templateYLabel[2] every 1,000 templateYLabel[4] . The following three positions of the ranking were occupied templateTitle[4] Southern regions : templateXValue[1] , templateXValue[2] , and templateXValue[3] . Indeed , South-Italy was the macro-region with the largest birth-rate in templateTitleSubject[0] .

generated_template: 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 the results of a survey conducted in the country in February templateTitleDate[0] on whether templateTitle[5] templateTitle[6] templateTitle[7] templateXValue[1] templateXValue[0] templateTitle[2] the American President Donald templateTitleSubject[0] . During the survey , templateYValue[max] percent of templateYLabel[1] stated they think that templateTitle[7] of templateTitle[5] countries do not templateXValue[1] templateXValue[1] templateXValue[0] templateTitle[2] Donald templateTitleSubject[0] .
generated: This statistic shows the results of a survey conducted in the country in February 2018 on whether UK 2018 Don't use ad blocker Use ad blocker usage the American President Donald United Kingdom . During the survey , 53 percent of respondents stated they think that 2018 of UK countries do not Don't use ad blocker Use ad blocker usage Donald United Kingdom .

Example 754:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2018']}
title: Number of pubs in the United Kingdom ( UK ) 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '48', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Number', 'of', 'pubs', 'in', 'thousands']: ['47.6', '48.35', '50.3', '50.8', '51.9', '52.5', '53.8', '54.7', '55.4', '52.5', '54.8', '56.8', '58.2', '58.6', '59.0', '59.4', '60.1', '60.7', '60.8']

gold: How many pubs are there in the UK ? There were approximately 47,600 pubs operating in the United Kingdom in 2018 . This represented a decrease of approximately 7,200 pubs in the last ten years , and over 13,200 pubs since 2000 . Pubs in decline Several factors have been suggested for the decline in pubs in the UK .
gold_template: How many templateYLabel[1] are there in the templateTitleSubject[1] ? There were approximately templateYValue[min] templateYLabel[1] operating in the templateTitleSubject[0] in templateXValue[max] . This represented a decrease of approximately 7,200 templateYLabel[1] in the last ten years , and over 13,200 templateYLabel[1] since templateXValue[18] . templateYLabel[1] in decline Several factors have been suggested for the decline in templateYLabel[1] in the templateTitleSubject[1] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] of all templateYLabel[1] in templateTitleSubject[0] templateTitle[4] templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] increased over this period . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] to templateTitleSubject[0] amounted to approximately 97.2 templateYLabel[2] British pounds .
generated: This statistic shows the pubs Number of all pubs in United Kingdom UK 48 to 2018 . The Number of pubs increased over this period . In 2018 , the Number of pubs to United Kingdom amounted to approximately 97.2 thousands British pounds .

Example 755:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Household income of white families in the U.S. 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Median', 'income', 'in', '2018', 'CPI-U-RS', 'adjusted', 'U.S.', 'dollars']: ['70642', '69851', '66440', '65133', '62453', '63588', '60979', '60526', '61361', '62374', '63378', '65089', '63892', '63900', '63627', '63832', '64084', '62773', '63609', '63654', '62480', '60548', '59128', '58184', '56297', '55914', '55842', '55568', '56917']

gold: This statistic shows the household income of white families in the U.S. from 1990 to 2018 . The median income in 2018 was at 70,642 U.S. dollars for white , non-Hispanic families . The median household income of the United States can be accessed here .
gold_template: This statistic shows the templateTitle[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the templateYLabel[5] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] in templateXValue[max] was at templateYValue[max] templateYLabel[5] templateYLabel[6] for templateTitle[2] , non-Hispanic templateTitle[3] . The templateYLabel[0] templateTitle[0] templateYLabel[1] of the templateTitle[4] can be accessed here .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[0] percent of templateTitleSubject[0] templateTitle[3] templateTitle[4] were living below the templateYLabel[0] line in the templateTitle[5] . templateYLabel[0] is the state of one who lacks a certain amount of material possessions or money .
generated: This statistic shows the Median income of U.S. families U.S. in the U.S. from 1990 to 2018 . In 2018 , 70642 percent of U.S. families U.S. were living below the Median line in the 1990 . Median is the state of one who lacks a certain amount of material possessions or money .

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 the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] templateYLabel[1] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In its templateXValue[max] fiscal templateXLabel[0] , the Switzerland-based electronics company employed templateYValue[0] vehicles worldwide .
generated: This statistic depicts the Expenditure of U.S. from the fiscal Year of 2010 to the fiscal Year of 2018 . In its 2018 fiscal Year , the Switzerland-based electronics company employed 3459 vehicles worldwide .

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] templateTitleSubject[0] templateTitleSubject[0] ' ( UAE ) templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[4] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , The templateTitleSubject[0] templateTitleSubject[0] ' templateYLabel[0] templateTitle[4] rate amounted to approximately templateYValue[min] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the Turkey ' ( UAE ) Ratio government expenditure gross from 2014 to 2018 , with projections up until 2024 . In 2018 , The Turkey ' Budgetary gross rate amounted to approximately 33.23 percent relation to the gross Year .

Example 758:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2014']}
title: Travelers in the U.S. who find family vacation planning stressful in 2014 , by gender
X_Axis['Response']: ['Women', 'Men']
Y_Axis['Share', 'of', 'respondents']: ['74', '67']

gold: This statistic shows the share of travelers who find family vacation planning stressful in the United States as of May 2014 , by gender . During the survey , 74 percent of women said that they found family vacation planning stressful .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] in the templateTitle[1] as of May templateTitleDate[0] , templateTitle[9] templateTitle[10] . During the survey , templateYValue[max] percent of templateXValue[0] said that they found templateTitle[4] templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: A July templateTitleDate[0] 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 July 2014 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: 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[1] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: 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 had amounted to about 1 percent compared to the previous 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] 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] 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']}
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 April templateTitleDate[0] . 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 April 2013 . 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[max] 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 894043 metric tons of country to Haiti that year .

Example 763:
titleEntities: {'Subject': ['National Football League', 'Dallas Cowboys'], 'Date': ['2001', '2018']}
title: National Football League : operating income of the Dallas Cowboys 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Operating', 'income', 'in', 'million', 'U.S.', 'dollars']: ['420.0', '365.0', '350.0', '300.0', '270.0', '245.7', '250.5', '226.7', '119.0', '143.3', '9.2', '30.6', '4.3', '37.1', '54.3', '37.5', '52.3', '75.0']

gold: The statistic depicts the operating income of the Dallas Cowboys , a franchise of the National Football League , from 2001 to 2018 . In the 2018 season , the operating income of the Dallas Cowboys was at 420 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[5] templateTitle[6] , a franchise of the templateTitleSubject[0] League , from templateXValue[min] to templateXValue[max] . In the templateXValue[max] season , the templateYLabel[0] templateYLabel[1] of the templateTitle[5] templateTitle[6] was at templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitleSubject[0] templateTitleSubject[0] franchise amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic depicts the Operating income of the National Football League from 2001 to 2018 . In 2018 , the Operating income of the National Football League 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: In the second templateXLabel[0] of templateXValue[0] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[0] templateYLabel[2] templateYLabel[3] templateYLabel[4] , up from templateYValue[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in the corresponding templateXLabel[0] of the previous templateXLabel[0] . templateTitleSubject[0] had an annual templateYLabel[0] templateYLabel[1] of templateYValue[max] templateYLabel[2] templateYLabel[3] , making it accounted for more than one templateYLabel[2] templateYLabel[3] templateYLabel[4] in the first templateXLabel[0] of 2012 .
generated: In the second Quarter of Q4 2019 , Tesla 's Number deliveries amounted to 112000 units , up from 97000 units in the corresponding Quarter of the previous Quarter . Tesla had an annual Number deliveries of 112000 units , making it accounted for more than one units in the first Quarter of 2012 .

Example 765:
titleEntities: {'Subject': ['Nigeria'], 'Date': ['2024']}
title: Inflation rate in Nigeria 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['11', '11.14', '11.4', '11.31', '11.73', '11.32', '12.09', '16.5', '15.7', '9.01', '8.05', '8.5', '12.23', '10.83', '13.74', '12.54', '11.58', '5.4', '8.22', '17.86', '15']

gold: Nigeria 's inflation has been higher than the average for African and Sub-Saharan countries for years now , and even exceeded 16 percent in 2017 – and a real , significant decrease is nowhere in sight . The bigger problem is its unsteadiness , however : An inflation rate that is bouncing all over the place , like this one , is usually a sign of a struggling economy , causing prices to fluctuate , and unemployment and poverty to increase . Nigeria 's economy - a so-called `` mixed economy '' , which means the market economy is at least in part regulated by the state – is not entirely in bad shape , though .
gold_template: templateTitleSubject[0] 's templateYLabel[0] has been higher than the average for African and Sub-Saharan countries for years now , and even exceeded templateYValue[8] percent in templateXValue[7] – and a real , significant decrease is nowhere in sight . The bigger problem is its unsteadiness , however : An templateYLabel[0] templateYLabel[1] that is bouncing all over the place , like this one , is usually a sign of a struggling economy , causing prices to fluctuate , and unemployment and poverty to increase . templateTitleSubject[0] 's economy - a so-called `` mixed economy '' , which means the market economy is at least in part regulated by the state – is not entirely in bad shape , though .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Nigeria from 2004 to 2018 , with projections up until 2024 . In 2018 , the average Inflation rate in Nigeria amounted to about 12.09 percent compared to the previous Year .

