Example 1:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2010', '2017']}
title: Grocery market growth year-on-year in Great Britain 2010 to 2017
X_Axis['Year']: ['28_Nov_2010', '27_Nov_2011', '25_Nov_2012', '10_Nov_2013', '09_Nov_2014', '09_Nov_2015', '06_Nov_2016', '05_Nov_2017']
Y_Axis['Percentage', 'growth', '(year-on-year)']: ['2.5', '4.2', '3.2', '3.2', '-0.2', '0.5', '0.8', '3.8']

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

generated_template: In templateXValue[max] , the templateTitle[1] templateYLabel[0] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] amounted to about templateYValue[idxmax(X)] 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 templateNegativeTrend to below templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] . templateTitle[3] templateTitle[4] The templateTitle[0] templateYLabel[0] serves as an indicator for a variety of different selling prices on the templateTitle[4] market , gathering all templateYLabel[0] ranges of templateTitle[3] wines purchased in templateTitleSubject[0] .
generated: In 28 Nov 2010 , the market Percentage of year-on-year Great in Great Britain amounted to about 3.8 growth (year-on-year) , all types included .  The Grocery realized Percentage of year-on-year Great remained fairly steady throughout the years until 10 Nov 2013 , when it dropped to below 2.5 percentage (year-on-year) .  year-on-year Great The Grocery Percentage serves as an indicator for a variety of different selling prices on the Great market , gathering all Percentage ranges of year-on-year wines purchased in Great Britain .

Example 2:
titleEntities: {'Subject': ['Brooklyn Nets'], 'Date': ['2001', '2019']}
title: Brooklyn Nets ' revenue 2001 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['304', '290', '273', '223', '220', '212', '190', '84', '89', '89', '92', '98', '102', '93', '87', '93', '94', '91']

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

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] The templateTitleSubject[0] are a professional basketball 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 3:
titleEntities: {'Subject': ['Kansas'], 'Date': ['2000', '2018']}
title: Kansas - poverty rate 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'population']: ['12', '11.9', '12.1', '13', '13.6', '14', '14', '13.8', '13.6', '13.4', '11.3', '11.2', '12.4', '11.7', '10.5', '10.8', '12.1', '11.3', '9.5']

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

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

Example 4:
titleEntities: {'Subject': ['Germany'], 'Date': ['2000', '2013']}
title: Trade union density in Germany from 2000 to 2013
X_Axis['Year']: ['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013']
Y_Axis['Percentage', 'of', 'employees']: ['24.57', '23.75', '23.51', '23.02', '22.17', '21.68', '20.72', '19.89', '19.13', '18.93', '18.56', '18.49', '18.3', '18.13']

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

generated_template: This statistic shows the templateScale of conceptions of under 16 girls templateYLabel[2] in templateYLabel[3] in templateTitleSubject[0] and templateTitleSubject[1] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of terminated templateYLabel[1] in this age group has remained around the same amount over the provided time period . In templateXValue[max] , approximately templateYValue[idxmax(X)] templateScale of the templateYLabel[1] led to templateYLabel[3] .
generated: This statistic shows the percentage of conceptions under 16 girls employees in Germany and from 2000 to 2013 .  The Percentage of terminated employees in this age group has remained around the same amount over the provided time period .  In 2013 , approximately 18.13 percentage of the employees led to employees .

Example 5:
titleEntities: {'Subject': ['Finland'], 'Date': ['2018']}
title: Urbanization in Finland 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['85.38', '85.33', '85.28', '85.23', '85.18', '85.13', '84.84', '84.31', '83.77', '83.43', '83.3']

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

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Finland from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 85.38 % of Finland 's total population lived in urban areas and cities .

Example 6:
titleEntities: {'Subject': ['Projected'], 'Date': ['1950', '2100']}
title: Projected global median age 1950 to 2100
X_Axis['Year']: ['2100', '2095', '2090', '2085', '2080', '2075', '2070', '2065', '2060', '2055', '2050', '2045', '2040', '2035', '2030', '2025', '2020', '2015', '2010', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960', '1955', '1950']
Y_Axis['Median', 'age']: ['41.9', '41.3', '40.8', '40.3', '39.7', '39.2', '38.7', '38.2', '37.6', '36.9', '36.2', '35.4', '34.6', '33.9', '33.0', '32.0', '30.9', '29.6', '28.5', '27.4', '26.3', '25.1', '24.0', '23.3', '22.6', '21.9', '21.5', '22.0', '22.6', '23.1', '23.6']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) denotes the aggregate value of all services and goods produced within a country in any given templateXLabel[0] . templateTitle[3] is an important indicator of a country 's economic power .
generated: The statistic shows the Median age in the Projected from 1950 to 2100 .  Median age age ( ) denotes the aggregate value of all services and goods produced within a country in any given Year .  age is an important indicator of a country 's economic power .

Example 7:
titleEntities: {'Subject': ['Automobile'], 'Date': ['2010']}
title: Automobile production - automatic transmission forecast 2010 to 2015
X_Axis['Year']: ['2010', '2011', '2012', '2013', '2014', '2015']
Y_Axis['Million', 'transmissions']: ['25.03', '25.36', '26.72', '27.82', '28.46', '28.65']

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

generated_template: This statistic presents 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[idxmax(X)] templateScale templateTitleSubject[0] , up from templateYValue[1] templateScale in templateXValue[1] . Being one of the most prominent eSports games , in templateXValue[1] LoL championship finals attracted 36 templateScale viewers worldwide .
generated: This statistic presents the Million of Automobile ( LoL ) monthly active transmissions worldwide from 2010 to 2015 .  In 2015 , LoL had 28.65 million Automobile , up from 25.36 million in 2011 .  Being one of the most prominent eSports games , in 2011 LoL championship finals attracted 36 million viewers worldwide .

Example 8:
titleEntities: {'Subject': ['General Motors'], 'Date': ['2015', '2019']}
title: General Motors - adjusted EBIT 2015 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015']
Y_Axis['EBIT', '(adjusted;', 'in', 'million', 'U.S.dollars)']: ['8393', '11783', '12844', '12848', '11449']

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

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

Example 9:
titleEntities: {'Subject': ['Germany'], 'Date': ['2012', '2028']}
title: Travel and tourism 's total contribution to GDP in Germany 2012 to 2028
X_Axis['Year']: ['2028', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Value', 'in', 'billion', 'euros']: ['411.0', '358.3', '348.1', '342.2', '336.2', '325.7', '315.9', '318.0']

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

generated_template: This statistic presents the templateTitle[3] templateTitle[4] of templateTitle[0] and templateTitle[1] to templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[1] , with a forecast for templateXValue[max] . Over this period , the templateTitle[4] of the templateTitle[0] and templateTitle[1] industry to templateTitle[5] in templateTitleSubject[0] has templatePositiveTrend , reaching templateYValue[2] templateScale templateYLabel[2] in templateXValue[2] .
generated: This statistic presents the 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 348.1 billion euros in 2017 .

Example 10:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2000']}
title: Argentina : internet penetration 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'individuals', 'using', 'internet']: ['74.29', '70.97', '68.04', '64.7', '59.9', '55.8', '51', '45', '34', '28.11', '25.95', '20.93', '17.72', '16.04', '11.91', '10.88', '9.78', '7.04']

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

generated_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In the most recently measured period , templateYValue[idxmax(X)] templateScale of the population accessed the templateYLabel[3] , up from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] . In templateXValue[2] , templateTitleSubject[0] 's population templatePositiveTrend by approximately 2.48 templateScale compared to the previous templateXLabel[0] .
generated: This statistic gives information on the internet penetration in Argentina from 2000 to 2017 .  In the most recently measured period , 74.29 percentage of the population accessed the internet , up from 7.04 percentage in 2000 .  In 2015 , Argentina 's population grew by approximately 2.48 percentage compared to the previous Year .

Example 11:
titleEntities: {'Subject': ['Southwest Airlines'], 'Date': ['2019']}
title: Revenue passenger miles ( RPMs ) of Southwest Airlines 2010 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['RPMs', 'in', 'billions']: ['131.35', '133.32', '129.04', '124.8', '117.5', '108.04', '104.35', '102.87', '97.58', '78.05']

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

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

Example 12:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2008', '2019']}
title: Online purchasing penetration in Great Britain 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'respondents']: ['82', '78', '77', '77', '76', '74', '72', '67', '66', '62', '61', '53']

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

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

Example 13:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2010', '2018']}
title: University applicants in the United Kingdom ( UK ) 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Number', 'of', 'university', 'applicants']: ['636960', '649700', '674890', '673040', '659030', '635910', '616700', '668150', '658560']

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

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[1] templateTitle[2] , a franchise of the National Football League , templateTitle[4] templateXValue[min] to templateXValue[max] . In the templateXValue[max] season , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[1] templateTitle[2] were at templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic presents the Number university of the UK United , a franchise of the National Football League , UK 2010 to 2018 .  In the 2018 season , the Number university of the UK United were at 636960 million applicants .

Example 14:
titleEntities: {'Subject': ['PepsiCo'], 'Date': ['2007', '2018']}
title: PepsiCo 's net revenue worldwide 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['64.66', '63.53', '62.8', '63.06', '66.68', '66.42', '65.49', '66.5', '57.84', '43.23', '43.25', '39.47']

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

generated_template: templateTitleSubject[0] 's templateYLabel[0] from printers and copiers reached about templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in the fiscal templateXLabel[0] templateXValue[max] . The templateTitle[1] templateTitle[2] is one of two main business segments of HP Inc . Personal systems is HP Inc 's second business templateTitle[2] , which in templateXValue[max] generated around 38.7 templateScale templateYLabel[2] templateYLabel[3] in templateYLabel[0] , bringing the company 's overall templateYLabel[0] to more than 58 templateScale templateYLabel[2] templateYLabel[3] in that templateXLabel[0] .
generated: PepsiCo 's Revenue from printers and copiers reached about 64.66 billion U.S. dollars in the fiscal Year 2018 .  The 's net is one of two main business segments of HP Inc .  Personal systems is HP Inc 's second business net , which in 2018 generated around 38.7 billion U.S. dollars in Revenue , bringing the company 's overall Revenue to more than 58 billion U.S. dollars in that Year .

Example 15:
titleEntities: {'Subject': ['Canada'], 'Date': ['2000', '2018']}
title: Canada : number of robberies 2000 to 2018
X_Axis['Year']: ['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['Number', 'of', 'robberies']: ['30582', '30756', '30036', '32084', '30990', '32437', '34641', '34182', '32372', '32463', '30478', '29790', '27748', '23249', '20932', '22149', '21958', '22831', '22450']

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

generated_template: This statistic shows the total templateTitleSubject[0] ( UK ) templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] templateXValue[0] . The overall trend was one of templatePositiveTrend , seeing a peak in templateXValue[0] with 27.99 templateScale British pounds and a total rise of over 5 templateScale British pounds throughout this period .
generated: This statistic shows the total Canada ( UK ) 2018 Number robberies from fiscal Year 2018 to fiscal Year 2000 .  The overall trend was one of increasing , seeing a peak in 2000 with 27.99 billion British pounds and a total rise of over 5 billion British pounds throughout this period .

Example 16:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. per capita consumption of fresh citrus fruit 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['23.95', '23.82', '24.07', '22.75', '23.28', '23.97', '23.54', '22.81', '21.59', '20.69', '20.62', '17.94', '21.64', '21.63', '22.7', '23.82', '23.37', '23.93', '23.54']

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

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[idxmin(Y)] .
generated: 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 2007 .

Example 17:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2002', '2017']}
title: Car rental industry in the U.S. - revenue 2002 to 2017
X_Axis['Year']: ['2002', '2007', '2012', '2017']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['16.43', '21.49', '23.63', '28.63']

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

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

Example 18:
titleEntities: {'Subject': ['Alibaba'], 'Date': ['2012', '2019']}
title: Alibaba : number of employees 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'employees']: ['101958', '66421', '50092', '36450', '34985', '22072', '20674', '21930']

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

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

Example 19:
titleEntities: {'Subject': ['Australia'], 'Date': ['2015', '2022']}
title: Number of social network users Australia 2015 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['19.27', '18.94', '18.58', '18.17', '17.7', '17.19', '16.62', '15.99']

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

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] is expected to reach templateYValue[idxmax(X)] templateScale , up from templateYValue[5] templateScale in templateXValue[min] .
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 2015 .

Example 20:
titleEntities: {'Subject': ['Nicaragua'], 'Date': ['2019']}
title: Unemployment rate in Nicaragua 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['4.87', '4.48', '4.21', '4.34', '4.4', '4.52', '5.28', '5.21', '6.38', '7.83', '8.16', '6.2', '4.89', '5.31', '5.37', '6.41', '7.6', '7.61', '7.44', '7.47', '7.35']

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

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

Example 21:
titleEntities: {'Subject': ['France'], 'Date': ['2000', '2016']}
title: France : number of internet users 2000 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Number', 'of', 'internet', 'users', 'in', 'millions']: ['55.86', '55.1', '53.7', '52.3', '51.76', '48.23', '48.65', '44.84', '44.04', '40.95', '28.87', '26.25', '23.82', '21.85', '18.13']

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

generated_template: This statistic provides information on 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[max] templateScale .
generated: This statistic provides information on the Number internet in France from 2002 to 2016 .  In 2016 , the Number internet in France amounted to approximately 55.86 millions .

Example 22:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2019']}
title: Employment rate of men in the U.S. 1990 to 2019
X_Axis['Year']: ["'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92", "'91", "'90"]
Y_Axis['Employment', 'rate']: ['66.6', '66.3', '66', '65.8', '65.3', '64.9', '64.4', '64.4', '63.9', '63.7', '64.5', '68.5', '69.8', '70.1', '69.6', '69.2', '68.9', '69.7', '70.9', '71.9', '71.6', '71.6', '71.3', '70.9', '70.8', '70.4', '70', '69.8', '70.4', '72']

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

generated_template: In templateTitleDate[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[2] in the templateTitle[3] has stayed more or less steady . In templateTitleDate[min] , the female templateYLabel[0] templateYLabel[1] was templateYValue[last] templateScale , and in 2018 , the templateYLabel[0] templateYLabel[1] was at templateYValue[0] templateScale . However , it reached a peak in 2000 at templateYValue[max] templateScale .
generated: In 2019 , the Employment rate of men in the U.S. has stayed more or less steady .  In 1990 , the female Employment rate was 72 % , and in 2018 , the Employment rate was at 66.6 % .  However , it reached a peak in 2000 at 72 % .

Example 23:
titleEntities: {'Subject': ['Utah Jazz'], 'Date': ['2001', '2019']}
title: Utah Jazz 's revenue 2001 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['258', '243', '221', '164', '146', '142', '131', '111', '120', '121', '118', '119', '114', '96', '91', '88', '85', '87']

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

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] The templateTitleSubject[0] are a professional basketball franchise of the National Basketball Association ( NBA ) , entering the league as an expansion team in 1968 .
generated: The statistic shows the Revenue of the Utah Jazz franchise from the 2001/02 season to the 2018/19 season .  In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 258 million U.S. dollars .  Utah Jazz The Utah Jazz are a professional basketball franchise of the National Basketball Association ( NBA ) , entering the league as an expansion team in 1968 .

Example 24:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2000', '2018']}
title: Hospitals in Belgium 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Number', 'of', 'hospitals']: ['174', '175', '177', '178', '187', '191', '192', '195', '198', '203', '209', '210', '215', '216', '214', '218', '219', '225', '228']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] at templateTitle[3] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Throughout this period there has been a clear trend of templateTitle[2] having children later in life , with the templateTitle[0] templateYLabel[1] of templateTitle[2] in the UK templatePositiveTrend from templateYValue[idxmin(X)] in templateXValue[idxmin(Y)] to templateYValue[idxmax(X)] by templateXValue[idxmax(Y)] .
generated: This statistic shows the Number hospitals of 2000 at 2018 in the Belgium from 2000 to 2018 .  Throughout this period there has been a clear trend of 2000 having children later in life , with the Hospitals of 2000 in the UK increasing from 228 in 2018 to 174 by 2000 .

Example 25:
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 26:
titleEntities: {'Subject': ['Norway'], 'Date': ['2008', '2018']}
title: Number of births in Norway 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'live', 'births']: ['55120', '56633', '58890', '59058', '59084', '58995', '60255', '60220', '61442', '61807', '60497']

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

generated_template: In templateXValue[max] , over templateYValue[max] thousand templateTitle[0] cars were born in templateTitleSubject[0] . This was an templatePositiveTrend of 0.65 templateScale compared to the previous templateXLabel[0] . During the past ten years , the amount fluctuated , peaking in templateXValue[2] at roughly templateYValue[max] thousand templateYLabel[0] .
generated: In 2018 , over 61807 thousand Number cars were born in Norway .  This was an increase of 0.65 % compared to the previous Year .  During the past ten years , the amount fluctuated , peaking in 2016 at roughly 61807 thousand Number .

Example 27:
titleEntities: {'Subject': ['Colombia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Colombia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['8096.64', '7729.02', '7381.73', '7053.53', '6744.01', '6508.13', '6641.51', '6325.49', '5799.62', '6088.68', '7998.84', '8103.49', '7950.38', '7264.11', '6285.2', '5170.53', '5444.82', '4684.01', '3717.55', '3384.61', '2737.58', '2240.21', '2347.51', '2383.31', '2455.11', '2588.92', '2992.93', '3291.58', '3037.63', '2939.1', '2640.39', '2139.18', '1923.58', '1658.07', '1637.5', '1640.39', '1660.55', '1572.58', '1542.36', '1572.19', '1711.86']

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

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

Example 28:
titleEntities: {'Subject': ['Morocco'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Morocco 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['4.53', '4.42', '4.3', '4.13', '3.69', '2.66', '2.96', '4.2', '1.06', '4.55', '2.67']

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

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

Example 29:
titleEntities: {'Subject': ['Net'], 'Date': ['2006']}
title: Net profit of airlines worldwide 2006 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Net', 'profit', 'in', 'billion', 'U.S.', 'dollars']: ['29.3', '25.9', '27.3', '37.6', '34.2', '36.0', '13.8', '10.7', '9.2', '8.3', '17.3', '-4.6', '-26.1', '14.7', '5.0']

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

generated_template: The graph shows the templateTitle[2] templateTitle[3] of the templateTitle[0] templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it is estimated that around templateYValue[idxmax(X)] templateYLabel[4] templateYLabel[5] templateYLabel[0] templateYLabel[1] were spent on military causes by the templateTitle[0] . A ranking of countries with the highest military templateTitle[3] can be accessed here .
generated: The graph shows the airlines worldwide of the Net profit from 2006 to 2020 .  In 2020 , it is estimated that around 29.3 dollars Net profit were spent on military causes by the Net .  A ranking of countries with the highest military worldwide can be accessed here .

Example 30:
titleEntities: {'Subject': ['PV'], 'Date': ['2018']}
title: U.S. residential sector annual solar PV capacity installations 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Capacity', 'in', 'megawatts']: ['2400', '2227', '2583', '2099', '1231', '792', '494', '304', '246', '164', '82', '58', '38', '27']

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

generated_template: This statistic shows the total templateYLabel[0] templateTitle[4] of templateTitleSubject[0] worldwide templateTitle[5] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateScale templateYLabel[4] dollars .
generated: This statistic shows the total Capacity solar of PV worldwide 2005 to 2018 .  In 2018 , PV 's Capacity megawatts amounted to 2583 million megawatts dollars .

Example 31:
titleEntities: {'Subject': ['American'], 'Date': ['2001', '2019']}
title: American imports of corn 2001 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Imports', 'in', 'million', 'bushels']: ['28', '36', '57', '57', '68', '32', '36', '160', '29', '28', '8', '14', '20', '12', '9', '11', '14', '14', '10']

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

generated_template: This statistic illustrates the templateYLabel[0] templateYLabel[1] of templateTitle[2] at templateTitle[3] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Throughout this period there has been a clear trend of templateTitle[2] having children later in life , with the templateTitle[0] templateYLabel[1] of templateTitle[2] in the UK templatePositiveTrend from templateYValue[idxmin(X)] in templateXValue[idxmin(Y)] to templateYValue[idxmax(X)] by templateXValue[idxmax(Y)] .
generated: This statistic illustrates the Imports million of corn at 2001 in the American from 2001 to 2019 .  Throughout this period there has been a clear trend of corn having children later in life , with the American million of corn in the UK rising from 10 in 2009 to 28 by 2012 .

Example 32:
titleEntities: {'Subject': ['Thailand'], 'Date': ['2017', '2023']}
title: Number of social network users Thailand 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['37.2', '36.3', '35.6', '34.7', '33.8', '33.0', '32.1']

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

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] is expected to reach templateYValue[idxmax(X)] templateScale , up from templateYValue[5] templateScale in templateXValue[min] .
generated: This statistic shows the Number of Thailand users in Thailand from 2017 to 2023 .  In 2023 , the Number of Thailand users in Thailand is expected to reach 37.2 millions , up from 33.0 millions in 2017 .

Example 33:
titleEntities: {'Subject': ['Zambia'], 'Date': ['2019']}
title: Youth unemployment rate in Zambia in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['15.94', '16.09', '16.12', '16.39', '16.49', '16.92', '17.05', '17.03', '21.45', '26.83', '21.44', '16.02', '19.3', '23.17', '28.26', '26.75', '25.55', '24.6', '23.08', '21.34', '20.88']

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

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

Example 34:
titleEntities: {'Subject': ['Worldwide'], 'Date': ['2013', '2019']}
title: Worldwide shipments of headphones 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Unit', 'shipments', 'in', 'millions']: ['400.0', '380.7', '363.0', '349.0', '331.3', '309.5', '286.0']

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

generated_template: The statistic displays the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . According to the source , there were templateYValue[idxmax(X)] templateScale templateTitleSubject[0] templateYLabel[4] in templateXValue[max] .
generated: The statistic displays the Unit shipments of Worldwide shipments headphones in the Worldwide from 2013 to 2019 .  According to the source , there were 400.0 millions Worldwide in 2019 .

Example 35:
titleEntities: {'Subject': ['Estonia'], 'Date': ['2010', '2017']}
title: Electricity prices for households in Estonia 2010 - 2017 , semi-annually
X_Axis['Year']: ['2010_S1', '2010_S2', '2011_S1', '2011_S2', '2012_S1', '2012_S2', '2013_S1', '2013_S2', '2014_S1', '2014_S2', '2015_S1', '2015_S2', '2016_S1', '2016_S2', '2017_S1']
Y_Axis['Euro', 'cents', 'per', 'kilowatt-hour']: ['9.7', '10.04', '9.73', '10.42', '10.96', '11.23', '13.51', '13.67', '13.07', '13.25', '13.02', '12.91', '12.08', '12.38', '12.07']

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

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

Example 36:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2008', '2018']}
title: Crude death rate in Sweden 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Death', 'rate', 'per', 'thousand', 'mid-year', 'population']: ['9.1', '9.1', '9.2', '9.3', '9.2', '9.4', '9.7', '9.5', '9.6', '9.7', '9.9']

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

generated_template: The statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the templateScale of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the total templateYLabel[2] in templateTitleSubject[0] lived in cities . templateTitleSubject[0] 's rural exodus templateTitleSubject[0] is one of the largest countries in the world regarding land area , second behind Russia .
generated: The statistic shows the degree of Crude in Sweden from 2008 to 2018 and details the thousand of the entire per , living in rate areas .  In 2018 , 9.1 thousand of the total per in Sweden lived in cities .  Sweden 's rural exodus Sweden is one of the largest countries in the world regarding land area , second behind Russia .

Example 37:
titleEntities: {'Subject': ['Tesco', 'United Kingdom', 'UK'], 'Date': ['2010']}
title: Edible grocery sales forecast for Tesco in the United Kingdom ( UK ) from 2010 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Sales', 'in', 'million', 'US', 'dollars']: ['52714.03', '50821.69', '49342.45', '47921.14', '46631.26', '45524.45', '47444.1', '44802.19', '45335.47', '44899.26', '40675.81']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the global templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] was about templateYValue[0] templateScale templateYLabel[3] dollars.H templateTitle[3] MH templateTitle[3] templateTitleSubject[0] is a leading global fashion company with strong values and a clear business concept . templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] constantly strives to have the best customer offering in each individual market – which includes giving customers the best price .
generated: This statistic shows the Sales million of the Tesco forecast worldwide from 2010 to 2020 .  In 2020 , the global Sales million of the Tesco forecast was about 52714.03 million dollars dollars.H forecast MH Tesco is a leading global fashion company with strong values and a clear business concept .  Tesco forecast Tesco constantly strives to have the best customer offering in each individual market – which includes giving customers the best price .

Example 38:
titleEntities: {'Subject': ['Tate Modern', 'London'], 'Date': ['2007', '2018']}
title: Number of visitors to the Tate Modern in London 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'visitors', 'in', 'millions']: ['5.83', '5.66', '5.84', '4.71', '5.79', '4.8', '5.3', '4.8', '5.06', '4.75', '4.95', '5.19']

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

generated_template: This statistic shows the templateYLabel[0] of recreational templateYLabel[1] to the templateTitleSubject[0] Veterans templateTitleSubject[0] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] of templateYLabel[1] to the templateTitleSubject[0] II templateTitleSubject[0] amounted to approximately templateYValue[0] templateYValue[idxmax(X)] in templateXValue[max] .
generated: This statistic shows the Number of recreational visitors to the Tate Modern Veterans in the 2018 from 2007 to 2018 .  The Number of visitors to the Tate Modern II amounted to approximately 5.83 in 2018 .

Example 39:
titleEntities: {'Subject': ['NFL'], 'Date': ['2010', '2018']}
title: NFL - New England Patriots revenue from ticket sales 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Ticket', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['104', '103', '103', '99', '100', '95', '94', '97', '93']

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

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

Example 40:
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] from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , there were a total of templateYValue[max] registered templateTitle[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] according to the International templateTitle[0] templateTitle[1] Federation .
generated: The statistics 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 41:
titleEntities: {'Subject': ['Phoenix Suns', 'NBA'], 'Date': ['2003', '2020']}
title: Franchise value of the Phoenix Suns ( NBA ) 2003 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1625', '1500', '1280', '1100', '1000', '910', '565', '474', '395', '411', '429', '452', '449', '410', '395', '356', '282', '272']

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

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] franchise had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Franchise value of the Phoenix Suns of the National Basketball Association from 2003 to 2020 .  In 2020 , the Phoenix Suns franchise had an estimated value of 1625 million U.S. dollars .

Example 42:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: U.S. average retail electricity prices 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995', '1990']
Y_Axis['Average', 'electricity', 'price', 'in', 'U.S.', 'cents', 'per', 'kilowatt', 'hour']: ['10.58', '10.48', '10.27', '10.41', '10.44', '10.07', '9.84', '9.9', '9.83', '9.82', '9.74', '9.13', '8.9', '8.14', '6.81', '6.89', '6.57']

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

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

Example 43:
titleEntities: {'Subject': ['Middle East', 'Africa'], 'Date': ['2005']}
title: PC penetration in the Middle East and Africa 2005 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2011', '2012', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Penetration', 'in', 'percent']: ['7', '5', '3', '2', '2', '1', '1', '1', '1', '1', '1']

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

generated_template: 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] templateYLabel[2] to one templateYLabel[4] while being of child-bearing age . templateTitleSubject[0] includes almost all countries south of the Sahara desert .
generated: This statistic shows the PC penetration of Middle East from 2005 to 2015 .  The PC penetration is the average Penetration of percent to one percent while being of child-bearing age .  Middle East includes almost all countries south of the Sahara desert .

Example 44:
titleEntities: {'Subject': ['SE annual'], 'Date': ['2009', '2016']}
title: Mast-Jägermeister SE - annual profit 2009 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Annual', 'profit', 'in', 'million', 'U.S.', 'dollars']: ['101.88', '106.95', '109.48', '135.87', '126.19', '132.32', '135.19', '135.21']

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

generated_template: In templateXValue[max] , the templateTitle[1] templateYLabel[0] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] amounted to about templateYValue[idxmax(X)] 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 templateNegativeTrend to below templateYValue[0] templateScale 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 2016 , the SE Annual of profit 2009 in SE annual amounted to about 101.88 profit million U.S. , all types included .  The Mast-Jägermeister realized Annual of profit 2009 remained fairly steady throughout the years until 2013 , when it fell to below 101.88 million U.S. profit 2009 The Mast-Jägermeister Annual serves as an indicator for a variety of different selling prices on the 2009 market , gathering all Annual ranges of profit wines purchased in SE annual .

Example 45:
titleEntities: {'Subject': ['Germany'], 'Date': ['2000', '2018']}
title: Revenue from used cars in Germany from 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['84.7', '82.1', '84.62', '77.85', '69.78', '66.81', '62.98', '66.32', '56.54', '51.65', '53.11', '52.6', '55.95', '55.44', '52.22', '55.65', '54.03', '59.92', '59.0']

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

generated_template: The statistic depicts the total templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] amounted to approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] .
generated: The statistic depicts the total Revenue of Germany used cars Germany from 2000 to 2018 .  In 2018 , the Revenue of Germany used cars Germany amounted to approximately 84.7 billion euros .

Example 46:
titleEntities: {'Subject': ['Bulgari'], 'Date': ['2011', '2017']}
title: Bulgari : turnover 2011 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Turnover', 'in', 'million', 'euros']: ['177.0', '194.9', '143.4', '121.6', '96.8', '104.7', '96.4']

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

generated_template: The statistic shows the templateYLabel[0] of templateTitle[1] television templateYLabel[1] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . According to the source , there were templateYValue[idxmax(X)] templateScale templateTitle[1] templateYLabel[1] in the templateTitle[3] in templateXValue[max] templateXValue[idxmin(Y)] a templateNegativeTrend of more almost 10 templateScale from templateXValue[min] .
generated: The statistic shows the Turnover of television million in the 2017 from 2011 to 2017 .  According to the source , there were 177.0 million turnover in the 2017 in 2011 a decrease of more almost 10 million from 2011 .

Example 47:
titleEntities: {'Subject': ['Washington Wizards', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price Washington Wizards ( NBA ) games 2015/16
X_Axis['Year']: ['2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['51.21', '32.7', '30.31', '32.24', '23.64', '24.52', '27.21', '29.14', '30.89', '46.83']

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

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

Example 48:
titleEntities: {'Subject': ['Nintendo'], 'Date': ['2011', '2018']}
title: Nintendo 3DS sales worldwide 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Sales', 'in', 'million', 'units']: ['6.4', '7.27', '6.79', '8.73', '12.24', '13.95', '13.53', '3.61']

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

generated_template: The statistic presents the templateYLabel[0] of templateTitle[0] templateTitle[1] and tourism templateTitle[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateYLabel[0] of templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] was approximately 0.99 templateScale templateYLabel[2] . The templateTitle[2] templateTitle[3] is an informal network of banks , brokers , dealers and financial institutions which are linked electronically .
generated: The statistic presents the Sales of Nintendo 3DS and tourism sales in the Nintendo from 2011 to 2018 .  In 2015 , the Sales of 3DS sales worldwide 2011 2018 in the 3DS was approximately 0.99 million units .  The sales worldwide is an informal network of banks , brokers dealers and financial institutions which are linked electronically .

Example 49:
titleEntities: {'Subject': ['Japan'], 'Date': ['2017', '2023']}
title: Japan : mobile internet users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'mobile', 'phone', 'internet', 'users', 'in', 'millions']: ['71.9', '71.5', '70.8', '69.9', '68.4', '66.6', '64.0']

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

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

Example 50:
titleEntities: {'Subject': ['Penguin Random House'], 'Date': ['2005', '2018']}
title: Number of employees of Penguin Random House 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Number', 'of', 'employees']: ['10351', '10615', '10594', '10582', '12812', '11838', '5712', '5343', '5264', '5432', '5779', '5764', '5804', '5395']

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

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

Example 51:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2019']}
title: Cinema box office revenue in the United Kingdom ( UK ) 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Revenue', 'in', 'million', 'GBP']: ['1251.84', '1277.12', '1277.88', '1246.56', '1240.38', '1057.68', '1082.1', '1099.1', '1038.0', '988.0', '944.0', '854.4', '821.0', '762.1', '776.3', '769.6', '742.0', '755.0', '645.0', '627.5']

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

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

Example 52:
titleEntities: {'Subject': ['CPI UAE'], 'Date': ['2012']}
title: Average CPI in the UAE 2012 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Consumer', 'price', 'index']: ['107.83', '105.75', '125.75', '120.84', '118.07', '116.78']

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

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateYLabel[2] templateYLabel[3] templateTitle[2] from templateXValue[min] to templateXValue[max] . For the 52 weeks ended on 1 , templateXValue[max] , there were approximately templateYValue[idxmax(X)] templateYValue[idxmax(X)] templateTitleSubject[0] cardholders all over the world .
generated: This statistic presents the Consumer price of CPI UAE index UAE from 2012 to 2017 .  For the 52 weeks ended on 1 , 2017 there were approximately 107.83 CPI UAE cardholders all over the world .

Example 53:
titleEntities: {'Subject': ['Global'], 'Date': ['2017']}
title: Global papaya production 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Million', 'metric', 'tons']: ['13.02', '13.05', '12.08', '12.67', '12.36', '12.01', '11.3', '11.19', '10.73', '10.02', '9.53', '8.91', '8.03', '8.67', '8.16', '8.42', '8.55', '7.25']

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

generated_template: 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] templateScale templateYLabel[1] templateYLabel[2] in templateXValue[idxmax(Y)] , an templatePositiveTrend from templateYValue[1] templateScale 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 2016 , 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 54:
titleEntities: {'Subject': ['Italian'], 'Date': ['2011', '2018']}
title: Revenues of Italian company Guccio Gucci in 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Revenues', 'in', 'million', 'euros']: ['267.63', '214.28', '162.31', '181.03', '184.24', '182.72', '177.24', '157.41']

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

generated_template: 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] templateScale templateYLabel[2] , a templateNegativeTrend 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 between 2011 and 2018 .  In 2018 , the total Revenues of Italian Gucci amounted to over 267.63 million euros , a decrease compared to the previous Year .

Example 55:
titleEntities: {'Subject': ['Houston Rockets', 'NBA'], 'Date': ['2003', '2020']}
title: Franchise value of the Houston Rockets ( NBA ) 2003 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['2475', '2300', '2200', '1650', '1500', '1250', '775', '568', '453', '443', '470', '469', '462', '439', '422', '369', '278', '255']

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

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

Example 56:
titleEntities: {'Subject': ['Minnesota Timberwolves'], 'Date': ['2001', '2018']}
title: Minnesota Timberwolves ' revenue 2001 to 2018
X_Axis['Year']: ['17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['223', '204', '154', '146', '128', '116', '96', '97', '95', '96', '100', '103', '103', '101', '97', '85', '85']

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

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

Example 57:
titleEntities: {'Subject': ['NASDAQ'], 'Date': ['1999', '2013']}
title: Market capitalization of the NASDAQ exchange in 1999 and 2013
X_Axis['Year']: ['1999', '2013']
Y_Axis['Market', 'cap', 'in', 'billion', 'U.S.', 'dollars']: ['1.16', '2.54']

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

generated_template: The templateTitleSubject[0] templateTitle[1] energy templateTitle[3] is expected to reach templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] , in comparison to templateYValue[min] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmin(Y)] . templateTitle[1] energy is one of the most popular renewable energy sources and in recent years more capacity was deployed than traditional energy sources . The success related to the templateTitle[1] energy segment can be attributed to the declining cost of installing templateTitle[1] photovoltaic systems .
generated: The NASDAQ capitalization energy exchange is expected to reach 2.54 billion U.S. dollars in 2013 , in comparison to 1.16 billion U.S. dollars in 1999 .  capitalization energy is one of the most popular renewable energy sources and in recent years more capacity was deployed than traditional energy sources .  The success related to the capitalization energy segment can be attributed to the declining cost of installing capitalization photovoltaic systems .

Example 58:
titleEntities: {'Subject': ['Armenia'], 'Date': ['2019']}
title: Unemployment rate in Armenia 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['17.71', '17.71', '17.83', '17.62', '18.26', '17.5', '16.18', '17.31', '18.44', '19.01', '18.74', '16.37', '9.81', '10.28', '10.8', '11.22', '11.23', '11.52', '11.44', '11.25', '11.2']

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

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

Example 59:
titleEntities: {'Subject': ['Peru'], 'Date': ['2015']}
title: Median age of the population in Peru 2015
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2015', '2010', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960', '1955', '1950']
Y_Axis['Median', 'age', 'in', 'years']: ['39.4', '38.0', '36.8', '35.6', '34.2', '32.6', '31.0', '27.5', '25.5', '24.1', '22.7', '21.2', '20.1', '19.2', '18.5', '17.9', '17.6', '17.7', '18.2', '18.7', '19.2']

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

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

Example 60:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2003', '2013']}
title: Attendance at performing arts events in the U.S. 2003 to 2013
X_Axis['Year']: ['2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Attendance', 'in', 'millions']: ['73.54', '74.94', '77.33', '74.98', '77.49', '76.68', '80.13', '82.28', '84.48', '85.6', '86.38']

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

generated_template: This statistic shows the approximate templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] in the templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale people attended a templateTitle[0] templateTitle[1] concert at least once . The templateTitle[1] concert industry – additional information In 2016 , Beyonce and Guns ' N ' templatePositiveTrend were among the most successful templateTitle[1] tours in North America , generating 169.4 templateScale templateTitleSubject[0] dollars and 130.8 templateScale templateTitleSubject[0] dollars , respectively in gross revenue .
generated: This statistic shows the approximate Attendance millions of Attendance performing in the arts from 2003 to 2013 .  In 2013 , 73.54 millions people attended a Attendance performing concert at least once .  The performing concert industry – additional information In 2016 , Beyonce and Guns ' N rose were among the most successful performing tours in North America , generating 169.4 millions U.S. dollars and 130.8 millions U.S. dollars , respectively in gross revenue .

Example 61:
titleEntities: {'Subject': ['Regal Entertainment Group'], 'Date': ['2006', '2017']}
title: Regal Entertainment Group 's net income 2006 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['112.3', '170.5', '153.2', '105.2', '157.6', '142.2', '40.1', '77.6', '95.5', '112.2', '360.4', '104.3']

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

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

Example 62:
titleEntities: {'Subject': ['Peru'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Peru 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['3.76', '3.9', '3.99', '4', '3.62', '2.6', '3.99', '2.47', '4.05', '3.27', '2.39']

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

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from between templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] had templatePositiveTrend by around 2.48 templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Peru from between 2014 to 2018 , with projections up until 2024 .  In 2018 , Peru 's real Gross domestic product had increased by around 2.48 % compared to the previous Year .