Example 766:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2019']}
title: Medicine : Online purchasing in Great Britain 2019 , by demographic
X_Axis['Year']: ['Men', 'Women', '16-24', '25-34', '35-44', '45-54', '55-64', '65+']
Y_Axis['Share', 'of', 'respondents']: ['15', '18', '14', '16', '26', '16', '15', '12']

gold: This statistic displays the share of individuals in Great Britain who purchased medicine online in 2019 , by age and gender . Purchasing online was most common among individuals within the 35 to 44 age demographic , at 26 percent of respondents .
gold_template: This statistic displays the templateYLabel[0] of individuals in templateTitleSubject[0] who purchased templateTitle[0] templateTitleSubject[0] in templateTitle[5] , templateTitle[6] age and gender . templateTitle[2] templateTitleSubject[0] was most common among individuals within the 35 to 44 age templateTitle[7] , at templateYValue[max] percent of templateYLabel[1] .

generated_template: In templateTitleDate[0] , templateYValue[1] percent of templateXValue[1] and around a quarter of templateXValue[0] had purchased templateTitle[0] or templateTitle[1] templateTitle[2] in the previous 12 months in templateTitleSubject[0] templateTitleSubject[0] . templateTitle[3] templateTitle[2] was most common among the 25 to 34-year-old templateTitle[8] , with templateYValue[max] percent of individuals within this group having made purchases in templateTitleDate[0] . European templateTitle[2] templateTitle[3] compared Overall , 29 percent of British adults bought templateTitle[1] templateTitle[2] in templateTitleDate[0] .
generated: In 2019 , 18 percent of Women and around a quarter of Men had purchased Medicine or Online purchasing in the previous 12 months in Great Britain . Great purchasing was most common among the 25 to 34-year-old demographic , with 26 percent of individuals within this group having made purchases in 2019 . European purchasing Great compared Overall , 29 percent of British adults bought Online purchasing in 2019 .

Example 767:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2006', '2018']}
title: Number of road deaths in the Netherlands 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'fatalities']: ['678', '613', '629', '621', '570', '570', '650', '661', '640', '720', '750', '791', '811']

gold: In 2018 , 678 people were killed on roads in the Netherlands . Between 2006 and 2018 , road traffic fatalities had seen a net decline of 16 percent , with the peak recorded at the beginning of the reporting period in 2006 . The Dutch Government is dedicated to making its roads even safer in the future and aims to bring the number of road deaths down to below 500 by 2020 .
gold_template: In templateXValue[max] , templateYValue[0] people were killed on roads in the templateTitleSubject[0] . Between templateXValue[min] and templateXValue[max] , templateTitle[1] traffic templateYLabel[1] had seen a net decline of 16 percent , with the peak recorded at the beginning of the reporting period in templateXValue[min] . The Dutch Government is dedicated to making its roads even safer in the future and aims to bring the templateYLabel[0] of templateTitle[1] templateTitle[2] down to below 500 by 2020 .

generated_template: There were templateYValue[0] 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 678 road deaths recorded in Netherlands in 2018 . Netherlands 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 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 statistic shows the templateYLabel[1] generated by templateTitleSubject[0] , templateTitleSubject[1] , and templateTitle[2] for the fiscal years templateXValue[min] to templateXValue[max] . templateTitleSubject[0] , templateTitleSubject[1] , and templateTitle[2] reported a record templateYLabel[1] of over templateYValue[3] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[3] , up templateTitle[5] templateYValue[4] templateYLabel[2] in the previous templateXLabel[0] .
generated: The statistic shows the number generated by Balfour Beatty Group , Balfour Beatty Group , and Group for the fiscal years 2011 to 2018 . Balfour Beatty Group , Balfour Beatty Group , and Group reported a record number of over 23316 employees in 2015 , up number 39751 employees in the previous Year .

Example 769:
titleEntities: {'Subject': ['Cintas'], 'Date': ['2012', '2019']}
title: Cintas - annual revenue 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['6.89', '6.48', '5.32', '4.8', '4.37', '4.09', '3.88', '3.76']

gold: This statistic depicts the annual revenue of Cintas Corporation between the fiscal year of 2012 and the fiscal year of 2019 . For the fiscal year of 2019 , the Cincinnati-based specialized facility services company reported an annual revenue of just under 6.9 billion U.S. dollars .
gold_template: This statistic depicts the templateTitle[1] templateYLabel[0] of templateTitleSubject[0] Corporation between the fiscal templateXLabel[0] of templateXValue[min] and the fiscal templateXLabel[0] of templateXValue[max] . For the fiscal templateXLabel[0] of templateXValue[max] , the Cincinnati-based specialized facility services company reported an templateTitle[1] templateYLabel[0] of just under templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: In templateXValue[max] , templateTitleSubject[0] , an e-commerce website company in craft and vintage items , generated templateYLabel[0] worth templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] , up from more than templateYValue[1] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the previous templateXLabel[0] . Most products on templateTitleSubject[0] are sold by independent sellers . templateTitleSubject[0] company details templateTitleSubject[0] launched in 2005 and went public in templateXValue[4] after a decade of operations , and is now one of the leading online marketplaces worldwide .
generated: In 2019 , Cintas , an e-commerce website company in craft and vintage items , generated Revenue worth 6.89 billion U.S. dollars , up from more than 6.48 billion U.S. dollars in the previous Year . Most products on Cintas are sold by independent sellers . Cintas company details Cintas launched in 2005 and went public in 2015 after a decade of operations , and is now one of the leading online marketplaces worldwide .

Example 770:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2002', '2017']}
title: U.S. wholesale sales of beer and wine 2002 to 2017
X_Axis['Year']: ['17', '16', '15', '14', '13', '12', '11', '10', '09', '08', '07', '06', '05', '04', '03', '02']
Y_Axis['Sales', 'in', 'billion', 'U.S.', 'dollars']: ['166.31', '161.16', '152.57', '147.34', '145.86', '136.77', '129.43', '122.9', '120.76', '121.58', '115.57', '108.56', '103.91', '96.25', '91.23', '87.56']

gold: The timeline shows the beer , wine , and distilled alcoholic beverages sales of merchant wholesalers in the United States from 2002 to 2017 . In 2017 , the beer , wine , and distilled alcoholic beverages sales of U.S. merchant wholesalers amounted to about 166.31 billion U.S. dollars . Alcohol in the United States During the recession , alcohol-related industries were some of the few seeing continued growth , proving alcohol to be a frequent purchase for many Americans .
gold_template: The timeline shows the templateTitle[3] , templateTitle[4] , and distilled alcoholic beverages templateYLabel[0] of merchant wholesalers in the templateTitle[0] from templateTitleDate[0] to templateTitleDate[1] . In templateTitleDate[1] , the templateTitle[3] , templateTitle[4] , and distilled alcoholic beverages templateYLabel[0] of templateYLabel[2] merchant wholesalers amounted to about templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . Alcohol in the templateTitle[0] During the recession , alcohol-related industries were some of the few seeing continued growth , proving alcohol to be a frequent purchase for many Americans .

generated_template: The timeline shows the templateYLabel[0] of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitle[4] 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: The timeline shows the Sales of U.S. wholesale sales in the wine 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: This statistic shows the projected templateTitle[0] templateYLabel[0] templateYLabel[1] of e-Readers from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYValue[2] templateYLabel[2] units of e-Readers were sold worldwide .
generated: This statistic shows the projected Average estimated of e-Readers from 2006 to 2011 . In 2008 , 801 amount units of e-Readers were sold worldwide .

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 templateYLabel[0] of chemical company templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] generated some templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateYLabel[0] . templateTitleSubject[0] , with full name E. I. du Pont de Nemours and Company , was a U.S.-based chemical company , and one of the largest companies in this industry worldwide .
generated: This statistic shows the Revenue of chemical company Advance Publications from 2006 to 2014 . In 2014 , Advance Publications generated some 7.14 billion U.S. dollars of Revenue . Advance Publications , with full name E. I. du Pont de Nemours and Company , was a U.S.-based chemical company , and one of the largest companies in this industry worldwide .

Example 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: The statistic shows the templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] on templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , there were templateYValue[2] templateYLabel[2] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] on the social platform . The volume of templateTitle[3] content on templateTitleSubject[0] is projected to amount to templateYValue[3] templateYLabel[2] templateYLabel[1] in templateXValue[3] .
generated: The statistic shows the brand of value Nike from value on Nike from 2016 to 2020 . In 2018 , there were 28030 million value Nike from value on the social platform . The volume of Nike content on Nike is projected to amount to 31762 million value in 2017 .

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 total amount of templateTitle[1] templateTitle[2] templateTitle[3] generated between templateXValue[min] and templateXValue[max] in the templateTitle[0] . In templateXValue[max] , approximately templateYValue[max] templateYLabel[1] templateYLabel[2] of templateTitle[1] templateTitle[2] templateTitle[3] were generated here .
generated: The statistic illustrates the total amount of retail sales turnover generated between 2008 and 2017 in the Stationery . In 2017 , approximately 4892 million GBP of retail sales turnover were generated here .

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: In templateXValue[max] , the number of monthly active templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] is projected to reach templateYValue[max] templateYLabel[2] individuals . This would be an increase of approximately six templateYLabel[2] new templateYLabel[1] compared to templateXValue[min] . The penetration rate is also expected to increase .
generated: In 2024 , the number of monthly active Smartphone users in Norway is projected to reach 5.19 millions individuals . This would be an increase of approximately six millions new users compared to 2018 . The penetration rate is also expected to increase .

Example 776:
titleEntities: {'Subject': ['Haiti'], 'Date': ['2018']}
title: Infant mortality rate in Haiti 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Deaths', 'per', '1,000', 'live', 'births']: ['49.5', '50.8', '52.1', '53.3', '54.5', '55.7', '56.8', '57.9', '85.6', '60.2', '61.4']

gold: The statistic shows the infant mortality rate in Haiti from 2008 to 2018 . In 2018 , the infant mortality rate in Haiti was at about 49.5 deaths per 1,000 live births .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[min] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[min] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Infant mortality rate in Haiti from 2008 to 2018 . In 2018 , the Infant mortality rate in Haiti was at about 49.5 Deaths per 1,000 live births .

Example 777:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: Important features of music streaming services in the U.S. 2018
X_Axis['Response']: ['The_variety_of_music_available', 'Low_price_point', 'The_ability_to_listen_on_multiple_divices', 'Clean_user_interface', 'Good_algorithms_to_find_new_music', 'The_ability_to_combine_your_music_library_with_your_streaming_service_library', 'The_ability_to_stream_on_smart_home_devices', 'Curated_playlists', 'Artist_exclusives']
Y_Axis['Share', 'of', 'respondents']: ['81', '80', '68', '66', '58', '64', '57', '52', '46']

gold: This statistic presents data on the most important features of music streaming services among adults in the United States as of March 2018 . During a survey , 81 percent of respondents stated that the variety of music available was the most important feature of music streaming services .
gold_template: This statistic presents data on the most templateTitle[0] templateTitle[1] of templateXValue[0] templateXValue[5] templateTitle[4] among adults in the templateTitle[5] as of March templateTitleDate[0] . During a survey , templateYValue[max] percent of templateYLabel[1] stated that the templateXValue[0] of templateXValue[0] was the most templateTitle[0] feature of templateXValue[0] templateXValue[5] templateTitle[4] .

generated_template: Swedish e-shoppers cared about fashion and templateXValue[1] in templateTitleDate[0] . The templateTitle[0] frequently templateTitle[1] templateXValue[1] templateTitle[3] by Swedish consumers , were templateXValue[0] and templateXValue[0] items , with templateYValue[max] percent of templateYLabel[1] ' vote . templateXValue[1] and templateXValue[1] templateXValue[1] followed , with templateYValue[1] percent of templateYLabel[1] ' vote .
generated: Swedish e-shoppers cared about fashion and Low price point in 2018 . The Important frequently features Low price point streaming by Swedish consumers , were The variety of music available and The variety of music available items , with 81 percent of respondents ' vote . Low price point and Low price point followed , with 80 percent of respondents ' vote .

Example 778:
titleEntities: {'Subject': ['Gannett'], 'Date': ['2013', '2018']}
title: Gannett 's revenue 2013 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['2.92', '3.15', '3.05', '2.89', '3.17', '3.32']

gold: This statistic presents Gannett Company 's annual revenue from 2013 to 2018 . In 2018 , the publisher of USA Today generated a total revenue of 2.92 billion U.S. dollars .
gold_template: This statistic presents templateTitleSubject[0] Company templateTitle[1] annual templateYLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the publisher of USA Today generated a total templateYLabel[0] of templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] for enterprise resource planning ( templateTitleSubject[0] ) templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] templateTitleSubject[0] generated a size of templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the 's Revenue for enterprise resource planning ( Gannett ) 2013 from 2013 to 2018 . In 2018 , the Gannett generated a size of 3.32 billion U.S. dollars .

Example 779:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2024']}
title: Forecast on U.S. petroleum refinery end-use market output 2020 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020']
Y_Axis['Change', 'from', 'year', 'to', 'year']: ['2', '0.5', '0.5', '0.8', '2.2']

gold: This statistic displays a forecast of the petroleum and refinery end-use market output in the United States from 2020 to 2024 . Through 2020 , the petroleum and refinery end-use market output is expected to increase by 2.2 percent . U.S. petroleum refinery market It is projected that the growth of output from the U.S. petroleum refinery end-use market will slow , from a rate of 2.2 percent in 2020 to 0.5 percent in 2023 , and grow again to 2.2 percent in 2024 .
gold_template: This statistic displays a templateTitle[0] of the templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[1] templateYLabel[1] templateXValue[min] to templateXValue[max] . Through templateXValue[min] , the templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] is expected to increase by templateYValue[max] percent . templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[5] It is projected that the growth of templateTitle[6] templateYLabel[1] the templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] will slow , templateYLabel[1] a rate of templateYValue[max] percent in templateXValue[min] to templateYValue[min] percent in templateXValue[1] , and grow again to templateYValue[max] percent in templateXValue[max] .

generated_template: This statistic displays a templateTitle[0] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . Through templateXValue[max] , the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] is expected to increase by templateYValue[max] percent . templateTitleSubject[0] templateTitle[2] templateTitle[4] It is expected that the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] will experience a decreasing rate of 1.3 percent from 2019 to templateXValue[min] .
generated: This statistic displays a Forecast of the petroleum refinery end-use market in the U.S. from 2020 to 2024 . Through 2024 , the petroleum refinery end-use market is expected to increase by 2.2 percent . U.S. petroleum end-use It is expected that the petroleum refinery end-use market will experience a decreasing rate of 1.3 percent from 2019 to 2020 .

Example 780:
titleEntities: {'Subject': ['Spanish'], 'Date': ['2008', '2018']}
title: Chocolate and cocoa products consumption in Spanish households 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Total', 'consumption', 'in', 'million', 'kilograms']: ['164.9', '162.4', '164.1', '165.5', '163.6', '165.3', '158.7', '151.5', '150.54', '147.44', '143.6']

gold: Chocolate has inspired uncountable desserts in many countries across the globe and throughout history . This delicacy originating in Mesoamerica is the favorite sweet of many places including Spain , which imported this delight to Europe during the country 's explorations in the Americas . Spain 's sweet tooth has been on the rise in recent years , with a peak in chocolate and cocoa product consumption amounting to 165.5 million kilograms in 2013 .
gold_template: templateTitle[0] has inspired uncountable desserts in many countries across the globe and throughout history . This delicacy originating in Mesoamerica is the favorite sweet of many places including Spain , which imported this delight to Europe during the country 's explorations in the Americas . Spain 's sweet tooth has been on the rise in recent years , with a peak in templateTitle[0] and templateTitle[1] product templateYLabel[1] amounting to templateYValue[max] templateYLabel[2] templateYLabel[3] in templateXValue[5] .

generated_template: The templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] manufactured in the templateTitleSubject[1] was approximately templateYValue[0] 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 Total consumption of Chocolate cocoa manufactured in the Spanish was approximately 164.9 thousand tons in 2018 , an increase of over eight thousand tons from 2017 . This constitutes a recovery of the production consumption , which collapsed between 2008 and 2011 and fell by just under eighty percent , before it stagnated in the early 2010s . A stagnating market ? The corresponding developments in Total values give the same picture as above .

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 average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . The templateYLabel[0] templateYLabel[1] is calculated using the price increase of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the templateXLabel[0] .
generated: The statistic shows the average Inflation rate in 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: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) as of October templateTitleDate[0] . Of templateYLabel[1] templateYValue[max] percent reported action or thriller .
generated: This statistic presents the Methods ordering food for in the U.S. April ( U.S. April ) as of October 2014 . Of respondents 50.5 percent reported action or thriller .

Example 783:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2019']}
title: Public sector expenditure as a share of GDP in the United Kingdom ( UK ) 2000 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02', '00/01']
Y_Axis['Expenditure', 'as', 'share', 'of', 'GDP']: ['34.1', '34.4', '34.8', '35.7', '36.4', '37.3', '38.3', '39.1', '39.7', '39.4', '36.9', '34.9', '34.5', '34.7', '34.9', '34', '32.8', '32.1', '31.8']

gold: This statistic shows total public sector current expenditure as a share of GDP in the United Kingdom ( UK ) from 2000/01 to 2018/19 . During this period public sector spending fluctuated , peaking in 2010/11 at 39.7 percent of GDP .
gold_template: This statistic shows total templateTitle[0] templateTitle[1] current templateYLabel[0] as a templateYLabel[1] of templateYLabel[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from 2000/01 to 2018/19 . During this period templateTitle[0] templateTitle[1] spending fluctuated , peaking in 2010/11 at templateYValue[max] percent of templateYLabel[2] .

generated_template: This statistic shows the total templateTitleSubject[0] templateTitleSubject[0] ( UK ) templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] 2018/19 . The overall trend was one of increase , seeing a peak in templateXValue[0] with 27.99 templateYLabel[3] British pounds and a total rise of over 5 templateYLabel[3] British pounds throughout this period .
generated: This statistic shows the total United Kingdom ( UK ) GDP Expenditure share GDP from fiscal Year 00/01 to fiscal Year 2018/19 . The overall trend was one of increase , seeing a peak in 18/19 with 27.99 GDP British pounds and a total rise of over 5 GDP British pounds throughout this period .