Example 63:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2024']}
title: Inflation rate in Argentina 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['16.98', '22.21', '26.87', '32.26', '51', '54.44', '34.28', '25.68', '10.62', '10.04', '9.78', '10.46', '6.27', '8.59', '8.83', '10.9', '9.64', '4.42']

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

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

Example 64:
titleEntities: {'Subject': ['Los Angeles Chargers'], 'Date': ['2006', '2019']}
title: Regular season home attendance of the Los Angeles Chargers 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Home', 'attendance']: ['177755', '262145', '202687', '456197', '534180', '523457', '513641', '479716', '523143', '524240', '540344', '545104', '524016', '531024']

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

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

Example 65:
titleEntities: {'Subject': ['CAC'], 'Date': ['1995', '2019']}
title: Annual CAC 40 performance 1995 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995']
Y_Axis['Index', 'points']: ['5978.06', '4730.69', '5312.56', '4862.31', '4637.06', '4272.75', '4295.95', '3641.07', '3159.81', '3804.78', '3936.33', '3217.97', '5614.08', '5541.76', '4715.23', '3821.16', '3557.9', '3063.91', '4624.58', '5926.42', '5958.32', '3942.66', '2998.9', '2315.7', '1872.0']

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

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[2] from templateXValue[min] to templateXValue[max] , as of last quarter of each templateXLabel[0] . The templateYLabel[1] templateYLabel[2] in the templateTitle[2] amounted to roughly templateYValue[0] templateScale templateYLabel[4] templateYLabel[5] in templateXValue[max] . templateYLabel[1] templateYLabel[2] in the templateTitle[2] It is generally the case that in times of austerity people hold on to capital .
generated: The statistic shows the Index of points in the 40 from 1995 to 2019 , as of last quarter of each Year .  The points points in the 40 amounted to roughly 5978.06 million points in 2019 .  points points in the 40 It is generally the case that in times of austerity people hold on to capital .

Example 66:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2004', '2018']}
title: Capital expenditure of the U.S. chemical industry 2004 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Capital', 'expenditure', 'in', 'million', 'U.S.', 'dollars']: ['33200', '31900', '31100', '31000', '26700', '28900', '28800', '25758', '20339', '23625', '23839', '27412', '18151', '18056', '18857']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Capital expenditure of the U.S. chemical industry from 2004 to 2018 .  In 2018 , the U.S. chemical industry amounted to about 33200 million U.S. dollars .

Example 67:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2009', '2014']}
title: Average price for a full set of gel toenails in nail salons in the U.S. 2009 to 2014
X_Axis['Year']: ['2009', '2010', '2011', '2012', '2013', '2014']
Y_Axis['Average', 'price', 'in', 'U.S.', 'dollars']: ['33.95', '37.03', '36.63', '39.16', '34.02', '34.01']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateYLabel[2] templateYLabel[3] templateTitle[2] from templateXValue[min] to templateXValue[max] . For the 52 weeks ended on 1 , templateXValue[max] , there were approximately templateYValue[idxmax(X)] templateYValue[idxmax(X)] templateTitleSubject[0] cardholders all over the world .
generated: This statistic shows the Average price of U.S. dollars for from 2009 to 2014 .  For the 52 weeks ended on 1 , 2014 there were approximately 34.01 U.S. cardholders all over the world .

Example 68:
titleEntities: {'Subject': ['Global'], 'Date': ['2011', '2017']}
title: Global spending on motorsports sponsorships 2011 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Spending', 'in', 'billion', 'U.S.', 'dollars']: ['5.75', '5.58', '5.43', '5.26', '5.12', '4.97', '4.83']

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

generated_template: The statistic shows the total 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] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the total Spending for motorsports sponsorship from 2011 to 2017 .  In the Year 2014 , the Global motorsports sponsorship Spending amounted to 5.26 billion U.S. dollars .

Example 69:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2018']}
title: Participants in indoor soccer in the U.S. from 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'participants', 'in', 'millions']: ['5.23', '5.4', '5.12', '4.81', '4.53', '4.8', '4.9', '4.34', '4.93', '4.91', '4.74', '4.24', '4.7']

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

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] templateTitle[2] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] templateTitle[2] amounted to approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Number of participants in 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 70:
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[max] templateYLabel[3] in templateXValue[idxmax(Y)] .
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 8.1 pounds in 2016 .

Example 71:
titleEntities: {'Subject': ['Mattel'], 'Date': ['2013', '2019']}
title: Global ad spending of Mattel from 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Ad', 'expenditure', 'in', 'million', 'U.S.', 'dollars']: ['551.5', '524.3', '642.3', '634.9', '717.85', '733.2', '750.2']

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

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , in templateScale templateYLabel[4] . The templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was around templateYValue[max] templateScale templateYLabel[4] dollars in templateXValue[idxmax(Y)] .
generated: The statistic presents the Ad expenditure of Mattel from 2013 to 2019 , in million dollars .  The Ad expenditure of Mattel was around 750.2 million dollars in 2013 .

Example 72:
titleEntities: {'Subject': ['Amazon'], 'Date': ['2018']}
title: U.S. Amazon Prime membership penetration 2018 , by income
X_Axis['Year']: ['Less_than_35K_USD', '35000_-_74999_USD', '75000_USD_and_over', '100000_USD_and_over', '150000_USD_and_over']
Y_Axis['Share', 'of', 'respondents']: ['31.7', '41.6', '55.7', '58.5', '63.3']

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

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

Example 73:
titleEntities: {'Subject': ['Texas'], 'Date': ['1992', '2018']}
title: Texas - Unemployment rate 1992 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92"]
Y_Axis['Unemployment', 'rate']: ['3.9', '4.3', '4.6', '4.4', '5.1', '6.3', '6.7', '7.8', '8.1', '7.6', '4.8', '4.3', '4.9', '5.4', '5.9', '6.7', '6.4', '5', '4.3', '4.7', '4.9', '5.3', '5.7', '6.1', '6.5', '7.2', '7.6']

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

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

Example 74:
titleEntities: {'Subject': ['USA'], 'Date': ['1990', '2018']}
title: USA - number of arrests for all offenses 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Number', 'of', 'arrests']: ['10310960', '10554985', '10662252', '10797088', '11205833', '11302102', '12196959', '12408899', '13120947', '13687241', '14005615', '14209365', '14380370', '14094186', '13938071', '13639500', '13741400', '13699300', '13980300', '14031100', '14528300', '15284300', '15168100', '15119800', '14648700', '14036300', '14075100', '14211900', '14195100']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] templateTitle[3] templateTitle[4] were living below the templateYLabel[0] line in the templateTitle[5] . templateYLabel[0] is the state of one who lacks a certain amount of material possessions or money .
generated: This statistic shows the Number arrests of USA for all in the USA from 1990 to 2018 .  In 2018 , 10310960 % 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 75:
titleEntities: {'Subject': ['Canada', 'Facebook'], 'Date': ['2015']}
title: Canada : Facebook user penetration 2015 , by age
X_Axis['Year']: ['18-34', '35-44', '45-54', '55-64', '65+', 'Total']
Y_Axis['Share', 'of', 'population']: ['75', '68', '63', '47', '32', '59']

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

generated_template: This statistic shows the estimated templateTitle[0] templateTitle[1] templateTitleSubject[0] templateYLabel[0] from templateXValue[last] to templateXValue[0] . During the eighteenth templateTitle[0] templateTitle[1] in templateXValue[0] , templateYLabel[0] templateYLabel[1] stood at templateYValue[0] templateScale . Republic of templateTitleSubject[0] , also often referred to as templateTitleSubject[0] , adopted the single-term system and holds its templateTitle[0] templateTitle[1] every five years .
generated: This statistic shows the estimated Canada Facebook Share from Total to 18-34 .  During the eighteenth Canada Facebook in 18-34 , Share population stood at 75 % .  Republic of Canada , also often referred to as Canada , adopted the single-term system and holds its Canada Facebook every five years .

Example 76:
titleEntities: {'Subject': ['Coast Guard'], 'Date': ['1995', '2010']}
title: Active duty U.S Coast Guard personnel numbers from 1995 to 2010
X_Axis['Year']: ['2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995']
Y_Axis['Number', 'of', 'Coast', 'Guard', 'personnel']: ['41327', '42426', '41362', '40650', '39980', '39361', '34804', '35582']

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

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

Example 77:
titleEntities: {'Subject': ['Wayfair'], 'Date': ['2013', '2018']}
title: Wayfair active customers 2013 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Number', 'of', 'active', 'customers', 'in', 'millions']: ['15.16', '10.99', '8.25', '5.36', '3.22', '2.09']

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

generated_template: What is the templateYLabel[1] of a house in templateTitleSubject[0] ? In templateXValue[max] , a house in templateTitleSubject[0] would cost approximately 251,584 templateYLabel[2] . This is an templatePositiveTrend of roughly 4.7 templateScale compared to the previous templateXLabel[0] and an templatePositiveTrend of approximately 11 templateScale compared to templateXValue[4] . Note , however , that there are large templateYLabel[1] differences between the three Belgian regions .
generated: What is the active of a house in Wayfair ? In 2018 , a house in Wayfair would cost approximately 251,584 customers .  This is an increase of roughly 4.7 millions compared to the previous Year and an increase of approximately 11 millions compared to 2014 .  Note , however that there are large active differences between the three Belgian regions .

Example 78:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2002', '2012']}
title: Car rental in the U.S. - total cars in service 2002 to 2012
X_Axis['Year']: ['2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012']
Y_Axis['Cars', 'in', 'service', 'in', 'thousands']: ['1643', '1617', '1665', '1714', '1768', '1861', '1813', '1637', '1629', '1761', '1857']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] had reached about templateYValue[idxmax(X)] years.Demographic development in templateTitleSubject[0] – additional information templateYLabel[0] templateYLabel[1] at templateYLabel[2] refers to the average number of years a group of people born in the same templateXLabel[0] would live , assuming constant mortality rates . The country with the highest templateYLabel[0] templateYLabel[1] at templateYLabel[2] was Japan , while templateTitleSubject[0] had reached a templateYLabel[0] templateYLabel[1] above global average .
generated: The statistic shows the Cars service at thousands in U.S. from 2002 to 2012 .  In 2012 , the average Cars service at thousands in U.S. had reached about 1857 years.Demographic development in U.S. – additional information Cars service at thousands 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 Cars service at thousands was Japan , while U.S. had reached a Cars service above global average .

Example 79:
titleEntities: {'Subject': ['Spain'], 'Date': ['2006', '2017']}
title: Number of arrivals in tourist accommodation Spain 2006 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'arrivals', 'in', 'millions']: ['129.4', '123.5', '114.4', '107.5', '101.7', '100.4', '103.1', '98.8', '93.7', '100.1', '101.8', '99.9']

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

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

Example 80:
titleEntities: {'Subject': ['Tonga'], 'Date': ['2019']}
title: Unemployment rate in Tonga 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['1.02', '1.03', '1', '1.06', '1.08', '1.11', '1.14', '1.11', '1.13', '1.15', '1.13', '0.96', '0.99', '1.09', '2.14', '3.49', '5.18', '4.74', '4.19', '3.8', '3.43']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Tonga from 1999 to 2019 .  In 2019 , the Unemployment rate in Tonga was 1.02 % .

Example 81:
titleEntities: {'Subject': ['Germany'], 'Date': ['2013', '2018']}
title: Forecast : Online retail sales value in Germany 2013 to 2018
X_Axis['Year']: ['2013', '2018']
Y_Axis['Sales', 'in', 'billion', 'euros']: ['25', '41']

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

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

Example 82:
titleEntities: {'Subject': ['Nuclear'], 'Date': ['2005', '2019']}
title: Nuclear power plants : permanent shutdowns 2005 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Number', 'of', 'shutdowns']: ['3', '7', '5', '4', '7', '1', '6', '3', '13', '1', '3', '2', '1', '8', '2']

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

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

Example 83:
titleEntities: {'Subject': ['U.S. Black Friday'], 'Date': ['2019']}
title: Share of U.S. consumers shopping on Black Friday 2015 to 2019
X_Axis['Year']: ['2015', '2016', '2017', '2018', '2019']
Y_Axis['Share', 'of', 'respondents']: ['59', '51', '35', '37', '36']

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

generated_template: This statistic shows the results of a survey conducted in the templateTitleSubject[0] in templateTitleDate[0] on how long the templateYLabel[1] thought the feeling of templateTitle[3] could last in a templateTitle[4] . According to templateYValue[max] templateScale of templateYLabel[1] , the feeling of templateTitle[3] in a templateTitle[4] can last a templateXValue[last] .
generated: This statistic shows the results of a survey conducted in the U.S. Black Friday in 2019 on how long the respondents thought the feeling of shopping could last in a Black .  According to 59 % of respondents , the feeling of shopping in a Black can last a 2019 .

Example 84:
titleEntities: {'Subject': ['BMW Group'], 'Date': ['2010', '2018']}
title: BMW Group - motorcycle production 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Production', 'in', 'units']: ['162687', '185682', '145555', '151004', '133615', '110127', '113811', '110360', '99236']

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

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[1] templateTitle[2] , a franchise of the National Football League , templateTitle[4] templateXValue[min] to templateXValue[max] . In the templateXValue[max] season , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[1] templateTitle[2] were at templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic presents the Production units of the BMW Group motorcycle , a franchise of the National Football League , 2010 to 2018 .  In the 2018 season , the Production units of the BMW Group motorcycle were at 162687 million units .

Example 85:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2017']}
title: Vietnam social media user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['52', '52', '51', '50', '49', '48', '46']

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

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

Example 86:
titleEntities: {'Subject': ['Easton Bell Sports'], 'Date': ['2013', '2013']}
title: Sporting goods industry : Revenue of Easton Bell Sports 2013
X_Axis['Year']: ['2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Revenue', '(in', 'million', 'U.S.', 'dollars)']: ['780.4', '827.2', '834.9', '772.8', '716.3', '775.5', '724.7', '639.0']

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

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] generated some templateYValue[idxmax(X)] templateScale templateYLabel[3] of templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . In 2016/2017 , India is projected to be the leading global producer of templateTitle[1] and to harvest about 44.5 templateScale templateYLabel[2] of templateTitle[1] .
generated: This statistic shows the Revenue of U.S. dollars) in Easton Bell Sports from 2006 to 2013 .  In 2013 , Easton Bell Sports generated some 780.4 million U.S. of Revenue (in million U.S. In 2016/2017 , India is projected to be the leading global producer of goods and to harvest about 44.5 million of goods .

Example 87:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2017', '2023']}
title: Mexico : number of social network users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['73.0', '71.1', '69.1', '66.7', '64.1', '61.7', '58.8']

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

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] is expected to reach templateYValue[idxmax(X)] templateScale , up from templateYValue[5] templateScale in templateXValue[5] .
generated: This statistic shows the Number of Mexico users in Mexico from 2017 to 2023 .  In 2023 , the Number of Mexico users in Mexico is expected to reach 73.0 millions , up from 61.7 millions in 2018 .

Example 88:
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 represents templateTitleSubject[0] 's templateYLabel[0] between the templateXValue[min] and templateXValue[max] fiscal years . In the templateXValue[max] fiscal templateXLabel[0] , the manufacturer of regional aircraft , business jets , mass transportation equipment and recreational equipment had revenues of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: The statistic represents Bombardier 's Revenue between the 2007 and 2018 fiscal years .  In the 2018 fiscal Year , the manufacturer of regional aircraft , business jets , mass transportation equipment and recreational equipment had revenues of 8.91 billion U.S. dollars .

Example 89:
titleEntities: {'Subject': ['Lauder'], 'Date': ['2012', '2024']}
title: Estée Lauder 's share of the makeup products market worldwide 2012 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Market', 'share']: ['24.4', '23.4', '22.4', '21.4', '20.4', '18.9', '17.4', '16.4', '13.6', '12.9', '14.2', '13.8', '12']

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

generated_template: This statistic shows templateTitle[0] templateTitleSubject[0] templateTitle[2] templateYLabel[1] of the templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitle[0] templateTitleSubject[0] templateTitle[2] templateYLabel[1] of the global templateTitle[4] templateTitle[5] templateTitle[6] templateYLabel[0] is estimated to be templateYValue[7] templateScale . The company is a manufacturer and marketer of prestige templateTitle[4] templateTitle[5] , makeup , fragrance and hair templateTitle[5] templateTitle[6] , with global net sales of over 11.2 templateScale U.S. dollars .
generated: This statistic shows Estée Lauder 's share of the makeup products market 2012 from to 2024 .  In 2018 , Estée Lauder 's share of the global makeup products market is estimated to be 16.4 billion .  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 90:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2015', '2022']}
title: South Korea : number of social network users 2015 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Number', 'of', 'users', 'in', 'millions']: ['28.16', '27.66', '27.1', '26.32', '25.53', '24.77', '23.99', '23.07']

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

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateTitleSubject[1] templateYLabel[1] in templateTitleSubject[0] is expected to reach templateYValue[idxmax(X)] templateScale , up from templateYValue[5] templateScale in templateXValue[min] .
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 2015 .

Example 91:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2015', '2050']}
title: Projected number of people with dementia in Malaysia 2015 to 2050
X_Axis['Year']: ['2050', '2030', '2015']
Y_Axis['Number', 'of', 'people', 'in', 'thousands']: ['590', '261', '123']

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

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

Example 92:
titleEntities: {'Subject': ['Qatar'], 'Date': ['2019']}
title: Unemployment rate in Qatar 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['0.15', '0.14', '0.14', '0.14', '0.16', '0.19', '0.27', '0.48', '0.56', '0.45', '0.31', '0.31', '0.52', '0.87', '1.21', '1.48', '1.53', '1.51', '1.46', '1.51', '1.57']

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

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

Example 93:
titleEntities: {'Subject': ['Bulgaria'], 'Date': ['2019']}
title: Unemployment rate in Bulgaria 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['4.82', '5.26', '6.16', '7.58', '9.14', '11.42', '12.94', '12.27', '11.26', '10.28', '6.82', '5.61', '6.88', '8.95', '10.08', '12.04', '13.73', '18.11', '19.92', '16.22', '14.1']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at about templateYValue[idxmax(X)] templateScale . Trade in templateTitleSubject[0] is a templatePositiveTrend market and recognized as one of the world 's newest industrialized countries .
generated: The statistic shows the Unemployment rate in Bulgaria from 1999 to 2019 .  In 2019 , the Unemployment rate in Bulgaria was at about 4.82 % .  Trade in Bulgaria is a growing market and recognized as one of the world 's newest industrialized countries .

Example 94:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2009', '2018']}
title: Retail sales value of yogurt & sour milk products in the United Kingdom 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Retail', 'value', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['2984.2', '2997.6', '3011.1', '3033.8', '3063.4', '3030.9', '2941.6', '2856.4', '2773.4', '2633.5']

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

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] ' templateYLabel[0] templateYLabel[1] amounted to around templateYValue[0] templateScale templateYLabel[3] . After spin-offs of other product segments , Koninklijke templateTitleSubject[0] N.V. today is a company focused on healthcare/medical technology .
generated: The statistic presents the Retail value of United Kingdom from 2009 to 2018 .  In 2018 , United Kingdom ' Retail value amounted to around 2984.2 million .  After spin-offs of other product segments , Koninklijke United Kingdom N.V. today is a company focused on healthcare/medical technology .

Example 95:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2010', '2019']}
title: Ice hockey players in Sweden 2010 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11']
Y_Axis['Number', 'of', 'players']: ['55431', '62701', '63901', '60408', '60089', '56839', '64214', '69921', '62003']

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

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

Example 96:
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 97:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2011']}
title: Camping equipment sales in the U.S. - sleeping bags 2011
X_Axis['Year']: ['2010', '2011']
Y_Axis['Equipment', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['191.76', '210.38']

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

generated_template: This statistic represents the templateYLabel[0] of migrant worker templateYLabel[1] templatePositiveTrend up away from their parents in templateTitleSubject[0] in templateXValue[min] and templateXValue[max] . The 6th National Population Census of the Republic of templateTitleSubject[0] estimated that templateYValue[max] templateScale templateTitle[1] templateYLabel[1] until the age of 17 templatePositiveTrend up without their parents .
generated: This statistic represents the Equipment of migrant worker sales growing up away from their parents in U.S. 2010 and 2011 .  The 6th National Population Census of the Republic of U.S. estimated that 210.38 million equipment sales until the age of 17 grew up without their parents .

Example 98:
titleEntities: {'Subject': ['Outback Steakhouse'], 'Date': ['2015', '2018']}
title: Average sales per unit of Outback Steakhouse restaurants in the U.S. 2015 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015']
Y_Axis['Average', 'sales', 'in', 'thousand', 'U.S.', 'dollars']: ['3580', '3542', '3354', '3430']

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

generated_template: In the templateYLabel[1] many templateTitle[0] pay a portion of templateTitle[2] templateTitle[3] templateYLabel[0] for employees . As of templateXValue[min] the total annual medical templateYLabel[0] for employees was 11.7 thousand templateYLabel[1] templateYLabel[2] . That cost is expected to templatePositiveTrend to templateYValue[max] thousand templateYLabel[1] templateYLabel[2] by templateXValue[idxmax(Y)] .
generated: In the sales many Average pay a portion of per unit Average for employees .  As of 2015 the total annual medical Average for employees was 11.7 thousand sales .  That cost is expected to increase 3580 thousand sales by 2018 .

Example 99:
titleEntities: {'Subject': ['Chicago Blackhawks'], 'Date': ['2019']}
title: Franchise value of the Chicago Blackhawks 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1085', '1050', '1000', '925', '925', '825', '625', '350', '306', '300', '258', '205', '179', '168']

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

generated_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Yankees from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Yankees was at templateYValue[0] templateYValue[idxmax(X)] . • Major League Baseball average per game templateYLabel[1] • Major League Baseball total templateYLabel[1]
generated: This graph depicts the total Franchise value of the Chicago Blackhawks Yankees from 2006 to 2019 .  In 2019 , the Franchise value of the Chicago Blackhawks Yankees was at 1085 .  • Major League Baseball average per game value • Major League Baseball total value . 

Example 100:
titleEntities: {'Subject': ['Nigeria'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Nigeria 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['3346.39', '3074.98', '3830.0', '2602.02', '2400.45', '2222.01', '2032.86', '1971.79', '2180.27', '2726.34', '3222.68', '3042.05', '2797.86', '2582.57', '2365.01', '1958.58', '2234.36', '1822.79', '1591.33', '1245.07', '982.98', '797.64', '748.31', '598.29', '570.17', '496.5', '1861.07', '1713.38', '1618.29', '1273.27', '792.81']

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

generated_template: The statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateYLabel[0] is the total value of all goods and services produced in a country in a templateXLabel[0] . It is considered to be a very important indicator of the economic strength of a country and a positive change is an indicator of economic growth .
generated: The statistic shows Gross domestic product ( GDP ) per capita in 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 101:
titleEntities: {'Subject': ['Latin America', 'Caribbean'], 'Date': ['2024', '2024']}
title: Gross domestic product of Latin America and the Caribbean 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'in', 'billion', 'U.S.', 'dollars']: ['6546.54', '6234.32', '5922.28', '5634.42', '5352.4', '5188.25', '5249.66', '5459.78', '5057.11', '5271.94', '5988.65']

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

generated_template: This statistic shows templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] in templateScale templateYLabel[2] templateYLabel[3] . In templateXValue[6] , the EU 's templateYLabel[0] amounted to about templateYValue[6] templateScale templateYLabel[2] templateYLabel[3] . Brexit and the economy of the templateTitleSubject[0] The templateTitleSubject[0] is still recovering from the crisis in 2008 , but it is by no means making an impressive comeback and templateXValue[8] has not started out on the right foot either either .
generated: This statistic shows Gross domestic product ( GDP ) of the Latin America from 2014 to 2024 in billion U.S. dollars .  In 2018 , the EU 's GDP amounted to about 5249.66 billion U.S. dollars .  Brexit and the economy of the Latin America is still recovering from the crisis in 2008 , but it is by no means making an impressive comeback and 2016 has not started out on the right foot either .

Example 102:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1995', '2019']}
title: U.S. retail price of grapes 1995 to 2019
X_Axis['Year']: ['1995', '1997', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019']
Y_Axis['Price', 'in', 'U.S.', 'dollars', 'per', 'pound']: ['1.86', '2.19', '2.4', '2.36', '2.31', '2.27', '2.25', '3.09', '2.76', '2.89', '2.72', '2.46', '3.14', '2.87', '2.86', '3.09', '2.57', '2.96', '2.83', '2.88', '2.61', '2.28', '2.39']

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

generated_template: This statistic shows the templateTitle[0] templateTitleSubject[0] templateTitle[2] templateYLabel[0] of templateTitle[4] from templateXValue[min] to templateXValue[max] , in templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . In templateXValue[13] , the annual templateTitle[0] templateYLabel[0] for one templateYLabel[4] of templateTitle[4] on the templateTitleSubject[0] exchange was templateYValue[13] templateYLabel[1] templateYLabel[2] . By templateXValue[max] , the annual templateTitle[0] templateYLabel[0] of templateTitle[4] was templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the U.S. price of 1995 from to 2019 , in U.S. dollars per pound .  In 2010 , the annual U.S. Price for one pound of 1995 on the U.S. exchange was 2.87 U.S. dollars .  By 2019 , the annual U.S. Price of 1995 was 2.39 U.S. dollars per pound .

Example 103:
titleEntities: {'Subject': ['Bolivia'], 'Date': ['2000']}
title: Bolivia : internet penetration 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'individuals', 'using', 'internet']: ['43.83', '39.7', '35.56', '34.6', '36.99', '35.34', '30', '22.4', '16.8', '12.5', '10.5', '6.2', '5.23', '4.44', '3.51', '3.12', '2.12', '1.44']

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

generated_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In the most recently measured period , templateYValue[idxmax(X)] templateScale of the population accessed the templateYLabel[3] , up from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] . In templateXValue[2] , templateTitleSubject[0] 's population templatePositiveTrend by approximately 2.48 templateScale compared to the previous templateXLabel[0] .
generated: This statistic gives information on the internet penetration in Bolivia from 2000 to 2017 .  In the most recently measured period , 43.83 percentage of the population accessed the internet , up from 1.44 percentage in 2000 .  In 2015 , Bolivia 's population grew by approximately 2.48 percentage compared to the previous Year .

Example 104:
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 typical American picture of a templateYLabel[4] templateTitle[7] 2.5 kids might not be as relevant as it once was : In templateXValue[max] , there was an templateYLabel[0] of templateYValue[idxmax(X)] templateYLabel[2] under 18 templateYLabel[3] templateYLabel[4] in the templateTitle[5] . This is a templateNegativeTrend from templateYValue[last] templateYLabel[2] under 18 templateYLabel[3] templateYLabel[4] in templateXValue[min] . Familial structure in the templateTitle[5] If there 's one thing the templateTitle[5] is known for , it 's diversity .
generated: The typical American picture of a years 2017 2.5 kids might not be as relevant it once was : In 2017 , there was an Life of 78.83 birth under 18 years in the 2017 .  This is a decrease from 45.19 birth under 18 years in 1960 .  Familial structure in the 2017 If there 's one thing the 2017 is known for , it 's diversity .

Example 105:
titleEntities: {'Subject': ['China'], 'Date': ['2007', '2017']}
title: Fertility rate in China 2007 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'children', 'born', 'per', 'woman']: ['1.6', '1.6', '1.57', '1.56', '1.56', '1.55', '1.54', '1.54', '1.54', '1.53', '1.53']

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

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] to one templateYLabel[4] while being of child-bearing age . templateTitleSubject[0] includes almost all countries south of the Sahara desert .
generated: This statistic shows the Fertility rate in China from 2007 to 2017 .  The Fertility rate is the average Number of children born to one woman while being of child-bearing age .  China includes almost all countries south of the Sahara desert .

Example 106:
titleEntities: {'Subject': ['Puma'], 'Date': ['2013', '2019']}
title: Global R & D expenditure of Puma from 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['R&D', 'expenditure', 'in', 'million', 'euros']: ['61.7', '54.0', '53.4', '52.0', '56.7', '46.2', '47.9']

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

generated_template: The statistic displays the templateYLabel[0] of templateTitle[1] television templateYLabel[1] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . According to the source , there were templateYValue[idxmax(X)] templateScale templateTitle[1] templateYLabel[1] in the templateTitle[3] in templateXValue[max] templateXValue[idxmin(Y)] a templateNegativeTrend of more almost 10 templateScale from templateXValue[min] .
generated: The statistic displays the R&D of R television expenditure in the D from 2013 to 2019 .  According to the source , there were 61.7 million R expenditure in the D in 2019 2014 a decrease of more almost 10 million from 2013 .

Example 107:
titleEntities: {'Subject': ['Azerbaijan'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Azerbaijan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.42', '2.35', '2.21', '2.15', '2.11', '2.7', '0.97', '0.15', '-3.06', '1.05', '2.8']

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

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

Example 108:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2024']}
title: Argentina 's budget balance in relation to GDP 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'balance', 'in', 'relation', 'to', 'GDP']: ['-2.93', '-2.72', '-2.99', '-3.13', '-2.68', '-3.97', '-5.23', '-6.7', '-6.66', '-6', '-4.25']

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

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a deficit . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] surplus amounted to around templateYValue[max] templateScale of templateYLabel[3] .
generated: The statistic shows Argentina 's Budget balance in relation to GDP between 2014 and 2018 , with projections up until 2024 .  A positive value indicates a Budget surplus , a negative value indicates a deficit .  In 2018 , Argentina 's Budget surplus amounted to around -2.68 % of GDP .

Example 109:
titleEntities: {'Subject': ['Los Angeles Angels'], 'Date': ['2002', '2019']}
title: Franchise value of the Los Angeles Angels 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1900', '1800', '1750', '1340', '1300', '775', '718', '656', '554', '521', '509', '500', '431', '368', '294', '241', '225', '195']

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

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

Example 110:
titleEntities: {'Subject': ['Detroit Pistons', 'NBA'], 'Date': ['2015/16', '2015/16']}
title: Average ticket price Detroit Pistons ( NBA ) games 2015/16
X_Axis['Year']: ['2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07']
Y_Axis['Average', 'ticket', 'price', 'in', 'U.S.', 'dollars']: ['31.42', '38.8', '40.1', '40.1', '41.26', '42.76', '47.5', '47.5', '47.5', '46.23']

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

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] templateYLabel[2] for templateTitleSubject[0] games of the National Basketball Association from templateXValue[last] to templateXValue[0] . In the templateXValue[last] season , the templateYLabel[0] templateYLabel[1] templateYLabel[2] was templateYValue[min] templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Average ticket price for Detroit Pistons games of the National Basketball Association from 2006/07 to 2015/16 .  In the 2006/07 season , the Average ticket price was 31.42 U.S. dollars .

Example 111:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1991', '2015']}
title: Median age of first alcohol use among U.S. youth 1991 to 2015
X_Axis['Year']: ['2013–2015', '2012–2014', '2011–2013', '2010–2012', '2009–2011', '2008–2010', '2007–2009', '2006–2008', '2005–2007', '2004–2006', '2003–2005', '2002–2004', '2001–2003', '2000–2002', '1999–2001', '1998–2000', '1997–1999', '1996–1998', '1995–1997', '1994–1996', '1993–1995', '1992–1994', '1991–1993']
Y_Axis['Average', 'age', 'in', 'years']: ['14.64', '14.56', '14.47', '14.4', '14.35', '14.29', '14.22', '14.14', '14.06', '13.97', '13.9', '13.85', '13.82', '13.81', '13.81', '13.88', '13.91', '13.94', '13.85', '13.8', '13.76', '13.76', '13.65']

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

generated_template: The templateYLabel[0] templateYLabel[1] templateYLabel[2] templatePositiveTrend to templateYValue[last] templateScale in templateXValue[max] after an unprecedented time below templateYValue[10] templateScale after the Financial Crisis . The templateYLabel[0] templateYLabel[1] templateYLabel[2] is the interest templateYLabel[2] from the templateYLabel[0] Reserve , the central bank in the templateTitleSubject[0] . It is a very important financial indicator and analysts all over the world watch this templateYLabel[2] .
generated: The Average age years rose to 13.65 % in 2013–2015 after an unprecedented time below 13.9 % after the Financial Crisis .  The Average age years is the interest years from the Average Reserve , the central bank in the U.S. It is a very important financial indicator and analysts all over the world watch this years .

Example 112:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2004', '2017']}
title: Companies on the insurance market in the United Kingdom ( UK ) 2004 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Number', 'of', 'companies']: ['436', '464', '496', '526', '613', '528', '540', '521', '534', '549', '576', '594', '640', '673']

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

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] of the templateTitle[3] amounted to approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic displays the Number companies of the insurance market United in United Kingdom from 2004 to 2017 .  In 2017 , the Number companies of the market United of the United amounted to approximately 436 million .

Example 113:
titleEntities: {'Subject': ['Los Angeles Rams', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Los Angeles Rams ( NFL ) 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['3800', '3200', '3000', '2900', '1450', '930', '875', '780', '775', '779', '913', '929', '908', '841', '757', '708', '602', '544']

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

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to around templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Jerry Jones who bought the templateYLabel[0] for 150 templateScale templateYLabel[3] templateYLabel[4] in 1989 .
generated: 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 came to around 3800 million U.S. dollars .  The Los Angeles Rams are owned by Jerry Jones who bought the Franchise for 150 million U.S. dollars in 1989 .

Example 114:
titleEntities: {'Subject': ['GDP'], 'Date': ['2007', '2018']}
title: Value added of manufacturing as a percentage of GDP 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Value', 'added', 'as', 'a', 'percentage', 'of', 'GDP']: ['11.4', '11.2', '11.1', '11.7', '11.7', '11.9', '11.9', '12', '12', '11.8', '12.2', '12.8']

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

generated_template: The templateTitleSubject[0] of templateTitleSubject[0] ( RBS ) is part of The 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 templateScale euros in templateYLabel[1] , the fourth highest in the United Kingdom . History of RBS Founded in Edinburgh in 1727 , the templateTitleSubject[0] of templateTitleSubject[0] is an amalgamation of hundreds of past banks .
generated: The GDP of ( RBS ) is part of The GDP of Group plc with Natwest and Ulster GDP and consists of 67 thousand employees over 187 branches across the United Kingdom .  As of 2017 , RBS had over 830 percentage euros in added , the fourth highest in the United Kingdom .  History of RBS Founded in Edinburgh 1727 , the GDP of is an amalgamation of hundreds past banks .

Example 115:
titleEntities: {'Subject': ['Texas'], 'Date': ['1990', '2018']}
title: Texas - Median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['59785', '59295', '58146', '56473', '53875', '53027', '51926', '49047', '47266', '47475', '46490', '46053', '43307', '41422', '41397', '39271', '40149', '40860', '38609', '38688', '35783', '35075', '33072', '32039', '30755', '28727', '27953', '27733', '28228']

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

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: 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 116:
titleEntities: {'Subject': ['UK'], 'Date': ['2019']}
title: Public expenditure on recreational and sporting services in the UK 2013 to 2019
X_Axis['Year']: ['2018//19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14']
Y_Axis['Expenditure', 'in', 'million', 'GBP']: ['2947', '3048', '3230', '3268', '3997', '3472']

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

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

Example 117:
titleEntities: {'Subject': ['Czech Republic'], 'Date': ['2019']}
title: Unemployment rate in the Czech Republic 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['2.47', '2.4', '2.89', '3.95', '5.05', '6.11', '6.95', '6.98', '6.71', '7.28', '6.66', '4.39', '5.32', '7.15', '7.93', '8.21', '7.54', '7.02', '7.99', '8.76', '8.49']

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

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

Example 118:
titleEntities: {'Subject': ['Italy'], 'Date': ['2024']}
title: Budget balance in Italy 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'balance', 'in', 'billion', 'euros']: ['-51.31', '-49.15', '-48.57', '-47.61', '-45.51', '-35.77', '-37.51', '-41.29', '-42.66', '-43.14', '-49.34']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a state surplus ; a negative value , a state deficit . In templateXValue[6] , the state deficit of templateTitleSubject[0] was at around 29.98 templateScale templateYLabel[3] .
generated: The statistic shows the Budget balance in Italy from 2014 to 2018 , with projections up until 2024 .  A positive value indicates a state surplus ; a negative value , a state deficit .  In 2018 , the state deficit of Italy was at around 29.98 billion euros .

Example 119:
titleEntities: {'Subject': ['Germany'], 'Date': ['2010', '2019']}
title: Electricity prices for households in Germany 2010 - 2019 , semi-annually
X_Axis['Year']: ['2019_S1', '2018_S2', '2018_S1', '2017_S2', '2017_S1', '2016_S2', '2016_S1', '2015_S2', '2015_S1', '2014_S2', '2014_S1', '2013_S2', '2013_S1', '2012_S2', '2012_S1', '2011_S2', '2011_S1', '2010_S2', '2010_S1']
Y_Axis['Euro', 'cents', 'per', 'kilowatt-hour']: ['30.88', '30.0', '29.87', '30.48', '30.48', '29.77', '29.69', '29.46', '29.51', '29.74', '29.81', '29.21', '29.19', '26.76', '25.95', '25.31', '25.28', '24.38', '23.75']

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

generated_template: Hungarian templateTitle[0] consumers have experienced a reduction in the price of templateTitle[0] over the past several years , with the price templateNegativeTrend from templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] kilowatt hour ( kWh ) in the first half of templateXValue[17] , to templateYValue[0] templateYLabel[1] templateYLabel[2] kWh in the first half of templateXValue[0] . Energy in templateTitleSubject[0] uses a diverse range of fuels to generate energy . Nuclear plays a significant role in the Hungarian energy mix , with energy derived from nuclear sources making up half of the country 's energy mix as of templateXValue[3] .
generated: Hungarian Electricity consumers have experienced a reduction in the price of Electricity over the past several years , with the price falling from 30.88 Euro cents per kilowatt hour ( kWh ) in the first half of 2010 S2 , to 30.88 cents per kWh in the first half of 2019 S1 .  Energy in Germany uses a diverse range of fuels to generate energy .  Nuclear plays a significant role in the Hungarian energy mix , with energy derived from nuclear sources making up half of the country 's energy mix as of 2017 S2 .