Example 784:
titleEntities: {'Subject': ['Luxembourg'], 'Date': ['2006', '2018']}
title: Number of arrivals in tourist accommodation Luxembourg 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012_', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'arrivals', 'in', 'thousands']: ['1139.0', '1156.0', '1161.8', '1196.1', '1142.9', '1044.3', '1021.7', '935.0', '854.72', '907.53', '936.65', '979.21', '967.88']

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Luxembourg from 2006 to 2018 . There were around 1.1 million arrivals at accommodation establishments in Luxembourg in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . There were around templateYValue[0] templateYLabel[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] in templateXValue[0] .

generated_template: The statistic shows the templateYLabel[0] of templateTitleSubject[0] 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: The statistic shows the Number of Luxembourg arrivals between the fiscal Year of 2018 and the fiscal Year of 2018 . In the fiscal Year of 2018 , the Germany-based multinational engineering and electronics company employed some 1196.1 people .

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: The statistic presents the templateYLabel[0] of templateTitleSubject[0] templateTitleSubject[0] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the developer of a popular templateYLabel[0] of templateYValue[0] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] templateTitleSubject[0] , founded in Oklahoma , templateTitle[3] in 1953 , is a drive-in fast-food restaurant chain .
generated: The statistic presents the Revenue of Iberdrola in the 2018 2009 to 2018 . In 2018 , the developer of a popular Revenue of 35075.9 million euros . Iberdrola , founded in Oklahoma , 2018 in 1953 , is a drive-in fast-food restaurant chain .

Example 786:
titleEntities: {'Subject': ['United States'], 'Date': ['2012']}
title: Reasons for opposing same-sex marriage in the United States in 2012
X_Axis['Month']: ['Religion/Bible_says_it_is_wrong', 'Marriage_should_be_between_a_man_and_a_woman', 'Morally_wrong/Have_traditional_beliefs', 'Civil_unions_are_sufficient', 'Unnatural/Against_laws_of_nature', 'Undermines_traditional_family_structure/Mother_and_father', 'Other', 'No_opinion']
Y_Axis['Share', 'of', 'respondents']: ['47', '20', '16', '6', '5', '5', '7', '4']

gold: This statistic shows the results of a 2012 survey among American adults opposing legal same-sex marriage . They were asked to give reasons for this decision . 47 percent of respondents stated that they oppose same-sex marriage because their religion and/or the Bible says it 's wrong .
gold_template: This statistic shows the results of a templateTitleDate[0] survey among American adults templateTitle[2] legal templateTitle[3] templateXValue[1] . They were asked to give templateTitle[0] templateTitle[1] this decision . templateYValue[max] percent of templateYLabel[1] stated that they oppose templateTitle[3] templateXValue[1] because their religion and/or the Bible templateXValue[0] it 's templateXValue[0] .

generated_template: As of the third quarter of templateTitleDate[0] , it was found that templateYValue[max] percent of templateTitleSubject[0] users in the templateTitleSubject[1] templateTitleSubject[1] accessed the photo sharing app templateXValue[0] templateXValue[0] a templateXValue[0] . A further templateYValue[1] percent of templateYLabel[1] claimed that they used templateTitleSubject[0] on a templateXValue[1] basis .
generated: As of the third quarter of 2012 , it was found that 47 percent of United States users in the United States accessed the photo sharing app Religion/Bible says it is wrong a Religion/Bible says it is wrong . A further 20 percent of respondents claimed that they used United States on a Marriage should be between a man and a woman basis .

Example 787:
titleEntities: {'Subject': ['Nissan', 'Europe'], 'Date': ['2018']}
title: Nissan car sales in Europe 2003 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Number', 'of', 'units', 'sold']: ['487017', '560415', '547343', '554046', '477703', '421134', '436169', '470004', '411084', '372096', '338169', '313437', '332150', '387325', '409717', '409511']

gold: This statistic shows the number of cars sold by Nissan in Europe between 2003 and 2018 . European sales of the Nissan cars rose from 400 thousand units sold in 2003 to over 560 thousand units sold by 2017 . In 2018 , there were 487 thousand units of Nissan cars sold in Europe .
gold_template: This statistic shows the templateYLabel[0] of cars templateYLabel[2] by templateTitleSubject[0] in templateTitleSubject[1] between templateXValue[min] and templateXValue[max] . European templateTitle[2] of the templateTitleSubject[0] cars rose from 400 thousand templateYLabel[1] templateYLabel[2] in templateXValue[min] to over templateYValue[max] thousand templateYLabel[1] templateYLabel[2] by templateXValue[1] . In templateXValue[max] , there were templateYValue[0] thousand templateYLabel[1] of templateTitleSubject[0] cars templateYLabel[2] in templateTitleSubject[1] .

generated_template: In templateXValue[max] , the templateTitleSubject[0] templateTitleSubject[0] 's global templateTitle[0] amounted to templateYValue[0] templateYLabel[1] templateYLabel[2] . The templateTitleSubject[0] templateTitleSubject[0] Ltd. is a Swiss company which primarily manufactures watches . templateTitleSubject[0] The templateTitleSubject[0] templateTitleSubject[0] was formed in 1983 through the merging of two Swiss watch manufacturers : ASUAG and SSIH .
generated: In 2018 , the Nissan 's global Nissan amounted to 487017 units sold . The Nissan Ltd. is a Swiss company which primarily manufactures watches . Nissan The Nissan was formed in 1983 through the merging of two Swiss watch manufacturers : ASUAG and SSIH .

Example 788:
titleEntities: {'Subject': ['Annual'], 'Date': ['2010', '2018']}
title: Annual growth in average global hotel rates 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Year-over-year', 'growth', 'in', 'average', 'hotel', 'rates']: ['3.7', '2.5', '2.5', '2.6', '1.8', '0', '-1.5', '7.4', '4.7']

gold: This statistic shows annual growth in average global hotel rates from 2010 to 2018 . Global hotel rates were forecasted to increase by 3.7 percent in 2018 . The average daily rate of the hotel industry in the Americas reached around 123.37 U.S. dollars in 2016 .
gold_template: This statistic shows templateTitleSubject[0] templateYLabel[1] in templateYLabel[2] templateTitle[3] templateYLabel[3] templateYLabel[4] from templateXValue[min] to templateXValue[max] . templateTitle[3] templateYLabel[3] templateYLabel[4] were forecasted to increase by templateYValue[0] percent in templateXValue[max] . The templateYLabel[2] daily rate of the templateYLabel[3] industry in the Americas reached around 123.37 U.S. dollars in templateXValue[2] .

generated_template: The motorcycle fleet size in templateTitleSubject[0] amounted to more than templateYValue[max] templateYLabel[2] templateYLabel[3] in templateXValue[max] . This represented an increase of almost 13.5 percent in comparison to the motorbike fleet reported a templateXLabel[0] earlier . In templateXValue[max] , the templateYLabel[0] of registered motor vehicles in templateTitle[2] in templateTitleSubject[0] reached almost 47.8 templateYLabel[2] templateYLabel[3] .
generated: The motorcycle fleet size in Annual amounted to more than 7.4 average hotel in 2018 . This represented an increase of almost 13.5 percent in comparison to the motorbike fleet reported a Year earlier . In 2018 , the Year-over-year of registered motor vehicles in average in Annual reached almost 47.8 average hotel .

Example 789:
titleEntities: {'Subject': ['Muslims', 'Spain'], 'Date': ['2018']}
title: Muslims in Spain 2018 , by nationality
X_Axis['Country']: ['Spain', 'Morocco', 'Pakistan', 'Senegal', 'Algeria', 'Nigeria', 'Mali', 'Gambia', 'Bangladesh', 'Guinea', 'Others']
Y_Axis['Number', 'of', 'Muslims']: ['847801', '769050', '82738', '66046', '60820', '39374', '23685', '19381', '15979', '10186', '58615']

gold: This statistic presents the number of Muslims in Spain in 2018 , broken down by nationality . That year , there were a total of approximately two million Muslims in Spain . Almost 848 thousand had Spanish nationality , followed by Muslims with a Moroccan nationality with figures that almost reached 770 thousand individuals .
gold_template: This statistic presents the templateYLabel[0] of templateYLabel[1] in templateXValue[0] in templateTitleDate[0] , broken down templateTitle[3] templateTitle[4] . That year , there were a total of approximately two million templateYLabel[1] in templateXValue[0] . Almost templateYValue[max] thousand had Spanish templateTitle[4] , followed templateTitle[3] templateYLabel[1] with a Moroccan templateTitle[4] with figures that almost reached 770 thousand individuals .

generated_template: This statistic shows the distribution of templateTitle[1] templateTitle[2] templateYLabel[1] templateTitle[5] region in templateTitleDate[0] . During this year , the templateXValue[0] templateXValue[2] consumed approximately templateYValue[max] percent of the world 's templateTitle[2] . This drug can be used as an analgesic and is about 100 times more potent than morphine .
generated: This statistic shows the distribution of Spain 2018 Muslims nationality region in 2018 . During this year , the Spain Pakistan consumed approximately 847801 percent of the world 's 2018 . This drug can be used as an analgesic and is about 100 times more potent than morphine .

Example 790:
titleEntities: {'Subject': ['Tampa Bay Buccaneers', 'NFL'], 'Date': ['2001', '2018']}
title: Revenue of the Tampa Bay Buccaneers ( NFL ) 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['400', '383', '367', '341', '313', '275', '267', '258', '245', '246', '241', '224', '205', '203', '195', '175', '168', '151']

gold: The statistic depicts the revenue of the Tampa Bay Buccaneers , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Tampa Bay Buccaneers was 400 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] Buccaneers , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] Buccaneers was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] templateTitleSubject[0] was templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Tampa Bay Buccaneers , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the Revenue of the Tampa Bay Buccaneers was 400 million U.S. dollars .