Example 120:
titleEntities: {'Subject': ['Cyprus'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Cyprus 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.51', '2.46', '2.61', '2.71', '2.89', '3.11', '3.88', '4.47', '4.81', '1.96', '-1.31']

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

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

Example 121:
titleEntities: {'Subject': ['NFL'], 'Date': ['2019']}
title: NFL Thursday Night Football average viewers 2015 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015']
Y_Axis['Viewers', 'in', 'millions']: ['15.4', '14.9', '10.94', '12.44', '12.43']

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

generated_template: This statistic presents the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYLabel[2] consumers spent , which shows a templateTitle[1] templateTitle[2] of approximately 28 templateScale ( women are 29 templateScale less likely than men to have equal opportunities ) . That same templateXLabel[0] , the templateTitle[1] templateTitle[2] in the area of political empowerment in templateTitleSubject[0] amounted to 72 templateScale .
generated: This statistic presents the Thursday Night Football in NFL from 2015 to 2019 .  In 2017 , millions consumers spent , which shows a Thursday Night of approximately 28 millions ( women are 29 millions less likely than men to have equal opportunities ) .  That same Year , the Thursday Night in the area of political empowerment in NFL amounted to 72 millions .

Example 122:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2004', '2018']}
title: U.S. airlines - domestic passenger enplanements 2004 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Passenger', 'enplanements', '(in', 'millions)']: ['777.91', '741.73', '719.99', '696.01', '662.82', '645.68', '642.29', '638.25', '629.54', '618.05', '651.71', '679.17', '658.36', '657.26', '629.77']

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

generated_template: In templateXValue[max] , the templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitle[0] generated a templateYLabel[0] templateYLabel[1] of about templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] . templateTitle[1] templateTitle[2] templateTitle[3] The templateTitle[1] templateTitle[2] templateTitle[3] consists of the generation , transmission , and distribution of templateTitle[1] templateTitle[2] to the public . First developed in the late 1800 templateTitle[4] , the templateTitle[1] templateTitle[2] templateTitle[3] has evolved tremendously .
generated: In 2018 , the airlines domestic passenger in the U.S. generated a Passenger enplanements of about 777.91 (in .  airlines domestic passenger The airlines domestic passenger consists of the generation , transmission and distribution of airlines domestic to the public .  First developed in the late 1800 enplanements , the airlines domestic passenger has evolved tremendously .

Example 123:
titleEntities: {'Subject': ['Bloomin Brands'], 'Date': ['2009', '2018']}
title: Revenue of Bloomin ' Brands , Inc. worldwide from 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['4.06', '4.21', '4.25', '4.38', '4.44', '4.13', '3.99', '3.84', '3.63', '3.6']

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

generated_template: templateTitle[0] templateTitle[1] templateYLabel[0] has templatePositiveTrend astronomically in the last five years alone – templatePositiveTrend from templateYValue[5] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[5] to templateYValue[max] templateScale in templateXValue[idxmax(Y)] . templateTitle[1] has become a popular pastime for templateYLabel[2] templateTitle[0] fans and a major source of templateYLabel[0] for the industry , though many traditional consumers lament the resulting decline of physical templateTitle[0] formats . Physical CD shipments have dwindled , whilst digital templateTitle[0] platforms are flourishing .
generated: Revenue Bloomin Revenue has increased astronomically in the last five years alone – rising from 4.13 billion U.S. dollars in 2013 to 4.44 billion in 2014 .  Bloomin has become a popular pastime for U.S. Revenue fans and a major source of Revenue for the industry , though many traditional consumers lament the resulting decline of physical Revenue formats .  Physical CD shipments have dwindled , whilst digital Revenue platforms are flourishing .

Example 124:
titleEntities: {'Subject': ['Autonomous'], 'Date': ['2015', '2030']}
title: Autonomous driving sensor components - global market size 2015 to 2030
X_Axis['Year']: ['2030', '2025', '2020', '2015']
Y_Axis['Market', 'size', 'in', 'billion', 'U.S.', 'dollars']: ['25.0', '15.0', '4.0', '0.4']

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

generated_template: 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] , templateYValue[2] templateScale of the households in the templateTitle[4] owned a templateTitle[2] camera .
generated: The statistic shows the Market size of sensor components in the global from 2015 to 2030 .  In 2020 , 4.0 billion of the households in the global owned a sensor camera .

Example 125:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: U.S. per capita consumption of oat products 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['4.8', '4.8', '4.6', '4.6', '4.5', '4.5', '4.7', '4.8', '4.7', '4.7', '4.7', '4.7', '4.7', '4.6', '4.7', '4.7', '4.5', '4.5', '4.4']

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

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

Example 126:
titleEntities: {'Subject': ['Samoa'], 'Date': ['2019']}
title: Unemployment rate in Samoa 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['8.46', '8.33', '8.32', '8.61', '8.62', '8.72', '8.8', '8.75', '5.68', '5.52', '5.35', '4.77', '4.75', '4.96', '5.14', '5.14', '5.15', '5.11', '4.96', '4.72', '4.43']

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

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

Example 127:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2003', '2016']}
title: Plywood and veneer production in the U.S. 2003 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Production', 'in', 'million', 'cubic', 'feet']: ['683', '693', '693', '703', '679', '653', '655', '616', '754', '898', '989', '1068', '1082', '1052']

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

generated_template: In templateXValue[max] , the templateTitle[3] of templateTitle[0] and calves in the templateTitleSubject[0] was approximately templateYValue[min] templateScale , a slight templateNegativeTrend from the previous templateXLabel[0] . This was the lowest templateYLabel[0] for the entire period shown in this graph . Despite a small rebound in templateXValue[4] and templateXValue[3] this constitutes a slow long-term decline of herd sizes .
generated: In 2016 , the U.S. of Plywood and calves in the U.S. was approximately 616 million , a slight decrease from the previous Year .  This was the lowest Production for the entire period shown in this graph .  Despite a small rebound in 2012 and 2013 this constitutes a slow long-term decline of herd sizes .

Example 128:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2018']}
title: Number of participants in sailing in the U.S. 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'participants', 'in', 'millions']: ['3.75', '3.97', '4.1', '4.1', '3.92', '3.92', '3.96', '3.73', '3.87', '4.34', '4.23', '3.79', '3.39']

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

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

Example 129:
titleEntities: {'Subject': ['France'], 'Date': ['2012', '2028']}
title: Domestic travel spending in France 2012 to 2028
X_Axis['Year']: ['2028', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Spending', 'in', 'billion', 'euros']: ['131.4', '114.9', '112.3', '110.9', '108.1', '108.3', '107.5', '107.2']

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

generated_template: This statistic presents the templateTitle[3] templateTitle[4] of templateTitle[0] and templateTitle[1] to templateTitle[5] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[1] , with an additional forecast for templateXValue[max] . Over this period , the templateTitle[4] of the templateTitle[0] and templateTitle[1] industry to templateTitle[5] in the templateTitleSubject[1] has templatePositiveTrend , reaching templateYValue[2] templateScale British pounds in templateXValue[2] .
generated: This statistic presents the France 2012 of Domestic and travel to 2028 in the France from 2012 to 2018 , with an additional forecast for 2028 .  Over this period , the 2012 of the Domestic and travel industry to 2028 in the France has increased , reaching 112.3 billion British pounds in 2017 .

Example 130:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1950', '2017']}
title: Deaths by unintentional drug overdose in the U.S. 1950 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1990', '1980', '1970', '1960', '1950']
Y_Axis['Death', 'rate', 'per', '100,000', 'population']: ['20.1', '18.2', '14.8', '13.1', '12.2', '11.5', '11.6', '10.6', '10.3', '10.2', '9.9', '9.2', '8.0', '7.2', '6.7', '6.1', '4.9', '4.5', '4.4', '3.9', '3.7', '3.5', '3.4', '2.3', '1.9', '2.8', '1.7', '2.5']

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

generated_template: This statistic shows the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] and templateTitle[7] for inmates on death row in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , an average of templateYValue[idxmax(X)] templateYLabel[2] templateTitle[4] templateTitle[5] templateTitle[6] and templateTitle[7] for inmates on death row . This is an templatePositiveTrend from templateTitleDate[min] , when an average of templateYValue[min] templateYLabel[2] passed templateTitle[5] templateTitle[6] and templateTitle[7] .
generated: This statistic shows the unintentional drug overdose U.S. 1950 and 2017 for inmates on death row in the Deaths from 1950 to 2017 .  In 2017 , an average of 20.1 per overdose U.S. 1950 and 2017 for inmates on death row .  This is an increase from 1950 , when an average of 1.7 per passed U.S. 1950 and 2017 .

Example 131:
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 templatePositiveTrend , templatePositiveTrend 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] templateNegativeTrend 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 , 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 fell noticeably between 2014 S2 and the first half of 2015 S2 , with households paying less than 10.48 Euro cents per kWh .

Example 132:
titleEntities: {'Subject': ['Luxembourg'], 'Date': ['2010', '2019']}
title: Electricity prices for households in Luxembourg 2010 - 2019 , semi-annually
X_Axis['Year']: ['2019_S1', '2018_S2', '2018_S1', '2017_S2', '2017_S1', '2016_S2', '2016_S1', '2015_S2', '2015_S1', '2014_S2', '2014_S1', '2013_S2', '2013_S1', '2012_S2', '2012_S1', '2011_S2', '2011_S1', '2010_S2', '2010_S1']
Y_Axis['Euro', 'cents', 'per', 'kilowatt-hour']: ['17.98', '16.91', '16.71', '16.18', '16.15', '16.98', '16.98', '17.67', '17.67', '17.38', '17.38', '16.46', '16.65', '17.06', '16.96', '16.62', '16.78', '17.47', '17.26']

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

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] household end users in templateTitleSubject[0] templateTitle[7] from templateXValue[17] to templateXValue[0] . In the second half of templateXValue[1] , the average templateTitle[0] price templateTitle[2] templateTitle[3] was templateYValue[max] templateYLabel[0] templateYLabel[1] templateYLabel[2] kWh . This was an templatePositiveTrend from the previous period .
generated: This statistic shows the Electricity prices for household end users in Luxembourg semi-annually from 2010 S2 to 2019 S1 .  In the second half of 2018 S2 , the average Electricity price for households was 17.98 Euro cents per kWh .  This was an increase from the previous period .

Example 133:
titleEntities: {'Subject': ['Switzerland'], 'Date': ['2006', '2018']}
title: Number of road deaths in Switzerland 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'fatalities']: ['233', '230', '216', '253', '243', '269', '339', '320', '327', '349', '357', '384', '370']

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

generated_template: There were templateYValue[0] reported templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateXValue[max] . This was a slight templatePositiveTrend compared to the previous templateXLabel[0] , but an templatePositiveTrend of 14 incidents compared to the low reported in templateXValue[4] . The Northern European island state is known for enforcing a strict templateTitle[1] safety policy in order to ensure the security of its residents and tourists in the country .
generated: There were 233 reported road deaths in Switzerland 2018 .  This was a slight increase compared to the previous Year , but an increase of 14 incidents compared to the low reported in 2014 .  The Northern European island state is known for enforcing a strict road safety policy in order to ensure the security of its residents and tourists in the country .

Example 134:
titleEntities: {'Subject': ['BNP Paribas'], 'Date': ['2003', '2018']}
title: Return on equity of BNP Paribas 2003 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Return', 'on', 'equity']: ['8.2', '8.9', '9.3', '8.3', '7.7', '6.1', '8.9', '8.8', '12.3', '10.8', '6.6', '19.6', '21.2', '20.2', '16.8', '15.3']

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

generated_template: The statistic shows the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] due to earthquakes templateTitle[4] templateXValue[min] to templateXValue[max] . Around templateYValue[idxmax(X)] 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 135:
titleEntities: {'Subject': ['Slovakia'], 'Date': ['2024']}
title: Inflation rate in Slovakia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2.01', '1.97', '1.97', '2.09', '2.06', '2.6', '2.53', '1.39', '-0.47', '-0.33', '-0.1', '1.46', '3.73', '4.07', '0.7', '0.94', '3.95', '1.9', '4.27', '2.78', '7.45', '8.42', '3.49', '7.14', '12.16', '10.46', '6.69', '6.03', '5.78', '9.9', '13.46']

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

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Slovakia from 1994 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Slovakia amounted to about 2.53 % compared to the previous Year .

Example 136:
titleEntities: {'Subject': ['Ethiopia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Ethiopia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['1411.77', '1328.18', '1248.69', '1161.57', '1066.2', '953.18', '852.88', '817.49', '777.29', '702.77', '613.1', '535.26', '493.32', '379.38', '360.83', '397.81', '334.15', '249.21', '198.34', '165.22', '138.55', '120.87', '116.79', '125.72', '129.68', '128.21', '134.23', '152.17', '155.12', '152.66', '151.97', '176.47', '293.35', '287.65', '269.08', '262.23', '257.56', '256.7', '247.98', '246.51', '217.42']

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

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

Example 137:
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] of templateTitle[5] templateTitle[6] in the templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] .
generated: This statistic shows the Average price acrylic a fill nail of salons U.S. in the 2006 from to 2019 .  In 2019 , the Average price U.S. of fill nail salons in the U.S. amounted to 29.91 dollars .

Example 138:
titleEntities: {'Subject': ['Italian'], 'Date': ['2011', '2018']}
title: Turnover of Italian fashion company Giorgio Armani 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Turnover', 'in', 'million', 'euros']: ['1360', '1515', '1676', '1702', '1644', '1650', '1584', '1275']

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

generated_template: The templateYLabel[0] of the templateTitleSubject[0] luxury brand templateTitle[4] templateTitle[5] has templatePositiveTrend twofold over the period surveyed , templatePositiveTrend from roughly templateYValue[min] templateScale templateYLabel[2] in templateXValue[idxmin(Y)] to templateYValue[max] templateScale templateYLabel[2] in the templateXLabel[0] templateXValue[idxmax(Y)] . Despite the steady templatePositiveTrend in templateYLabel[0] during the period considered , the templateTitle[3] reported a net loss of approximately 25 templateScale templateYLabel[2] in templateXValue[max] . Worldwide recognition Founded in 1978 in Milan , templateTitle[4] templateTitle[5] is one of the leading international templateTitle[2] design houses .
generated: The Turnover of the Italian luxury brand Giorgio Armani has increased twofold over the period surveyed , rising from roughly 1275 million euros in 2011 to 1702 million euros in the Year 2015 .  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 Milan , Giorgio Armani is one of the leading international fashion design houses .

Example 139:
titleEntities: {'Subject': ['Vinci Group revenue'], 'Date': ['2009', '2018']}
title: Vinci Group - revenue 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['43.52', '40.25', '38.07', '38.5', '38.7', '40.34', '38.63', '36.96', '33.38', '30.74']

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

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

Example 140:
titleEntities: {'Subject': ['Portugal'], 'Date': ['2012', '2028']}
title: International tourism spending in Portugal 2012 to 2028
X_Axis['Year']: ['2028', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Spending', 'in', 'billion', 'euros']: ['26.4', '19.4', '18.1', '15.6', '14.5', '13.9', '12.8', '12.2']

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

generated_template: This statistic presents the templateTitle[3] templateTitle[4] of templateTitle[0] and templateTitle[1] to templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[1] , with a forecast for templateXValue[max] . Over this period , the templateTitle[4] of the templateTitle[0] and templateTitle[1] industry to templateTitle[5] in templateTitleSubject[0] templatePositiveTrend , reaching around templateYValue[2] templateScale templateYLabel[2] in templateXValue[2] .
generated: This statistic presents the Portugal 2012 of International and tourism to 2028 in Portugal from 2012 to 2018 , with a forecast for 2028 .  Over this period , the 2012 of the International and tourism industry to 2028 in Portugal increased , reaching around 18.1 billion euros in 2017 .

Example 141:
titleEntities: {'Subject': ['Johnson Johnson'], 'Date': ['2013']}
title: Johnson & Johnson 's share of the skin care products market worldwide 2013 to 2021
X_Axis['Year']: ['2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021']
Y_Axis['Market', 'share']: ['4.3', '4.2', '4.15', '4.1', '4.05', '3.95', '3.85', '3.75', '3.65']

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

generated_template: This statistic shows templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateYLabel[0] templateYLabel[1] of the over-the-counter and templateTitle[8] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[1] , templateTitleSubject[0] templateTitle[1] templateTitleSubject[0] templateTitle[3] templateYLabel[0] templateYLabel[1] of the templateTitle[6] over-the-counter and templateTitle[8] templateYLabel[0] is estimated to be templateYValue[6] templateScale .
generated: This statistic shows Johnson Johnson & 's Market share of the over-the-counter and market worldwide from 2013 to 2021 .  In 2014 , Johnson Johnson & 's Market share of the care over-the-counter and market is estimated to be 3.85 % .

Example 142:
titleEntities: {'Subject': ['Jamaica'], 'Date': ['2018']}
title: Urbanization in Jamaica 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['55.67', '55.38', '55.1', '54.83', '54.58', '54.35', '54.13', '53.93', '53.74', '53.56', '53.37']

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

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

Example 143:
titleEntities: {'Subject': ['Green Bay Packers', 'NFL'], 'Date': ['2001', '2018']}
title: Revenue of the Green Bay Packers ( NFL ) 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['456', '434', '421', '391', '347', '299', '282', '276', '259', '242', '232', '218', '197', '194', '189', '138', '152', '132']

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

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the 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 U.S. dollars .

Example 144:
titleEntities: {'Subject': ['NFL'], 'Date': ['2001', '2018']}
title: Revenue of the Baltimore Ravens ( NFL ) 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['438', '417', '403', '378', '345', '304', '292', '279', '262', '255', '240', '226', '205', '201', '192', '172', '155', '148']

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

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] Chargers , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] Chargers was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the NFL Chargers , a franchise of the National Football League , from 2001 to 2018 .  In 2018 , the Revenue of the NFL Chargers was 438 U.S. dollars .

Example 145:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2008', '2018']}
title: Gross domestic product ( GDP ) at current prices in Denmark 2008 to 2018
X_Axis['Year']: ['2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['GDP', 'in', 'million', 'Danish', 'kroner']: ['1801470', '1722143', '1810926', '1846854', '1895002', '1929677', '1981165', '2036356', '2107808', '2175106', '2245954']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] had reached about templateYValue[idxmax(X)] years.Demographic development in templateTitleSubject[0] – additional information templateYLabel[0] templateYLabel[1] at templateYLabel[2] refers to the average number of years a group of people born in the same templateXLabel[0] would live , assuming constant mortality rates . The country with the highest templateYLabel[0] templateYLabel[1] at templateYLabel[2] was Japan , while templateTitleSubject[0] had reached a templateYLabel[0] templateYLabel[1] above global average .
generated: The statistic shows the GDP million at Danish in Denmark from 2008 to 2018 .  In 2018 , the average GDP million at Danish in Denmark had reached about 2245954 years.Demographic development in Denmark – additional information GDP million at Danish 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 GDP million at Danish was Japan , while Denmark had reached a GDP million above global average .

Example 146:
titleEntities: {'Subject': ['Malawi'], 'Date': ['2000']}
title: Malawi : internet penetration 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'individuals', 'using', 'the', 'internet']: ['13.78', '11.47', '5.3', '5.83', '5.05', '4.35', '3.33', '2.26', '1.07', '0.7', '0.97', '0.43', '0.38', '0.35', '0.28', '0.22', '0.16', '0.13']

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

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

Example 147:
titleEntities: {'Subject': ['South Sudan'], 'Date': ['2018']}
title: Infant mortality rate in South Sudan 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Deaths', 'per', '1,000', 'live', 'births']: ['63.7', '63.7', '63.7', '63.7', '63.7', '64.0', '64.9', '66.9', '69.7', '72.8', '76.1']

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

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

Example 148:
titleEntities: {'Subject': ['Global'], 'Date': ['2007', '2019']}
title: Global mine production of asbestos 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Production', 'in', 'thousand', 'metric', 'tons']: ['1100', '1150', '1170', '1280', '2000', '2020', '2020', '1970', '2030', '2010', '2070', '2090', '2200']

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

generated_template: This statistic represents the templateTitle[1] templateTitle[2] templateYLabel[0] in the templateTitle[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , approximately templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[1] products were sold in the templateTitle[0] . Building materials in the templateTitle[0] : templateTitle[1] After its tremendous downturn following the templateXValue[11] subprime mortgage crisis , the housing industry in the templateTitle[0] is making a comeback , especially in markets like New York , Dallas and Houston .
generated: This statistic represents the mine production in the Global between 2007 and 2019 .  In 2019 , approximately 1100 thousand metric tons of mine products were sold in the Global .  Building materials in the Global : mine After its tremendous downturn following the 2008 subprime mortgage crisis , the housing industry in the Global is making a comeback , especially in markets like New York , Dallas and Houston .

Example 149:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: Book readers in the U.S. 2019 , by age group
X_Axis['Year']: ['18-29', '30-49', '50-64', '65+']
Y_Axis['Share', 'of', 'respondents']: ['81', '72', '67', '68']

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

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

Example 150:
titleEntities: {'Subject': ['SAP'], 'Date': ['2006', '2018']}
title: SAP 's net profit 2006 to 2018
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Net', 'profit', 'in', 'million', 'euros']: ['3321', '4083', '4008', '3646', '3064', '3280', '3326', '2803', '3437', '1813', '1750', '1847', '1906', '1871']

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

generated_template: The median templateYLabel[0] earnings for templateTitle[0] templateTitle[1] templatePositiveTrend by 3.77 pounds between templateXValue[min] and templateXValue[max] , reaching templateYValue[idxmax(X)] 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] , employers are expected to templateYLabel[1] their templateTitle[1] a minimum templateTitle[4] that is determined by how old they are .
generated: The median Net earnings for SAP 's increased by 3.77 pounds between 2006 and 2019 , reaching 3321 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 2006 in the SAP In the SAP , employers are expected to profit their 's a minimum 2006 that is determined by how old they are .

Example 151:
titleEntities: {'Subject': ['Texas'], 'Date': ['2000', '2018']}
title: Texas - poverty rate 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'population']: ['14.9', '14.7', '15.6', '15.9', '17.2', '17.5', '17.9', '18.5', '17.9', '17.2', '15.8', '16.3', '16.9', '17.6', '16.6', '16.3', '15.6', '15', '15.1']

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

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

Example 152:
titleEntities: {'Subject': ['Taiwan'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Taiwan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['31483.8', '29870.22', '28324.43', '26861.07', '25525.81', '24827.9', '25007.75', '24389.68', '22572.7', '22373.56', '22638.92', '21887.99', '21269.61', '20911.64', '19261.67', '16959.78', '18102.95', '17780.93', '16984.54', '16503.31', '15360.72', '14094.37', '13715.53', '13408.38', '14876.88', '13768.27', '12787.26', '13968.1', '13597.25', '13706.01', '12108.75', '11200.81', '10725.7', '9092.11', '8178.15', '7577.05', '6337.5', '5325.22', '4010.11', '3295.11', '3203.47']

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

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

Example 153:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1992', '2017']}
title: Luggage and leather goods store sales in the U.S. from 1992 to 2017
X_Axis['Year']: ['17', '16', '15', '14', '13', '12', '11', '10', '9', '8', '7', '6', '5', '4', '3', '2', '1', '0', '99', '98', '97', '96', '95', '94', '93', '92']
Y_Axis['Billion', 'U.S.', 'dollars']: ['33.92', '33.97', '34.14', '33.83', '33.21', '32.32', '31.25', '27.78', '27.46', '30.91', '32.77', '31.96', '30.13', '29.28', '27.17', '26.27', '25.2', '26.7', '25.38', '22.79', '21.12', '21.63', '20.28', '19.02', '17.59', '16.12']

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

generated_template: This timeline shows templateTitle[0] , templateTitle[1] , and templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[1] templateTitle[0] , templateTitle[1] , and templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[0] templateScale 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 templateNegativeTrend 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 33.92 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 fell 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 154:
titleEntities: {'Subject': ['Italy'], 'Date': ['2011', '2018']}
title: Italy : number of banks 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Number', 'of', 'banks']: ['505', '538', '604', '643', '664', '684', '706', '740']

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

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

Example 155:
titleEntities: {'Subject': ['Trade'], 'Date': ['1950', '2018']}
title: Trade : export volume worldwide 1950 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960', '1955', '1950']
Y_Axis['Export', 'volume', 'in', 'billion', 'U.S.', 'dollars']: ['19453.36', '17731.08', '16021.98', '16539.16', '18986.54', '18950.64', '18513.19', '18339.81', '15302.68', '12556.2', '16149.3', '14021.16', '12128.03', '10502.74', '6452.32', '5176.2', '3495.69', '1964.84', '2049.41', '876.89', '318.02', '189.62', '130.09', '93.92', '61.81']

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

generated_template: templateYLabel[0] templateYLabel[1] templateYLabel[2] is a measure of the difference between the templateYLabel[1] income generated by templateTitle[3] or other financial institutions and the amount of templateYLabel[1] paid out to their lenders relative to the amount of their ( interest-earning ) assets . It is similar to the gross templateYLabel[2] of non-financial companies . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitleSubject[0] templateTitle[3] amounted to templateYValue[idxmax(X)] templateScale .
generated: Export volume billion is a measure of the difference between the volume income generated by worldwide or other financial institutions and the amount of volume paid out to their lenders relative to the amount of their ( interest-earning ) assets .  It is similar to the gross billion of non-financial companies .  In 2018 , the average Export volume billion of the Trade worldwide amounted to 19453.36 billion .

Example 156:
titleEntities: {'Subject': ['Research'], 'Date': ['2010', '2019']}
title: Research and development spending of 3M from 2010 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Spending', 'in', 'million', 'U.S.', 'dollars']: ['1911', '1821', '1870', '1735', '1763', '1770', '1715', '1634', '1570', '1434']

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

generated_template: The statistic presents the templateTitle[3] templateYLabel[0] of templateTitleSubject[0] Holdings from templateTitle[5] years templateXValue[min] to templateXValue[max] . In the last templateTitle[5] templateXLabel[0] that ended 31 , templateXValue[max] , templateTitleSubject[0] reported a profit of templateYValue[0] templateYValue[idxmax(X)] templateYValue[idxmax(X)] templateYLabel[3] .
generated: The statistic presents the 3M Spending of Research Holdings from 2010 years to 2019 .  In the last 2010 Year that ended 31 , 2019 Research reported a profit of 1911 dollars .

Example 157:
titleEntities: {'Subject': ['Nepal'], 'Date': ['2017']}
title: Fertility rate in Nepal 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'children', 'born', 'per', 'woman']: ['1.97', '2.03', '2.1', '2.18', '2.26', '2.35', '2.44', '2.54', '2.64', '2.75', '2.87']

gold: This statistic shows the fertility rate of Nepal from 2007 to 2017 . The fertility rate is the average number of children a woman will have during her child-bearing years . In 2017 , the fertility rate in Nepal was 1.97 children per woman .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] a templateYLabel[4] will have during her child-bearing years . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] by one templateYLabel[4] while being of child-bearing age . templateTitleSubject[0] includes almost all countries south of the Sahara desert .
generated: This statistic shows the Fertility rate in Nepal from 2007 to 2017 .  The Fertility rate is the average Number of children born by one woman while being of child-bearing age .  Nepal includes almost all countries south of the Sahara desert .

Example 158:
titleEntities: {'Subject': ['earth', 'gadolinium'], 'Date': ['2009']}
title: Forecast of rare earth oxide gadolinium oxide price globally 2009 to 2025
X_Axis['Year']: ['2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022', '2023', '2024', '2025']
Y_Axis['Price', 'in', 'U.S.', 'dollars', 'per', 'metric', 'ton']: ['10707', '62724', '24167', '24033', '25485', '26046', '26447', '28055', '28473', '23916', '19700', '19700', '19982', '20603', '21907', '23437']

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

generated_template: This statistic displays the templateYLabel[0] of rare earth templateTitle[2] templateTitle[1] templateTitle[2] templateTitle[4] from templateXValue[min] to templateXValue[max] . It is expected that the templateYLabel[0] of templateTitle[1] templateTitle[2] will reach some templateYValue[8] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[8] .
generated: This statistic displays the Price of rare earth gadolinium from 2010 to 2025 .  It is expected that the Price of rare earth will reach some 28473 U.S. dollars per metric ton in 2018 .

Example 159:
titleEntities: {'Subject': ['Illinois'], 'Date': ['1990', '2018']}
title: Illinois - median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['70145', '64609', '61386', '60413', '54916', '57196', '51738', '50637', '50728', '52870', '53254', '52506', '48671', '48398', '46077', '45153', '42710', '46171', '46064', '46330', '43178', '41283', '39554', '38071', '35081', '32857', '31551', '31884', '32542']

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

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: 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 160:
titleEntities: {'Subject': ['Ireland'], 'Date': ['2018']}
title: Urbanization in Ireland 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['63.17', '62.95', '62.74', '62.54', '62.34', '62.14', '61.94', '61.74', '61.54', '61.34', '61.14']

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

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

Example 161:
titleEntities: {'Subject': ['Singapore'], 'Date': ['2017', '2023']}
title: Singapore : mobile phone internet users 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Number', 'of', 'mobile', 'phone', 'internet', 'users', 'in', 'millions']: ['4.9', '4.9', '4.8', '4.7', '4.6', '4.5', '4.3']

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

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

Example 162:
titleEntities: {'Subject': ['India'], 'Date': ['2024']}
title: National debt of India in relation to gross domestic product ( GDP ) 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'to', 'GDP', 'ratio']: ['65.61', '66.19', '66.93', '67.75', '68.52', '69.04', '68.05', '67.83', '67.67', '68.78', '66.83']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to about templateYValue[min] templateScale of templateTitle[4] templateTitle[5] templateTitle[6] .
generated: The statistic shows the National debt in India from 2014 to 2018 in relation to gross domestic product ( GDP ) , with projections up until 2024 .  In 2018 , the National debt of India amounted to about 65.61 % of gross domestic product .

Example 163:
titleEntities: {'Subject': ['Pakistan'], 'Date': ['2024']}
title: National debt of Pakistan in relation to gross domestic product ( GDP ) 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'in', 'relation', 'to', 'GDP']: ['65.41', '68.98', '72.48', '76.11', '78.65', '76.73', '71.69', '67.05', '67.63', '63.32', '63.47']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[7] in templateYLabel[2] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[3] ) , with projections up until templateXValue[max] . In templateXValue[7] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[max] templateScale of the templateYLabel[3] .
generated: The statistic shows the National debt of Pakistan from 2014 to 2017 in relation to the gross domestic product ( GDP ) , with projections up until 2024 .  In 2017 , Pakistan 's National debt amounted to approximately 78.65 % of the GDP .

Example 164:
titleEntities: {'Subject': ['Ukraine'], 'Date': ['2010', '2024']}
title: National debt of Ukraine in relation to gross domestic product 2010 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['National', 'debt', 'as', 'percent', 'of', 'GDP']: ['43.72', '46.24', '49.77', '53.83', '57.89', '62.03', '63.86', '71.92', '81.18', '79.5', '70.32', '40.52', '37.54', '36.88', '40.63']

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

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

Example 165:
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 166:
titleEntities: {'Subject': ['Growth'], 'Date': ['2008', '2012']}
title: Growth of crowdfunding platforms worldwide 2008 to 2012
X_Axis['Year']: ['2012', '2011', '2010', '2009', '2008']
Y_Axis['Growth', 'in', 'the', 'number', 'of', 'CFPs']: ['60', '54', '47', '45', '38']

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

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateTitle[1] a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateYValue[2] templateTitle[7] in the templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] templateYLabel[1] had attended a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateXLabel[0] .
generated: This statistic presents the Growth of number crowdfunding a platforms worldwide 2008 at least once in the 2012 47 in the 2012 from 2008 to 2012 .  In 2012 , 60 % of Growth number had attended a platforms worldwide 2008 at least once in the 2012 Year .

Example 167:
titleEntities: {'Subject': ['Fresh'], 'Date': ['1990', '2018']}
title: Fresh fruit production worldwide 1990 to 2018
X_Axis['Year']: ['1990', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['Production', 'in', 'million', 'metric', 'tons']: ['401.61', '576.65', '591.63', '609.53', '621.92', '645.87', '661.62', '687.42', '702.61', '718.82', '736.13', '740.54', '766.21', '778.94', '810.44', '822.81', '833.74', '835.34', '842.84', '868.09']

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

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

Example 168:
titleEntities: {'Subject': ['Germany'], 'Date': ['2017']}
title: Life expectancy at birth in Germany 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Life', 'expectancy', 'at', 'birth', 'in', 'years']: ['80.99', '80.99', '80.64', '81.09', '80.49', '80.54', '80.44', '79.99', '79.84', '79.74', '79.53']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] in templateXValue[max] was templateYValue[idxmax(X)] templateYLabel[3] . Standard of living in templateTitleSubject[0] is one of the so-called BRIC countries , an acronym which stands for Brazil , Russia , templateTitleSubject[0] and China , the four states considered the major emerging market countries .
generated: The statistic shows the Life expectancy at birth in Germany from 2007 to 2017 .  The average Life expectancy at birth in Germany 2017 was 80.99 years .  Standard of living in Germany is one of the so-called BRIC countries , an acronym which stands for Brazil , Russia Germany and China , the four states considered the major emerging market countries .

Example 169:
titleEntities: {'Subject': ['Illinois'], 'Date': ['1960', '2018']}
title: Population density in Illinois 1960 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2010', '2000', '1990', '1980', '1970', '1960']
Y_Axis['Number', 'of', 'residents', 'per', 'square', 'mile']: ['229.5', '230.6', '230.6', '231.6', '232.0', '232.0', '231.9', '231.1', '223.7', '205.9', '205.8', '200.1', '181.3']

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

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

Example 170:
titleEntities: {'Subject': ['European Union'], 'Date': ['2009', '2018']}
title: Number of textile and clothing manufacturers in the European Union ( EU28 ) 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'companies']: ['171072', '176354', '177684', '174862', '171437', '174254', '180200', '186101', '191459', '188464']

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

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of templateTitleSubject[0] amounted to approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic presents the Number companies of the European Union of from 2009 to 2018 .  In 2018 , the Number companies of the European Union of amounted to approximately 171072 million companies .

Example 171:
titleEntities: {'Subject': ['French'], 'Date': ['2010', '2017']}
title: French households savings rate 2010 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Savings', 'rate']: ['14.6', '14', '14.5', '14.4', '14.3', '15.1', '15.6', '15.8']

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

generated_template: In templateXValue[max] , the templateTitle[1] templateYLabel[0] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] amounted to about templateYValue[idxmax(X)] 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 templateNegativeTrend to below templateYValue[0] templateScale 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 fell 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 172:
titleEntities: {'Subject': ['Philadelphia', 'NBA'], 'Date': ['2003', '2020']}
title: Franchise value of the Philadelphia 76ers ( NBA ) 2003 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['2000', '1650', '1180', '800', '700', '700', '469', '418', '314', '330', '344', '360', '380', '373', '351', '342', '328', '298']

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

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] franchise of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale 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 173:
titleEntities: {'Subject': ['Guatemala'], 'Date': ['2024']}
title: Inflation rate in Guatemala 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['4.33', '4.23', '4.18', '4.15', '4.18', '4.21', '3.75', '4.43', '4.45', '2.39', '3.42', '4.34', '3.78', '6.22', '3.86', '1.86', '11.36', '6.82', '6.56', '9.11', '7.58', '5.6', '8.14', '7.28', '5.98', '5.21', '6.62', '9.24', '11.06', '8.41', '12.51', '13.36', '10.24', '35.11', '37.98', '12.99', '10.3', '10.85', '32.78', '19.18', '3.21']

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

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

Example 174:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Change in U.S. exports of trade goods 1990 to 2018
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Percentage', 'of', 'change']: ['-1.3', '7.8', '6.6', '-3.6', '-7.6', '2.6', '2', '4.3', '16.2', '20.5', '-18.2', '12.3', '11.9', '14', '10.9', '12.8', '4.6', '-4.6', '-6.8', '12.4', '4.2', '-1.2', '10.8', '6.4', '14.4', '10', '3.9', '6.2', '6.9', '7.6']

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

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

Example 175:
titleEntities: {'Subject': ['China'], 'Date': ['2016']}
title: Consumption of cement in China 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Consumption', 'in', 'thousand', 'metric', 'tons']: ['2395070', '2339000', '2454081', '2206000', '2160000', '2055224', '1872921', '1642216', '1375720', '1345338', '1218128']

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

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

Example 176:
titleEntities: {'Subject': ['Senegal'], 'Date': ['2019']}
title: Unemployment rate in Senegal 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['6.52', '6.46', '6.44', '6.61', '6.76', '7.63', '8.51', '9.31', '10.36', '10.54', '10.36', '9.32', '9.5', '10.03', '9.09', '7.96', '6.78', '5.65', '5.61', '5.6', '5.7']

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

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

Example 177:
titleEntities: {'Subject': ['Canada'], 'Date': ['2024']}
title: Canada 's share of global gross domestic product ( GDP ) 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Share', 'of', 'global', 'GDP']: ['1.23', '1.25', '1.27', '1.3', '1.32', '1.34', '1.36', '1.38', '1.39', '1.42', '1.46']

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

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] of the templateYLabel[1] templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) adjusted for Purchasing Power Parity ( PPP ) from templateXValue[min] to templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] of templateYLabel[2] ( in relation to PPP dollars ) was about templateYValue[6] templateScale . For further information , see templateTitleSubject[0] templateTitle[1] templateYLabel[2] .
generated: The statistic shows Canada 's Share of the global gross domestic product ( GDP ) adjusted for Purchasing Power Parity ( PPP ) from 2014 to 2024 .  In 2018 , Canada 's Share of GDP ( in relation to PPP dollars ) was about 1.36 % .  For further information , see Canada 's GDP .

Example 178:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2008', '2018']}
title: Poverty risk rate in Belgium 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['At-risk-of-poverty', 'rate']: ['16.4', '15.9', '15.5', '14.9', '15.5', '15.1', '15.3', '15.3', '14.6', '14.6', '14.7']

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

generated_template: After reaching a record high in templateXValue[2] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] templateNegativeTrend to around templateYValue[0] templateScale templateYLabel[3] in templateXValue[max] . A country 's templateYLabel[0] templateYLabel[1] is defined as the total number of employable templateYLabel[3] and incorporates both the employed and the unemployed population . The share of the population participating in the templateYLabel[0] market in templateTitleSubject[0] also templateNegativeTrend over the past years , reaching about 68 templateScale in templateXValue[max] .
generated: After reaching a record high in 2016 , Belgium 's At-risk-of-poverty rate fell to around 16.4 % rate in 2018 .  A country 's At-risk-of-poverty rate is defined as the total number of employable rate and incorporates both the employed and the unemployed population .  The share of the population participating in the At-risk-of-poverty market in Belgium also decreased over the past years , reaching about 68 % in 2018 .