Example 791:
titleEntities: {'Subject': ['Afghanistan'], 'Date': ['2018']}
title: Population growth in Afghanistan 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Population', 'growth', 'compared', 'to', 'previous', 'year']: ['2.38', '2.55', '2.78', '3.08', '3.36', '3.49', '3.41', '3.14', '2.75', '2.4', '2.27']

gold: This timeline shows the population growth in Afghanistan from 2008 to 2018 . In 2018 , Afghanistan 's population grew by an estimated 2.38 percent compared to the previous year . See Afghanistan 's population figures for comparison .
gold_template: This timeline shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] grew by an estimated templateYValue[0] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . See templateTitleSubject[0] 's templateYLabel[0] figures for comparison .

generated_template: In templateXValue[max] , the templateTitleSubject[0] templateTitleSubject[0] produced approximately templateYValue[max] templateYLabel[2] templateYLabel[3] of templateYLabel[0] worldwide , an increase of 53 templateYLabel[2] templateYLabel[3] on the previous templateXLabel[0] . The company templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] has increased by about 75 percent since templateXValue[6] . Asia at the center of templateTitle[3] operations network The templateTitleSubject[0] templateTitleSubject[0] templateTitle[2] templateYLabel[0] templateYLabel[1] has risen year-on-year since templateXValue[6] .
generated: In 2018 , the Afghanistan produced approximately 3.49 compared previous of Population worldwide , an increase of 53 compared previous on the previous Year . The company Afghanistan 2018 Population growth has increased by about 75 percent since 2012 . Asia at the center of 2018 operations network The Afghanistan Population growth has risen year-on-year since 2012 .

Example 792:
titleEntities: {'Subject': ['Philippines'], 'Date': ['2017', '2023']}
title: Philippines social media user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['55', '54', '54', '53', '52', '51', '49']

gold: The social media penetration in the Philippines was at 49 percent in 2017 , amounting to about 54 million people using a social network in the Philippines as of 2018 . Considering that the number of internet users in the Philippines was at just under 70 million in that year , the social media penetration was projected to increase to 55 percent of the population by 2023 . Social media in the Philippines The Philippines are an archipelagic country , which poses logistical problems for social interaction and communication between residents from the various islands .
gold_template: The templateTitle[1] templateTitle[2] templateTitle[4] in the templateTitleSubject[0] was at templateYValue[min] percent in templateXValue[min] , amounting to about templateYValue[1] million people using a templateTitle[1] network in the templateTitleSubject[0] as of templateXValue[5] . Considering that the number of internet users in the templateTitleSubject[0] was at just under 70 million in that templateXLabel[0] , the templateTitle[1] templateTitle[2] templateTitle[4] was projected to increase to templateYValue[max] percent of the templateYLabel[1] by templateXValue[max] . templateTitle[1] templateTitle[2] in the templateTitleSubject[0] The templateTitleSubject[0] are an archipelagic country , which poses logistical problems for templateTitle[1] interaction and communication between residents from the various islands .

generated_template: This statistic provides information on templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , 55.65 percent of the Brazilian templateYLabel[1] were accessing the templateTitle[1] . These figures are projected to grow to templateYValue[max] percent in templateXValue[max] .
generated: This statistic provides information on social media user in Philippines from 2017 to 2023 . In 2017 , 55.65 percent of the Brazilian population were accessing the social . These figures are projected to grow to 55 percent in 2023 .

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: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the 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 the templateTitleSubject[0] was at templateYValue[0] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Price per pound in the U.S. from 1990 and 2018 . According to the source , the data are ILO estimates . In 2018 , the estimated Price per pound in the U.S. was at 86.85 U.S. cents .

Example 795:
titleEntities: {'Subject': ['Spain'], 'Date': ['2006', '2018']}
title: Number of deaths in Spain 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'deaths']: ['474523', '424523', '410611', '422568', '395830', '390419', '402950', '387911', '382047', '384933', '386324', '385361', '371478']

gold: According to data provided by the Spanish Statistics Institute , more people died in Spain than were being born in 2018 , with figures reaching over 427 thousand deaths versus 370 thousand newborns . The number of deaths experienced an upward trend over the 11-year period , presumably due to Spain 's aging population . Circulatory system diseases and cancer ranked as the most common causes of death in Spain The cause of death can vary significantly across the globe and depends highly on economic development , presence of a competent healthcare system and one 's choices in lifestyle .
gold_template: According to data provided by the Spanish Statistics Institute , more people died in templateTitleSubject[0] than were being born in templateXValue[max] , with figures reaching over 427 thousand templateYLabel[1] versus 370 thousand newborns . The templateYLabel[0] of templateYLabel[1] experienced an upward trend over the 11-year period , presumably due to templateTitleSubject[0] 's aging population . Circulatory system diseases and cancer ranked as the most common causes of death in templateTitleSubject[0] The cause of death can vary significantly across the globe and depends highly on economic development , presence of a competent healthcare system and one 's choices in lifestyle .

generated_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] of official templateYLabel[1] templateTitle[2] increased from approximately templateYValue[min] thousand to templateYValue[0] thousand , after which it experienced a slight decline . As of templateXValue[1] , England is the leader with 1.87 thousand official templateYLabel[1] templateTitle[2] followed by Germany and France with respectively 731 and 602 registered templateYLabel[1] templateTitle[2] . The rise in the templateYLabel[0] of templateYLabel[1] templateTitle[2] in templateTitleSubject[0] was accompanied with a rise in templateYLabel[1] popularity .
generated: Between 2006 and 2018 , the Number of official deaths Spain increased from approximately 371478 thousand to 474523 thousand , after which it experienced a slight decline . As of 2017 , England is the leader with 1.87 thousand official deaths Spain followed by Germany and France with respectively 731 and 602 registered deaths Spain . The rise in the Number of deaths Spain in Spain was accompanied with a rise in deaths popularity .

Example 796:
titleEntities: {'Subject': ['Winter Olympic Games'], 'Date': ['2014']}
title: Number of participants Winter Olympic Games 2014
X_Axis['Year']: ['2014_Sochi', '2010_Vancouver', '2006_Torino', '2002_Salt_Lake_City', '1998_Nagano', '1994_Lillehammer', '1992_Albertville', '1988_Calgary', '1984_Sarajevo', '1980_Lake_Placid', '1976_Innsbruck', '1972_Sapporo', '1968_Grenoble', '1964_Innsbruck', '1960_Squaw_Valley', "1956_Cortina_d'Ampezzo", '1952_Oslo', '1948_St._Moritz', '1936_Garmisch-Partenkirchen', '1932_Lake_Placid', '1928_St._Moritz', '1924_Chamonix']
Y_Axis['Number', 'of', 'participants']: ['2800', '2536', '2494', '2402', '2180', '1738', '1801', '1424', '1273', '1072', '1129', '1008', '1160', '1094', '665', '821', '694', '668', '668', '252', '461', '292']

gold: The statistic shows the number of participants in the Winter Olympic Games from 1924 to 2014 . At the first Olympic Winter Games in Chamonix in 1924 , 292 athletes participated . This figure grew to 2,536 participating athletes from 82 nations during the 2010 Vancouver Winter Olympics .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] Games from templateXValue[last] to templateXValue[0] . At the first templateTitleSubject[0] Games in templateXValue[last] in templateXValue[last] , templateYValue[21] athletes participated . This figure grew to templateYValue[1] participating athletes from 82 nations during the templateXValue[1] Winter Olympics .

generated_template: In templateTitleDate[1] , the templateTitle[3] templateTitle[4] amount of individual templateTitle[1] templateTitle[2] templateTitle[6] templateTitle[7] in the templateTitleSubject[0] templateTitleSubject[0] was about templateYValue[max] thousand templateYLabel[2] . The largest amount of templateTitle[1] templateTitle[2] in the templateTitleSubject[0] templateTitleSubject[0] is directed to reach about templateYValue[6] templateYLabel[2] people .
generated: In 2014 , the Olympic Games amount of individual participants Winter 2014 in the Winter Olympic Games was about 2800 thousand participants . The largest amount of participants Winter in the Winter Olympic Games is directed to reach about 1801 participants people .

Example 797:
titleEntities: {'Subject': ['California'], 'Date': ['2018']}
title: California - real GDP 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Real', 'GDP', 'in', 'billion', 'U.S.', 'dollars']: ['2677.94', '2587.57', '2498.84', '2426.14', '2309.93', '2220.87', '2144.5', '2091.59', '2058.14', '2026.49', '2111.14', '2103.62', '2072.18', '1990.14', '1902.32', '1825.42', '1743.65', '1702.78', '1709.94']

gold: This statistic shows the development of California 's real GDP from 2000 to 2018 . In 2018 , the real GDP of California was 2.67 trillion U.S. dollars .
gold_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was 2.67 templateYLabel[2] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[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 California 's Real GDP from 2000 to 2018 . In 2018 , the Real GDP of California was about 2677.94 billion U.S. dollars . The annual Real GDP growth of the U.S. can be accessed here .