Example 179:
titleEntities: {'Subject': ['Capital'], 'Date': ['2000']}
title: Capital spending in the semiconductor industry worldwide 2000 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Spending', 'in', 'billion', 'U.S.', 'dollars']: ['94.3', '97.8', '105.9', '95.6', '67.8', '65.2', '66.1', '55.3', '59.0', '67.4', '54.0', '26.1', '43.4', '61.0', '57.5', '48.1', '47.7', '31.3', '27.5', '38.7', '61.3']

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

generated_template: The statistic shows the total templateTitleSubject[0] energy-related templateYLabel[4] templateYLabel[5] templateYLabel[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , around templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] of CO2 templateYLabel[0] were produced from energy consumption in the templateTitle[0] . In templateXValue[3] , around 34.8 templateScale templateYLabel[2] templateYLabel[3] of templateYLabel[4] templateYLabel[5] was emitted globally .
generated: The statistic shows the total Capital energy-related dollars Spending between 2000 and 2020 .  In 2020 , around 94.3 billion U.S. dollars of CO2 Spending were produced from energy consumption in the Capital .  In 2017 , around 34.8 billion U.S. dollars of was emitted globally .

Example 180:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019', '2050']}
title: Carbon dioxide emissions per person in the U.S. 2019 to 2050
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2019']
Y_Axis['Emissions', 'in', 'metric', 'tons', 'of', 'CO2', 'equivalent/', 'person']: ['12.6', '12.5', '12.6', '12.8', '13.1', '13.7', '15.0', '15.5']

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

generated_template: The statistic shows the templateYLabel[0] forecast of the templateTitle[3] templateTitle[1] templateTitle[2] sector in the United Kingdom ( templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . The estimated templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[1] templateTitle[2] sector in templateXValue[max] is templateYValue[idxmax(X)] templateScale British pounds ( templateYLabel[3] ) . templateTitle[1] templateTitle[2] exists because the threats and costs are great enough to warrant these measure .
generated: The statistic shows the Emissions forecast of the per dioxide emissions sector in the United Kingdom ( U.S. ) from 2019 to 2050 .  The estimated Emissions metric of the per dioxide emissions sector in 2050 is 12.6 billion British pounds ( CO2 ) .  dioxide emissions exists because the threats and costs are great enough to warrant these measure .

Example 181:
titleEntities: {'Subject': ['North America'], 'Date': ['2011', '2016']}
title: Online gaming traffic in North America 2011 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Data', 'volume', 'in', 'petabytes']: ['96', '62', '44', '37', '29', '22']

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

generated_template: This graph shows templateTitleSubject[0] templateTitle[1] templateYLabel[2] as a templateYLabel[0] of templateYLabel[1] templateTitle[5] templateYLabel[2] from templateXValue[min] to templateXValue[max] . templateTitle[1] systems have vastly improved vehicle safety over the evolution of the templateTitle[5] , such as airbags and anti-lock braking systems . In templateXValue[max] , it is forecasted that on average , electronic systems will account for half of the templateYLabel[1] price of a new templateTitle[5] .
generated: This graph shows North America gaming petabytes as a Data of volume 2011 petabytes from 2011 to 2016 .  gaming systems have vastly improved vehicle safety over the evolution of the 2011 , such as airbags and anti-lock braking systems .  In 2016 , it is forecasted that on average , electronic systems will account for half of the volume price of a new 2011 .

Example 182:
titleEntities: {'Subject': ['Jordan'], 'Date': ['2019']}
title: Youth unemployment rate in Jordan in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['36.68', '37.24', '34.86', '35.63', '30.9', '28.43', '29.87', '28.84', '30.03', '28.88', '29.12', '28.51', '29.04', '30.45', '31.75', '31.37', '31.06', '32.62', '31.08', '29.4', '29.65']

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

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

Example 183:
titleEntities: {'Subject': ['NCAA'], 'Date': ['2013']}
title: NCAA college basketball tournament TV/television revenue 2013
X_Axis['Year']: ['2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984', '1983', '1982', '1981', '1980']
Y_Axis['Television', 'revenue', '(in', 'million', 'U.S.', 'dollars)']: ['684.3', '670.0', '653.0', '633.8', '571.0', '543.8', '503.0', '453.0', '432.0', '400.0', '370.04', '272.78', '242.1', '227.7', '213.8', '200.9', '188.4', '178.3', '166.2', '137.06', '129.06', '120.06', '112.44', '63.51', '57.16', '57.79', '36.64', '33.03', '28.33', '20.14', '16.88', '14.63', '10.32', '8.86']

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

generated_template: In templateXValue[max] , a total of templateTitle[2] templateTitle[3] to the templateTitle[0] amounted to about templateYValue[0] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] ; a significant templatePositiveTrend templateTitle[4] templateXValue[min] levels , when templateTitle[1] templateTitle[4] templateTitleSubject[0] amounted to about templateYValue[idxmin(X)] templateScale templateYLabel[3] templateYLabel[4] . templateYLabel[3] exports to templateTitleSubject[0] Compared to templateYLabel[3] templateTitle[1] templateTitle[4] templateTitleSubject[0] , the value of templateYLabel[3] exports to templateTitleSubject[0] in templateXValue[max] amounted to 106.63 templateScale templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] is the templateTitle[0] ' largest trading partner , while templateTitleSubject[0] was the templateTitle[0] third largest templateTitle[3] export market .
generated: In 2013 , a total of basketball tournament to the NCAA amounted to about 684.3 million U.S. ; a significant increased TV/television 1980 levels , when college TV/television NCAA amounted to about 8.86 million U.S. million exports to NCAA Compared to million college TV/television NCAA , the value of million exports to NCAA in 2013 amounted to 106.63 million U.S. NCAA is the NCAA ' largest trading partner , while NCAA was the NCAA third largest tournament export market .

Example 184:
titleEntities: {'Subject': ['Fiji'], 'Date': ['2019']}
title: Unemployment rate in Fiji 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['4.15', '4.15', '4.14', '4.32', '4.55', '4.53', '4.36', '4.52', '4.41', '4.35', '4.16', '3.62', '3.76', '3.83', '3.89', '4.06', '4.07', '4.16', '4.15', '4.15', '4.12']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Fiji from 1999 to 2019 .  In 2019 , the Unemployment rate in Fiji was 4.15 % .

Example 185:
titleEntities: {'Subject': ['Connecticut'], 'Date': ['2000', '2018']}
title: Connecticut - Poverty rate 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'population']: ['10.4', '9.6', '9.8', '10.5', '10.8', '10.7', '10.7', '10.9', '10.1', '9.4', '9.3', '7.9', '8.3', '8.3', '7.6', '8.1', '7.5', '7.3', '7.7']

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

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

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

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

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

Example 187:
titleEntities: {'Subject': ['Newmont Mining'], 'Date': ['2007', '2018']}
title: Newmont Mining 's revenue 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['7253', '7379', '6680', '6085', '6819', '7891', '9297', '10441', '9610', '7705', '6124', '5465']

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

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

Example 188:
titleEntities: {'Subject': ['Brazil'], 'Date': []}
title: Fertility rate in Brazil
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'children', 'born', 'per', 'woman']: ['1.74', '1.75', '1.75', '1.76', '1.76', '1.77', '1.78', '1.8', '1.82', '1.85', '1.88']

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

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] to one templateYLabel[4] while being of child-bearing age . templateTitleSubject[0] includes almost all countries south of the Sahara desert .
generated: This statistic shows the Fertility rate in Brazil from 2007 to 2017 .  The Fertility rate is the average Number of children born to one woman while being of child-bearing age .  Brazil includes almost all countries south of the Sahara desert .

Example 189:
titleEntities: {'Subject': ['Pennsylvania'], 'Date': ['1992', '2018']}
title: Pennsylvania - Unemployment rate 1992 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92"]
Y_Axis['Unemployment', 'rate']: ['4.3', '4.9', '5.4', '5.3', '5.9', '7.4', '7.8', '7.9', '8.5', '8', '5.3', '4.4', '4.6', '5', '5.4', '5.7', '5.6', '4.8', '4.1', '4.4', '4.6', '5.1', '5.4', '5.9', '6.3', '7', '7.6']

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

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

Example 190:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2005', '2017']}
title: Major kitchen/laundry appliances : unit shipments in the U.S. 2005 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Unit', 'shipments', 'in', 'millions']: ['64.61', '62.29', '60.52', '58.76', '55.71', '52.11', '53.53', '53.51', '52.02', '57.54', '63.41', '68.45', '69.13']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of Swiss-based pharmaceutical company templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] is one of the largest global pharmaceutical companies . In templateXValue[max] , templateTitleSubject[0] reported approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] of templateYLabel[0] templateYLabel[1] .
generated: The statistic shows the Unit shipments of Swiss-based pharmaceutical company U.S. from 2005 to 2017 .  U.S. is one of the largest global pharmaceutical companies .  In 2017 , U.S. reported approximately 69.13 millions of Unit shipments .

Example 191:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: U.S. tea market : total wholesale value 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '1990']
Y_Axis['Sales', 'in', 'billion', 'U.S.', 'dollars']: ['12.66', '12.5', '12.0', '11.5', '10.84', '10.41', '9.79', '1.84']

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

generated_template: This statistic shows the templateYLabel[0] made by templateTitleSubject[0] templateTitle[1] restaurants templateTitle[4] templateXValue[min] to templateXValue[max] . templateTitleSubject[0] , owned by Jack in the Box Inc. , generated templateTitle[2] templateYLabel[0] of approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[max] .
generated: This statistic shows the Sales made by U.S. tea restaurants wholesale 1990 to 2018 .  U.S. , owned by Jack in the Box Inc. , generated market Sales of approximately 12.66 billion U.S. dollars in 2018 .

Example 192:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2024']}
title: Ratio of government expenditure to gross domestic product ( GDP ) in Argentina 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Ratio', 'of', 'government', 'expenditure', 'to', 'GDP']: ['38.28', '37.74', '37.28', '37.04', '36.86', '37.56', '38.9', '41.18', '41.52', '41.37', '38.85']

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

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

Example 193:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019']}
title: U.S. nuclear refueling outage days 1995 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995']
Y_Axis['Average', 'number', 'of', 'days']: ['32', '34', '34', '34', '36', '37', '41', '46', '45', '40', '41', '38', '40', '39', '38', '42', '40', '33', '37', '44', '40', '51', '81', '66', '66']

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

generated_template: templateYLabel[0] templateYLabel[1] templateYLabel[2] is a measure of the difference between the templateYLabel[1] income generated by templateTitle[3] or other financial institutions and the amount of templateYLabel[1] paid out to their lenders relative to the amount of their ( interest-earning ) assets . It is similar to the gross templateYLabel[2] of non-financial companies . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitleSubject[0] templateTitle[3] amounted to templateYValue[idxmax(X)] templateScale .
generated: Average number days is a measure of the difference between the number income generated by outage or other financial institutions and the amount of number paid out to their lenders relative to the amount of their ( interest-earning ) assets .  It is similar to the gross days of non-financial companies .  In 2019 , the average number days of the U.S. outage amounted to 32 million .

Example 194:
titleEntities: {'Subject': ['Portland Trail Blazers'], 'Date': ['2001', '2019']}
title: Portland Trail Blazers ' revenue 2001 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['287', '246', '223', '178', '157', '153', '140', '117', '132', '127', '121', '114', '82', '77', '78', '88', '97', '96']

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

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

Example 195:
titleEntities: {'Subject': ['Teva'], 'Date': ['2006', '2019']}
title: Teva : expenditure on research and development 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Expenditure', 'in', 'million', 'U.S.', 'dollars']: ['1010', '1213', '1778', '2077', '1525', '1488', '1427', '1356', '1095', '951', '825', '786', '581', '495']

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

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

Example 196:
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 197:
titleEntities: {'Subject': ['New Brunswick', 'Canada'], 'Date': ['2000', '2018']}
title: GDP of New Brunswick , Canada 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['GDP', 'in', 'million', 'chained', '2012', 'Canadian', 'dollars']: ['30446.6', '30223.9', '29546.0', '29275.7', '29039.6', '29017.0', '29117.4', '29467.6', '29404.4', '28802.1', '29215.2', '28962.4', '28787.2', '28281.0', '28008.6', '27240.8', '26653.8', '25444.8', '25058.0']

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

generated_template: This statistic shows the gross domestic product ( templateYLabel[0] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] was templateYValue[max] templateScale templateYLabel[2] templateXValue[6] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the gross domestic product ( GDP ) of New Brunswick from 2000 to 2018 .  In 2018 , New Brunswick 's GDP was 30446.6 million chained 2012 Canadian dollars .

Example 198:
titleEntities: {'Subject': ['Southwest Airlines'], 'Date': ['2011', '2019']}
title: Southwest Airlines - available seat miles 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['ASMs', 'in', 'billions']: ['157.25', '159.8', '153.81', '148.52', '140.5', '131.0', '130.34', '128.14', '120.58']

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

generated_template: This statistic represents the templateTitle[3] templateTitle[4] templateYLabel[0] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[min] templateYLabel[2] templateYLabel[3] of templateYLabel[4] templateYLabel[5] was produced . templateYLabel[4] templateTitle[2] and templateTitle[3] are important in various industries .
generated: This statistic represents the seat miles ASMs in the miles from 2011 to 2019 .  In 2019 , 120.58 billions of was produced .  billions available and seat are important in various industries .

Example 199:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2001', '2018']}
title: Total recalls of children 's products in the U.S. 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Number', 'of', 'recalls']: ['52', '93', '76', '68', '75', '114', '97', '124', '148', '143', '209', '232', '111', '122', '87', '66', '90', '118']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] worldwide in the fiscal years templateXValue[min] to templateXValue[max] . In the fiscal templateXLabel[0] templateXValue[max] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[3] , up templateTitle[4] templateYValue[1] templateScale templateYLabel[3] a templateXLabel[0] earlier .
generated: This statistic shows the Number recalls of U.S. worldwide in the fiscal years 2001 to 2018 .  In the fiscal Year 2018 , U.S. recalls Number amounted to 52 million recalls , up products 93 million recalls a Year earlier .

Example 200:
titleEntities: {'Subject': ['Sudan'], 'Date': ['2019']}
title: Unemployment rate in Sudan 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['13', '12.88', '12.77', '13.01', '13.16', '13.21', '13.26', '13.18', '13.04', '13.35', '13', '14.8', '14.9', '15.26', '16.06', '16.34', '16.61', '16.49', '16.62', '16.94', '16.71']

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

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

Example 201:
titleEntities: {'Subject': ['Tanzania'], 'Date': ['2019']}
title: Youth unemployment rate in Tanzania in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['3.43', '3.45', '3.43', '3.66', '3.7', '3.71', '5.69', '6.34', '6.76', '5.9', '4.88', '4.58', '5.04', '6.07', '6.29', '6.19', '6.04', '5.81', '5.36', '5.64', '5.88']

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

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

Example 202:
titleEntities: {'Subject': ['Canada'], 'Date': ['2010', '2019']}
title: Ice hockey players in Canada 2010 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11']
Y_Axis['Number', 'of', 'players']: ['621026', '637000', '631295', '639500', '721504', '721504', '625152', '617107', '572411']

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

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

Example 203:
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 204:
titleEntities: {'Subject': ['UK'], 'Date': ['2012', '2019']}
title: Number of female directors in FTSE 100 companies UK 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'female', 'directors']: ['292', '264', '259', '244', '233', '205', '169', '141']

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

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of the sports company templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] had a templateYLabel[0] templateYLabel[1] of templateYValue[last] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] in templateXValue[max] .
generated: The statistic presents the Number female of the sports company UK from 2012 to 2019 .  UK had a Number female of 141 292 directors in 2019 .

Example 205:
titleEntities: {'Subject': ['Louis Cardinals'], 'Date': ['2019']}
title: Franchise value of the St. Louis Cardinals 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['2100', '1900', '1800', '1600', '1400', '820', '716', '591', '518', '488', '486', '484', '460', '429', '370', '314', '308', '271']

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

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] franchise of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Lewis Wolff and John Fisher , who bought the templateYLabel[0] for 180 templateScale templateYLabel[3] templateYLabel[4] in templateXValue[14] .
generated: This graph depicts the value of the Louis Cardinals franchise of Major League Baseball from 2002 to 2019 .  In 2019 , the Franchise had an estimated value of 2100 million U.S. dollars .  The Louis Cardinals are owned by Lewis Wolff and John Fisher , who bought the Franchise for 180 million U.S. dollars in 2005 .

Example 206:
titleEntities: {'Subject': ['Louisiana'], 'Date': ['2000', '2018']}
title: Louisiana - poverty rate 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'population']: ['18.6', '19.7', '20.2', '19.6', '19.8', '19.8', '19.9', '20.4', '18.7', '17.3', '17.3', '18.6', '19', '19.8', '19.4', '20.3', '18.8', '19.1', '20']

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

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

Example 207:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2004', '2008']}
title: Global market share of the U.S. athletic and non-athletic footwear retail market 2004 to 2008
X_Axis['Year']: ['2004', '2005', '2006', '2007', '2008']
Y_Axis['Global', 'market', 'share']: ['27.3', '26.6', '25.7', '24.6', '24.3']

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

generated_template: In templateTitleDate[0] , templateYValue[max] templateScale of EU templateYLabel[1] said they templateTitle[2] templateXValue[2] or templateXValue[0] templateTitle[1] templateTitle[3] templateTitle[4] , of which templateYValue[1] templateScale smoke an templateXValue[last] of templateXValue[1] 10 and the remaining templateYValue[0] templateScale said they smoke templateXValue[0] or less templateTitle[1] templateTitle[3] templateTitle[4] . Most frequently consumers smoke templateTitle[1] from packs . Smoking in the EU templateYLabel[1] were those who smoke templateTitle[1] and are from the templateTitleSubject[0] member states .
generated: In 2004 , 27.3 % of EU market said they share 2006 or 2004 market U.S. athletic , of which 26.6 % smoke an 2008 of 2005 10 and the remaining 27.3 % said they smoke 2004 or less market U.S. athletic .  Most frequently consumers smoke market from packs .  Smoking in the EU market were those who smoke market and are from the U.S. member states .

Example 208:
titleEntities: {'Subject': ['HSBC'], 'Date': ['2009', '2019']}
title: Return on average ordinary shareholders ' equity at HSBC 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Return', 'on', 'equity']: ['3.6', '7.7', '5.9', '0.8', '7.2', '7.3', '9.2', '8.4', '10.9', '9.5', '5.1']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of carmaker templateTitleSubject[0] from the fiscal templateXLabel[0] of 2008 to the fiscal templateXLabel[0] of templateXValue[max] . templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] amounted to around templateYValue[max] templateScale templateYLabel[3] in the fiscal templateXLabel[0] of templateXValue[idxmax(Y)] . The templateTitleSubject[0] Motor Corporation is a Japanese multinational company and one of the largest automobile manufacturers in the world .
generated: This statistic shows the Return equity of carmaker HSBC from the fiscal Year of 2008 to the fiscal Year of 2019 .  HSBC average Return equity amounted to around 10.9 % equity in the fiscal Year of 2011 .  The HSBC Motor Corporation is a Japanese multinational company and one of the largest automobile manufacturers in the world .

Example 209:
titleEntities: {'Subject': ['FourFourTwo', 'United Kingdom', 'UK'], 'Date': ['2006', '2016']}
title: Readership of FourFourTwo magazine in the United Kingdom ( UK ) 2006 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['AIR', 'in', 'thousands']: ['483', '469', '541', '647', '631', '633', '664', '639', '589', '619', '554']

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

generated_template: The templateTitle[3] shows the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the worldwide templateYLabel[0] templateYLabel[1] is expected at templateYValue[7] templateScale templateYLabel[3] templateYLabel[4] . templateYLabel[0] includes , for example , application development and integration ; business intelligence and data management ; network , storage and systems management ; security and operating systems .
generated: The United shows the FourFourTwo AIR thousands from 2006 to 2016 .  In 2010 , the worldwide AIR thousands is expected at 639 thousands .  AIR includes , for example , application development and integration ; business intelligence and data management ; network , storage and systems management ; security and operating systems .

Example 210:
titleEntities: {'Subject': ['Manchester Metrolink', 'United Kingdom'], 'Date': ['1992', '2019']}
title: Passenger journeys on the Manchester Metrolink in the United Kingdom 1992 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05', '2003/04', '2002/03', '2001/02', '2000/01', '1999/00', '1998/99', '1997/98', '1996/97', '1995/96', '1994/95', '1993/94', '1992/93']
Y_Axis['Passenger', 'journeys', 'in', 'millions']: ['43.7', '41.2', '37.8', '34.3', '31.2', '29.2', '25.0', '22.3', '19.2', '19.6', '21.1', '20.0', '19.8', '19.9', '19.7', '18.9', '18.8', '18.2', '17.2', '14.2', '13.2', '13.8', '13.4', '12.6', '12.3', '11.3', '8.1']

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

generated_template: This statistic shows the U.S. templateTitleSubject[0] templateTitle[1] among templateTitle[3] and young adult templateYLabel[3] , templateYLabel[4] templateTitle[5] - templateYValue[1] templateYLabel[6] , between templateTitleDate[min] and templateTitleDate[max] . In templateTitleDate[max] , the teenage templateTitleSubject[0] templateTitle[1] within this age group stood at templateYValue[min] templateYLabel[0] templateYLabel[1] every thousand templateYLabel[3] . Teenage pregnancy and templateTitleSubject[0] Teenage pregnancy and templateYLabel[0] are related to a number of negative outcomes .
generated: This statistic shows the U.S. Manchester Metrolink journeys among Metrolink and young adult millions , Kingdom - 41.2 millions , between 1992 and 2019 .  In 2019 , the teenage Manchester Metrolink journeys within this age group stood at 8.1 Passenger journeys every thousand millions .  Teenage pregnancy and Manchester Metrolink Teenage pregnancy and Passenger are related to a number of negative outcomes .

Example 211:
titleEntities: {'Subject': ['Thailand'], 'Date': ['2024']}
title: Inflation rate in Thailand 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2', '1.8', '1.6', '1.2', '0.92', '0.86', '1.06', '0.67', '0.19', '-0.9', '1.9', '2.19', '3.01', '3.81', '3.29', '-0.85', '5.46', '2.2', '4.66', '4.52', '2.76']

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

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

Example 212:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Denmark 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['72325.94', '69413.32', '66656.96', '64106.78', '61732.57', '59795.27', '60897.23', '57380.2', '54665.22', '53478.5', '62729.5', '61325.58', '58623.41', '61864.09', '58177.16', '58286.54', '64531.12', '58641.19', '52121.25', '48872.1', '46571.28', '40512.05', '33275.56', '30806.61', '30798.72', '33492.35', '33426.97', '32897.57', '35732.69', '35471.26', '30050.88', '27640.5', '29622.47', '27052.65', '26920.58', '21913.16', '22528.11', '21349.95', '17215.43', '12259.28', '11561.77']

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

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

Example 213:
titleEntities: {'Subject': ['Portugal'], 'Date': ['2006', '2018']}
title: Volume of wine produced in Portugal 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Thousands', 'of', 'hectoliters']: ['6.1', '6.7', '6.0', '7.0', '6.2', '6.2', '6.3', '5.6', '7.13', '5.87', '5.69', '6.07', '7.54']

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

generated_template: In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateYLabel[1] were committed templateTitle[1] templateTitle[2] in the templateTitle[3] . While this figure has remained relatively steady since templateXValue[min] , it has been templatePositiveTrend since templateXValue[2] . Firearms in the templateTitle[3] Firearms in the templateTitle[3] have become a large part of American culture .
generated: In 2018 , 6.1 thousands of hectoliters were committed wine produced in the Portugal .  While this figure has remained relatively steady since 2006 , it has been increasing since 2016 .  Firearms in the Portugal Firearms in the Portugal have become a large part of American culture .

Example 214:
titleEntities: {'Subject': ['Lithuania'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Lithuania 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['25936.96', '24437.92', '23066.81', '21708.62', '20355.0', '19266.79', '18994.38', '16845.33', '14988.57', '14299.1', '16571.43', '15695.74', '14354.29', '14386.61', '12010.68', '11866.63', '15047.25', '12313.17', '9246.51', '7880.35', '6706.03', '5505.59', '4146.11', '3530.2', '3297.45', '3113.64', '3166.96', '2830.75', '2328.22', '1845.67']

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

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

Example 215:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2001', '2017']}
title: Head value of sheep and lambs in the U.S. 2001 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Value', 'per', 'head', 'in', 'U.S.', 'dollars']: ['203', '202', '214', '188', '177', '221', '170', '135', '133', '138', '134', '141', '130', '119', '104', '92', '100']

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

generated_template: This statistic shows the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] from templateTitle[1] templateTitle[2] in the templateTitle[0] templatePositiveTrend by templateYValue[0] templateYValue[idxmax(X)] compared to the previous templateXLabel[0] . Wine industryWine is classified as alcoholic beverage which goes well with a large variety of occasions : some often choose to serve this classy drink as an aperitif , others see wine as a perfect accompaniment to a multi-course meal , and others again prefer drinking wine while spending time with friends or family .
generated: This statistic shows the U.S. Value per of value sheep from 2001 to 2017 .  In 2017 , Value from sheep in the Head increased by 203 compared to the previous Year .  Wine industryWine is classified as alcoholic beverage which goes well with a large variety of occasions : some often choose to serve this classy drink as an aperitif , others see wine as a perfect accompaniment to a multi-course meal , and others again prefer drinking wine while spending time with friends or family .

Example 216:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2019']}
title: United Kingdom ( UK ) : National debt as a percentage of GDP 2000 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05', '2003/04', '2002/03', '2001/02', '2001/01']
Y_Axis['Percentage', 'of', 'GDP']: ['85.2', '85.3', '86.5', '86.4', '86.5', '85.5', '83.3', '81.8', '75.6', '69.6', '52.6', '40.9', '40', '39.2', '38', '35.5', '33.8', '33.7', '35.2']

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

generated_template: The total templateTitle[0] expenditure in templateTitleSubject[0] in templateXValue[max] accounted for approximately templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's Gross Domestic Product ( templateYLabel[1] ) that templateXLabel[0] . This was the result of the steepest templatePositiveTrend in the past ten years and was the first time templateTitle[0] templateTitle[1] as a share of the templateYLabel[1] exceeded templateYValue[max] templateScale . This share saw a continuous templatePositiveTrend over the past decade , indicating that as the templateYLabel[1] templatePositiveTrend , templateTitle[0] templateTitle[1] templatePositiveTrend at an even faster rate .
generated: The total United expenditure in United Kingdom 2018/19 accounted for approximately 35.2 percentage of United Kingdom 's Gross Domestic Product ( GDP ) that Year .  This was the result of the steepest increase in the past ten years and was the first time United Kingdom as a share of the GDP exceeded 86.5 percentage .  This share saw a continuous increase over the past decade , indicating that as the GDP grew , United Kingdom grew at an even faster rate .

Example 217:
titleEntities: {'Subject': ['Portugal'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita in Portugal 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['27920.05', '26788.05', '25744.8', '24727.89', '23731.13', '23030.79', '23437.39', '21482.86', '19986.36', '19252.01', '22109.32', '21625.48', '20588.87', '23217.34', '22580.68', '23122.56', '24933.17', '22811.57', '19837.97', '18815.44', '18064.47', '15799.89', '12922.15', '11737.17', '11533.83', '12490.93', '12220.18', '11597.7', '12187.56', '11788.47', '9978.59', '9548.58', '10864.55', '9027.18', '7958.02', '5978.16', '5533.16', '4724.91', '3774.95', '2716.91', '2596.33']

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

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

Example 218:
titleEntities: {'Subject': ['North Carolina'], 'Date': ['1990', '2018']}
title: North Carolina - Median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['53369', '50343', '53764', '50797', '46784', '41208', '41553', '45206', '43830', '41906', '42930', '43513', '39797', '42056', '40238', '37279', '36515', '38162', '38317', '37254', '35838', '35840', '35601', '31979', '30114', '28820', '27771', '26853', '26329']

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

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Carolina Household income in North Carolina from 1990 to 2018 .  In 2018 , the Carolina Household income in North Carolina amounted to 53369 U.S. dollars .

Example 219:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2017', '2024']}
title: United Kingdom ( UK ) oil price forecast in U.S. dollars 2017 to 2024
X_Axis['Year']: ['2023/24', '2022/23', '2021/22', '2020/21', '2019/20', '2018/19', '2017/18']
Y_Axis['U.S.', 'dollars', 'per', 'barrel']: ['64.5', '63.3', '62.0', '61.6', '62.1', '71.3', '54.6']

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

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

Example 220:
titleEntities: {'Subject': ['Slovakia'], 'Date': ['2006', '2018']}
title: Number of arrivals in tourist accommodation Slovakia 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'arrivals', 'in', 'millions']: ['5.49', '5.29', '4.94', '4.27', '3.69', '4.0', '3.73', '3.54', '3.36', '3.34', '4.03', '3.74', '3.55']

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

generated_template: Approximately templateYValue[max] templateScale templateYLabel[1] were recorded at travel templateTitle[3] in templateTitleSubject[0] in templateXValue[max] International tourism was the highest contributor , with around two thirds of templateYLabel[1] in visitor templateTitle[3] coming from a foreign country . templateTitleSubject[0] tourism growth Since templateXValue[9] international visitor numbers to templateTitleSubject[0] have steadily templatePositiveTrend . Including both overnight and same-day visitors , figures reached over 30 templateScale by templateXValue[1] .
generated: Approximately 5.49 millions arrivals were recorded at travel accommodation in Slovakia 2018 International tourism was the highest contributor , with around two thirds of arrivals in visitor accommodation coming from a foreign country .  Slovakia tourism growth Since 2009 international visitor numbers to Slovakia have steadily increased .  Including both overnight and same-day visitors , figures reached over 30 millions by 2017 .

Example 221:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2013', '2018']}
title: Annual levels of brick production in Great Britain ( GB ) 2013 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Production', 'level', 'in', 'million', 'bricks']: ['2025', '1877', '1800', '1915', '1824', '1555']

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

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[0] snow sports market from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] templateTitle[3] in the templateTitleSubject[1] amounted to templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic presents the Production level of levels brick production in the Great Britain snow sports market from 2013 to 2018 .  In 2018 , the Production level of levels brick production in the Great Britain amounted to 2025 million bricks .

Example 222:
titleEntities: {'Subject': ['Ecuador'], 'Date': ['2017']}
title: Ecuador : poverty headcount ratio at 3.20 U.S. dollars a day 2005 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Percentage', 'of', 'population']: ['8.7', '9.4', '9.4', '8.6', '10.1', '11.4', '12.2', '14.7', '17.2', '18.1', '19.2', '19.8', '25.7']

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

generated_template: In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateYLabel[1] were committed templateTitle[1] templateTitle[2] in the templateTitle[3] . While this figure has remained relatively steady since templateXValue[min] , it has been templatePositiveTrend since templateXValue[2] . Firearms in the templateTitle[3] Firearms in the templateTitle[3] have become a large part of American culture .
generated: In 2017 , 8.7 percentage of population were committed poverty headcount in the ratio .  While this figure has remained relatively steady since 2005 , it has been increasing since 2015 .  Firearms in the ratio Firearms in the ratio have become a large part of American culture .

Example 223:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1950', '2050']}
title: U.S. - seniors as a percentage of the population 1950 to 2050
X_Axis['Year']: ['2050', '2040', '2030', '2020', '2018', '2010', '2000', '1990', '1980', '1970', '1960', '1950']
Y_Axis['Percentage', 'of', 'total', 'population']: ['22', '21.6', '20.6', '16.9', '16', '13.1', '12.4', '12.5', '11.3', '10', '9', '8']

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

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] of templateTitle[0] in the templateTitleSubject[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , in templateYLabel[1] templateYLabel[2] . In templateXValue[max] , templateTitle[1] templateYLabel[0] of templateTitle[0] in DR templateTitleSubject[0] totaled some templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] . Recently , worldwide templateYLabel[0] of templateTitle[0] has exceed consumption which has templateNegativeTrend prices for the mineral .
generated: This statistic shows the seniors Percentage of U.S. in the U.S. of from 1950 to 2050 , in total population .  In 2050 , seniors Percentage of U.S. in DR U.S. totaled some 22 total population .  Recently , worldwide Percentage of U.S. has exceed consumption which has decreased prices for the mineral .

Example 224:
titleEntities: {'Subject': ['Buffalo Bills', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Buffalo Bills ( NFL ) 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1900', '1600', '1600', '1500', '1400', '935', '870', '805', '792', '799', '909', '885', '821', '756', '708', '637', '564', '458']

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

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to around templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Jerry Jones who bought the templateYLabel[0] for 150 templateScale templateYLabel[3] templateYLabel[4] in 1989 .
generated: 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 around 1900 million U.S. dollars .  The Buffalo Bills are owned by Jerry Jones who bought the Franchise for 150 million U.S. dollars in 1989 .

Example 225:
titleEntities: {'Subject': ['Uruguay'], 'Date': ['2017']}
title: Uruguay : poverty headcount ratio at 3.20 U.S. dollars a day 2006 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Percentage', 'of', 'population']: ['0.4', '0.5', '0.6', '0.7', '0.8', '1.1', '1', '1.3', '1.8', '1.8', '2.9', '3.7']

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

generated_template: In templateXValue[max] , the templateTitle[1] templateTitle[2] templateTitle[3] at templateTitle[4] templateTitle[5] templateTitle[6] a templateTitle[7] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateScale , which means that almost templateYValue[0] templateScale of the Brazilian templateYLabel[1] was living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] . The templateTitle[1] rate has templatePositiveTrend since templateXValue[3] , when the templateTitle[2] templateTitle[3] was below templateYValue[min] templateScale .
generated: In 2017 , the poverty headcount ratio at 3.20 U.S. dollars a day in Uruguay amounted to 0.4 percentage , which means that almost 0.4 percentage of the Brazilian population was living on less than 3.20 U.S. dollars per day .  The poverty rate has increased since 2014 , when the headcount ratio was below 0.4 percentage .

Example 226:
titleEntities: {'Subject': ['Denver Broncos'], 'Date': ['2019']}
title: Regular season home attendance of the Denver Broncos 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Home', 'attendance']: ['607497', '611571', '610846', '614193', '615381', '615517', '614977', '613062', '602618', '599264', '600928', '604074', '612888', '610776']

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

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

Example 227:
titleEntities: {'Subject': ['France'], 'Date': ['2024']}
title: Ratio of government expenditure to gross domestic product ( GDP ) in France 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Ratio', 'of', 'government', 'expenditure', 'to', 'GDP']: ['53.94', '53.96', '54', '54.19', '54.5', '55.65', '56.04', '56.38', '56.59', '56.8', '57.21']

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

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to 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] , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale of the templateTitle[3] templateTitle[4] templateTitle[5] .
generated: 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 , government expenditure in France amounted to about 56.04 % of the gross domestic product .

Example 228:
titleEntities: {'Subject': ['Ireland'], 'Date': ['2024']}
title: Total population of Ireland 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['5.2', '5.15', '5.11', '5.06', '5.01', '4.95', '4.89', '4.83', '4.77', '4.71', '4.67']

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

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[7] templateScale templateYLabel[0] .
generated: This statistic shows the Total population of 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 229:
titleEntities: {'Subject': ['Russia'], 'Date': ['2024']}
title: Total population of Russia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['145.74', '146.02', '146.27', '146.47', '146.62', '146.73', '146.8', '146.9', '146.8', '146.5', '146.3']

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

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateScale templateYLabel[0] . See the figures for the templateTitle[1] of South Korea for comparison .
generated: The 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 around 146.8 millions Inhabitants .  See the figures for the population of South Korea for comparison .

Example 230:
titleEntities: {'Subject': ['Italy'], 'Date': ['2004', '2019']}
title: Youth unemployment rate in Italy 2004 to 2019
X_Axis['Year']: ['2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019']
Y_Axis['Unemployment', 'rate']: ['23.5', '24.1', '21.8', '20.4', '21.2', '25.3', '27.9', '29.2', '35.3', '40', '42.7', '40.3', '37.8', '34.7', '32.2', '28.9']

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

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

Example 231:
titleEntities: {'Subject': ['Brazil'], 'Date': ['2017', '2023']}
title: Brazil : mobile phone internet user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['51', '50', '49', '47', '45', '43', '41']

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

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

Example 232:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: U.S. per capita consumption of fresh peaches and nectarines 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['2.2', '2.66', '2.73', '2.91', '3.15', '3.02', '3.86', '4.47', '4.73', '4.41', '5.08', '4.46', '4.58', '4.83', '5.15', '5.17', '5.23', '5.16', '5.3']

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

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[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 about templateYValue[1] templateYLabel[3] in templateXValue[1] .
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 about 2.66 pounds in 2017 .

Example 233:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. per capita consumption of fresh carrots 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['8.5', '7.4', '7.8', '8.8', '8.5', '8.0', '7.9', '7.5', '7.8', '7.4', '8.1', '8.0', '8.1', '8.7', '8.7', '8.8', '8.4', '9.4', '9.2']

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

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] templateTitle[6] amounted to about templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[idxmax(Y)] .
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 8.5 pounds in 2001 .

Example 234:
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] . In templateXValue[max] , templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows Gross domestic product ( GDP ) in Syria from 1984 to 2010 .  In 2010 , Gross domestic product of Syria amounted to 60.04 U.S. dollars .

Example 235:
titleEntities: {'Subject': ['Spain'], 'Date': ['2007', '2018']}
title: Annual average housing prices Spain 2007 to 2018
X_Axis['Year']: ['2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['Price', 'in', 'euros', 'per', 'square', 'meter', 'built']: ['2246', '2285', '2185', '2060', '1907', '1768', '1602', '1477', '1431', '1447', '1532', '1613']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] were templateTitle[5] .
generated: The statistic shows the Price euros at per in Spain the from 2007 to 2018 .  In 2018 , 1613 square meter were 2007 .