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': ['2000', '2018']}
title: U.S. petroleum imports from Iraq 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Imports', 'in', 'thousand', 'barrels', 'per', 'day']: ['521', '604', '424', '229', '369', '341', '476', '459', '415', '450', '627', '484', '553', '531', '656', '481', '459', '795', '620']

gold: This statistic represents U.S. petroleum imports from Iraq between 2000 and 2018 . In 2018 , the United States imported an average of approximately 521,000 barrels of petroleum per day from the Middle Eastern country .
gold_template: This statistic represents templateTitle[0] templateTitle[1] templateYLabel[0] templateTitle[3] templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateTitle[0] imported an average of approximately templateYValue[0] templateYLabel[2] of templateTitle[1] templateYLabel[3] templateYLabel[4] templateTitle[3] the Middle Eastern country .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[2] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[0] templateYLabel[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[2] in the templateTitle[6] .
generated: The statistic shows the Imports of thousand imports from Iraq barrels in the 2018 from 2000 to 2018 . In 2018 , there were 521 thousand imports from Iraq barrels in the 2018 .

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 statistic presents the annual templateTitle[2] templateYLabel[1] of mobile messaging platform templateTitleSubject[0] . In templateXValue[max] , the chat app had generated 1.03 templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateTitle[2] losses , a slight improvement from 1.56 templateYLabel[2] templateYLabel[3] templateYLabel[4] in losses in the preceding templateXLabel[0] .
generated: The statistic presents the annual revenue of mobile messaging platform Cree . In 2019 , the chat app had generated 1.03 million U.S. dollars in revenue losses , a slight improvement from 1.56 million U.S. dollars in losses in the preceding Year .

Example 801:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2018']}
title: Population growth in Malaysia 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Population', 'growth', 'compared', 'to', 'previous', 'year']: ['1.35', '1.36', '1.36', '1.34', '1.34', '1.37', '1.45', '1.56', '1.69', '1.82', '1.91']

gold: This statistic shows the population growth in Malaysia from 2008 to 2018 . In 2018 , Malaysia 's population increased by approximately 1.35 percent compared to the previous year .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] increased by approximately templateYValue[0] percent templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: In templateXValue[max] , the templateTitleSubject[0] templateTitleSubject[0] produced approximately templateYValue[max] templateYLabel[2] templateYLabel[3] of templateYLabel[0] worldwide , an increase of 53 templateYLabel[2] templateYLabel[3] on the previous templateXLabel[0] . The company templateTitle[2] templateTitle[3] templateYLabel[0] templateYLabel[1] has increased by about 75 percent since templateXValue[6] . Asia at the center of templateTitle[3] operations network The templateTitleSubject[0] templateTitleSubject[0] templateTitle[2] templateYLabel[0] templateYLabel[1] has risen year-on-year since templateXValue[6] .
generated: In 2018 , the Malaysia produced approximately 1.91 compared previous of Population worldwide , an increase of 53 compared previous on the previous Year . The company Malaysia 2018 Population growth has increased by about 75 percent since 2012 . Asia at the center of 2018 operations network The Malaysia Population growth has risen year-on-year since 2012 .

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 templateTitle[1] in templateTitleSubject[0] grew to templateYValue[6] templateYLabel[1] people in templateXValue[6] . This is in line with a steady positive trend that has been happening since at least templateXValue[min] and is forecast to continue until at least templateXValue[max] , as well as with the growth rates in other ASEAN countries . Malaysian demographics As the fertility rate slowly declines , the templateTitle[1] growth rate should slowly decline as well .
generated: The population in Nepal grew to 28.09 millions people in 2018 . This is in line with a steady positive trend that has been happening since at least 2014 and is forecast to continue until at least 2024 , as well as with the growth rates in other ASEAN countries . Malaysian demographics As the fertility rate slowly declines , the population growth rate should slowly decline as well .

Example 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 presents the templateTitle[2] of templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] templateTitleSubject[1] as of January templateTitleDate[0] . During an online survey conducted by We Are Flint , it was found that templateYValue[0] percent of internet users in the templateTitleSubject[1] templateTitleSubject[1] accessed the messenger app templateXValue[1] templateXValue[2] templateXValue[2] templateXValue[2] .
generated: This statistic presents the consumer of U.S. in the U.S. as of January 2016 . During an online survey conducted by We Are Flint , it was found that 53 percent of internet users in the U.S. accessed the messenger app Yahoo Outlook (Hotmail) .

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: Ethical templateTitleSubject[0] and beauty company templateTitleSubject[0] generated the largest portion of its templateYLabel[0] from its operations in the United Kingdom in templateTitleDate[0] . This was followed templateTitle[6] the brand 's retail activities in templateXValue[1] , where the number of templateTitleSubject[0] shops is almost as high as retail templateXValue[0] stores . Young shopper profile templateTitleSubject[0] manufactures and sells hand-made beauty , hair and skincare products .
generated: Ethical Global and beauty company Global generated the largest portion of its Price from its operations in the United Kingdom in 2019 . This was followed 2019 the brand 's retail activities in United States , where the number of Global shops is almost as high as retail China stores . Young shopper profile Global manufactures and sells hand-made beauty , hair and skincare products .

Example 805:
titleEntities: {'Subject': ['Minnesota Wilds revenue'], 'Date': ['2019']}
title: Minnesota Wilds ' revenue 2005 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['149', '142', '139', '136', '125', '111', '81', '99', '97', '92', '95', '94', '78', '71']

gold: This graph depicts the annual National Hockey League revenue of the Minnesota Wild from the 2005/06 season to the 2018/19 season . The revenue of the Minnesota Wild amounted to 149 million U.S. dollars in the 2018/19 season .
gold_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] Wild from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] Wild amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .

generated_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] templateTitle[1] amounted to templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateXValue[0] season .
generated: This graph depicts the annual National Hockey League Revenue of the Minnesota Wilds revenue Wilds from the 2005/06 season to the 2018/19 season . The Revenue of the Minnesota Wilds revenue Wilds amounted to 149 million U.S. dollars in the 2018/19 season .

Example 806:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: New issue volume of U.S. asset-backed securities 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2010', '2005', '2000']
Y_Axis['Volume', 'in', 'billion', 'U.S.', 'dollars']: ['517', '550', '325', '333', '393', '126', '474', '240']

gold: This statistic presents the new issue volume of the asset-backed securities of the United States from 2000 to 2018 . In 2018 , the new issue volume of the asset-backed securities of the United States was 517 billion U.S. dollars .
gold_template: This statistic presents the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitle[4] templateTitle[5] of the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitle[4] templateTitle[5] of the templateTitle[3] was templateYValue[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic depicts the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] was approximately templateYValue[max] templateYLabel[1] templateYLabel[2] templateYLabel[3] . The templateTitle[2] templateTitle[3] is an informal network of banks , brokers , dealers and financial institutions which are linked electronically .
generated: This statistic depicts the Volume of volume U.S. asset-backed securities in the issue from 2000 to 2018 . In 2018 , the Volume of the volume U.S. asset-backed securities in the issue was approximately 550 billion U.S. dollars . The volume U.S. is an informal network of banks , brokers , dealers and financial institutions which are linked electronically .

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] templateXValue[0] templateXValue[0] 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] 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 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: In templateXValue[max] , the proportion of templateYLabel[0] templateTitle[3] who smoked in templateTitleSubject[0] was at templateYValue[max] percent . The templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateTitleSubject[0] had increased since templateXValue[min] , despite measures taken by the templateTitleSubject[0] government to reduce the templateYLabel[2] templateYLabel[3] . These include increasing taxes on cigarettes and restricting the areas in which templateYLabel[2] was allowed .
generated: In 2017 , the proportion of Retail market who smoked in U.S. was at 9732.2 percent . The Retail sales million U.S. in U.S. had increased since 2011 , despite measures taken by the U.S. government to reduce the million U.S. . These include increasing taxes on cigarettes and restricting the areas in which million was allowed .

Example 809:
titleEntities: {'Subject': ['American'], 'Date': []}
title: American teenagers ' belief in existence of a God
X_Axis['Response']: ['Absolutely_certain_that_there_is_a_God', 'Somewhat_certain_that_there_is_a_God', 'Somewhat_certain_that_there_is_no_God', 'Absolutely_certain_that_there_is_no_God', 'Not_sure_whether_or_not_there_is_a_God']
Y_Axis['Share', 'of', 'respondents']: ['54', '15', '7', '9', '16']

gold: This survey , conducted by Harris Poll across the United States in February 2014 , shows the share of American teenagers who are certain or uncertain about the existence of a God . 54 percent of American teenagers are absolutely certain that there is a God .
gold_template: This survey , conducted by Harris Poll across the templateTitle[0] in February 2014 , shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[1] who are templateXValue[0] or uncertain about the templateTitle[4] of a templateXValue[0] . templateYValue[max] percent of templateTitleSubject[0] templateTitle[1] are templateXValue[0] that templateXValue[0] is a templateXValue[0] .

generated_template: This statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] templateTitleSubject[0] ( templateTitleSubject[1] ) as of October templateTitleDate[0] . Of templateYLabel[1] templateYValue[max] percent reported action or thriller .
generated: This statistic presents the American teenagers ' belief in the American ( American ) as of October . Of respondents 54 percent reported action or thriller .