Example 236:
titleEntities: {'Subject': ['Brunswick Corporation'], 'Date': ['2007', '2019']}
title: Global revenue of the Brunswick Corporation 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['4108.4', '4120.9', '3802.2', '3508.1', '3311.1', '3838.7', '3599.7', '3416.8', '3367.0', '3039.6', '2776.1', '4708.7', '5671.2']

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

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

Example 237:
titleEntities: {'Subject': ['Russia'], 'Date': ['2010', '2019']}
title: Ice hockey players in Russia 2010 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11']
Y_Axis['Number', 'of', 'players']: ['112236', '110624', '105059', '102179', '99172', '84270', '66551', '64326', '63580']

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

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

Example 238:
titleEntities: {'Subject': ['Southwest Airlines'], 'Date': ['2019']}
title: Net income of Southwest Airlines 2010 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['2300', '2465', '3357', '2183', '2181', '1136', '754', '421', '178', '459']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to approximately templateYValue[0] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] in templateXValue[max] .
generated: This statistic shows the Net income of Southwest Airlines from 2010 to 2019 .  The Net income of Southwest Airlines amounted to approximately 2300 U.S. dollars in 2019 .

Example 239:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2008', '2018']}
title: Number of births in Belgium 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'births']: ['117800', '119102', '121713', '121713', '124415', '124862', '126993', '127655', '129173', '127297', '128049']

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

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

Example 240:
titleEntities: {'Subject': ['Faroe Islands'], 'Date': ['1993', '2019']}
title: World ranking of Faroe Islands ' national football team 1993 to 2019
X_Axis['Year']: ['1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019']
Y_Axis['FIFA', 'World', 'Ranking', 'position']: ['115', '133', '120', '135', '117', '125', '112', '117', '117', '114', '126', '131', '132', '181', '194', '184', '117', '136', '116', '153', '170', '104', '97', '83', '95', '98', '102']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of the Norwegian templateTitle[4] templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . The highest templateYLabel[3] ever reached was templateYValue[min] in templateXValue[idxmin(Y)] . Rank templateYValue[max] was the lowest result of the templateTitle[6] , which was reached in templateXValue[idxmax(Y)] .
generated: This statistic shows the FIFA World Ranking of the Norwegian ' national football from 1993 to 2019 .  The highest position ever reached was 83 in 2016 .  Rank 194 was the lowest result of the football , which was reached in 2007 .

Example 241:
titleEntities: {'Subject': ['Germany'], 'Date': ['2000', '2018']}
title: Consumption of wine in Germany 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2004', '2003', '2001', '2000']
Y_Axis['Consumption', 'in', 'million', 'hectoliters']: ['20.0', '19.7', '20.1', '19.6', '20.2', '20.4', '20.3', '19.7', '20.2', '20.2', '20.7', '20.8', '19.6', '20.2', '20.0', '20.2']

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

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateTitle[2] of the templateTitleSubject[0] Yankees from templateXValue[min] to templateXValue[max] . For the templateXValue[1] season the templateTitleSubject[0] templateYLabel[2] templateYValue[1] templateScale templateYLabel[4] templateYLabel[5] in templateYLabel[0] templateYLabel[1] .
generated: The statistic shows the Consumption million Germany of the Germany Yankees from 2000 to 2018 .  For the 2017 season the Germany hectoliters 19.7 million hectoliters in Consumption million .

Example 242:
titleEntities: {'Subject': ['Thailand'], 'Date': ['2015', '2035']}
title: Share of aging population Thailand 2015 to 2035
X_Axis['Year']: ['2035', '2030', '2025', '2020', '2015']
Y_Axis['Share', 'of', 'population', 'older', 'than', '65', 'years', 'old']: ['22.8', '19.4', '16', '12.9', '10.6']

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

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateTitle[1] a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateYValue[2] templateTitle[7] in the templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] templateYLabel[1] had attended a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateXLabel[0] .
generated: This statistic presents the Share of population aging a population Thailand 2015 at least once in the 2035 16 in the 2035 from 2015 to 2035 .  In 2035 , 22.8 % of Thailand population had attended a population Thailand 2015 at least once in the 2035 Year .

Example 243:
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 statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] in templateTitleDate[0] , broken down templateTitle[5] templateTitle[6] group . In templateTitleDate[0] , more than templateYValue[2] templateScale of Brazilian templateYLabel[1] templateYLabel[2] were between 18 and 19 years old .
generated: This statistic gives information on the Number of new books in U.S. 2002 , broken down 'fiction ' group .  In 2002 , more than 38832 % of Brazilian new books were between 18 and 19 years old .

Example 244:
titleEntities: {'Subject': ['Japan'], 'Date': ['2024']}
title: Budget balance in Japan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'balance', 'in', 'trillion', 'yen']: ['-11.67', '-10.46', '-10.24', '-11.12', '-12.25', '-16.48', '-17.64', '-17.27', '-19.8', '-20.23', '-28.96']

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

generated_template: The statistic shows 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] . In templateXValue[6] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[max] templateScale of templateTitleSubject[0] templateTitle[4] templateTitle[5] .
generated: The statistic shows the Budget balance of Japan from 2014 to 2018 in trillion to the 2024 ( yen ) , with projections up until 2024 .  In 2018 , Japan 's Budget balance amounted to approximately -10.24 trillion of Japan 2024 .

Example 245:
titleEntities: {'Subject': ['Global'], 'Date': ['2010', '2016']}
title: Global spending on golf sponsorships 2010 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Spending', 'in', 'billion', 'U.S.', 'dollars']: ['1.82', '1.73', '1.65', '1.6', '1.51', '1.44', '1.36']

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

generated_template: This statistic shows the total templateTitle[0] templateYLabel[0] in the templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , about 5.91million metric tons of templateTitle[0] were produced in the templateTitle[2] , up from templateYValue[2] templateScale metric tons in templateXValue[2] .
generated: This statistic shows the total Global Spending in the golf from 2010 to 2016 .  In 2015 , about 5.91million metric tons of Global were produced in the golf , up from 1.65 billion metric tons in 2014 .

Example 246:
titleEntities: {'Subject': ['Angola'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Angola 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['3.78', '4.13', '3.33', '2.87', '1.15', '-0.27', '-1.2', '-0.15', '-2.58', '0.94', '4.82']

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

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[max] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Angola from 2014 to 2018 , with projections up until 2024 .  In 2018 , Angola 's real Gross domestic product increased by around 4.82 % compared to the previous Year .

Example 247:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1980', '2016']}
title: U.S. public schools - average expenditure per pupil 1980 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1980']
Y_Axis['Expenditures', 'per', 'pupil', 'in', 'U.S.', 'dollars']: ['12617', '12224', '11819', '11509', '11362', '11433', '11427', '11239', '10982', '10336', '9778', '9316', '8900', '8610', '8259', '7904', '7394', '7013', '6676', '6393', '6147', '5989', '5767', '5584', '5421', '5258', '4980', '4645', '4240', '3970', '3756', '3470', '2272']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around 8.32 templateScale of templateTitle[2] templateTitle[3] in the templateTitle[4] .
generated: This statistic shows the Expenditures per of schools average in the expenditure from 1980 to 2016 .  In 2016 , around 8.32 % of schools average in the expenditure .

Example 248:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1987', '2019']}
title: U.S. exports of trade goods to the world 1987 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987']
Y_Axis['Exports', 'in', 'billion', 'U.S.', 'dollars']: ['1645.53', '1665.99', '1546.47', '1451.46', '1503.33', '1621.87', '1578.52', '1545.82', '1482.51', '1278.49', '1056.04', '1287.44', '1148.2', '1025.97', '901.08', '814.87', '724.77', '693.1', '729.1', '781.92', '695.8', '682.14', '689.18', '625.07', '584.74', '512.63', '465.09', '448.16', '421.73', '393.59', '363.81', '322.43', '254.12']

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

generated_template: In templateXValue[max] , Chinese exports of templateTitle[2] templateTitle[3] to the templateTitle[0] amounted to about templateYValue[0] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] ; a significant templatePositiveTrend templateTitle[4] templateXValue[min] levels , when templateTitle[1] templateTitle[4] templateTitleSubject[0] amounted to about templateYValue[idxmin(X)] templateScale templateYLabel[4] templateYLabel[5] . templateYLabel[3] exports to templateTitleSubject[0] Compared to templateYLabel[3] templateTitle[1] templateTitle[4] templateTitleSubject[0] , the value of templateYLabel[3] exports to templateTitleSubject[0] in templateXValue[max] amounted to 106.63 templateScale templateYLabel[3] templateYLabel[4] . templateTitleSubject[0] is the templateTitle[0] ' largest trading partner , while templateTitleSubject[0] was the templateTitle[0] third largest templateTitle[3] export market .
generated: In 2019 , Chinese exports of trade goods to the U.S. amounted to about 1645.53 dollars ; a significant increase world 1987 levels , when exports world U.S. amounted to about 254.12 billion dollars .  dollars exports to U.S. Compared to dollars exports world U.S. , the value of dollars exports to U.S. in 2019 amounted to 106.63 billion dollars .  U.S. is the U.S. ' largest trading partner , while U.S. was the U.S. third largest goods export market .

Example 249:
titleEntities: {'Subject': ['UEFA European Championships'], 'Date': ['2016']}
title: Revenue of the UEFA European Championships 1992 to 2016
X_Axis['Year']: ['2016_France', '2012_Poland_&_Ukraine', '2008_Switzerland_&_Austria', '2004_Portugal', '2000_Belgium_&_the_Netherlands', '1996_England', '1992_Sweden']
Y_Axis['Revenue', 'in', 'million', 'euros']: ['1916.0', '1390.9', '1350.9', '855.2', '229.9', '147.3', '40.9']

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

generated_template: This statistic shows the share of internet users in the templateTitleSubject[0] who were using templateTitle[0] templateTitle[1] as of 2018 , sorted templateTitle[7] templateTitle[8] . We Are Flint found that templateYValue[max] templateScale of internet users aged from 25 to 34 years used the social networking site 's templateTitle[1] platform .
generated: This statistic shows the share of internet users in the UEFA European Championships who were using Revenue UEFA as of 2018 , sorted 2016 .  We Are Flint found that 1916.0 million of internet users aged from 25 to 34 years used the social networking site 's UEFA platform .

Example 250:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2005']}
title: Comic book reading by young people in the United Kingdom ( UK ) 2005 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2005']
Y_Axis['Share', 'of', 'respondents']: ['26.3', '28.6', '27.3', '25.1', '27.9', '26.8', '50.6']

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

generated_template: This statistic gives information on the templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] as of 2019 . During this period of time , it was found that templateYValue[max] templateScale of templateTitle[3] templateTitle[4] in the Latin American country were aged between 25 and 34 templateXValue[0] .
generated: This statistic gives information on the book reading of by young in United Kingdom as of 2019 .  During this period of time , it was found that 50.6 % of by young in the Latin American country were aged between 25 and 34 2015 .

Example 251:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1999', '2013']}
title: Consumer expenditure on musical instruments in the U.S. 1999 to 2013
X_Axis['Year']: ['1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013']
Y_Axis['Expenditure', 'in', 'billion', 'U.S.', 'dollars']: ['3.93', '4.75', '4.93', '5.18', '5.06', '5.33', '5.32', '5.52', '5.39', '5.13', '4.57', '4.58', '4.67', '5.14', '5.2']

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

generated_template: The statistic shows the templateYLabel[0] amount of templateTitle[1] generated templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitle[4] templateTitle[5] templateTitle[6] generated some templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of templateTitle[1] .
generated: The statistic shows the Expenditure amount of expenditure generated instruments U.S. 1999 2013 in the U.S. between 1999 and 2013 .  In 2013 , U.S. 1999 2013 generated some 5.52 billion U.S. dollars of expenditure .

Example 252:
titleEntities: {'Subject': ['Retail'], 'Date': ['2014', '2019']}
title: Retail revenue from smart wearable devices worldwide 2014 and 2019
X_Axis['Year']: ['2019', '2014']
Y_Axis['Retail', 'revenue', 'in', 'billion', 'U.S.', 'dollars']: ['53.2', '4.5']

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

generated_template: The templateTitleSubject[0] templateTitle[1] energy templateTitle[3] is expected to reach templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] , in comparison to templateYValue[min] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmin(Y)] . templateTitle[1] energy is one of the most popular renewable energy sources and in recent years more capacity was deployed than traditional energy sources . The success related to the templateTitle[1] energy segment can be attributed to the declining cost of installing templateTitle[1] photovoltaic systems .
generated: The Retail revenue energy smart is expected to reach 53.2 billion U.S. dollars in 2019 , in comparison to 4.5 billion U.S. dollars in 2014 .  revenue energy is one of the most popular renewable energy sources and in recent years more capacity was deployed than traditional energy sources .  The success related to the revenue energy segment can be attributed to the declining cost of installing revenue photovoltaic systems .

Example 253:
titleEntities: {'Subject': ['Google'], 'Date': ['2001', '2019']}
title: Google network sites : advertising revenue 2001 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['21.55', '20.01', '17.62', '15.6', '15.03', '14.54', '13.65', '12.47', '10.39', '8.79', '7.17', '6.71', '5.79', '4.16', '2.69', '1.55', '0.63', '0.1', '0.0']

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

generated_template: templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[3] templatePositiveTrend by 21.8 templateScale in templateXValue[max] compared to templateXValue[1] , templateTitle[4] templateYValue[1] templateScale to 107 templateScale templateTitle[3] templateYLabel[3] . This is the first time the figure has surpassed 100 templateScale templateYLabel[3] , owing to the emergence of new channels and formats including virtual and augmented reality , podcasts and OTT content as well as strong growth templateTitle[4] existing channels such as mobile . templateTitle[0] templateTitle[1] at a glance Search is dominating the internet templateTitle[1] scene among the remaining formats in the templateTitle[3] , with a 45 templateScale share .
generated: Google network Revenue in the advertising grew by 21.8 billion in 2019 compared to 2018 , revenue 20.01 billion to 107 billion advertising dollars .  This is the first time the figure has surpassed 100 billion dollars , owing to the emergence of new channels and formats including virtual and augmented reality , podcasts and OTT content as well strong growth revenue existing channels such as mobile .  Google network at a glance Search is dominating the internet network scene among the remaining formats in the advertising , with a 45 billion share .

Example 254:
titleEntities: {'Subject': ['Arsenal FC'], 'Date': ['2009']}
title: Value of Arsenal FC 's jersey sponsorship 2009 to 2020
X_Axis['Year']: ['2019/20_(Fly_Emirates)', '2018/19_(Fly_Emirates)', '2017/18_(Fly_Emirates)', '2016/17_(Fly_Emirates)', '2015/16_(Fly_Emirates)', '2014/15_(Fly_Emirates)', '2013/14_(Fly_Emirates)', '2012/13_(Fly_Emirates)', '2011/12_(Fly_Emirates)', '2010/11_(Fly_Emirates)', '2009/10_(Fly_Emirates)']
Y_Axis['Jersey', 'sponsorship', 'revenue', 'in', 'million', 'GBP']: ['40.0', '40.0', '30.0', '30.0', '30.0', '30.0', '30.0', '5.5', '5.5', '5.5', '5.5']

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

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

Example 255:
titleEntities: {'Subject': ['United States'], 'Date': ['2017', '2017']}
title: Mechanical engineering in the United States - market size 2017
X_Axis['Year']: ['2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['340.0', '331.37', '353.55', '293.19', '316.59', '347.39', '394.2', '370.79', '380.65', '423.76', '390.5', '401.59']

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

generated_template: This statistic represents templateTitleSubject[0] 's templateYLabel[0] between the templateXValue[min] and templateXValue[max] fiscal years . In the templateXValue[max] fiscal templateXLabel[0] , the manufacturer of regional aircraft , business jets , mass transportation equipment and recreational equipment had revenues of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic represents United States 's Revenue between the 2006 and 2017 fiscal years .  In the 2017 fiscal Year , the manufacturer of regional aircraft , business jets , mass transportation equipment and recreational equipment had revenues of 401.59 billion U.S. dollars .

Example 256:
titleEntities: {'Subject': ['Rome'], 'Date': ['2011', '2019']}
title: Hotel occupancy rate in Rome 2011 to 2019
X_Axis['Year']: ['2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019']
Y_Axis['Occupancy', 'rate']: ['67', '66', '67', '69', '69', '70', '70', '70', '71']

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

generated_template: This statistic depicts the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] used in templateTitle[4] templateTitle[5] between templateXValue[min] and templateXValue[max] . It is estimated that this templateYLabel[0] will exceed templateYValue[min] templateScale templateYLabel[3] templateYLabel[4] by templateXValue[3] .
generated: This statistic depicts the Occupancy rate Rome used in 2011 2019 between 2011 and 2019 .  It is estimated that this Occupancy will exceed 66 % rate by 2014 .

Example 257:
titleEntities: {'Subject': ['Lithuania'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Lithuania 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.29', '2.3', '2.39', '2.46', '2.72', '3.41', '3.49', '4.14', '2.35', '2.02', '3.54']

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

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

Example 258:
titleEntities: {'Subject': ['Wisconsin'], 'Date': ['1990', '2018']}
title: Wisconsin - Median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['62629', '63451', '59817', '55425', '58080', '55258', '53079', '52058', '50351', '51237', '51200', '51277', '51692', '44650', '45732', '46269', '45903', '45346', '45088', '45667', '41327', '39595', '40001', '40955', '35388', '31766', '33308', '31133', '30711']

gold: This statistic shows the median household income in Wisconsin from 1990 to 2018 . In 2018 , the median household income in Wisconsin amounted to 62,629 U.S. dollars .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Median Household income in Wisconsin from 1990 to 2018 .  In 2018 , the Median Household income in Wisconsin amounted to 62629 U.S. dollars .

Example 259:
titleEntities: {'Subject': ['Royal Dutch Shell'], 'Date': ['2010', '2018']}
title: Royal Dutch Shell 's exploration costs 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Costs', 'in', 'million', 'U.S.', 'dollars']: ['208', '141', '494', '1290', '1439', '5278', '3104', '2266', '2036']

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

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

Example 260:
titleEntities: {'Subject': ['Pakistan'], 'Date': ['2024']}
title: Total population of Pakistan 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['224.66', '220.53', '216.47', '212.48', '208.57', '204.73', '200.96', '197.26', '193.56', '189.87', '186.19']

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

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateScale templateYLabel[0] .
generated: This statistic shows the Total population of Pakistan from 2014 to 2018 , with projections up until 2024 .  In 2018 , the Total population of Pakistan amounted to approximately 200.96 millions Inhabitants .

Example 261:
titleEntities: {'Subject': ['Global'], 'Date': ['2017', '2022']}
title: Global sexual wellness market size 2017 to 2022
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Market', 'size', 'in', 'billion', 'U.S.', 'dollars']: ['37.19', '35.07', '33.08', '31.2', '29.42', '27.75', '26.17']

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

generated_template: The statistic shows the templateYLabel[1] of the templateTitle[1] templateTitle[2] templateTitle[3] ( VPN ) templateYLabel[0] worldwide , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] VPN templateYLabel[0] is forecast to reach templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . templateTitle[1] templateTitle[2] networks are designed to extend a templateTitle[3] securely from a templateTitle[2] location , such as a business or home , across a public templateTitle[3] , as if the templateTitle[3] were directly linked .
generated: The statistic shows the size of the sexual wellness market ( VPN ) Market worldwide , from 2017 to 2023 .  In 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 262:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2008', '2018']}
title: Unemployment rate in Belgium 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Unemployment', 'rate']: ['6', '7.1', '7.9', '8.6', '8.6', '8.5', '7.6', '7.2', '8.4', '8', '7']

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

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

Example 263:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2005', '2018']}
title: Expenditure on beer in the United Kingdom 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Expenditure', 'in', 'million', 'GBP']: ['5631', '5091', '4640', '4571', '4573', '4435', '4188', '4119', '3994', '4142', '3677', '3802', '3976', '3714']

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

generated_template: This statistic shows the total annual templateYLabel[0] on templateTitle[0] and furnishings in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , based on volume . In templateXValue[max] , templateTitleSubject[1] households purchased approximately 19.1 templateScale British pounds worth of templateTitle[0] and furnishings , an templatePositiveTrend on the previous templateXLabel[0] of roughly one and a half templateScale British pounds . According to results of the templateXValue[1] Statista Global Consumer survey , 24 templateScale of templateTitleSubject[1] consumers have bought templateTitle[0] and household goods online in the last 12 months , while 33 templateScale claim to mostly look online for information about these products .
generated: This statistic shows the total annual Expenditure on and furnishings in the United Kingdom from 2005 to 2018 , based on volume .  In 2018 , United Kingdom households purchased approximately 19.1 million British pounds worth of Expenditure and furnishings , an increase on the previous Year of roughly one and a half million British pounds .  According to results of the 2017 Statista Global Consumer survey , 24 million of United Kingdom consumers have bought Expenditure and household goods online in the last 12 months , while 33 million claim to mostly look online for information about these products .

Example 264:
titleEntities: {'Subject': ['European'], 'Date': ['2005', '2018']}
title: European ATM numbers 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Total', 'number', 'of', 'ATMs']: ['406532', '413414', '420200', '411243', '409136', '407001', '412799', '403996', '398040', '391175', '383951', '362244', '335083', '324797']

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

generated_template: In templateXValue[max] , the templateTitle[3] of templateTitle[0] and calves in the templateTitleSubject[0] was approximately templateYValue[min] templateScale , a slight templateNegativeTrend from the previous templateXLabel[0] . This was the lowest templateYLabel[0] for the entire period shown in this graph . Despite a small rebound in templateXValue[4] and templateXValue[3] this constitutes a slow long-term decline of herd sizes .
generated: In 2018 , the 2005 of European and calves in the European was approximately 324797 million , a slight decrease from the previous Year .  This was the lowest Total for the entire period shown in this graph .  Despite a small rebound in 2014 and 2015 this constitutes a slow long-term decline of herd sizes .

Example 265:
titleEntities: {'Subject': ['Bosnia-Herzegovina'], 'Date': ['2019']}
title: Unemployment rate in Bosnia-Herzegovina 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['21.22', '20.84', '20.47', '25.41', '27.65', '27.52', '27.45', '28.01', '27.58', '27.31', '24.07', '23.41', '28.98', '31.11', '30.49', '29.87', '29.03', '28.22', '27.13', '26.19', '25.31']

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

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

Example 266:
titleEntities: {'Subject': ['North Macedonia'], 'Date': ['2024']}
title: Inflation rate in North Macedonia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2.2', '2.2', '2.2', '2.2', '1.7', '1.26', '1.46', '1.35', '-0.24', '-0.3', '-0.28', '2.79', '3.32', '3.91', '1.51', '-0.74', '7.52', '2.79', '3.27', '-0.57', '-0.66', '1.11', '2.31', '5.2', '6.61', '-1.28', '0.54', '1.29', '2.47', '16.37', '126.58']

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

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

Example 267:
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 268:
titleEntities: {'Subject': ['Average'], 'Date': ['2014']}
title: Average prices for zinc worldwide from 2014 to 2025
X_Axis['Year']: ['2025', '2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Price', 'in', 'nominal', 'U.S.', 'dollars', 'per', 'metric', 'ton']: ['2475', '2470', '2465', '2460', '2455', '2450', '2570', '2922', '2891', '2090', '1932', '2161']

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

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

Example 269:
titleEntities: {'Subject': ['Georgia'], 'Date': ['1992', '2018']}
title: Georgia - unemployment rate 1992 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92"]
Y_Axis['Unemployment', 'rate']: ['3.9', '4.7', '5.4', '6', '7.1', '8.2', '9.2', '10.2', '10.5', '9.9', '6.2', '4.5', '4.7', '5.3', '4.8', '4.8', '5', '4', '3.6', '3.9', '4.3', '4.6', '4.7', '4.8', '5.2', '6', '6.9']

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

generated_template: This statistic displays the templateYLabel[0] templateYLabel[1] in the state of templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[0] in templateTitleSubject[0] was templateYValue[min] templateScale .
generated: This statistic displays the Unemployment rate in the state of Georgia from 1992 to 2018 .  In 2018 , Unemployment in Georgia was 3.6 % .

Example 270:
titleEntities: {'Subject': ['European'], 'Date': ['2020']}
title: Revenue of the top European soccer leagues ( Big Five ) 2006 to 2020
X_Axis['Year']: ['2019/20', '2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07']
Y_Axis['Revenue', 'in', 'billion', 'euros']: ['17.95', '17.0', '15.59', '14.66', '13.42', '12.1', '11.3', '9.8', '9.3', '8.6', '8.4', '7.9', '7.7', '7.16']

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

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . According to Deloitte , in the templateXValue[0] season the templateYLabel[0] of the Spanish football club templatePositiveTrend by 22 templateScale , to templateYValue[max] templateScale templateYLabel[2] , with record matchday and commercial templateYLabel[0] for a football club . templateTitleSubject[0] - additional information templateTitleSubject[0] 's brand and team value templatePositiveTrend significantly in 2014 , thanks in part to the high-profile signing of Luis Suarez from Liverpool templateTitleSubject[0] in 2014 .
generated: The statistic shows the Revenue of the European from the 2006/07 season to the 2019/20 season .  According to Deloitte , in the 2019/20 season the Revenue of the Spanish football club increased by 22 billion , to 17.95 billion euros , with record matchday and commercial Revenue for a football club .  European - additional information European 's brand and team value increased significantly in 2014 , thanks in part to the high-profile signing of Luis Suarez from Liverpool European in 2014 .

Example 271:
titleEntities: {'Subject': ['Jordan'], 'Date': ['2015']}
title: Median age of the population in Jordan 2015
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2015', '2010', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960', '1955', '1950']
Y_Axis['Median', 'age', 'in', 'years']: ['34.9', '33.1', '31.2', '29.3', '27.5', '25.6', '23.8', '22.1', '21.3', '20.6', '19.6', '19.1', '16.8', '16.3', '15.5', '16.4', '17.1', '17.4', '18.0', '17.6', '17.2']

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

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

Example 272:
titleEntities: {'Subject': ['Saudi Arabia'], 'Date': ['2012', '2028']}
title: Total contribution of travel and tourism to GDP in Saudi Arabia 2012 to 2028
X_Axis['Year']: ['2028', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Value', 'in', 'billion', 'Saudi', 'Riyal']: ['573.1', '258.1', '240.9', '228.1', '232.3', '215.4', '21.7', '209.2']

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

generated_template: The statistic presents the templateYLabel[0] of templateTitle[0] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were a total amount of around templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] in the previous templateXLabel[0] .
generated: The statistic presents the Value of Total billion in the Saudi Arabia from 2012 to 2028 .  In 2028 , there were a total amount of around 573.1 billion Riyal in the previous Year .

Example 273:
titleEntities: {'Subject': ['Norway'], 'Date': ['2024']}
title: Norway 's budget balance in relation to GDP 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'balance', 'in', 'relation', 'to', 'GDP']: ['8.6', '8.21', '7.84', '7.75', '7.82', '7.57', '7.25', '4.92', '4.04', '6.07', '8.77']

gold: The statistic shows Norway 's budget balance in relation to GDP between 2014 and 2018 , with projections up until 2024 . A positive value indicates a budget surplus , a negative value indicates a deficit . In 2018 , Norway 's surplus amounted to around 7.25 percent of GDP .
gold_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a deficit . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] surplus amounted to around templateYValue[6] templateScale of templateYLabel[3] .

generated_template: The statistic shows templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateYLabel[1] in templateYLabel[2] to templateYLabel[3] between templateXValue[min] and templateXValue[6] , with projections up until templateXValue[max] . A positive value indicates a templateYLabel[0] surplus , a negative value indicates a deficit . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] surplus amounted to around templateYValue[max] templateScale of templateYLabel[3] .
generated: The statistic shows Norway 's Budget balance in relation to GDP between 2014 and 2018 , with projections up until 2024 .  A positive value indicates a Budget surplus , a negative value indicates a deficit .  In 2018 , Norway 's Budget surplus amounted to around 8.77 % of GDP .

Example 274:
titleEntities: {'Subject': ['U.S'], 'Date': ['1990', '2010']}
title: U.S active duty military suicides 1990 to 2010
X_Axis['Year']: ['2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Number', 'of', 'suicides']: ['289', '302', '259', '211', '213', '182', '197', '190', '174', '153', '153', '150', '165', '159', '188', '250', '232', '236', '238', '256', '232']

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

generated_template: The statistic shows the templateTitleSubject[0] of the templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] was around templateYValue[0] templateScale people in templateXValue[7] . The global templateYLabel[1] As shown above , the total number of people living on Earth has more than doubled since the 1950s , and continues to templatePositiveTrend .
generated: The statistic shows the U.S of the Number suicides from 1990 to 2010 .  The Number suicides was around 289 million people in 2003 .  The global suicides As shown above , the total number of people living on Earth has more than doubled since the 1950s , and continues to increase .

Example 275:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2012', '2014']}
title: PC online games revenue in Malaysia 2012 to 2014
X_Axis['Year']: ['2014', '2013', '2012']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['66.5', '54.1', '44.1']

gold: The statistic presents a forecast of the PC online games revenue in Malaysia from 2012 to 2014 . It was estimated that the 2014 PC online games revenue for Malaysia would be 66.5 million U.S. dollars .
gold_template: The statistic presents a forecast of the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . It was estimated that the templateXValue[max] templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] for templateTitleSubject[0] would be templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] Canada company from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the health templateTitle[1] chain is expected to generate a templateYLabel[0] of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] . In templateXValue[1] , the templateTitleSubject[0] based company operated 307 clubs .
generated: The statistic depicts the Revenue of the Malaysia Canada company from 2012 to 2014 .  In 2014 , the health online chain is expected to generate a Revenue of 66.5 million U.S. dollars .  In 2013 , the Malaysia based company operated 307 clubs .

Example 276:
titleEntities: {'Subject': ['Liberia'], 'Date': ['2019']}
title: Unemployment rate in Liberia 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['2.03', '2.03', '2.03', '2.15', '2.18', '2.21', '2.29', '2.26', '2.28', '2.27', '2.25', '1.96', '2.03', '2.23', '2.42', '2.53', '2.61', '2.66', '2.61', '2.77', '2.78']

gold: This statistic shows the unemployment rate in Liberia from 1999 to 2019 . In 2019 , the unemployment rate in Liberia was at 2.03 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .

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

Example 277:
titleEntities: {'Subject': ['Zimbabwe'], 'Date': ['2019']}
title: Youth unemployment rate in Zimbabwe in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['8.13', '8.16', '8.18', '8.56', '8.79', '8.86', '8.96', '8.86', '8.47', '8.15', '8.06', '7.39', '6.96', '6.85', '7.21', '7.41', '8.54', '9.5', '10.11', '11.02', '11.95']

gold: The statistic shows the youth unemployment rate in Zimbabwe from 1999 and 2019 . The data are ILO estimates . In 2019 , the estimated youth unemployment rate in Zimbabwe was at 8.13 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . The data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .
generated: The statistic shows the Youth unemployment rate in Zimbabwe from 1999 and 2019 .  According to the source , the data are ILO estimates .  In 2019 , the estimated Youth unemployment rate in Zimbabwe was at 8.13 % .

Example 278:
titleEntities: {'Subject': ['EU'], 'Date': ['2009', '2018']}
title: Number of illegal entries between BCPs to the EU 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'illegal', 'entries', 'in', 'thousands']: ['150.1', '204.72', '511.05', '1822.18', '282.93', '107.37', '72.44', '141.05', '104.06', '104.6']

gold: This statistic shows the total number of individuals detected entering the European Union ( EU ) illegally between border-crossing points ( BCPs ) from 2009 to 2018 . In 2013 , there was a total of approximately 107 thousand illegal entries between BCPs , making it a 48 percent increase on the previous year . By 2015 the number of individuals had increased to almost two million illegal entries .
gold_template: This statistic shows the total templateYLabel[0] of individuals detected entering the European Union ( templateTitleSubject[0] ) illegally templateTitle[3] border-crossing points ( templateTitle[4] ) from templateXValue[min] to templateXValue[max] . In templateXValue[5] , there was a total of approximately templateYValue[5] thousand templateYLabel[1] templateYLabel[2] templateTitle[3] templateTitle[4] , making it a 48 templateScale templatePositiveTrend on the previous templateXLabel[0] . By templateXValue[3] the templateYLabel[0] of individuals had templatePositiveTrend to almost templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] .

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] ' templateYLabel[0] templateYLabel[1] amounted to around templateYValue[0] templateScale templateYLabel[3] . After spin-offs of other product segments , Koninklijke templateTitleSubject[0] N.V. today is a company focused on healthcare/medical technology .
generated: The statistic presents the Number illegal of EU from 2009 to 2018 .  In 2018 , EU ' Number illegal amounted to around 150.1 thousands .  After spin-offs of other product segments , Koninklijke EU N.V. today is a company focused on healthcare/medical technology .

Example 279:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2016']}
title: Self-paced e-learning industry revenue in the U.S. 2016 to 2021
X_Axis['Year']: ['2021', '2020', '2019', '2018', '2017', '2016']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['15.86', '17.35', '18.57', '19.53', '20.33', '20.85']

gold: This statistic presents the self-paced e-learning market revenue in the United States from 2016 to 2021 . The self-paced online learning market , in the U.S. , reached $ 20.85 billion in revenues in 2016 . The figure is estimated to decrease to $ 15.86 billion by 2021 .
gold_template: This statistic presents the templateTitle[0] templateTitle[1] market templateYLabel[0] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateTitle[0] online learning market , in the templateYLabel[2] , reached $ templateYValue[idxmin(X)] templateScale in revenues in templateXValue[idxmax(Y)] . The figure is estimated to templateNegativeTrend to $ templateYValue[idxmax(X)] templateScale by templateXValue[idxmin(Y)] .

generated_template: This statistic gives information 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] templateScale templateYLabel[2] templateYLabel[3] . This number is expected to grow to templateYValue[idxmax(X)] templateScale by the end of templateXValue[idxmax(Y)] .
generated: This statistic gives information on the e-learning industry revenue U.S. sales Revenue worldwide from 2016 to 2021 .  The source estimates that the U.S. VR market size in 2017 will be worth 20.33 billion U.S. dollars .  This number is expected to grow 15.86 billion by the end of 2016 .

Example 280:
titleEntities: {'Subject': ['Boston Bruins'], 'Date': ['2005', '2019']}
title: Boston Bruins ' revenue 2005 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['228', '191', '176', '169', '158', '164', '114', '129', '125', '110', '108', '97', '87', '86']

gold: The statistic shows the revenue of the Boston Bruins from the 2005/06 season to the 2018/19 season . The revenue of the Boston Bruins amounted to 228 million U.S. dollars in the 2018/19 season .
gold_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] Leafs from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] Leafs amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .
generated: The statistic shows the Revenue of the Boston Bruins Leafs from the 2005/06 season to the 2018/19 season .  The Revenue of the Boston Bruins Leafs amounted to 228 million U.S. dollars in the 2018/19 season .

Example 281:
titleEntities: {'Subject': ['Rwanda'], 'Date': ['2018']}
title: Urbanization in Rwanda 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['17.21', '17.13', '17.06', '17', '16.97', '16.95', '16.94', '16.94', '16.93', '16.93', '16.93']

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

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

Example 282:
titleEntities: {'Subject': ['Production'], 'Date': ['2013', '2020']}
title: Production of pork worldwide 2013 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Thousand', 'tons', 'carcass', 'weight']: ['96.38', '106.0', '112.94', '112.07', '111.39', '112.01', '110.65', '108.85']

gold: This statistic depicts the production of pork worldwide from 2013 to 2020 . The net production of pork worldwide amounted to about 113 million tons carcass weight in 2018 , and forecasted to decrease to 96.4 million metric tones by 2020 .
gold_template: This statistic depicts the templateTitleSubject[0] of templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . The net templateTitleSubject[0] of templateTitle[1] templateTitle[2] amounted to about templateYValue[max] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] , and forecasted to templateNegativeTrend to templateYValue[min] templateScale metric tones by templateXValue[idxmin(Y)] .

generated_template: This statistic presents the templateYLabel[0] of templateTitle[1] at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , as of 31 of each templateXLabel[0] . As of 31 , templateXValue[max] , templateTitleSubject[0] was employing some templateYValue[idxmax(X)] people templateYValue[idxmax(X)] . Abbott was a U.S.-based global pharmaceutical and healthcare products company , headquartered in Chicago , Illinois .
generated: This statistic presents the Thousand of pork at Production from 2013 to 2020 , as of 31 each Year .  As of 31 , 2020 Production was employing some 96.38 people .  Abbott was a U.S.-based global pharmaceutical and healthcare products company , headquartered in Chicago , Illinois .

Example 283:
titleEntities: {'Subject': ['UEFA Champions League'], 'Date': ['2005', '2018']}
title: UEFA Champions League total performance and bonus payments to clubs 2005 to 2018
X_Axis['Year']: ['2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Bonus', 'payments', 'in', 'million', 'euros']: ['1412.6', '1396.13', '1349.43', '1033.43', '904.6', '910.0', '754.1', '786.3', '757.5', '583.4', '585.6', '584.9', '437.1']

gold: The statistic shows the total amount of bonus payments to the participating clubs in the UEFA Champions League from the 2005/06 season to the 2017/18 season . In the 2017/18 season , the total bonus payments to the participating clubs amounted to 1,412.6 million euros .
gold_template: The statistic shows the templateTitle[3] amount of templateYLabel[0] templateYLabel[1] to the participating templateTitle[7] in the templateTitleSubject[0] League from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[0] season , the templateTitle[3] templateYLabel[0] templateYLabel[1] to the participating templateTitle[7] amounted to templateYValue[max] templateScale templateYLabel[3] .

generated_template: In the period templateXValue[0] , templateYValue[min] templateScale of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] smoked at the time of delivery . The share of templateYLabel[1] templateYLabel[2] templateTitle[2] has templateNegativeTrend since templateTitleDate[min] when almost templateYValue[max] templateScale of templateYLabel[1] templateYLabel[2] did so . templateTitle[2] templateTitle[3] templateTitle[4] can lead to many birth complications , so it is advised that the expecting mother quits templateTitle[2] for the health of the baby .
generated: In the period 2017/18 , 437.1 million of payments million in UEFA Champions League smoked at the time of delivery .  The share of payments million League has decreased since 2005 when almost 1412.6 million of payments million did so .  League total performance can lead to many birth complications , so it is advised that the expecting mother quits League for the health of the baby .

Example 284:
titleEntities: {'Subject': ['Syngenta'], 'Date': ['2009', '2018']}
title: Syngenta 's R & D expenditure worldwide 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Expenditure', 'in', 'million', 'U.S.', 'dollars']: ['-1300', '-1273', '-1299', '1362', '1430', '1376', '1257', '1191', '1032', '952']

gold: The statistic shows Syngenta AG 's expenditure on research and development ( R & D ) worldwide from 2009 to 2018 . Syngenta is one of the largest global agribusiness companies . It is based in Basel , Switzerland , and notably markets seeds and pesticides .
gold_template: The statistic shows templateTitleSubject[0] AG templateTitle[1] templateYLabel[0] on research and development ( templateTitle[2] templateTitle[3] templateTitle[4] ) templateTitle[6] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] is one of the largest global agribusiness companies . It is based in Basel , Switzerland , and notably markets seeds and pesticides .

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

Example 285:
titleEntities: {'Subject': ['The Cheesecake Factory'], 'Date': ['2009', '2018']}
title: The Cheesecake Factory 's number of establishments 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'restaurants']: ['201', '199', '194', '188', '177', '168', '162', '156', '149', '160']

gold: This statistic shows the number of The Cheesecake Factory restaurants from 2009 to 2018 . In 2018 , U.S.-based restaurant chain The Cheesecake Factory had 201 restaurants , up from 199 the previous year .
gold_template: This statistic shows the templateYLabel[0] of The templateTitleSubject[0] restaurants from templateXValue[min] to templateXValue[max] . In templateXValue[max] , U.S.-based restaurant chain The templateTitleSubject[0] had templateYValue[max] templateYValue[idxmax(X)] , up from templateYValue[1] the previous templateXLabel[0] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[5] , there were templateYValue[idxmax(X)] incidents of templateYLabel[0] .
generated: This statistic shows the Number of The Cheesecake Factory in the number establishments 2009 to 2018 .  In 2013 , there were 201 incidents of Number .

Example 286:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2017']}
title: Life expectancy at birth in Vietnam 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Life', 'expectancy', 'at', 'birth', 'in', 'years']: ['75.24', '75.17', '75.11', '75.06', '75.01', '74.96', '74.9', '74.84', '74.75', '74.63', '74.47']

gold: This statistic shows the life expectancy at birth in Vietnam from 2007 to 2017 . In 2017 , the average life expectancy at birth in Vietnam was 75.24 years .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] was templateYValue[idxmax(X)] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] had reached about templateYValue[idxmax(X)] years.Demographic development in templateTitleSubject[0] – additional information templateYLabel[0] templateYLabel[1] at templateYLabel[2] refers to the average number of years a group of people born in the same templateXLabel[0] would live , assuming constant mortality rates . The country with the highest templateYLabel[0] templateYLabel[1] at templateYLabel[2] was Japan , while templateTitleSubject[0] had reached a templateYLabel[0] templateYLabel[1] above global average .
generated: The statistic shows the Life expectancy at birth in Vietnam from 2007 to 2017 .  In 2017 , the average Life expectancy at birth in Vietnam had reached about 75.24 years.Demographic development in Vietnam – additional information Life expectancy at birth refers to the average number of years a group of people born in the same Year would live , assuming constant mortality rates .  The country with the highest Life expectancy at birth was Japan , while Vietnam had reached a Life expectancy above global average .

Example 287:
titleEntities: {'Subject': ['Eritrea'], 'Date': ['2024']}
title: Total population of Eritrea 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['6.68', '6.58', '6.48', '6.38', '6.27', '6.16', '6.05', '5.93', '5.82', '5.7', '5.58']

gold: This statistic shows the total population of Eritrea from 2014 to 2024 . All figures are estimates . In 2018 , the total population of Eritrea was estimated to amount to approximately 6.05 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . All figures are estimates . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was estimated to amount to approximately templateYValue[6] templateScale templateYLabel[0] .

generated_template: 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] templateScale templateYLabel[0] . See the figures for the templateTitle[1] of South Korea for comparison .
generated: The statistic shows the Total population of Eritrea from 2014 to 2018 , with projections up until 2024 .  In 2018 , the Total population of Eritrea amounted to around 5.93 millions Inhabitants .  See the figures for the population of South Korea for comparison .

Example 288:
titleEntities: {'Subject': ['Europe'], 'Date': ['2020']}
title: Europe : forecasted distribution of golfers in 2020 , by age group
X_Axis['Year']: ['20_or_younger', '20_to_40_years', '40_to_50_years', '50_to_60_years', '60_or_older']
Y_Axis['Share', 'of', 'average', 'increase']: ['6', '11', '18', '24', '42']

gold: The statistic displays the forecast of a golf player distribution in Europe in 2020 , by age group . With data from five European countries it was forecasted that in 2020 approximately 24 percent of golf players will be between 50 and 60 years old .
gold_template: The statistic displays the forecast of a golf player templateTitle[2] in templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateTitle[6] templateTitle[7] . With data from five European countries it was templateTitle[1] that in templateTitleDate[0] approximately templateYValue[3] templateScale of golf players will be between templateXValue[2] and templateXValue[3] templateXValue[1] old .

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateTitle[1] a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateYValue[2] templateTitle[7] in the templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] templateYLabel[1] had attended a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateXLabel[0] .
generated: This statistic presents the Share of average forecasted a distribution golfers 2020 at least once in the by 18 group in the group from 20 or younger to 50 to 60 years .  In 50 to 60 years , 42 % of Europe average had attended a distribution golfers 2020 at least once in the by Year .

Example 289:
titleEntities: {'Subject': ['earth'], 'Date': ['2010']}
title: Forecast of rare earth oxide holmium oxide price globally 2010 to 2025
X_Axis['Year']: ['2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022', '2023', '2024', '2025']
Y_Axis['Price', 'in', 'U.S.', 'dollars', 'per', 'kilogram']: ['41', '303', '107', '66', '63', '55', '51', '49', '46', '38', '20', '20', '20', '21', '21', '22']

gold: This statistic displays the price development of rare earth oxide holmium oxide globally from 2009 to 2025 . It expected that the price of holmium oxide will reach some 49 U.S. dollars per kilogram in 2017 .
gold_template: This statistic displays the templateYLabel[0] development of templateTitle[1] templateTitleSubject[0] templateTitle[3] templateTitle[4] templateTitle[3] templateTitle[7] from 2009 to templateXValue[max] . It expected that the templateYLabel[0] of templateTitle[4] templateTitle[3] will reach some templateYValue[7] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] in templateXValue[7] .

generated_template: In templateXValue[max] , the templateYLabel[1] of templateTitle[2] and templateTitle[3] to the Italian gross domestic product amounted to templateYValue[10] templateScale templateYLabel[3] . The industry , which is one of the most important ones for the country 's economy , constituted about 13.3 templateScale of the Italian templateYLabel[0] in the templateXLabel[0] considered and is predicted to reach 14.3 templateScale in templateXValue[max] . In search of the Italian dolce vita Every templateXLabel[0] more and more international visitors come to templateTitleSubject[0] to discover the real Italian dolce vita – breathtaking landscapes , rich history , great art , interesting culture and delectable cuisine .
generated: In 2025 , the U.S. of earth and oxide to the Italian gross domestic product amounted to 20 billion per .  The industry , which is one of the most important ones for the country 's economy , constituted about 13.3 % of the Italian Price in the Year considered and is predicted to reach 14.3 % in 2025 .  In search of the Italian dolce vita Every Year more and international visitors come to earth discover the real Italian dolce vita – breathtaking landscapes , rich history , great art , interesting culture and delectable cuisine .

Example 290:
titleEntities: {'Subject': ['United States'], 'Date': ['1998', '2018']}
title: Natural gas production - United States 1998 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1998']
Y_Axis['Production', 'in', 'billion', 'cubic', 'meters']: ['831.8', '745.8', '727.4', '740.3', '704.7', '655.7', '649.1', '617.4', '575.2', '557.6', '546.1', '521.9', '524.0', '511.1', '526.4', '540.8', '536.0', '555.5', '543.2', '538.7']

gold: Production of natural gas in the United States has been increasing for the past decade and amounted to 831.8 billion cubic meters in 2018 . An increase in production corresponded with rising demand for natural gas in the United States , particularly after the 2008 Recession . Natural gas becomes competitive Since the early 2000s , the price of coal had been going up , and increased more rapidly following the 2008 Recession , which affected the cost of crude oil to an even greater degree .
gold_template: templateYLabel[0] of templateTitle[0] templateTitle[1] in the templateTitleSubject[0] has been templatePositiveTrend for the past decade and amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] . An templatePositiveTrend in templateYLabel[0] corresponded with templatePositiveTrend demand for templateTitle[0] templateTitle[1] in the templateTitleSubject[0] , particularly after the templateXValue[10] Recession . templateTitle[0] templateTitle[1] becomes competitive Since the early 2000s , the price of coal had been going up , and templatePositiveTrend more rapidly following the templateXValue[10] Recession , which affected the cost of crude oil to an even greater degree .

generated_template: The templateYLabel[0] templateTitle[2] templateYLabel[1] of templateTitle[4] templateTitle[5] has risen steadily in the templateTitle[0] , reaching a peak of almost templateYValue[max] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] in templateXValue[idxmax(Y)] . In the templateTitle[0] , templateTitle[5] prices tend to reflect costs related to construction , finance , maintenance , and operation of power plants and templateTitle[5] grids . How do templateTitle[5] rates differ ? The templateYLabel[1] of templateTitle[5] can vary widely across the states with Hawaii having one of the highest rates and Idaho one of the lowest .
generated: The Production production billion of States 1998 has risen steadily in the Natural , reaching a peak of almost 831.8 cubic meters in 2018 .  In the Natural , 1998 prices tend to reflect costs related to construction , finance maintenance , and operation of power plants and 1998 grids .  How do 1998 rates differ ? The billion of 1998 can vary widely across the states with Hawaii having one of the highest rates and Idaho one of the lowest .

Example 291:
titleEntities: {'Subject': ['Atlanta Falcons'], 'Date': ['2019']}
title: Regular season home attendance of the Atlanta Falcons 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Home', 'attendance']: ['572811', '583184', '575681', '559998', '562845', '493515', '561795', '560773', '551892', '542800', '545384', '512520', '547610', '563456']

gold: This graph depicts the total regular season home attendance of the Atlanta Falcons franchise of the National Football League from 2006 to 2019 . In 2019 , the regular season home attendance of the franchise was 572,811 .
gold_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[idxmax(X)] .

generated_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[idxmax(X)] .
generated: This graph depicts the total Regular season Home attendance of the Atlanta Falcons franchise of the National Football League from 2006 to 2019 .  In 2019 , the Regular season Home attendance of the franchise was 572811 .

Example 292:
titleEntities: {'Subject': ['Europe'], 'Date': ['2012', '2016']}
title: Forecast for the number of new hotel rooms opening in Europe from 2012 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'hotel', 'rooms']: ['34060', '34451', '39178', '30982', '37818']

gold: This statistic shows a forecast for the number of new hotel rooms opening in Europe from 2012 to 2016 . In 2013 , 30,982 new hotel rooms opened in the European hotel market . It was forecasted that 34,060 new hotel rooms would open in 2016 .
gold_template: This statistic shows a templateTitle[0] templateTitle[1] the templateYLabel[0] of templateTitle[3] templateYLabel[1] templateYLabel[2] templateTitle[6] in templateTitleSubject[0] templateTitle[8] templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateYValue[min] templateTitle[3] templateYLabel[1] templateYLabel[2] opened in the European templateYLabel[1] market . It was forecasted that templateYValue[idxmax(X)] templateTitle[3] templateYLabel[1] templateYLabel[2] would open in templateXValue[max] .

generated_template: This statistic presents 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[idxmax(X)] templateScale templateTitleSubject[0] , up from templateYValue[1] templateScale in templateXValue[min] .
generated: This statistic presents the Number of Europe ( LoL ) monthly active rooms worldwide from 2012 to 2016 .  In 2016 , LoL had 34060 % Europe , up from 34451 % in 2012 .

Example 293:
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 294:
titleEntities: {'Subject': ['Switzerland'], 'Date': ['2024']}
title: National debt of Switzerland 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'in', 'billion', 'U.S.', 'dollars']: ['258.2', '261.41', '264.78', '267.72', '270.63', '273.8', '280.14', '286.05', '276.79', '281.84', '279.83']

gold: The statistic shows the national debt of Switzerland from 2014 to 2017 , with projections up until 2024 . In 2017 , the national debt of Switzerland amounted to around 286.05 billion U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to around templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was around templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . In a ranking of templateYLabel[1] to GDP per country , templateTitleSubject[0] is currently ranked second .
generated: This statistic shows the National debt of Switzerland from 2014 to 2018 , with projections until 2024 .  In 2018 , the National debt in Switzerland was around 286.05 billion U.S. dollars .  In a ranking of debt to GDP per country , Switzerland is currently ranked second .

Example 295:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2017', '2023']}
title: Mexico : mobile phone internet user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['59', '58', '56', '53', '50', '47', '43']

gold: The statistic shows the mobile phone internet user penetration in Mexico from 2017 to 2023 . In 2017 , 43 percent of the population users accessed the internet through their mobile device . This figure is projected to grow to 59percent in 2023 .
gold_template: The statistic shows the templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the templateYLabel[1] users accessed the templateTitle[3] through their templateTitle[1] device . This figure is projected to grow to 59percent in templateXValue[max] .

generated_template: This statistic provides information on the templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , it was estimated that templateYValue[idxmax(X)] templateScale of the Indian templateYLabel[1] will be accessing templateTitle[1] networks , up from templateYValue[5] templateScale in templateXValue[5] .
generated: This statistic provides information on the mobile phone internet in Mexico from 2017 to 2023 .  In 2023 , it was estimated that 59 % of the Indian population will be accessing mobile networks , up from 47 % in 2018 .

Example 296:
titleEntities: {'Subject': ['Stryker'], 'Date': ['2011', '2019']}
title: Stryker 's annual research , development and engineering expenses 2011 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Expenses', 'in', 'million', 'U.S.', 'dollars']: ['971', '862', '787', '715', '625', '614', '536', '471', '462']

gold: The statistic shows the annual research , development and engineering expenses of Stryker from 2011 to 2019 . Stryker 's research , development and engineering expenses have gradually increased since 2011 , reaching 971 million U.S. dollars in 2019 . The Stryker Corporation is a U.S. medical technology company headquartered in Kalamazoo , Michigan .
gold_template: The statistic shows the templateTitle[2] templateTitle[3] , templateTitle[4] and templateTitle[5] templateYLabel[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] templateTitle[1] templateTitle[3] , templateTitle[4] and templateTitle[5] templateYLabel[0] have gradually templatePositiveTrend since templateXValue[min] , reaching templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] . The templateTitleSubject[0] Corporation is a templateYLabel[2] medical technology company headquartered in Kalamazoo , Michigan .

generated_template: This statistic represents the templateTitle[3] templateTitle[4] templateYLabel[0] of the British automotive company templateTitleSubject[0] Rover from financial templateXLabel[0] templateXValue[min] to financial templateXLabel[0] templateXValue[max] in templateScale British pounds . Between templateXValue[min] and templateXValue[1] , the company made a templateTitle[4] templateYLabel[0] higher than templateYValue[7] templateScale 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 templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic represents the research development Expenses of the British automotive company Stryker Rover from financial Year 2011 to financial Year 2019 in million British pounds .  Between 2011 and 2018 , the company made a development Expenses higher than 471 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 U.S. dollars .

Example 297:
titleEntities: {'Subject': ['China'], 'Date': ['1990', '2018']}
title: Average size of households in China 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2000', '1995', '1990']
Y_Axis['Number', 'of', 'persons']: ['3.03', '3.17', '3.11', '3.1', '2.97', '2.98', '3.02', '2.87', '2.88', '2.89', '3.13', '3.23', '3.5']

gold: This graph shows the average size of households in China from 1990 to 2018 . That year , approximately three people were living in an average Chinese household.Average number of people per household in China – additional information A household is commonly defined as one person living alone or a group of people living together and sharing certain living accommodations . The average number of people living in one household in China dropped from 3.5 in 1990 to 2.87 in 2011 .
gold_template: This graph shows the templateTitle[0] templateTitle[1] of templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . That templateXLabel[0] , approximately templateYValue[0] people were living in an templateTitle[0] Chinese household.Average templateYLabel[0] of people per household in templateTitleSubject[0] – additional information A household is commonly defined as one person living alone or a group of people living together and sharing certain living accommodations . The templateTitle[0] templateYLabel[0] of people living in one household in templateTitleSubject[0] templateNegativeTrend from templateYValue[idxmin(X)] in templateXValue[idxmax(Y)] to templateYValue[min] in templateXValue[idxmin(Y)] .

generated_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Global hotel company templateTitleSubject[0] Corporation generated approximately templateYValue[0] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] in templateYLabel[0] in templateXValue[max] .
generated: This statistic shows the Number of China from 1990 to 2018 .  Global hotel company China Corporation generated approximately 3.03 persons in Number 2018 .

Example 298:
titleEntities: {'Subject': ['Phoenix Suns'], 'Date': ['2001', '2019']}
title: Phoenix Suns ' revenue 2001 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['246', '235', '218', '173', '154', '145', '137', '121', '136', '147', '148', '148', '145', '132', '132', '111', '109', '107']

gold: The statistic shows the revenue of the Phoenix Suns franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise amounted to 246 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] The templateTitleSubject[0] are a professional basketball franchise of the National Basketball Association ( NBA ) , entering the league as an expansion team in 1968 .
generated: The statistic shows the Revenue of the Phoenix Suns franchise from the 2001/02 season to the 2018/19 season .  In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 246 million U.S. dollars .  Phoenix Suns The Phoenix Suns are a professional basketball franchise of the National Basketball Association ( NBA ) , entering the league as an expansion team in 1968 .

Example 299:
titleEntities: {'Subject': ['U.S. January TV'], 'Date': ['2020', '2020']}
title: Leading trailers in the U.S. January 2020 , by weekly TV ad spend
X_Axis['Year']: ['1917', 'Dolittle', 'Bad_Boys_for_Life', 'Like_a_Boss', 'Just_Mercy']
Y_Axis['Spending', 'in', 'million', 'U.S.', 'dollars']: ['10.41', '5.77', '4.18', '3.9', '3.72']

gold: The leading movie commercial in the United States based on weekly television advertising spending for the week ending January 5 , 2020 was for war drama film ' 1917 ' _ , with a 10.41 million U.S. dollar spend by studio Universal Pictures . Universal also spent 5.77 million U.S. dollars on TV promotion of 'Dolittle ' _ .
gold_template: The templateTitle[0] movie commercial in the templateTitle[2] based on templateTitle[6] television advertising templateYLabel[0] templateXValue[2] the week ending templateTitleSubject[0] 5 , templateTitleDate[0] was templateXValue[2] war drama film ' templateXValue[0] ' _ , with a templateYValue[max] templateScale templateYLabel[2] dollar templateTitle[9] templateTitle[5] studio Universal Pictures . Universal also spent templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] on templateTitleSubject[0] promotion of 'Dolittle ' _ .

generated_template: This statistic provides information on the templateTitle[2] of templateTitleSubject[0] templateTitle[0] templateYLabel[2] as of 2016 , sorted by templateTitle[1] group . During the survey period it was found that templateYValue[0] templateScale of templateTitle[0] templateYLabel[2] in the templateTitle[3] were aged between 13 and 17 years . As of spring 2017 , templateTitle[0] ranks as the most important social network of teens in the templateTitle[3] , ahead of other platforms such as Instagram , Twitter or Facebook .
generated: This statistic provides information on the U.S. of U.S. January TV Leading U.S. as of 2016 , sorted by trailers group .  During the survey period it was found that 10.41 million of Leading U.S. in the January were aged between 13 and 17 years .  As of spring 2017 , Leading ranks as the most important social network of teens in the January , ahead of other platforms such as Instagram , Twitter or Facebook .

Example 300:
titleEntities: {'Subject': ['Premier League'], 'Date': ['2010', '2019']}
title: Premier League total broadcasting payments to clubs 2010 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11']
Y_Axis['Broadcasting', 'payments', 'in', 'million', 'GBP']: ['2456.01', '2419.6', '2398.5', '1633.9', '1605.3', '1563.0', '1061.0', '1055.0', '953.0']

gold: The statistic depicts the broadcasting payments to Premier League clubs from 2010/11 to 2018/19 . In the 2018/19 season , all Premier League clubs combined received a total of 2.46 billion British Pounds in broadcasting payments .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] to templateTitleSubject[0] clubs from templateXValue[last] to templateXValue[0] . In the templateXValue[0] season , all templateTitleSubject[0] clubs combined received a templateTitle[2] of templateYValue[max] templateScale British Pounds in templateYLabel[0] templateYLabel[1] .

generated_template: In the academic templateXLabel[0] templateXValue[0] there were approximately templateYValue[min] thousand templateYLabel[1] in the templateTitleSubject[0] , around 655 templateYLabel[1] less than there were in the templateXValue[last] academic templateXLabel[0] . Throughout most of this period there has been a steady decline in the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] .
generated: In the academic Year 2018/19 there were approximately 953.0 thousand payments in the Premier League , around 655 payments less than there were in the 2010/11 academic Year .  Throughout most of this period there has been a steady decline in the Broadcasting of payments in the Premier League .

Example 301:
titleEntities: {'Subject': ['Canada'], 'Date': ['2000', '2018']}
title: Canada : reported arson rate 2000 to 2018
X_Axis['Year']: ['2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018']
Y_Axis['Rate', 'of', 'arson', 'per', '100,000', 'residents']: ['44.5', '46.27', '41.36', '43.12', '40.22', '40.48', '40.54', '39.57', '40.22', '39.86', '35.98', '30.29', '31.96', '25.41', '24.06', '25.12', '23.67', '23.4', '21.59']

gold: This statistic shows the reported arson rate in Canada from 2000 to 2018 . There were about 21.59 arsons per 100,000 residents in Canada in 2018 .
gold_template: This statistic shows the templateTitle[1] templateYLabel[1] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . There were about templateYValue[idxmax(X)] arsons templateYLabel[2] 100,000 templateYLabel[4] in templateTitleSubject[0] in templateXValue[idxmin(Y)] .

generated_template: The statistic shows the total templateTitleSubject[0] ( UK ) templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] templateXValue[0] . The overall trend was one of templatePositiveTrend , seeing a peak in templateXValue[0] with 27.99 templateScale British pounds and a total rise of over 5 templateScale British pounds throughout this period .
generated: The statistic shows the total Canada ( UK ) 2000 Rate arson per from fiscal Year 2018 to fiscal Year 2000 .  The overall trend was one of increasing , seeing a peak in 2000 with 27.99 billion British pounds and a total rise of over 5 billion British pounds throughout this period .

Example 302:
titleEntities: {'Subject': ['Marathon Oil'], 'Date': ['2010', '2018']}
title: Marathon Oil 's number of employees 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Number', 'of', 'employees']: ['2400', '2300', '2117', '2611', '3330', '3359', '3367', '3322', '29677']

gold: This statistic outlines Marathon Oil 's number of employees from 2010 to 2018 . Marathon Oil Corporation is an internationally leading United States-based oil and natural gas exploration and production company . In 2018 , the company had 2,400 employees .
gold_template: This statistic outlines templateTitleSubject[0] 's templateYLabel[0] of templateYLabel[1] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] Corporation is an internationally leading United States-based templateTitleSubject[0] and natural gas exploration and production company . In templateXValue[max] , the company had templateYValue[idxmax(X)] templateYValue[idxmax(X)] .

generated_template: This statistic depicts the templateYLabel[0] of templateTitleSubject[0] employees from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In its templateXValue[max] fiscal templateXLabel[0] , the Switzerland-based electronics company employed templateYValue[idxmax(X)] 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 303:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015']}
title: Percentage of population volunteering in the U.S. in 2015 , by age
X_Axis['Year']: ['16_to_24_years', '25_to_34_years', '35_to_44_years', '45_to_54_years', '55_to_64_years', '65_years_and_over']
Y_Axis['Percentage', 'of', 'population', 'volunteering']: ['21.8', '22.3', '28.9', '28', '25.1', '23.5']

gold: This statistic displays the percentage of population volunteering in the U.S. in 2015 , by age . In 2015 , 21.8 percent of Americans 16 to 24 years old volunteered at least once during the year .
gold_template: This statistic displays the templateScale of templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateTitle[6] . In templateTitleDate[0] , templateYValue[min] templateScale of Americans templateXValue[0] to templateXValue[0] old volunteered at least once during the templateXLabel[0] .

generated_template: This statistic presents the worldwide templateTitleSubject[0] templateTitle[1] templateYLabel[0] from to 2019 , sorted templateTitle[6] templateTitle[7] templateTitle[8] . During the measured period , accounts with 1,000 to 10,000 followers templatePositiveTrend their followers on average templateTitle[6] templateYValue[1] templateScale .
generated: This statistic presents the worldwide U.S. population Percentage from to 2019 , sorted age .  During the measured period , accounts with 1,000 to 10,000 followers increased their followers on average age 22.3 percentage .

Example 304:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2018']}
title: U.S. sweet cherry production 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Production', 'in', 'thousand', 'tons']: ['344.4', '437.6', '350.2', '335.5', '363.6', '332.1', '424.0', '334.4', '313.2', '442.9', '248.1', '310.7', '294.2', '250.8', '283.1', '245.7', '181.4', '230.4', '207.9']

gold: This statistic shows the total amount of sweet cherries produced in the United States from 2000 to 2018 . In 2018 , around 344 thousand tons of sweet cherries were produced in the U.S .
gold_template: This statistic shows the total amount of templateTitle[1] cherries produced in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around templateYValue[0] thousand templateYLabel[2] of templateTitle[1] cherries were produced in the templateTitle[0] .

generated_template: This statistic shows the estimated templateTitle[1] amount of strawberries produced in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around templateYValue[0] templateScale templateYLabel[2] of strawberries were produced in the templateTitle[0] .
generated: This statistic shows the estimated sweet amount of strawberries produced in the U.S. from 2000 to 2018 .  In 2018 , around 344.4 thousand tons of strawberries were produced in the U.S. .

Example 305:
titleEntities: {'Subject': ['Los Angeles Rams'], 'Date': ['2019']}
title: Regular season home attendance of the Los Angeles Rams 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Home', 'attendance']: ['498605', '579439', '507136', '665318', '419220', '456146', '455657', '396925', '451153', '423376', '441896', '479840', '514352', '522608']

gold: This graph depicts the total regular season home attendance of the St. Louis / Los Angeles Rams franchise of the National Football League from 2006 to 2019 . In 2019 , the regular season home attendance of the franchise was 498,605 . The franchise moved from St. Louis to Los Angeles before the 2016 season .
gold_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the St. Louis / templateTitleSubject[0] Rams franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[idxmax(X)] . The franchise moved from St. Louis to templateTitleSubject[0] before the templateXValue[3] templateTitle[1] .

generated_template: This graph depicts the total templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] franchise of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] templateYLabel[1] of the franchise was templateYValue[idxmax(X)] .
generated: This graph depicts the total Regular season Home attendance of the Los Angeles Rams franchise of the National Football League from 2006 to 2019 .  In 2019 , the Regular season Home attendance of the franchise was 498605 .

Example 306:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000']}
title: Estimated U.S. clothing and clothing accessories per capita sales 2000 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'sales', 'in', 'U.S.', 'dollars']: ['795', '804', '797', '786', '774', '763', '734', '689', '667', '709', '734', '714', '680', '650', '616', '599', '587', '594']

gold: The timeline shows the estimated clothing and clothing accessories per capita sales in the United States from 2000 to 2017 . In 2017 , clothing and clothing accessories sales amounted to 795 U.S. dollars per capita .
gold_template: The timeline shows the templateTitle[0] templateTitle[2] and templateTitle[2] templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] and templateTitle[2] templateTitle[4] templateYLabel[2] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] templateYLabel[0] templateYLabel[1] .

generated_template: The timeline shows the templateTitle[0] templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[1] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[2] templateYLabel[2] amounted to about templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] templateYLabel[0] templateYLabel[1] .
generated: The timeline shows the Estimated clothing Per capita sales in the U.S. capita 2000 to 2017 .  In 2017 , clothing sales amounted to about 795 U.S. dollars Per capita .

Example 307:
titleEntities: {'Subject': ['Forecast'], 'Date': ['2014', '2020']}
title: Forecast of sulfur fertilizer demand worldwide 2014 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Total', 'demand', 'in', 'million', 'metric', 'tons']: ['69.0', '67.7', '70.38', '68.08', '65.65', '63.47', '61.44']

gold: This statistic displays a forecast of total global demand for sulfur fertilizer from 2014 to 2020 . By 2020 , the annual demand for sulfur fertilizer is expected to reach some 69 million metric tons . Increasing crop prices lead to increased fertilizer demands and has been especially noted in recent years in South Asia .
gold_template: This statistic displays a templateTitleSubject[0] of templateYLabel[0] global templateYLabel[1] for templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . By templateXValue[max] , the annual templateYLabel[1] for templateTitle[1] templateTitle[2] is expected to reach some templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] . templatePositiveTrend crop prices lead to templatePositiveTrend templateTitle[2] demands and has been especially noted in recent years in South Asia .

generated_template: templateYLabel[0] of the templateTitleSubject[0] Brewing templateTitleSubject[0] reached around templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateTitleSubject[1] in templateXValue[max] , an templatePositiveTrend of just over 0.7 templateScale templateYLabel[2] templateYLabel[3] over the six-year survey period . In comparison , their worldwide net templateYLabel[0] amounted to around 10.8 templateScale templateYLabel[3] in templateXValue[idxmin(Y)] . templateTitleSubject[0] Brewing templateTitleSubject[0] origins The templateTitleSubject[0] was formed in 2005 through a merger between templateTitleSubject[0] of templateTitleSubject[1] and templateTitleSubject[0] of the country .
generated: Total of the Forecast Brewing reached around 69.0 million metric in Forecast 2020 , an increase of just over 0.7 million metric over the six-year survey period .  In comparison , their worldwide net Total amounted to around 10.8 million metric in 2014 .  Forecast Brewing Forecast origins The Forecast was formed in 2005 through a merger between Forecast of and Forecast of the country .

Example 308:
titleEntities: {'Subject': ['Smartphone'], 'Date': ['2016']}
title: Smartphone users worldwide 2016 to 2021
X_Axis['Year']: ['2021', '2020', '2019', '2018', '2017', '2016']
Y_Axis['Smartphone', 'users', 'in', 'billions']: ['3.8', '3.5', '3.2', '2.9', '2.7', '2.5']

gold: How many people have smartphones worldwide ? The number of smartphone users worldwide today surpasses three billion and is forecast to further grow by several hundred million in the next few years . China , India , and the United States are the countries with the highest number of smartphone users , with each country easily surpassing the 100 million user mark . Smartphone unit sales levelling off In the past five years , about 1.4 billion smartphones were sold worldwide annually , reflecting stagnation in the smartphone market during the last few years .
gold_template: How many people have smartphones templateTitle[2] ? The number of templateYLabel[0] templateYLabel[1] templateTitle[2] today surpasses templateYValue[2] templateScale and is forecast to further grow by several hundred templateScale in the next few years . China , India , and the country are the countries with the highest number of templateYLabel[0] templateYLabel[1] , with each country easily surpassing the 100 templateScale user mark . templateYLabel[0] unit sales levelling off In the past five years , about 1.4 templateScale smartphones were sold templateTitle[2] annually , reflecting stagnation in the templateYLabel[0] market during the last few years .

generated_template: This statistic shows the predicted templateXLabel[0] on templateXLabel[0] templateYLabel[1] in templateTitle[0] templateTitle[1] in the United Kingdom ( templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . The templateTitle[3] predicts a steady and slight templatePositiveTrend throughout the entire period , by 0.1 templateScale . The only templateNegativeTrend is expected to happen between templateXValue[min] and templateXValue[4] , by a decline of templateDelta[5,4] templateScale of templateYLabel[1] .
generated: This statistic shows the predicted Year on users in Smartphone users in the United Kingdom ( Smartphone ) from 2016 to 2021 .  The 2016 predicts a steady and slight increase throughout the entire period , by 0.1 billions .  The only decrease is expected to happen between 2016 and 2017 , by a decline of 0 billions of users .

Example 309:
titleEntities: {'Subject': ['Iran'], 'Date': ['2014', '2024']}
title: National debt of Iran 2014 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['National', 'debt', 'in', 'billion', 'U.S.', 'dollars']: ['575.52', '436.78', '344.33', '256.69', '205.66', '167.57', '142.95', '139.13', '143.56', '101.62', '31.64']

gold: The statistic shows the national debt of Iran from 2014 to 2018 , with projections up until 2024 . In 2018 , the national debt of Iran amounted to around 142.95 billion U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was around templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . In a ranking of templateYLabel[1] to GDP per country , templateTitleSubject[0] is currently ranked second .
generated: This statistic shows the National debt of Iran from 2014 to 2018 , with projections until 2024 .  In 2018 , the National debt in Iran was around 575.52 billion U.S. dollars .  In a ranking of debt to GDP per country , Iran is currently ranked second .

Example 310:
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 presents the templateYLabel[0] of templateYLabel[1] templateTitle[1] a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateYValue[2] templateTitle[7] in the templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] templateYLabel[1] had attended a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateXLabel[0] .
generated: This statistic presents the Imports of thousand rice a import volume 2013/14 at least once in the 2017/18 768 in the 2017/18 from 2013/14 to 2017/18 .  In 2017/18 , 775 thousand of U.S. thousand had attended a import volume 2013/14 at least once in the 2017/18 Year .

Example 311:
titleEntities: {'Subject': ['Italy'], 'Date': ['2018']}
title: Population density in Italy 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Inhabitants', 'per', 'square', 'kilometer']: ['205.45', '205.81', '206.12', '206.47', '206.67', '204.78', '202.42', '201.87', '201.53', '200.91', '200.0']

gold: The statistic shows the population density in Italy from 2008 to 2018 . In 2018 , the population density in Italy amounted to about 205.45 inhabitants per square kilometer . See the population of Italy for comparison .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] . See the templateTitle[0] of templateTitleSubject[0] for comparison .

generated_template: 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 templateScale 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 312:
titleEntities: {'Subject': ['Sweden'], 'Date': ['2009', '2019']}
title: Population in Sweden 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Inhabitants', 'in', 'millions']: ['10.33', '10.23', '10.12', '10.0', '9.85', '9.75', '9.64', '9.56', '9.48', '9.42', '9.34']

gold: This statistic shows the total population in Sweden from 2009 to 2019 . The number of inhabitants in Sweden has increased by nearly one million in this time period . In 2009 , there were approximately 9.34 million inhabitants in Sweden and by the end of 2019 the Swedish population reached 10.33 million people .
gold_template: This statistic shows the total templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The number of templateYLabel[0] in templateTitleSubject[0] has templatePositiveTrend by nearly one templateScale in this time period . In templateXValue[min] , there were approximately templateYValue[idxmin(X)] templateScale templateYLabel[0] in templateTitleSubject[0] and by the end of templateXValue[max] the Swedish templateTitle[0] reached templateYValue[idxmax(X)] templateScale people .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was at approximately templateYValue[6] templateScale templateYLabel[0] .
generated: This statistic shows the Population Sweden of from 2009 to 2019 .  In 2013 , the Population Sweden of was at approximately 9.64 millions Inhabitants .

Example 313:
titleEntities: {'Subject': ['Canada'], 'Date': ['2027']}
title: Projected increase of the national defense budget in Canada fiscal years 2018 to 2027
X_Axis['Year']: ['2026-2027', '2025-2026', '2024-2025', '2023-2024', '2022-2023', '2021-2022', '2020-2021', '2019-2020', '2018-2019', '2017-2018']
Y_Axis['Annual', 'funding', 'increase', 'in', 'million', 'Canadian', 'dollars']: ['2300', '2020', '1751', '1495', '1250', '1016', '793', '580', '377', '184']

gold: This statistic shows the projected increase over baseline spending for the national defense budget of Canada between the fiscal years 2018 to 2027 . In fiscal year 2027 , the defense budget for Canada is projected to be 2.3 billion Canadian dollars greater than 2026 budget .
gold_template: This statistic shows the templateTitle[0] templateYLabel[2] over baseline spending for the templateTitle[2] templateTitle[3] templateTitle[4] of templateTitleSubject[0] between the templateTitle[6] templateTitle[7] templateTitleDate[min] to templateTitleDate[max] . In templateTitle[6] templateXLabel[0] templateTitleDate[max] , the templateTitle[3] templateTitle[4] for templateTitleSubject[0] is templateTitle[0] to be templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] greater than 2026 templateTitle[4] .

generated_template: This statistic represents the templateYLabel[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] in the templateTitle[5] from FY templateXValue[2] to FY templateXValue[last] , in templateScale templateYLabel[3] templateYLabel[4] . The largest amount of templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[1] is projected for fiscal templateXLabel[0] templateXValue[last] with some templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] requested for templateTitle[2] templateTitle[3] funding .
generated: This statistic represents the Annual increase national defense funding in the Canada from FY 2024-2025 to FY 2017-2018 , in million Canadian .  The largest amount of increase national defense funding is projected for fiscal Year 2017-2018 with some 2300 million Canadian requested for national defense funding .

Example 314:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2015']}
title: Purchasing power change in the Netherlands 2015 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Year', 'on', 'year', 'percentage', 'change']: ['1.3', '1.6', '0.3', '0.3', '2.6', '1']

gold: In 2018 , the purchasing power in the Netherlands increased for the fourth year in a row . Purchasing power growth slowed down considerably though in 2017 and 2018 in comparison to 2015 and especially 2016 . In the next two years , the purchasing power was forecast to increase further .
gold_template: In templateXValue[2] , the templateTitle[0] templateTitle[1] in the templateTitleSubject[0] templatePositiveTrend for the fourth templateXLabel[0] in a row . templateTitle[0] templateTitle[1] growth slowed down considerably though in templateXValue[3] and templateXValue[2] in comparison to templateXValue[min] and especially templateXValue[4] . In the next templateYValue[1] years , the templateTitle[0] templateTitle[1] was forecast to templatePositiveTrend further .

generated_template: This statistic displays the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] in templateXValue[min] and templateXValue[4] , with a forecast to templateXValue[max] . In templateXValue[min] , templateYLabel[1] templateYLabel[2] made in templateTitleSubject[0] amounted to roughly templateYValue[max] thousands .
generated: This statistic displays the Year of percentage in Netherlands 2015 and 2016 , with a forecast to 2020 .  In 2015 , year percentage made in Netherlands amounted to roughly 2.6 thousands .