Example 810:
titleEntities: {'Subject': ['Nepal'], 'Date': ['2018']}
title: Population density in Nepal 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Inhabitants', 'per', 'square', 'kilometer']: ['195.94', '192.73', '190.17', '188.46', '187.7', '187.78', '188.28', '188.64', '188.44', '187.54', '186.02']

gold: The statistic shows the population density in Nepal from 2008 to 2018 . In 2018 , the population density in Nepal amounted to about 195.94 inhabitants per square kilometer .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: In templateXValue[max] , templateTitleSubject[0] had a templateTitle[0] templateTitle[1] of about templateYValue[max] people templateYLabel[1] templateYLabel[2] templateYLabel[3] . The country has one of the lowest templateTitle[0] densities in the world , as the total templateTitle[0] is very small in relation to the dimensions of the land . templateTitleSubject[0] has a relatively stable templateTitle[0] size , consistently with a growth of around one percent compared to the previous templateXLabel[0] .
generated: In 2018 , Nepal had a Population density of about 195.94 people per square kilometer . The country has one of the lowest Population densities in the world , as the total Population is very small in relation to the dimensions of the land . Nepal has a relatively stable Population size , consistently with a growth of around one percent compared to the previous Year .

Example 811:
titleEntities: {'Subject': ['Vending'], 'Date': ['2010']}
title: Vending machines : sales volume of vended products 2010
X_Axis['Year']: ['1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010']
Y_Axis['Vended', 'volume', '(in', 'billion', 'U.S.', 'dollars)']: ['36.6', '38.7', '41.0', '41.1', '42.2', '44.2', '46.0', '46.8', '47.5', '45.6', '42.9', '42.2']

gold: This graph depicts the total sales volume of products sold through vending machines in the U.S. from 1999 to 2010 . In 1999 , the sales volume was 36.6 billion U.S. dollars .
gold_template: This graph depicts the total templateTitle[2] templateYLabel[1] of templateTitle[5] sold through templateTitleSubject[0] templateTitle[1] in the templateYLabel[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[2] templateYLabel[1] was templateYValue[min] templateYLabel[3] templateYLabel[4] dollars .

generated_template: The statistic shows the 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] .
generated: The statistic shows the total Vended of sales and volume vended products in Indonesian territorial waters from 1999 to 2010 . In 2010 , there were 36.6 sales and volume vended products in Vending .

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 illustrates templateTitleSubject[0] templateTitle[1] templateTitle[2] to how many templateYLabel[1] they had in templateTitleDate[0] . There were templateYValue[max] templateYLabel[1] in the templateXValue[0] templateXValue[0] .
generated: This statistic illustrates U.S. arriving by to how many persons they had in 2018 . There were 2914 persons in the Under 1 year .

Example 814:
titleEntities: {'Subject': ['Cyber'], 'Date': ['2019']}
title: Cyber bullying : common types of bullying 2019
X_Axis['Response']: ['I_have_been_cyber_bullied', 'Mean_or_hurtful_comments_online', 'Rumors_online', 'Threatened_to_hurt_me_through_a_cell_phone_text', 'Posted_mean_names_or_comments_online_about_me_with_a_sexual_meaning', 'Threatened_to_hurt_me_online', 'Posted_a_mean_or_hurtful_picture_online_of_me', 'Pretended_to_be_me_online', 'Posted_mean_names_or_comments_about_my_race_or_color', 'Posted_a_mean_or_hurtful_video_online_of_me', 'Posten_mean_names_or_comments_online_about_my_religion', 'Created_a_mean_or_hurtful_web_page_about_me', 'One_or_more_of_above_two_or_more_times']
Y_Axis['Share', 'of', 'respondents']: ['17.4', '24.9', '22.2', '12.2', '12', '11.7', '10.8', '10.1', '9.5', '7.1', '6.7', '6.4', '30.1']

gold: This statistic presents the percentage of middle and high school students in the United States who were cyber bullied , divided by the type of cyber bullying endured . During the April 2019 survey , 10.1 percent of cyber bullying victims had been impersonated online during the last 30 days . Cyber bullying includes unwanted contact by threatening or insulting the respondent via digital communications as well as sharing private information online .
gold_template: This statistic presents the percentage of middle and high school students in the country who were templateXValue[0] , divided by the type of templateXValue[0] templateTitle[1] endured . During the April templateTitleDate[0] survey , templateYValue[7] percent of templateXValue[0] templateTitle[1] victims had templateXValue[0] impersonated templateXValue[1] during the last templateYValue[max] days . templateXValue[0] templateTitle[1] includes unwanted contact by threatening or insulting the respondent via digital communications as well as sharing private information templateXValue[1] .

generated_template: This statistic shows the results of a survey that asked British adults which types of templateXValue[last] , if any , they ever templateXValue[last] in templateTitleSubject[0] templateTitleSubject[0] ( GB ) in templateTitleDate[0] . templateXValue[0] templateXValue[0] templateXValue[last] ranked highest with a templateYValue[max] percent templateYLabel[0] of templateYLabel[1] , followed by templateXValue[1] templateXValue[last] ( templateYValue[1] percent ) and templateXValue[2] templateXValue[2] ( templateYValue[2] percent ) . A majority of British consumers templateXValue[last] templateXValue[last] on a daily basis , with earlier times in the day the peak period for templateXValue[last] drinking .
generated: This statistic shows the results of a survey that asked British adults which types of One or more of above two or more times , if any , they ever One or more of above two or more times in Cyber ( GB ) in 2019 . I have been cyber bullied One or more of above two or more times ranked highest with a 30.1 percent Share of respondents , followed by Mean or hurtful comments online One or more of above two or more times ( 24.9 percent ) and Rumors online ( 22.2 percent ) . A majority of British consumers One or more of above two or more times on a daily basis , with earlier times in the day the peak period for One or more of above two or more times drinking .

Example 815:
titleEntities: {'Subject': ['LEGO Group'], 'Date': ['2009', '2018']}
title: LEGO Group operating profit 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Operating', 'profit', 'in', 'million', 'Euros']: ['1440.6', '1391.21', '1674.3', '1645.35', '1302.45', '1117.43', '1019.49', '762.19', '667.1', '389.5']

gold: This statistic shows the operating profit of the LEGO Group from 2009 to 2018 . In 2015 , the LEGO Group 's operating profit amounted to approximately 1.65 billion euros .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[3] templateYLabel[2] templateYLabel[3] .

generated_template: In templateXValue[max] , approximately templateYValue[0] templateYLabel[1] templateYLabel[0] in templateTitleSubject[0] disposed of one television or more . In templateXValue[max] , the templateTitle[0] of French templateYLabel[0] owning at least one television amounted to templateYValue[max] thousand . Due to the implementation of new broadcasting resolution technologies , such as the 4K UHD , and to the growing affordability of high-end televisions , this figure could increase in the coming years .
generated: In 2018 , approximately 1440.6 profit Operating in LEGO Group disposed of one television or more . In 2018 , the LEGO of French Operating owning at least one television amounted to 1674.3 thousand . Due to the implementation of new broadcasting resolution technologies , such as the 4K UHD , and to the growing affordability of high-end televisions , this figure could increase in the coming years .

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[0] 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[3] 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 92.15 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 depicts the templateYLabel[0] of unlinked templateYLabel[1] templateYLabel[2] provided templateTitle[1] templateTitleSubject[0] templateTitleSubject[0] in the fiscal year of templateTitleDate[0] , broken down templateTitle[1] templateTitleSubject[0] templateTitle[3] . In that year , templateTitleSubject[0] templateTitleSubject[0] reported some templateYValue[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] on its templateXValue[2] templateXValue[2] lines . templateTitleSubject[0] templateTitleSubject[0] is the second largest commuter templateXValue[2] provider in the country .
generated: The statistic depicts the Earnings of unlinked million U.S. provided R.R R.R Martin in the fiscal year of 2011 , broken down R.R R.R Martin earnings . In that year , R.R Martin reported some 15.0 dollars million U.S. on its June 2013 to June 2014 lines . R.R Martin is the second largest commuter June 2013 to June 2014 provider in the country .

Example 819:
titleEntities: {'Subject': ['Lexus', 'United Kingdom', 'UK'], 'Date': ['2019']}
title: Lexus car sales in the United Kingdom ( UK ) 2016 to 2019
X_Axis['Month']: ['Dec_19', 'Nov_19', 'Oct_19', 'Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16']
Y_Axis['Units', 'sold']: ['745', '1044', '1098', '3852', '424', '1118', '1199', '943', '884', '3137', '262', '1007', '420', '663', '674', '2686', '270', '784', '1306', '851', '678', '3006', '180', '887', '772', '775', '725', '2908', '205', '843', '1161', '715', '672', '2888', '206', '800', '750', '931', '812', '2998', '234', '774']

gold: This statistic shows the monthly amount of cars sold by Lexus in the United Kingdom ( UK ) between July 2016 and December 2019 . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In September 2019 , 3,852 new Lexus cars were sold in the UK
gold_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] in the templateTitleSubject[1] ( templateTitleSubject[2] ) between July templateTitleDate[0] and December templateTitleDate[1] . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In September templateTitleDate[1] , templateYValue[max] new templateTitleSubject[0] cars were templateYLabel[1] in the templateTitleSubject[2]

generated_template: This statistic shows the monthly amount of cars templateYLabel[1] by templateTitleSubject[0] templateTitle[1] in the templateTitleSubject[1] templateTitleSubject[1] ( templateTitleSubject[2] ) between July templateTitleDate[0] 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 July 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 July 2018 .
generated: This statistic shows the monthly amount of cars sold by Lexus car in the United Kingdom ( UK ) between July 2019 and December 2019 . Peaks in registration numbers were recorded in March and September of each year , which was due to the issuing of license plates by the Driver & Vehicle Licensing Agency ( DVLA ) in those months . In July 2019 , 1118 new Lexus car Units had been sold , a decrease of roughly ten percent in comparison to 784 Units as of July 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: This statistic presents the templateTitle[3] templateTitle[4] of templateTitle[0] and templateTitle[1] to templateTitle[5] in the templateTitleSubject[0] templateTitleSubject[0] from templateXValue[min] to templateXValue[1] , with an additional forecast for templateXValue[max] . Over this period , the templateTitle[4] of the templateTitle[0] and templateTitle[1] industry to templateTitle[5] in the templateTitleSubject[1] has increased , reaching templateYValue[2] templateYLabel[1] British pounds in templateXValue[2] .
generated: This statistic presents the numbers globally of Banco and Santander to 2012 in the Banco Santander from 2012 to 2018 , with an additional forecast for 2019 . Over this period , the globally of the Banco and Santander industry to 2012 in the Banco Santander has increased , reaching 133.0 customers British pounds in 2017 .