Example 315:
titleEntities: {'Subject': ['General Motors'], 'Date': ['1999', '2014']}
title: General Motors - passenger cars produced worldwide 1999 to 2014
X_Axis['Year']: ['2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Passenger', 'cars', 'produced', '(in', 'millions)']: ['6.64', '6.73', '6.61', '6.87', '6.27', '5.0', '6.02', '6.26', '5.78', '5.66', '4.5', '4.68', '4.9', '4.66', '5.27', '5.34']

gold: The timeline shows the passenger car production of General Motors worldwide from 1999 to 2014 . In 2013 , GM produced 6.7 million passenger cars worldwide . The U.S. automaker is world 's fourth largest manufacturer of passenger cars in terms of production .
gold_template: The timeline shows the templateYLabel[0] car production of templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[1] , GM templateYLabel[2] templateYValue[1] templateScale templateYLabel[0] templateYLabel[1] templateTitle[5] . The U.S. automaker is world 's fourth largest manufacturer of templateYLabel[0] templateYLabel[1] in terms of production .

generated_template: The timeline shows the templateYLabel[0] car production of templateTitleSubject[0] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateYLabel[2] around templateYValue[idxmax(X)] templateScale templateYLabel[0] templateYLabel[1] templateTitle[4] . templateTitleSubject[0] is ranked among the 15 largest automakers templateTitle[4] .
generated: The timeline shows the Passenger car production of General Motors produced from 1999 to 2014 .  In 2014 , General Motors produced around 6.64 million Passenger cars produced .  General Motors is ranked among the 15 largest automakers produced .

Example 316:
titleEntities: {'Subject': ['Boeing'], 'Date': ['2004', '2019']}
title: Boeing 737 - orders 2004 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Number', 'of', 'aircraft']: ['69', '837', '865', '701', '666', '1196', '1208', '1184', '625', '508', '197', '488', '850', '733', '574', '152']

gold: In 2019 , Boeing received gross orders for 69 units of its 737 narrow-body jet airliner series , but net orders after cancellations and changes ( under the ASC 606 revenue recognition standard ) came to a devastating negative 183 units . The slump in net orders came as a result of the jet maker 's 737 MAX crisis . Boeing delivered some 18 units of its 737 aircraft to Delta Air Lines in 2019 .
gold_template: In templateXValue[max] , templateTitleSubject[0] received gross templateTitle[2] for templateYValue[idxmax(X)] units of its templateTitle[1] narrow-body jet airliner series , but net templateTitle[2] after cancellations and changes ( under the ASC 606 revenue recognition standard ) came to a devastating negative 183 units . The slump in net templateTitle[2] came as a result of the jet maker 's templateTitle[1] MAX crisis . templateTitleSubject[0] delivered some 18 units of its templateTitle[1] templateYLabel[1] to Delta Air Lines in templateXValue[idxmin(Y)] .

generated_template: This statistic represents the templateTitle[2] templateTitle[3] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] through templateXValue[max] . templateTitleSubject[0] is estimated to consume around templateYValue[1] templateScale more templateTitle[2] from templateXValue[4] to templateXValue[3] . templateTitle[2] is an important building material .
generated: This statistic represents the orders 2004 aircraft in Boeing from 2004 through 2019 .  Boeing is estimated to consume around 837 million more orders from 2015 to 2016 .  orders is an important building material .

Example 317:
titleEntities: {'Subject': ['Argentina'], 'Date': ['2017', '2023']}
title: Argentina : internet user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['94', '93', '91', '88', '85', '82', '77']

gold: This statistic provides information on internet user penetration in Argentina from 2017 to 2023 . In 2017 , 77 percent of the population in Argentina were accessing the internet . This figure is projected to grow to 94 percent by 2023 .
gold_template: This statistic provides information on templateTitle[1] templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the templateYLabel[1] in templateTitleSubject[0] were accessing the templateTitle[1] . This figure is projected to grow to templateYValue[idxmax(X)] templateScale by templateXValue[idxmax(Y)] .

generated_template: This statistic provides information on the templateTitle[1] templateTitle[3] rate in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the Singaporean templateYLabel[1] were using the templateTitle[1] . In templateXValue[max] , this figure is projected to grow to templateYValue[idxmax(X)] templateScale .
generated: This statistic provides information on the internet penetration rate in Argentina from 2017 to 2023 .  In 2017 , 77 % of the Singaporean population were using the internet .  In 2023 , this figure is projected to grow 94 % .

Example 318:
titleEntities: {'Subject': ['Belgium'], 'Date': ['2010', '2018']}
title: Migration balance in Belgium 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Migration', 'balance']: ['50180', '44536', '42239', '47682', '39954', '34843', '44365', '62157', '79446']

gold: In 2018 , the migration balance in Belgium was roughly 50,000 , meaning that the number of immigrants moving to Belgium outnumbered the number of people leaving the country by about 50,000 . This was an increase in comparison to the previous year , but significantly lower than for example in 2010 and 2011 , when the migration balance was 79,446 and 62,157 respectively . It was also considerably lower than in neighboring country the Netherlands , which in 2018 had a positive migration balance of over 86,000 .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was roughly 50,000 , meaning that the number of immigrants moving to templateTitleSubject[0] outnumbered the number of people leaving the country by about 50,000 . This was an templatePositiveTrend in comparison to the previous templateXLabel[0] , but significantly lower than for example in templateXValue[min] and templateXValue[7] , when the templateYLabel[0] templateYLabel[1] was templateYValue[idxmin(X)] and templateYValue[7] respectively . It was also considerably lower than in neighboring country the Netherlands , which in templateXValue[max] had a positive templateYLabel[0] templateYLabel[1] of over 86,000 .

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[1] templateTitle[2] , a franchise of the National Football League , templateTitle[4] templateXValue[min] to templateXValue[max] . In the templateXValue[max] season , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[1] templateTitle[2] were at templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic presents the Migration balance of the Belgium , a franchise of the National Football League , 2018 2010 to 2018 .  In the 2018 season , the Migration balance of the Belgium were at 50180 % balance .

Example 319:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2001', '2019']}
title: Employment in U.S. publishing industries 2001 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Employment', 'in', '1,000s']: ['733.7', '730.5', '730.8', '728.7', '725.5', '727.4', '735.7', '741.1', '751.1', '768.6', '837.8', '897.4', '902.8', '901.2', '901.5', '913.8', '942.2', '986.6', '1045.7']

gold: The statistic above presents employment data for the U.S. publishing industries from 2001 to 2019 . In January 2019 , over 733 thousand people were estimated to be working in print or software publishing companies , down from the 730.5 thousand people recorded in January of the previous year .
gold_template: The statistic above presents templateYLabel[0] data for the templateTitleSubject[0] templateTitle[2] templateTitle[3] from templateXValue[min] to templateXValue[max] . In 2019 , over 733 thousand people were estimated to be working in print or software templateTitle[2] companies , down from the templateYValue[1] thousand people recorded in of the previous templateXLabel[0] .

generated_template: The statistic above presents templateYLabel[0] data for the templateTitleSubject[0] templateTitle[2] templateTitle[3] and sound templateTitle[4] industry from templateXValue[min] to templateXValue[max] . In 2019 , this industry employed an estimated templateYValue[0] templateYValue[idxmax(X)] people , down slightly from templateYValue[1] recorded in the previous templateXLabel[0] .
generated: The statistic above presents Employment data for the U.S. publishing industries and sound 2001 industry from 2001 to 2019 .  In 2019 , this industry employed an estimated 733.7 people , down slightly from 730.5 recorded in the previous Year .

Example 320:
titleEntities: {'Subject': ['Switzerland'], 'Date': ['2020']}
title: Number of employed persons in Switzerland 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Employed', 'persons', 'in', 'millions']: ['5.02', '4.97', '5.06', '5.01', '4.96', '4.9', '4.82', '4.73', '4.67', '4.66', '4.56']

gold: The statistic shows the number of employed persons in Switzerland from 2010 to 2018 , with projections up until 2020 . In 2018 , the amount of gainfully employed persons in Switzerland amounted to 5.06 million .
gold_template: The statistic shows the templateTitle[0] of templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[2] , with projections up until templateXValue[max] . In templateXValue[2] , the amount of gainfully templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the global templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] was about templateYValue[0] templateScale templateYLabel[3] dollars.H templateTitle[3] MH templateTitle[3] templateTitleSubject[0] is a leading global fashion company with strong values and a clear business concept . templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] constantly strives to have the best customer offering in each individual market – which includes giving customers the best price .
generated: This statistic shows the Employed persons of the Switzerland worldwide from 2010 to 2020 .  In 2020 , the global Employed persons of the Switzerland was about 5.02 millions dollars.H Switzerland MH is a leading global fashion company with strong values and a clear business concept .  Switzerland Switzerland Switzerland constantly strives to have the best customer offering in each individual market – which includes giving customers the best price .

Example 321:
titleEntities: {'Subject': ['Afghanistan'], 'Date': ['2017']}
title: Fertility rate in Afghanistan 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'children', 'born', 'per', 'woman']: ['4.63', '4.8', '4.98', '5.16', '5.36', '5.56', '5.77', '5.98', '6.18', '6.37', '6.56']

gold: This timeline shows the fertility rate in Afghanistan from 2007 to 2017 . In 2017 , Afghanistan 's fertility rate amounted to 4.63 children born per woman . Today , Afghanistan is among the countries with the highest fertility rate on the world fertility rate ranking .
gold_template: This timeline shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateTitle[0] templateTitle[1] amounted to templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] . Today , templateTitleSubject[0] is among the countries with the highest templateTitle[0] templateTitle[1] on the world templateTitle[0] templateTitle[1] ranking .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] to one templateYLabel[4] while being of child-bearing age . templateTitleSubject[0] includes almost all countries south of the Sahara desert .
generated: This statistic shows the Fertility rate in Afghanistan from 2007 to 2017 .  The Fertility rate is the average Number of children born to one woman while being of child-bearing age .  Afghanistan includes almost all countries south of the Sahara desert .

Example 322:
titleEntities: {'Subject': ['Russia'], 'Date': ['2017']}
title: Fertility rate in Russia 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'children', 'born', 'per', 'woman']: ['1.76', '1.76', '1.78', '1.75', '1.71', '1.69', '1.58', '1.57', '1.54', '1.5', '1.42']

gold: This statistic shows the fertility rate of Russia from 2007 to 2017 . The fertility rate is the average number of children a woman will have during her child-bearing years . In 2017 , the fertility rate of Russia 's population was 1.76 children per woman .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] a templateYLabel[4] will have during her child-bearing years . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] 's population was templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] to one templateYLabel[4] while being of child-bearing age . templateTitleSubject[0] includes almost all countries south of the Sahara desert .
generated: This statistic shows the Fertility rate in Russia from 2007 to 2017 .  The Fertility rate is the average Number of children born to one woman while being of child-bearing age .  Russia includes almost all countries south of the Sahara desert .

Example 323:
titleEntities: {'Subject': ['King'], 'Date': ['2010', '2018']}
title: King annual income 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Operating', 'income', 'in', 'million', 'U.S.', 'dollars']: ['750.0', '700.0', '537.0', '516.78', '574.85', '567.59', '7.85', '-1.32', '1.74']

gold: This statistic shows a timeline with the global annual operating income of King.com from 2010 to 2018 . In 2018 , the company reported an income of 750 million U.S. dollars . Popular King titles include Candy Crush Saga , Farm Ville Saga as well as Bubble Witch Saga .
gold_template: This statistic shows a timeline with the global templateTitle[1] templateYLabel[0] templateYLabel[1] of King.com from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company reported an templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . Popular templateTitleSubject[0] titles include Candy Crush Saga , Farm Ville Saga as well as Bubble Witch Saga .

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[1] templateTitle[2] , a franchise of the National Football League , templateTitle[4] templateXValue[min] to templateXValue[max] . In the templateXValue[max] season , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[1] templateTitle[2] were at templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic presents the Operating income of the King income , a franchise of the National Football League , 2018 2010 to 2018 .  In the 2018 season , the Operating income of the King income were at 750.0 million U.S. dollars .

Example 324:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1995', '2019']}
title: Number of drive-in cinema sites in the U.S. 1995 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995']
Y_Axis['Number', 'of', 'drive-in', 'cinema', 'sites']: ['321', '321', '349', '349', '349', '393', '393', '366', '366', '374', '381', '383', '383', '396', '401', '402', '400', '432', '440', '442', '446', '524', '577', '583', '593']

gold: The number of drive-in cinema sites in the United States remained at 321 in 2019 , the same as in the previous year . The figure tends to remain the same for years at a time , and is always far lower than the number of indoor sites , which make up the vast majority of cinemas in the country .
gold_template: The templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[4] remained at templateYValue[min] in templateXValue[idxmin(Y)] , the same as in the previous templateXLabel[0] . The figure tends to remain the same for years at a time , and is always far lower than the templateYLabel[0] of indoor templateYLabel[3] , which make up the vast majority of cinemas in the country .

generated_template: templateYLabel[0] templateYLabel[1] templateYLabel[2] is a measure of the difference between the templateYLabel[1] income generated by templateTitle[3] or other financial institutions and the amount of templateYLabel[1] paid out to their lenders relative to the amount of their ( interest-earning ) assets . It is similar to the gross templateYLabel[2] of non-financial companies . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] templateYLabel[2] of the templateTitleSubject[0] templateTitle[3] amounted to templateYValue[idxmax(X)] templateScale .
generated: Number drive-in cinema is a measure of the difference between the drive-in income generated by sites or other financial institutions and the amount of drive-in paid out to their lenders relative to the amount of their ( interest-earning ) assets .  It is similar to the gross cinema of non-financial companies .  In 2019 , the average Number drive-in cinema of the U.S. sites amounted to 321 million .

Example 325:
titleEntities: {'Subject': ['Hispanics'], 'Date': ['1990', '2018']}
title: Birth rate of Hispanics in the U.S. 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Births', 'per', '1,000', 'of', 'Hispanic', 'population']: ['14.8', '15.2', '16.0', '16.3', '16.5', '16.7', '17.1', '17.6', '18.7', '20.3', '21.8', '23.0', '23.3', '22.9', '22.8', '22.8', '22.7', '22.9', '23.1', '22.5', '22.7', '23.0', '23.8', '24.1', '24.7', '25.4', '26.1', '26.5', '26.7']

gold: This graph displays the birth rate of Hispanics in the United States from 1990 to 2018 . In 2018 , about 14.8 children were born per 1,000 of Hispanic population .
gold_template: This graph displays the templateTitle[0] templateTitle[1] of templateTitleSubject[0] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] children were born templateYLabel[1] 1,000 of templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateScale of templateYLabel[2] templateYLabel[3] in the templateTitleSubject[0] who live below the templateTitle[0] level from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateYLabel[2] templateYLabel[3] were living below the templateTitle[0] line in the templateTitle[4] . templateTitle[0] is the state of one who lacks a certain amount of material possessions or money .
generated: This statistic shows the percentage of 1,000 Hispanic in the Hispanics who live below the Birth level from 1990 to 2018 .  In 2018 , 14.8 % of 1,000 Hispanic were living below the Birth line in the 1990 .  Birth is the state of one who lacks a certain amount of material possessions or money .

Example 326:
titleEntities: {'Subject': ['Greece'], 'Date': ['2007', '2018']}
title: Household internet access in Greece 2007 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Share', 'of', 'households']: ['76', '71', '69', '68', '66', '56', '54', '50', '46', '38', '31', '25']

gold: This statistic shows the share of households in Greece that had access to the internet from 2007 to 2018 . Internet penetration grew in Greece during this period . In 2018 , 76 percent of Greek households had internet access .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] that had templateTitle[2] to the templateTitle[1] from templateXValue[min] to templateXValue[max] . templateTitle[1] penetration templatePositiveTrend in templateTitleSubject[0] during this period . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of Greek templateYLabel[1] had templateTitle[1] templateTitle[2] .

generated_template: There is no use denying that the way people consume newspapers and magazines has changed . As of templateXValue[max] , more than two thirds of individuals in templateTitleSubject[0] were either reading or downloading templateTitle[0] templateTitle[1] , newspapers or magazines . This was more than three times the templateYLabel[0] of templateTitle[0] readers as compared to templateXValue[min] .
generated: There is no use denying that the way people consume newspapers and magazines has changed .  As of 2018 , more than two thirds of individuals in Greece were either reading or downloading Household internet , newspapers or magazines .  This was more than three times the Share of Household readers as compared to 2007 .

Example 327:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2011', '2018']}
title: Number of enrolled university students in South Korea 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Enrolled', 'university', 'students', 'in', 'millions']: ['2.03', '2.05', '2.08', '2.11', '2.13', '2.13', '2.1', '2.07']

gold: This statistic illustrates the number of students enrolled in universities in South Korea from 2011 to 2018 . In 2018 , there were approximately 2.03 million students enrolled in universities in South Korea .
gold_template: This statistic illustrates the templateTitle[0] of templateYLabel[2] templateYLabel[0] in universities in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[0] in universities in templateTitleSubject[0] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] ( templateTitle[2] ) of templateTitleSubject[0] Shell Plc from templateXValue[min] to templateXValue[max] , in templateScale templateYLabel[3] templateYLabel[4] . In templateXValue[max] , templateTitleSubject[0] 's templateTitle[2] amounted to some templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Enrolled university ( ) of South Korea Shell Plc from 2011 to 2018 , in millions .  In 2018 , South Korea 's university amounted to some 2.03 millions .

Example 328:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2019', '2029']}
title: Forecast of population growth in Denmark 2019 to 2029
X_Axis['Year']: ['2029', '2028', '2027', '2026', '2025', '2024', '2023', '2022', '2021', '2020', '2019']
Y_Axis['Number', 'of', 'inhabitants', '(in', 'millions)']: ['6.09', '6.07', '6.04', '6.02', '6.0', '5.97', '5.95', '5.92', '5.89', '5.87', '5.83']

gold: The statistic shows a forecast of the Danish population growth from 2019 to 2029 . The total number of inhabitants will keep on increasing . According to the forecast there will be roughly over 6 million of people living in Denmark by 2029 .
gold_template: The statistic shows a templateTitle[0] of the Danish templateTitle[1] templateTitle[2] from templateXValue[min] to templateXValue[max] . The total templateYLabel[0] of templateYLabel[1] will keep on templatePositiveTrend . According to the templateTitle[0] there will be roughly over templateYValue[max] templateScale of people living in templateTitleSubject[0] by templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the global templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] was about templateYValue[0] templateScale templateYLabel[3] dollars.H templateTitle[3] MH templateTitle[3] templateTitleSubject[0] is a leading global fashion company with strong values and a clear business concept . templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] constantly strives to have the best customer offering in each individual market – which includes giving customers the best price .
generated: This statistic shows the Number inhabitants of the Denmark worldwide from 2019 to 2029 .  In 2029 , the global Number inhabitants of the Denmark was about 6.09 million millions) dollars.H Denmark MH is a leading global fashion company with strong values and a clear business concept .  Denmark Denmark Denmark constantly strives to have the best customer offering in each individual market – which includes giving customers the best price .

Example 329:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2018']}
title: Frozen yogurt production in the U.S. 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Production', 'in', 'million', 'gallons']: ['50.2', '62.5', '66.93', '74.23', '66.76', '74.48', '74.0', '62.7', '50.1', '46.0', '78.6', '74.7', '66.0']

gold: This statistic shows the frozen yogurt production in the United States from 2006 to 2018 . In 2018 , about 50.2 million gallons of frozen yogurt were produced . Frozen yogurt is a frozen , low-calorie dessert , which is often served in a large variety of flavors .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateYLabel[0] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale templateYLabel[2] of templateTitle[0] templateTitle[1] were produced . templateTitle[0] templateTitle[1] is a templateTitle[0] , low-calorie dessert , which is often served in a large variety of flavors .

generated_template: This statistic shows the templateTitle[2] earnings before interest and tax ( templateTitle[3] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] had a templateTitle[2] templateTitle[3] of approximately templateYValue[0] templateScale templateYLabel[1] , representing an templatePositiveTrend over the previous templateXLabel[0] .
generated: This statistic shows the production earnings before interest and tax ( U.S. ) of U.S. from 2006 to 2018 .  In 2018 , U.S. had a production U.S. of approximately 50.2 million , representing an increase over the previous Year .

Example 330:
titleEntities: {'Subject': ['Welsh Assembly'], 'Date': ['1999', '2011']}
title: Welsh Assembly elections : turnout rates 1999 to 2011
X_Axis['Year']: ['1999', '2003', '2007', '2011']
Y_Axis['Turnout', 'rate']: ['46.4', '38.2', '43.5', '41.8']

gold: This statistic shows the voter turnout rates for constituency votes in the Welsh Assembly elections from 1999 to 2011 . Over the last four elections there was a variation in voter turnout of 8.2 percent . The peak , in 1999 , was followed in 2003 by the lowest turnout rate of this period .
gold_template: This statistic shows the voter templateYLabel[0] templateTitle[4] for constituency votes in the templateTitleSubject[0] elections from templateXValue[min] to templateXValue[max] . Over the last four templateTitle[2] there was a variation in voter templateYLabel[0] of 8.2 templateScale . The peak , in templateXValue[min] , was followed in templateXValue[1] by the lowest templateYLabel[0] templateYLabel[1] of this period .

generated_template: According to the Dutch business registry , the templateTitleSubject[0] templatePositiveTrend around 600 new small and medium enterprises between early templateXValue[1] and early templateXValue[max] . The source defined SME ( or mkb , a Dutch abbreviation of midden- en kleinbedrijf ) as a company having between two to 249 employees , as well as companies with one employee that are not classified as a freelancer/self-employed person . templateYLabel[1] were the templateTitleSubject[0] ' second largest type of business enterprise .
generated: According to the Dutch business registry , the Welsh Assembly gained around 600 new small and medium enterprises between early 2003 and early 2011 .  The source defined SME ( or mkb , a Dutch abbreviation of midden- en kleinbedrijf ) as a company having between two to 249 employees , as well companies with one employee that are not classified as a freelancer/self-employed person .  rate were the Welsh Assembly ' second largest type of business enterprise .

Example 331:
titleEntities: {'Subject': ['Germany'], 'Date': ['2001', '2018']}
title: Share of internet users in Germany 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Share', 'of', 'internet', 'users']: ['84', '81', '79', '77.6', '76.8', '76.5', '75.6', '74.7', '72', '69.1', '65.1', '60.2', '58.2', '55.1', '52.7', '50.1', '41.7', '37']

gold: In 2018 , the share of German internet users amounted to 84 percent , an increase compared to the previous year at 81 percent . This share has only been growing in recent years . Considering current German population numbers stand at almost 83 million , such a high share of internet users is significant in itself and also for predicting future trends on digitalization and online connectivity in the country .
gold_template: In templateXValue[max] , the templateYLabel[0] of German templateYLabel[1] templateYLabel[2] amounted to templateYValue[idxmax(X)] templateScale , an templatePositiveTrend compared to the previous templateXLabel[0] at templateYValue[1] templateScale . This templateYLabel[0] has only been templatePositiveTrend in recent years . Considering current German population numbers stand at almost 83 templateScale , such a high templateYLabel[0] of templateYLabel[1] templateYLabel[2] is significant in itself and also for predicting future trends on digitalization and online connectivity in the country .

generated_template: This statistic gives information on the templateYLabel[3] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In the most recently measured period , templateYValue[idxmax(X)] templateScale of the population accessed the templateYLabel[3] , up from templateYValue[idxmin(X)] templateScale in templateXValue[idxmin(Y)] . In templateXValue[2] , templateTitleSubject[0] 's population templatePositiveTrend by approximately 2.48 templateScale compared to the previous templateXLabel[0] .
generated: This statistic gives information on the users in Germany from 2001 to 2018 .  In the most recently measured period , 84 % of the population accessed the users , up from 37 % in 2001 .  In 2016 , Germany 's population grew by approximately 2.48 % compared to the previous Year .

Example 332:
titleEntities: {'Subject': ['GDP'], 'Date': ['1990']}
title: U.S. exports , as a percentage of GDP 1990 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Exports', 'as', 'a', 'percentage', 'of', 'GDP']: ['12.06', '11.85', '12.43', '13.53', '13.54', '13.53', '13.53', '12.32', '11.01', '12.51', '11.5', '10.65', '10', '9.63', '9.04', '9.13', '9.67', '10.66', '10.27', '10.48', '11.08', '10.71', '10.61', '9.86', '9.52', '9.68', '9.64', '9.23']

gold: In 2017 , exports of goods and services from the United States made up just over 12 percent of its gross domestic product ( GDP ) . This is an increase from 9.23 percent of the GDP of the United States in 1990 . Trade and foreign relations The United States ' GDP is the largest in the world , clocking in at around 18.57 trillion U.S. dollars in 2018. International trade is a huge boon to the U.S. economy , both financially and regarding foreign relations .
gold_template: In templateXValue[max] , templateYLabel[0] of goods and services from the templateTitle[0] made up just over templateYValue[0] templateScale of its gross domestic product ( templateYLabel[2] ) . This is an templatePositiveTrend from templateYValue[last] templateScale of the templateYLabel[2] of the templateTitle[0] in templateXValue[min] . Trade and foreign relations The templateTitle[0] ' templateYLabel[2] is the largest in the world , clocking in at around 18.57 templateScale templateTitle[0] dollars in 2018. International trade is a huge boon to the templateTitle[0] economy , both financially and regarding foreign relations .

generated_template: In templateXValue[max] , there were about templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] in the templateTitle[4] with a templateTitle[2] mother . This is an templatePositiveTrend from templateXValue[min] levels , when there were about templateYValue[min] templateScale templateYLabel[1] templateYLabel[2] with a templateTitle[2] mother . templateTitle[2] parenthood The typical family is comprised of two parents and at least one child .
generated: In 2017 , there were about 12.06 percentage GDP in the 1990 with a percentage mother .  This is an increase from 1990 levels , when there were about 9.04 percentage GDP with a percentage mother .  percentage parenthood The typical family is comprised of two parents and at least one child .

Example 333:
titleEntities: {'Subject': ['CVS Health'], 'Date': ['2012']}
title: CVS Health 's share of retail prescriptions filled in the U.S. 2012 to 2025
X_Axis['Year']: ['2025', '2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Share', 'of', 'retail', 'prescriptions', 'filled']: ['46.55', '44.55', '42.55', '40.55', '38.55', '36.55', '34.55', '33.55', '32.72', '30.1', '23.45', '21.63', '20.99', '17.25']

gold: This statistic depicts CVS Caremark 's share of retail prescriptions filled in the United States from 2012 to 2025 . The CVS Caremark Corporation is a U.S. drug retailing company , with three major segments : CVS/pharmacy , Caremark Pharmacy Services and MinuteClinic . CVS Caremark is headquartered in Woonsocket , Rhode Island .
gold_template: This statistic depicts templateTitleSubject[0] Caremark templateTitle[2] templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateYLabel[3] in the templateTitle[7] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] Caremark Corporation is a templateTitle[7] drug retailing company , with three major segments : CVS/pharmacy , Caremark Pharmacy Services and MinuteClinic . templateTitleSubject[0] Caremark is headquartered in Woonsocket , Rhode Island .

generated_template: The graph depicts the templateYLabel[0] templateYLabel[1] for templateTitleSubject[0] templateTitle[1] from templateXValue[min] to templateXValue[8] , with forecasts for up to templateXValue[max] . In templateXValue[6] , templateTitle[4] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[1] amounted to around templateYValue[min] templateScale templateYLabel[3] templateYLabel[4] .
generated: The graph depicts the Share retail for CVS Health Health from 2012 to 2017 , with forecasts for up to 2025 .  In 2019 , retail Share of CVS Health Health amounted to around 17.25 % filled .

Example 334:
titleEntities: {'Subject': ['Zimbabwe'], 'Date': ['2015']}
title: Median age of the population in Zimbabwe 2015
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2015', '2010', '2005', '2000', '1995', '1990', '1985', '1980', '1975', '1970', '1965', '1960', '1955', '1950']
Y_Axis['Median', 'age', 'in', 'years']: ['26.9', '25.7', '24.4', '22.8', '21.1', '19.6', '18.7', '18.4', '18.6', '18.3', '18.2', '17.6', '16.9', '16.0', '15.1', '15.4', '15.6', '16.0', '17.2', '18.1', '19.0']

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

generated_template: The statistic depicts the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of a templateTitle[2] is an index that divides the templateTitle[2] into two equal groups : half of the templateTitle[2] is older than the templateYLabel[0] templateYLabel[1] and the other half younger . In templateXValue[7] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] 's templateTitle[2] was templateYValue[7] templateYLabel[2] .
generated: The statistic depicts the Median age in Zimbabwe from 1950 to 2050 .  The Median age of a population is an index that divides the population into two equal groups : half of the population is older than the Median age and the other half younger .  In 2015 , the Median age of Zimbabwe 's population was 18.4 years .

Example 335:
titleEntities: {'Subject': ['GlaxoSmithKline'], 'Date': ['2011', '2018']}
title: GlaxoSmithKline 's advertising spending 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Profit', 'in', 'million', 'British', 'pounds']: ['1376', '1351', '1265', '1059', '671', '808', '839', '910']

gold: This statistic describes the advertising spending of GlaxoSmithKline from 2011 to 2018 . In 2018 , the company reported ad spending of some 1.38 billion British pounds . GlaxoSmithKline plc is a global pharmaceutical and biotech company , headquartered in London .
gold_template: This statistic describes the templateTitle[2] templateTitle[3] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company reported ad templateTitle[3] of some templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] plc is a global pharmaceutical and biotech company , headquartered in London .

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of the sports company templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] had a templateYLabel[0] templateYLabel[1] of templateYValue[last] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] in templateXValue[max] .
generated: The statistic presents the Profit million of the sports company GlaxoSmithKline from 2011 to 2018 .  GlaxoSmithKline had a Profit million of 910 1376 pounds in 2018 .

Example 336:
titleEntities: {'Subject': ['Births'], 'Date': ['2018']}
title: Births - number by age of mother 2018
X_Axis['Year']: ['15_to_19_years', '20_to_24_years', '25_to_29_years', '30_to_34_years', '35_to_39_years', '40_to_44_years', '45_to_50_years']
Y_Axis['Number', 'of', 'children', 'born', 'in', 'thousands']: ['469', '3268', '9668', '15269', '19902', '20145', '25038']

gold: This statistic displays the total number of births in the United States as of June 2018 , by age of mother . In 2018 , women aged between 15 and 19 years gave birth to 469,000 children in the United States .
gold_template: This statistic displays the total templateYLabel[0] of templateTitleSubject[0] in the country as of 2018 , templateTitle[2] templateTitle[3] of templateTitle[4] . In templateTitleDate[0] , women aged between templateXValue[0] and templateXValue[0] gave birth to templateYValue[min] templateYLabel[1] in the country .

generated_template: This statistic illustrates the templateYLabel[0] templateYLabel[1] templateYLabel[2] of public rental housing templateTitle[4] in templateTitleSubject[1] in selected years from templateXValue[min] to templateXValue[max] . As of templateXValue[idxmax(Y)] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in public rental housing in templateTitleSubject[1] was around templateYValue[max] templateYLabel[3] templateYValue[idxmax(X)] templateYLabel[5] templateYLabel[6] .
generated: This statistic illustrates the Number children born of public rental housing mother in Births selected years from 15 to 19 years to 45 to 50 years .  As of 45 to 50 years , the Number children born in public rental housing in Births was around 25038 thousands .

Example 337:
titleEntities: {'Subject': ['Orlando Magic'], 'Date': ['2001', '2019']}
title: Orlando Magic 's revenue 2001 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['244', '223', '211', '166', '143', '143', '139', '126', '140', '108', '107', '100', '92', '89', '82', '78', '80', '82']

gold: The statistic shows the revenue of the Orlando Magic franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated revenue of the National Basketball Association franchise amounted to 244 million U.S. dollars .
gold_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] franchise from the 2001/02 season to the 2018/19 season . In 2018/19 , the estimated templateYLabel[0] of the National Basketball Association franchise amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] The templateTitleSubject[0] are a professional basketball franchise of the National Basketball Association ( NBA ) , entering the league as an expansion team in 1968 .
generated: The statistic shows the Revenue of the Orlando Magic franchise from the 2001/02 season to the 2018/19 season .  In 2018/19 , the estimated Revenue of the National Basketball Association franchise amounted to 244 million U.S. dollars .  Orlando Magic The Orlando Magic are a professional basketball franchise of the National Basketball Association ( NBA ) , entering the league as an expansion team in 1968 .

Example 338:
titleEntities: {'Subject': ['North Carolina'], 'Date': ['1960', '2018']}
title: Population density in North Carolina 1960 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2010', '2000', '1990', '1980', '1970', '1960']
Y_Axis['Number', 'of', 'residents', 'per', 'square', 'mile']: ['213.6', '211.3', '208.7', '206.6', '204.5', '202.6', '200.6', '196.1', '165.6', '136.4', '120.9', '104.6', '93.5']

gold: This graph shows the population density in the federal state of North Carolina from 1960 to 2018 . In 2018 , the population density of North Carolina stood at 213.6 residents per square mile of land area .
gold_template: This graph shows the templateTitle[0] templateTitle[1] in the federal state of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] stood at templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] templateYLabel[4] of land area .

generated_template: This graph shows the templateTitle[0] templateTitle[1] in the federal state of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] stood at templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] of land area .
generated: This graph shows the Population density in the federal state of North Carolina from 1960 to 2018 .  In 2018 , the Population density of North Carolina stood at 213.6 residents per square mile of land area .

Example 339:
titleEntities: {'Subject': ['Indonesia'], 'Date': ['2000', '2016']}
title: Household electricity consumption per capita in Indonesia 2000 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Consumption', 'in', 'kilowatt', 'hours', 'per', 'capita']: ['348.3', '333.3', '319.9', '296.5', '281.9', '258.8', '238.2', '222.7', '203.3', '195.2', '183.5', '174.5', '171.9', '153.9', '148.0', '147.2', '135.9']

gold: This statistic represents the household consumption of electricity per capita in Indonesia from the year 2000 to 2016 , in kilowatt hours . In the year 2016 , household consumption of electricity per capita in Indonesia was about 348 kilowatts per hour .
gold_template: This statistic represents the templateTitle[0] templateYLabel[0] of templateTitle[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from the templateXLabel[0] templateXValue[min] to templateXValue[max] , in templateYLabel[1] templateYLabel[2] . In the templateXLabel[0] templateXValue[max] , templateTitle[0] templateYLabel[0] of templateTitle[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] was about templateYValue[max] kilowatts templateYLabel[3] hour .

generated_template: This statistic represents the templateTitle[0] templateYLabel[0] of templateTitle[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from the templateXLabel[0] templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateYLabel[0] of templateTitle[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] was about templateYValue[0] kilowatts templateYLabel[3] hour .
generated: This statistic represents the Household Consumption of electricity per capita in Indonesia from the Year 2000 to 2016 .  In 2016 , Household Consumption of electricity per capita in Indonesia was about 348.3 kilowatts per hour .

Example 340:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['1999']}
title: Fairtrade food and drink sales revenue in the United Kingdom 1999 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Sales', 'in', 'million', 'GBP']: ['1720', '1608', '1572', '1612', '1710', '1553', '1253', '1064', '749', '635', '458', '285', '195', '141', '92', '63', '51', '33', '22']

gold: This statistic illustrates the sales of Fairtrade food and drink products in the United Kingdom ( UK ) from 1999 to 2017 . In 2005 , 195 million British pounds was spent on Fairtrade food and drink products . Sales rose during the period under consideration to approximately 1.72 billion British pounds in sales in 2017 .
gold_template: This statistic illustrates the templateYLabel[0] of templateTitle[0] templateTitle[1] and templateTitle[2] products in the templateTitleSubject[0] ( UK ) from templateXValue[min] to templateXValue[max] . In templateXValue[12] , templateYValue[12] templateScale British pounds was spent on templateTitle[0] templateTitle[1] and templateTitle[2] products . templateYLabel[0] templatePositiveTrend during the period under consideration to approximately templateYValue[max] templateScale British pounds in templateYLabel[0] in templateXValue[idxmax(Y)] .

generated_template: This statistic illustrates the total templateTitleSubject[0] ( UK ) templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] templateXValue[0] . The overall trend was one of templatePositiveTrend , seeing a peak in templateXValue[0] with 27.99 templateScale British pounds and a total rise of over 5 templateScale British pounds throughout this period .
generated: This statistic illustrates the total United Kingdom ( UK ) revenue Sales million GBP from fiscal Year 1999 to fiscal Year 2017 .  The overall trend was one of increasing , seeing a peak in 2017 with 27.99 million British pounds and a total rise of over 5 million British pounds throughout this period .

Example 341:
titleEntities: {'Subject': ['Europe'], 'Date': ['2000', '2018']}
title: Direct investment position of the U.S. in Europe 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Direct', 'investments', 'in', 'trillion', 'U.S.', 'dollars']: ['3.61', '3.55', '3.31', '3.08', '2.9', '2.6', '2.45', '2.25', '2.03', '1.99', '1.84', '1.68', '1.4', '1.21', '1.18', '0.98', '0.86', '0.77', '0.69']

gold: This statistic shows the direct investment position of the United States in Europe from 2000 to 2018 , on a historical-cost basis . In 2018 , the U.S. investments made in Europe were valued at approximately 3.61 trillion U.S. dollars . The total direct position of the United States abroad amounted to 5.95 trillion U.S. dollars in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitleSubject[0] were valued at approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . The total templateYLabel[0] templateTitle[2] of the templateTitle[3] abroad amounted to 5.95 templateScale templateYLabel[3] templateYLabel[4] in templateXValue[max] templateXValue[idxmax(Y)]

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] amounted to about templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[0] templateYLabel[5] in templateXValue[max] .
generated: The timeline shows the Direct investments trillion of Europe in the Direct from 2000 to 2018 .  According to the report , the Europe Direct investments trillion amounted to about 3.61 U.S. Direct dollars in 2018 .

Example 342:
titleEntities: {'Subject': ['Carolina Panthers', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Carolina Panthers ( NFL ) 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['2400', '2300', '2300', '2075', '1560', '1250', '1057', '1048', '1002', '1037', '1049', '1040', '956', '936', '878', '760', '642', '609']

gold: This graph depicts the franchise value of the Carolina Panthers from the National Football League from 2002 to 2019 . In 2019 , the franchise value came to 2.4 billion U.S. dollars.The Carolina Panthers are owned by David Tepper , who bought the franchise for about 2.3 billion U.S. dollars in 2018 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateScale templateYLabel[3] dollars.The templateTitleSubject[0] are owned by David Tepper , who bought the templateYLabel[0] for about templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[1] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Dee and Jimmy Haslam .
generated: This graph depicts the Franchise value of the Carolina Panthers of the National Football League from 2002 to 2019 .  In 2019 , the Franchise value came to 2400 million U.S. dollars .  The Carolina Panthers are owned by Dee and Jimmy Haslam .