Example 821:
titleEntities: {'Subject': ['Guatemala'], 'Date': ['2019']}
title: Unemployment rate in Guatemala 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['2.75', '2.73', '2.68', '2.83', '2.51', '2.72', '3.02', '2.77', '4.13', '3.5', '3.31', '2.84', '2.8', '2.89', '2.99', '2.97', '2.81', '2.85', '2.78', '2.9', '2.92']

gold: This statistic shows the unemployment rate in Guatemala from 1999 to 2019 . In 2019 , the unemployment rate in Guatemala was 2.75 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[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 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 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 illustrates the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] templateTitleSubject[0] as of December templateTitleDate[0] , based on reach . During the survey period it was found that templateYValue[3] percent of templateTitle[2] templateTitle[3] users had a templateXValue[0] account .
generated: This statistic illustrates the Survey amount money spent in U.S. as of December 2012 , based on reach . During the survey period it was found that 17.4 percent of money spent users had a under 500$ account .

Example 824:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013']}
title: Most popular leisure activities among men in the U.S. 2013
X_Axis['Response']: ['Watch_TV', 'Reading', 'Computer/internet', 'Playing_video_games_and_computer/internet_games', 'Spending_time_with_families_and_friends', 'Watching/going_to_the_movies', 'Exercise/working_out', 'Concerts/listening_to/playing_music', 'Walking/running/jogging', 'Golf']
Y_Axis['Share', 'of', 'respondents']: ['43', '24', '20', '13', '13', '11', '10', '10', '7', '7']

gold: This statistic shows the most popular leisure activities among men in the United States as of September 2013 . During the survey , 43 percent of the male respondents named watching TV as their most preferred activity during leisure time .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] as of September templateTitleDate[0] . During the survey , templateYValue[max] percent of the male templateYLabel[1] named watching templateXValue[0] as their templateTitle[0] preferred activity during templateTitle[2] templateXValue[4] .

generated_template: This statistic shows the templateTitle[1] templateTitle[2] problems templateTitle[4] templateTitle[5] templateXValue[4] in the templateTitleSubject[0] in January templateTitleDate[0] . 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] templateXValue[0] .
generated: This statistic shows the popular leisure problems among men Spending time with families and friends in the U.S. in January 2013 . 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 February templateTitleDate[0] , 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[1] 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 February 2018 , Other was the Canadian Month home to the most stores operated stores in the whole of Canadian Tire Corporation , with 503 . SportChek 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': ['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: This statistic illustrates the templateTitle[0] templateYLabel[2] of the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] volume from templateTitle[6] exporters in templateTitleDate[0] . In that year , templateXValue[0] accounted for a templateYLabel[2] of templateYValue[max] percent of templateYLabel[0] templateTitle[4] to the templateTitle[2] .
generated: This statistic illustrates the Deaths of the United Kingdom Share deaths volume from United exporters in 2018 . In that year , Gas mains accounted for a deaths of 35 percent of Share monoxide to the unintentional .

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: 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 El Salvador from 1999 to 2019 . In 2019 , the Unemployment rate in El Salvador was at approximately 3.69 percent .

Example 828:
titleEntities: {'Subject': ['Canada'], 'Date': ['2024']}
title: Total population in Canada 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['39.22', '38.87', '38.52', '38.17', '37.81', '37.46', '36.99', '36.49', '36.05', '35.68', '35.39']

gold: The statistic shows the total population in Canada from 2014 to 2018 , with projections up until 2024 . In 2018 , the total population in Canada amounted to about 36.99 million inhabitants . Population of Canada Canada ranks second among the largest countries in the world in terms of area size , right behind Russia , despite having a relatively low total population .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] ranks second among the largest countries in the world in terms of area size , right behind Russia , despite having a relatively low templateTitle[0] templateTitle[1] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[7] templateYLabel[1] templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] Although templateTitle[0] templateTitle[1] increased on a yearly basis from 2004 to templateXValue[min] , templateTitle[1] growth has slowly decreased annually as of 2011 , despite remaining positive .
generated: This statistic shows the Total population of Canada from 2014 to 2018 , with projections up until 2024 . In 2018 , the Total population of Canada amounted to approximately 36.49 millions Inhabitants . population of Canada Although Total population increased on a yearly basis from 2004 to 2014 , population growth has slowly decreased annually as of 2011 , despite remaining positive .

Example 829:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['16', '19']}
title: Monthly watch and jewelry retail sales value index in Great Britain 2016 to 2019
X_Axis['Month']: ['Sep_19', 'Aug_19', 'Jul_19', 'Jun_19', 'May_19', 'Apr_19', 'Mar_19', 'Feb_19', 'Jan_19', 'Dec_18', 'Nov_18', 'Oct_18', 'Sep_18', 'Aug_18', 'Jul_18', 'Jun_18', 'May_18', 'Apr_18', 'Mar_18', 'Feb_18', 'Jan_18', 'Dec_17', 'Nov_17', 'Oct_17', 'Sep_17', 'Aug_17', 'Jul_17', 'Jun_17', 'May_17', 'Apr_17', 'Mar_17', 'Feb_17', 'Jan_17', 'Dec_16', 'Nov_16', 'Oct_16', 'Sep_16', 'Aug_16', 'Jul_16', 'Jun_16', 'May_16', 'Apr_16', 'Mar_16', 'Feb_16', 'Jan_16']
Y_Axis['Index', 'number', 'of', 'sales', 'per', 'week']: ['140.0', '136.8', '138.8', '139.3', '140.3', '132.8', '134.7', '126.7', '124.8', '129.6', '132.5', '131.2', '133.0', '135.8', '137.5', '112.8', '124.1', '118.7', '111.5', '116.8', '120.2', '114.3', '128.1', '115.7', '118.8', '118.6', '117.0', '114.4', '113.3', '116.5', '115.2', '118.3', '114.8', '107.9', '104.0', '107.3', '101.2', '100.3', '99.6', '104.1', '97.2', '97.7', '92.5', '95.3', '91.6']

gold: This statistic shows the monthly trend in the amount spent on watches and jewelry ( sales value ) in Great Britain from January 2016 to September 2019 , as an index of sales per week . During this period of time , retail sales increased significantly , measuring at 140 index points in September 2019 . The figures are seasonally adjusted estimates , measured using the Retail Sales Index ( RSI ) and published in index form with a reference year of 2016 equal to 100 .
gold_template: This statistic shows the templateTitle[0] trend in the amount spent on watches and templateTitle[2] ( templateYLabel[2] templateTitle[5] ) in templateTitleSubject[0] from January templateTitleDate[0] to September templateTitleDate[1] , as an templateYLabel[0] of templateYLabel[2] templateYLabel[3] templateYLabel[4] . During this period of time , templateTitle[3] templateYLabel[2] increased significantly , measuring at templateYValue[0] templateYLabel[0] points in September templateTitleDate[1] . The figures are seasonally adjusted estimates , measured using the templateTitle[3] templateYLabel[2] templateYLabel[0] ( RSI ) and published in templateYLabel[0] form with a reference year of templateTitleDate[0] equal to templateYValue[37] .

generated_template: Cosmetics and templateTitle[1] are bought in larger quantities each passing templateXLabel[0] in the UK . Most recently , in September templateTitleDate[1] , the templateTitle[2] templateYLabel[2] templateTitle[4] of articles in these two categories reached an templateYLabel[0] templateYLabel[1] of templateYValue[0] as presented in the graph . This was lower than the templateTitle[4] recorded for the previous months of the year , but still higher than the figures recorded in templateTitleDate[0] , 2017 and the first half of 2018 .
generated: Cosmetics and watch are bought in larger quantities each passing Month in the UK . Most recently , in September 19 , the jewelry sales of articles in these two categories reached an Index number of 140.0 as presented in the graph . This was lower than the sales recorded for the previous months of the year , but still higher than the figures recorded in 16 , 2017 and the first half of 2018 .

Example 830:
titleEntities: {'Subject': ['Norway'], 'Date': ['2009', '2019']}
title: Population in Norway 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Inhabitants', 'in', 'millions']: ['5.33', '5.3', '5.26', '5.21', '5.17', '5.12', '5.05', '4.99', '4.92', '4.86', '4.8']

gold: This statistic shows the population in Norway over the years from 2009 to 2019 . In 2009 , the population of Norway was around 4.8 million people . In 2019 , the number of inhabitants increased to around 5.33 million .
gold_template: This statistic shows the templateTitle[0] in templateTitleSubject[0] over the years from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[0] of templateTitleSubject[0] was around templateYValue[min] templateYLabel[1] people . In templateXValue[max] , the number of templateYLabel[0] increased to around templateYValue[max] templateYLabel[1] .

generated_template: 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 .