Example 343:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2019', '2050']}
title: U.S. production of energy from biomass forecast 2019 to 2050
X_Axis['Year']: ['2050', '2045', '2040', '2035', '2030', '2025', '2020', '2019']
Y_Axis['Production', 'in', 'quadrillion', 'Btu']: ['5.54', '5.39', '5.27', '5.2', '5.13', '4.96', '4.74', '4.82']

gold: This statistic gives outlook figures on the production of biomass energy between 2019 and 2050 . In 2050 , U.S. biomass energy production is forecast to increase to around 5.54 quadrillion British thermal units .
gold_template: This statistic gives outlook figures on the templateYLabel[0] of templateTitle[4] templateTitle[2] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[4] templateTitle[2] templateYLabel[0] is templateTitle[5] to templatePositiveTrend to around templateYValue[idxmax(X)] templateYLabel[1] British thermal units .

generated_template: This statistic shows the templateYLabel[0] forecast of the templateTitle[3] templateTitle[1] templateTitle[2] sector in the United Kingdom ( templateTitleSubject[0] ) from templateXValue[min] to templateXValue[max] . The estimated templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[1] templateTitle[2] sector in templateXValue[max] is templateYValue[idxmax(X)] templateScale British pounds ( templateYLabel[3] ) . templateTitle[1] templateTitle[2] exists because the threats and costs are great enough to warrant these measure .
generated: This statistic shows the Production forecast of the from production energy sector in the United Kingdom ( U.S. ) from 2019 to 2050 .  The estimated Production quadrillion of the from production energy sector in 2050 is 5.54 billion British pounds ( Btu ) .  production energy exists because the threats and costs are great enough to warrant these measure .

Example 344:
titleEntities: {'Subject': ['China'], 'Date': ['2008', '2015']}
title: Market value of honey in China based on sale price 2008 to 2015
X_Axis['Year']: ['2015', '2011', '2010', '2009', '2008']
Y_Axis['Market', 'value', 'in', 'million', 'U.S.', 'dollars']: ['553.6', '419.6', '380.8', '348.2', '328.7']

gold: The statistic shows the market value of honey in China between 2008 and 2010 , including a forecast for 2015 , based on sales price .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] in templateTitleSubject[0] between templateXValue[min] and templateXValue[2] , including a forecast for templateXValue[max] , templateTitle[4] on sales templateTitle[6] .

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateTitle[1] a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateYValue[2] templateTitle[7] in the templateTitle[8] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] templateYLabel[1] had attended a templateTitle[2] templateTitle[3] templateTitle[4] at least once in the templateTitle[5] templateXLabel[0] .
generated: This statistic presents the Market of value a honey China based at least once in the sale 380.8 2008 in the 2015 from 2008 to 2015 .  In 2015 , 553.6 million of China value had attended a honey China based at least once in the sale Year .

Example 345:
titleEntities: {'Subject': ['Nokia'], 'Date': ['1999', '2019']}
title: Nokia 's net sales 1999 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Net', 'sales', 'in', 'billion', 'euros']: ['23.32', '22.56', '23.15', '23.64', '12.5', '11.76', '12.71', '30.18', '38.66', '42.45', '40.98', '50.71', '51.06', '41.12', '34.19', '29.37', '29.53', '30.02', '31.19', '30.38', '19.77']

gold: In 2018 , Nokia had 22.5 billion euros in net sales , which is a small decrease from the year before . In the past decade , the company has made several directional shifts from handheld devices to network infrastructure in order to keep up with the quickly evolving electronics and telecommunications industry . Focus on B2B markets and products After selling its mobile and devices segment to Microsoft in 2014 , Nokia has focused on its network infrastructure business .
gold_template: In templateXValue[1] , templateTitleSubject[0] had 22.5 templateScale templateYLabel[3] in templateYLabel[0] templateYLabel[1] , which is a small templateNegativeTrend from the templateXLabel[0] before . In the past decade , the company has made several directional shifts from handheld devices to network infrastructure in order to keep up with the quickly evolving electronics and telecommunications industry . Focus on B2B markets and products After selling its mobile and devices segment to Microsoft in templateXValue[5] , templateTitleSubject[0] has focused on its network infrastructure business .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitle[3] templateTitle[4] templateTitle[5] 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 Net sales of the sales 1999 2019 from 1999 to 2019 .  The Net sales is the sales that divides a net into two numerically equal groups ; that is , half the people are younger than this sales and half are older .  It is a single index that summarizes the sales distribution of a net .

Example 346:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1970', '2018']}
title: Pre-primary school enrollment numbers in the U.S. 1970 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2000', '1995', '1990', '1985', '1980', '1975', '1970']
Y_Axis['Number', 'of', 'children', 'enrolled', '(in', 'millions)']: ['8.74', '8.64', '8.76', '8.61', '8.76', '8.83', '8.77', '9.16', '9.01', '8.84', '8.66', '8.76', '8.73', '8.52', '8.73', '8.65', '8.04', '8.03', '8.23', '5.16', '5.14', '4.28']

gold: This graph shows the number of children enrolled in pre-primary school institutions ( kindergarten or nursery ) in the United States from 1970 to 2018 . In 2018 , around 8.74 million children were enrolled in nursery or kindergarten programs in the United States .
gold_template: This graph shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitle[0] templateTitle[1] institutions ( kindergarten or nursery ) in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , around templateYValue[idxmax(X)] templateScale templateYLabel[1] were templateYLabel[2] in nursery or kindergarten programs in the templateTitle[4] .

generated_template: This statistic shows the templateTitle[3] templateTitle[4] of the NRA in the country from templateXValue[min] to templateXValue[max] . As of templateXValue[idxmin(Y)] , the NRA spent about templateYValue[min] templateYLabel[2] templateYLabel[3] on templateTitle[3] .
generated: This statistic shows the numbers U.S. of the NRA in the country from 1970 to 2018 .  As of 1970 , the NRA spent about 4.28 enrolled (in on numbers .

Example 347:
titleEntities: {'Subject': ['North Carolina'], 'Date': ['1992', '2018']}
title: North Carolina - Unemployment rate 1992 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92"]
Y_Axis['Unemployment', 'rate']: ['3.9', '4.5', '5.1', '5.7', '6.3', '8', '9.3', '10.3', '10.9', '10.6', '6.1', '4.7', '4.7', '5.2', '5.5', '6.4', '6.6', '5.5', '3.7', '3.2', '3.5', '3.7', '4.3', '4.3', '4.4', '5', '6']

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

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[min] templateScale . You can access the monthly templateYLabel[0] templateYLabel[1] for the country here .
generated: This statistic shows the Unemployment rate in North Carolina from 1992 to 2018 .  In 2018 , the Unemployment rate in North Carolina was 3.2 % .  You can access the monthly Unemployment rate for the country here .

Example 348:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2019']}
title: U.S. unemployment level 1990 to 2019
X_Axis['Year']: ["'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92", "'91", "'90"]
Y_Axis['Unemployed', 'in', 'millions']: ['6.0', '6.31', '6.98', '7.75', '8.3', '9.62', '11.46', '12.51', '13.75', '14.83', '14.27', '8.92', '7.08', '7.0', '7.59', '8.15', '8.77', '8.38', '6.8', '5.69', '5.88', '6.21', '6.74', '7.24', '7.4', '8.0', '8.94', '9.61', '8.63', '7.05']

gold: This statistic shows the unemployment level in the United States from 1990 to 2019 . National unemployment level decreased to an average of six million people looking for work in 2019 . See the United States unemployment rate and the monthly unemployment rate for further information .
gold_template: This statistic shows the templateTitle[1] templateTitle[2] in the templateTitle[0] from templateTitleDate[min] to templateTitleDate[max] . National templateTitle[1] templateTitle[2] templateNegativeTrend to an average of templateYValue[0] templateScale people looking for work in templateTitleDate[max] . See the templateTitle[0] templateTitle[1] rate and the monthly templateTitle[1] rate for further information .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[2] templateYLabel[3] templateYLabel[4] for templateTitle[3] in the country of America from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateYLabel[0] change of the PPI amounted to templateYValue[0] templateScale . The PPI for templateTitle[3] stood at 199.8 in templateTitleDate[max] .
generated: This statistic shows the Unemployed millions of the millions for 1990 in the country of America from 1990 to 2019 .  In 2019 , the Unemployed change of the PPI amounted to 6.0 millions .  The PPI for 1990 stood at 199.8 in 2019 .

Example 349:
titleEntities: {'Subject': ['Arizona Coyotes'], 'Date': ['2005', '2019']}
title: Arizona Coyotes ' revenue 2005 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['102', '96', '98', '101', '92', '80', '67', '83', '70', '67', '66', '68', '67', '63']

gold: This graph depicts the annual National Hockey League revenue of the Arizona Coyotes from the 2005/06 season to the 2018/19 season . The revenue of the Arizona Coyotes amounted to 102 million U.S. dollars in the 2018/19 season .
gold_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] Leafs from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] Leafs amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .
generated: The statistic shows the Revenue of the Arizona Coyotes Leafs from the 2005/06 season to the 2018/19 season .  The Revenue of the Arizona Coyotes Leafs amounted to 102 million U.S. dollars in the 2018/19 season .

Example 350:
titleEntities: {'Subject': ['Hays'], 'Date': ['2007', '2019']}
title: Revenue of Hays worldwide 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'million', 'GBP']: ['1129.7', '1072.8', '954.6', '810.3', '764.2', '724.9', '719.0', '734.0', '672.1', '557.7', '670.8', '786.8', '633.6']

gold: This statistic shows the revenue of Hays worldwide from 2007 to 2019 . In 2019 , the UK-based recruitment specialist Hays generated over 1.1 billion British pounds in revenue worldwide , up from one billion the previous year .
gold_template: This statistic shows the templateYLabel[0] of templateTitleSubject[0] templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the UK-based recruitment specialist templateTitleSubject[0] generated over templateYValue[max] templateScale British pounds in templateYLabel[0] templateTitle[2] , up from templateYValue[max] templateScale the previous templateXLabel[0] .

generated_template: templateTitleSubject[0] posted a profit of templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[max] , down from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[1] . In the same time period , operating income of the bank templatePositiveTrend from 63.6 templateScale templateYLabel[2] templateYLabel[3] to 71 templateScale templateYLabel[2] templateYLabel[3] . templateTitleSubject[0] bank templateTitleSubject[0] is a British banking and financial services company and one of the major players on the global banking market .
generated: Hays posted a profit of 1129.7 million GBP in 2019 , down from 1072.8 million GBP in 2018 .  In the same time period , operating income of the bank increased from 63.6 million GBP to 71 million GBP .  Hays bank Hays is a British banking and financial services company and one of the major players on the global banking market .

Example 351:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1982', '2020']}
title: Mass shootings in the U.S. 1982 to 2020
X_Axis['Year']: ['1982', '1983', '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020']
Y_Axis['Total', 'number', 'of', 'mass', 'shootings']: ['1', '0', '2', '0', '1', '1', '1', '2', '1', '3', '2', '4', '1', '1', '1', '2', '3', '5', '1', '1', '0', '1', '1', '2', '3', '4', '3', '4', '1', '3', '7', '5', '4', '7', '6', '11', '12', '10', '1']

gold: As of February 26 , there was one mass shootings in the United States in 2020 . This is compared to one mass shooting in 1982 , one in 2000 , and 12 mass shootings in 2018 . School shootings The United States sees the most school shootings in the world .
gold_template: As of 26 , there was templateYValue[last] templateYLabel[2] templateYLabel[3] in the templateTitle[2] in templateXValue[max] . This is compared to templateYValue[last] templateYLabel[2] shooting in templateXValue[min] , templateYValue[last] in templateXValue[18] , and templateYValue[max] templateYLabel[2] templateYLabel[3] in templateXValue[idxmax(Y)] . School templateYLabel[3] The templateTitle[2] sees the most school templateYLabel[3] in the world .

generated_template: In templateXValue[max] , the templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] as a templateYLabel[2] of Gross Domestic Product was templateYValue[min] templateYValue[idxmax(X)] . Since templateXValue[min] , the templateTitleSubject[1] 's templateYLabel[0] templateYLabel[1] was at it 's highest in templateXValue[34] when templateYValue[max] templateScale of the templateTitleSubject[1] 's templateYLabel[3] was spent on the military . After templateXValue[idxmax(Y)] , templateYLabel[0] templateYLabel[1] declined gradually , and then at a much faster pace after the end of the Cold War in templateXValue[27] , with the templateTitleSubject[0] only just reaching the templateYValue[min] templateScale benchmark set by NATO by templateXValue[idxmin(Y)] templateXValue[idxmin(Y)]
generated: In 2020 , the U.S. 's Total number as a mass of Gross Domestic Product was 0 1 .  Since 1982 , the U.S. 's Total number was at it 's highest in 2016 when 12 % of the U.S. 's shootings was spent on the military .  After 2018 , Total number declined gradually , and then at a much faster pace after the end of the Cold War in 2009 , with the U.S. only just reaching the 0 % benchmark set by NATO 1983 . 

Example 352:
titleEntities: {'Subject': ['Cineplex'], 'Date': ['2010', '2018']}
title: Attendance at Cineplex cinemas 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Attendance', 'in', 'millions']: ['69.27', '70.4', '74.6', '77.0', '73.6', '72.7', '71.2', '66.1', '67.0']

gold: The timeline presents the attendance figures at Cineplex from 2010 to 2018 . In 2018 , 69.27 million people attended movies at the Canadian movie theater chain , down from 70.4 million visitors a year earlier .
gold_template: The timeline presents the templateYLabel[0] figures at templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale people attended movies at the Canadian movie theater chain , down from templateYValue[1] templateScale visitors a templateXLabel[0] earlier .

generated_template: This statistic represents templateTitleSubject[0] 's templateYLabel[0] and templateYLabel[1] templateYLabel[2] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , the company incurred around templateYValue[idxmax(X)] templateScale templateYLabel[4] templateYLabel[5] in templateYLabel[0] and templateYLabel[1] templateYLabel[2] .
generated: This statistic represents Cineplex 's Attendance and millions from the fiscal Year of 2010 to the fiscal Year of 2018 .  In the fiscal Year of 2018 , the company incurred around 69.27 millions in Attendance and millions .

Example 353:
titleEntities: {'Subject': ['Angola'], 'Date': ['2019']}
title: Unemployment rate in Angola 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['7.25', '7.25', '7.14', '7.28', '7.28', '7.43', '7.45', '7.36', '7.36', '9.09', '10.61', '12.04', '14.63', '17.67', '20.53', '23.64', '23.93', '23.9', '23.12', '22.89', '20.9']

gold: This statistic shows the unemployment rate in Angola from 1999 to 2019 . In 2019 , the unemployment rate in Angola was 7.25 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Angola from 1999 to 2019 .  In 2019 , the Unemployment rate in Angola was at approximately 7.25 % .

Example 354:
titleEntities: {'Subject': ['Aston Villa'], 'Date': ['2016']}
title: Value of Aston Villa 's jersey sponsorship 2016
X_Axis['Year']: ['2015/16_(Intuit_Quickbooks)', '2014/15_(Dafabet.com)', '2013/14_(Dafabet.com)', '2012/13_(Genting)', '2011/12_(Genting)', '2010/11_(FxPro)', '2009/10_(Acorns)']
Y_Axis['Jersey', 'sponsorship', 'revenue', 'in', 'million', 'GBP']: ['5', '5', '5', '8', '8', '5', '0']

gold: The statistic shows the revenue Aston Villa generated from its jersey sponsorship deal from the 2009/10 season to the 2015/16 season . In the 2012/13 season Aston Villa received 8 million GBP from its jersey sponsor Genting .
gold_template: The statistic shows the templateYLabel[2] templateTitleSubject[0] generated from its templateYLabel[0] templateYLabel[1] deal from the templateXValue[last] season to the templateXValue[0] season . In the templateXValue[3] season templateTitleSubject[0] received templateYValue[max] templateScale templateYLabel[4] from its templateYLabel[0] sponsor Genting .

generated_template: The statistic shows the templateYLabel[0] templateTitle[1] templateYLabel[1] in the templateTitleSubject[0] in templateTitleDate[0] , templateTitle[5] templateTitle[6] of householder . In templateTitleDate[0] , the real templateYLabel[0] templateTitle[1] templateYLabel[1] for householder aged 15 - 24 was at templateYValue[min] templateYLabel[2] templateYLabel[3] .
generated: The statistic shows the Jersey Aston sponsorship in the Aston Villa in 2016 , sponsorship 2016 of householder .  In 2016 , the real Jersey Aston sponsorship for householder aged 15 - 24 was at 0 revenue million .

Example 355:
titleEntities: {'Subject': ['England', 'Wales'], 'Date': ['2005', '2019']}
title: Road deaths involving police pursuit in England and Wales from 2005 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05']
Y_Axis['Fatailities']: ['30', '17', '28', '13', '7', '10', '27', '12', '13', '19', '22', '17', '19', '32', '23']

gold: This statistic shows the number of road traffic fatalities related to police pursuits in England and Wales from 2004/05 to 2018/19 . During the period concerned , the number of road traffic fatalities related to police pursuits fluctuated , peaking in 2005/06 at 32 deaths .
gold_template: This statistic shows the number of templateTitle[0] traffic fatalities related to templateTitle[3] pursuits in templateTitleSubject[0] and templateTitleSubject[1] templateTitle[7] templateXValue[last] to templateXValue[0] . During the period concerned , the number of templateTitle[0] traffic fatalities related to templateTitle[3] pursuits fluctuated , peaking in templateXValue[13] at templateYValue[max] templateTitle[1] .

generated_template: This statistic shows the number of templateTitle[0] templateTitle[2] by templateTitle[1] in templateTitleSubject[0] and templateTitleSubject[1] from templateXValue[last] to templateXValue[0] . During this period , the number of templateTitle[0] templateTitle[2] by templateTitle[1] fluctuated , peaking in templateXValue[2] at templateYValue[max] templateTitle[2] . By templateXValue[0] it templateNegativeTrend down to templateYValue[last] templateTitle[2] .
generated: This statistic shows the number of Road involving by deaths in England and Wales from 2004/05 to 2018/19 .  During this period , the number of Road involving by deaths fluctuated , peaking in 2016/17 at 32 involving .  By 2018/19 it dropped down to 23 involving .

Example 356:
titleEntities: {'Subject': ['Burger King', 'EBITDA'], 'Date': ['2011', '2014']}
title: Burger King 's EBITDA margin worldwide 2011 to 2014
X_Axis['Year']: ['2014', '2013', '2012', '2011']
Y_Axis['EBITDA', 'margin']: ['16.71', '51.3', '27', '21.3']

gold: This statistic shows Burger King 's EBITDA margin worldwide from 2011 to 2014 . Between 2012 and 2013 fast food chain Burger King 's earnings before interest , taxes , depreciation and amortization increased by 51.3 percent .
gold_template: This statistic shows templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] templateTitle[5] from templateXValue[min] to templateXValue[max] . Between templateXValue[2] and templateXValue[1] fast food chain templateTitleSubject[0] 's earnings before interest , taxes , depreciation and amortization templatePositiveTrend by templateYValue[max] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[2] templateTitleSubject[1] brand templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[2] templateTitleSubject[1] brand generated templateYLabel[0] templateYLabel[1] amounting to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] globally , up from templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] the previous templateXLabel[0] .
generated: This statistic shows the EBITDA margin of Burger King 's EBITDA brand 2011 from to 2014 .  In 2014 , Burger King 's EBITDA brand generated EBITDA margin amounting to 51.3 % margin globally , up from 51.3 % margin the previous Year .

Example 357:
titleEntities: {'Subject': ['Golden State Warriors', 'NBA'], 'Date': ['2018/19', '2018/19']}
title: Gate receipts of the Golden State Warriors ( NBA ) 2018/19
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11']
Y_Axis['Gate', 'receipts', 'in', 'million', 'U.S.', 'dollars']: ['178', '164', '143', '134', '77', '55', '50', '31', '41']

gold: The statistic depicts the gate receipts/ticket sales of the Golden State Warriors , franchise of the National Basketball Association , from 2010/11 to 2018/19 . In the 2018/19 season , the gate receipts of the Golden State Warriors were at 178 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] receipts/ticket sales of the templateTitleSubject[0] Warriors , franchise of the National Basketball Association , from 2010/11 to templateTitle[6] . In the templateTitle[6] season , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Warriors were at templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: In the academic templateXLabel[0] templateXValue[0] there were approximately templateYValue[min] thousand templateYLabel[1] in the templateTitleSubject[0] , around 655 templateYLabel[1] less than there were in the templateXValue[last] academic templateXLabel[0] . Throughout most of this period there has been a steady decline in the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] .
generated: In the academic Year 18/19 there were approximately 31 thousand receipts in the Golden State Warriors , around 655 receipts less than there were in the 10/11 academic Year .  Throughout most of this period there has been a steady decline in the Gate of receipts in the Golden State Warriors .

Example 358:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2008', '2018']}
title: Number of butcher shops and meat retailers in the United Kingdom ( UK ) 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'enterprises']: ['5690', '5864', '5929', '5874', '5940', '6056', '6134', '6220', '6283', '6399', '6633']

gold: Between 2008 and 2018 , the number of stores that specialize in the sales of meat has been shrinking In the United Kingdom . During this period , the number of meat specialty stores has seen a decrease of almost 943 . Of these stores , approximately 1.75 thousand generated a turnover between one hundred thousand and 250 thousand British pounds in 2018 .
gold_template: Between templateXValue[min] and templateXValue[max] , the templateYLabel[0] of stores that specialize in the sales of templateTitle[3] has been templateNegativeTrend In the templateTitleSubject[0] . During this period , the templateYLabel[0] of templateTitle[3] specialty stores has seen a templateNegativeTrend of almost 943 . Of these stores , approximately 1.75 thousand generated a turnover between one hundred thousand and 250 thousand British pounds in templateXValue[max] .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] 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 templateTitleSubject[0] amounted to approximately templateYValue[0] templateYValue[idxmax(X)] .
generated: This statistic shows the Number of enterprises in United Kingdom the meat retailers 2008 to 2018 .  In 2018 , the Number of enterprises ( aged six years and older ) in United Kingdom amounted to approximately 5690 .

Example 359:
titleEntities: {'Subject': ['Spain'], 'Date': ['2000', '2018']}
title: Average annual wages in Spain 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Average', 'annual', 'wages', 'in', 'euros']: ['27946', '28171', '28738', '28902', '28405', '28400', '28336', '29166', '29585', '30101', '28198', '27101', '26751', '26853', '26697', '26976', '27049', '26851', '26856']

gold: This statistic shows the average annual wages in Spain from 2000 to 2018 . Over this 18-year period , annual wages in Spain have fluctuated greatly , peaking at approximately 30 thousand euros in 2009 and decreasing to approximately 28 thousand euros yearly in 2012 . The average annual wage stood at approximately 28 thousand euros in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Over this 18-year period , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] have fluctuated greatly , peaking at approximately templateYValue[8] thousand templateYLabel[3] in templateXValue[9] and templateNegativeTrend to approximately templateYValue[0] thousand templateYLabel[3] yearly in templateXValue[6] . The templateYLabel[0] templateYLabel[1] wage stood at approximately templateYValue[0] thousand templateYLabel[3] in templateXValue[max] .

generated_template: As of templateXValue[max] , the templateYLabel[0] templateYLabel[1] wage of templateTitleSubject[0] was templateYValue[max] thousand templateYLabel[3] per templateXLabel[0] , a growth of just over 5.8 thousand templateYLabel[3] when compared with templateXValue[min] . From templateXValue[min] until templateXValue[11] , templateYLabel[2] templatePositiveTrend by less than a thousand templateYLabel[3] , with wage growth accelerating mainly in the period after templateXValue[8] . The 607 Euro templatePositiveTrend recorded between templateXValue[8] and templateXValue[7] was the largest wage rise seen during this period .
generated: As of 2018 , the Average annual wage of Spain was 30101 thousand euros per Year , a growth of just over 5.8 thousand euros when compared with 2000 .  From 2000 until 2007 , wages grew by less than a thousand euros , with wage growth accelerating mainly in the period after 2010 .  The 607 Euro increase recorded between 2010 and 2011 was the largest wage rise seen during this period .

Example 360:
titleEntities: {'Subject': ['Guyana'], 'Date': ['2019']}
title: Unemployment rate in Guyana 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['12.22', '12.15', '12.12', '12.34', '12.55', '12.37', '12.28', '11.93', '11.74', '11.66', '11.4', '10.47', '10.48', '10.7', '11.09', '11.58', '11.76', '11.81', '11.76', '11.86', '12.06']

gold: This statistic shows the unemployment rate in Guyana from 1999 to 2019 . In 2019 , the unemployment rate in Guyana was 12.22 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Guyana from 1999 to 2019 .  In 2019 , the Unemployment rate in Guyana was at approximately 12.22 % .

Example 361:
titleEntities: {'Subject': ['Vegetables'], 'Date': ['2000', '2018']}
title: Vegetables : global production volume 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Production', 'volume', 'in', 'million', 'metric', 'tons']: ['1088.9', '1094.34', '1075.2', '1051.52', '1030.32', '997.84', '978.52', '954.89', '921.52', '900.66', '876.15', '843.23', '809.33', '779.82', '760.29', '750.86', '721.42', '700.09', '682.43']

gold: This statistic depicts the total production volume of vegetables ( including melons ) worldwide from 1990 to 2018 . In 2014 , some 1169.45 million metric tons of vegetables and melons were produced worldwide .
gold_template: This statistic depicts the total templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] ( including melons ) worldwide from 1990 to templateXValue[max] . In templateXValue[4] , some 1169.45 templateScale templateYLabel[3] templateYLabel[4] of templateTitleSubject[0] and melons were produced worldwide .

generated_template: In templateXValue[max] , over templateYValue[max] templateScale templateTitleSubject[0] templateTitle[3] departed on outbound travels from India , marking an templatePositiveTrend from the previous templateXLabel[0] . Interestingly , since templateXValue[min] , an exponential rise was seen in the templateTitle[0] of templateTitleSubject[0] templateTitle[3] traveling outside the country for leisure and other reasons . Travel bug and the economy templateTitleSubject[0] templateTitle[3] are traveling more than ever before .
generated: In 2018 , over 1094.34 million Vegetables volume departed on outbound travels from India , marking an increase from the previous Year .  Interestingly , since 2000 , an exponential rise was seen in the Vegetables of volume traveling outside the country for leisure and other reasons .  Travel bug and the economy Vegetables volume are traveling more than ever before .

Example 362:
titleEntities: {'Subject': ['Latvia'], 'Date': ['2006', '2018']}
title: Number of arrivals in tourist accommodation Latvia 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'arrivals', 'in', 'millions']: ['2.8', '2.58', '2.3', '2.14', '2.1', '1.84', '1.64', '1.58', '1.31', '1.11', '1.56', '1.49', '1.33']

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Latvia from 2006 to 2018 . Since 2009 there has been an increasing trend in arrivals . In 2018 , the number of arrivals ( including both foreign and domestic ) at accommodation in Latvia amounted to approximately 2.8 million .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Since templateXValue[9] there has been an templatePositiveTrend trend in templateYLabel[1] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( including both foreign and domestic ) at templateTitle[3] in templateTitleSubject[0] amounted to approximately templateYValue[idxmax(X)] templateScale .

generated_template: Approximately templateYValue[max] templateScale templateYLabel[1] were recorded at travel templateTitle[3] in templateTitleSubject[0] in templateXValue[max] International tourism was the highest contributor , with around two thirds of templateYLabel[1] in visitor templateTitle[3] coming from a foreign country . templateTitleSubject[0] tourism growth Since templateXValue[9] international visitor numbers to templateTitleSubject[0] have steadily templatePositiveTrend . Including both overnight and same-day visitors , figures reached over 30 templateScale by templateXValue[1] .
generated: Approximately 2.8 millions arrivals were recorded at travel accommodation in Latvia 2018 International tourism was the highest contributor , with around two thirds of arrivals in visitor accommodation coming from a foreign country .  Latvia tourism growth Since 2009 international visitor numbers to Latvia have steadily increased .  Including both overnight and same-day visitors , figures reached over 30 millions by 2017 .

Example 363:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013']}
title: Age distribution of mobile gamers in the U.S. 2013
X_Axis['Year']: ['18-24', '25-34', '35-44', '45-54', '55-64', '65+']
Y_Axis['Percentage', 'of', 'respondents']: ['7', '17', '19', '22', '24', '11']

gold: This statistic gives information on the age distribution of mobile gamers in the United States as of May 2013 . During the survey period , it was found that 17 percent of mobile games were 25 to 34 years old . The average age of a mobile gamer was 46.5 years .
gold_template: This statistic gives information on the templateTitle[0] templateTitle[1] of templateTitle[2] templateTitle[3] in the templateTitle[4] as of 2013 . During the survey period , it was found that templateYValue[1] templateScale of templateTitle[2] games were 25 to 34 years old . The average templateTitle[0] of a templateTitle[2] gamer was 46.5 years .

generated_template: This statistic displays the templateTitle[7] distribution of templateTitle[1] at templateTitleSubject[0] in the templateTitleSubject[1] as of templateTitleDate[0] . According to their annual report , templateYValue[max] templateScale 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 at U.S. in the U.S. as of 2013 .  According to their annual report , 24 percentage 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 364:
titleEntities: {'Subject': ['NASA'], 'Date': ['2014', '2024']}
title: NASA - budget 2014 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'in', 'billion', 'U.S.', 'dollars']: ['21.87', '21.66', '21.44', '21.23', '21.02', '21.5', '20.74', '19.65', '19.29', '18.01', '17.65']

gold: This graph show NASA 's projected budget from 2014 to 2024 . NASA 's budget is projected to be at around 21 billion U.S. dollars in 2020 . The National Aeronautics and Space Administration ( NASA ) is the U.S. agency responsible for aeronautics and aerospace research .
gold_template: This graph show templateTitleSubject[0] 's projected templateYLabel[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] 's templateYLabel[0] is projected to be at around templateYValue[2] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[4] . The National Aeronautics and Space Administration ( templateTitleSubject[0] ) is the templateYLabel[2] agency responsible for aeronautics and aerospace research .

generated_template: 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] . In templateXValue[6] , templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[min] templateScale of the templateTitle[3] templateTitle[4] templateTitle[5] .
generated: This statistic shows the Budget billion of NASA from 2014 to 2018 in U.S. to the 2024 ( dollars ) , with projections up until 2024 .  In 2018 , NASA 's Budget billion amounted to approximately 17.65 billion of the 2024 .

Example 365:
titleEntities: {'Subject': ['Chinese'], 'Date': ['2008/09', '2018/19']}
title: Number of Chinese students in the U.S. 2008/09 - 2018/19
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09']
Y_Axis['Number', 'of', 'students']: ['369548', '363341', '350755', '328547', '304040', '274439', '235597', '194029', '157558', '127628', '98235']

gold: Colleges and universities in the United States are becoming an increasingly popular study destination for Chinese students , with over 369 thousand choosing to take courses there in the 2018/19 academic year . This made China the leading source of international students in the U.S. education market . The education exodus Business and management courses led the field in terms of what Chinese students were studying in the United States , followed closely by engineering , mathematics and computer science programs .
gold_template: Colleges and universities in the templateTitle[3] are becoming an increasingly popular study destination for templateTitleSubject[0] templateYLabel[1] , with over 369 thousand choosing to take courses there in the templateXValue[0] academic templateXLabel[0] . This made China the leading source of international templateYLabel[1] in the templateTitle[3] education market . The education exodus Business and management courses led the field in terms of what templateTitleSubject[0] templateYLabel[1] were studying in the templateTitle[3] , followed closely by engineering , mathematics and computer science programs .

generated_template: In the fall semester templateXValue[max] , templateYValue[idxmax(X)] 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 templatePositiveTrend , while the individuals without templateNegativeTrend . In templateXValue[max] , 21.7 templateScale of the female population had an upper secondary education degree of three years and 24.9 templateScale of the male population .
generated: In the fall semester 2018/19 , 98235 students were Chinese in universities and other higher education institutions in Chinese .  Since 2000 , the Number of individuals in Chinese with an upper secondary education increased , while the individuals without decreased .  In 2018/19 , 21.7 % of the female population had an upper secondary education degree of three years and 24.9 % of the male population .

Example 366:
titleEntities: {'Subject': ['Vale'], 'Date': ['2009', '2018']}
title: Vale 's employee number 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'employees']: ['70270', '73596', '73062', '74098', '76531', '83286', '85305', '79646', '70785', '60036']

gold: This statistic shows mining company Vale 's number of employees worldwide from 2009 to 2018 . In 2018 , the company employed some 70,300 people . Vale S.A. , formerly called by the full name Companhia Vale do Rio Doce , is a Brazilian multinational metals and mining company , also specializing in logistic operations .
gold_template: This statistic shows mining company templateTitleSubject[0] templateTitle[1] templateYLabel[0] of templateYLabel[1] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the company employed some 70,300 people . templateTitleSubject[0] S.A. , formerly called by the full name Companhia templateTitleSubject[0] do Rio Doce , is a Brazilian multinational metals and mining company , also specializing in logistic operations .

generated_template: templateTitleSubject[0] is an international pharmaceutical company based out of Germany . As of templateXValue[max] , the company had a total of templateYValue[idxmax(X)] templateYLabel[1] globally . This is a significant templatePositiveTrend from the previous years .
generated: Vale is an international pharmaceutical company based out of Germany .  As of 2018 , the company had a total of 70270 employees globally .  This is a significant increase from the previous years .

Example 367:
titleEntities: {'Subject': ['Manitoba', 'Canada'], 'Date': ['2000', '2018']}
title: GDP of Manitoba , Canada 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['GDP', 'in', 'million', 'chained', '2012', 'Canadian', 'dollars']: ['62862.5', '61932.6', '59966.8', '59082.5', '58276.3', '57169.9', '55676.4', '54057.9', '52841.8', '51522.1', '51668.8', '50017.3', '48918.3', '47127.8', '45727.8', '44494.6', '44031.4', '43301.5', '42734.1']

gold: This statistic shows the gross domestic product ( GDP ) of Manitoba from 2000 to 2018 . In 2018 , Manitoba 's GDP was 62.86 billion chained 2012 Canadian dollars .
gold_template: This statistic shows the gross domestic product ( templateYLabel[0] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] was templateYValue[max] templateScale templateYLabel[2] templateXValue[6] templateYLabel[4] templateYLabel[5] .

generated_template: This statistic shows the gross domestic product ( templateYLabel[0] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] was templateYValue[max] templateScale templateYLabel[2] templateXValue[6] templateYLabel[4] templateYLabel[5] .
generated: This statistic shows the gross domestic product ( GDP ) of Manitoba from 2000 to 2018 .  In 2018 , Manitoba 's GDP was 62862.5 million chained 2012 Canadian dollars .

Example 368:
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: In the academic templateXLabel[0] templateXValue[0] there were approximately templateYValue[min] thousand templateYLabel[1] in the templateTitleSubject[0] , around 655 templateYLabel[1] less than there were in the templateXValue[last] academic templateXLabel[0] . Throughout most of this period there has been a steady decline in the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] .
generated: In the academic Year 2018/2019 there were approximately 205000 thousand consumption in the U.S. , around 655 consumption less than there were in the 2010/2011 academic Year .  Throughout most of this period there has been a steady decline in the Domestic of consumption in the U.S. .

Example 369:
titleEntities: {'Subject': ['Texas'], 'Date': ['2000', '2018']}
title: Per capita real GDP of Texas 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'real', 'GDP', 'in', 'chained', '2012', 'U.S.', 'dollars']: ['59674', '58125', '57280', '58077', '56457', '55573', '54097', '52397', '51568', '51264', '52481', '53470', '51811', '49732', '49241', '47583', '48031', '47932', '47664']

gold: This statistic shows the per capita real Gross Domestic Product of Texas from 2000 to 2018 . In 2018 , the per capita real GDP of Texas stood at 59,674 chained 2012 U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[idxmax(X)] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[idxmax(X)] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .
generated: This statistic shows the Per capita real Gross Domestic Product of Texas from 2000 to 2018 .  In 2018 , the Per capita real GDP of Texas stood at 59674 chained 2012 U.S. dollars .

Example 370:
titleEntities: {'Subject': ['Disneyland Paris'], 'Date': ['2016']}
title: Disneyland Paris visitors spending per day 2006 to 2016
X_Axis['Year']: ['2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Average', 'spend', 'in', 'euros', '(excluding', 'VAT)']: ['54.0', '53.7', '50.7', '48.1', '46.4', '46.2', '45.3']

gold: This statistic displays daily expenditure per person at Disneyland Paris theme parks in France between 2006 and 2016 . Visitors spending includes entrance fee , catering , merchandise and other services in the theme parks . In the fiscal year 2016 , the average spending dipped to 54 euros ( before VAT ) .
gold_template: This statistic displays daily expenditure templateTitle[4] person at templateTitleSubject[0] theme parks in France between templateTitleDate[min] and templateXValue[max] . templateTitle[2] templateTitle[3] includes entrance fee , catering , merchandise and other services in the theme parks . In the fiscal templateXLabel[0] templateXValue[max] , the templateYLabel[0] templateTitle[3] dipped to templateYValue[max] templateYLabel[2] ( before VAT ) .

generated_template: This statistic 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 Paris Average of spend enrolled in Canadian colleges in 2016 , per day 2006 .  In the academic Year 2016 , around 54.0 spend aged between 2016 and 2015 2016 were enrolled in Canadian colleges .

Example 371:
titleEntities: {'Subject': ['Under Armour'], 'Date': ['2009', '2019']}
title: Global revenue growth of Under Armour 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'growth']: ['1', '4', '3', '22', '28', '32', '27', '25', '38', '24', '18']

gold: This statistic depicts the growth of Under Armour 's revenue worldwide from 2009 to 2019 . In 2019 , Under Armour 's net revenue increased by one percent . Under Armour is an American sporting goods manufacturer , based in Baltimore , Maryland .
gold_template: This statistic depicts the templateYLabel[1] of templateTitleSubject[0] 's templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's net templateYLabel[0] templatePositiveTrend by templateYValue[min] templateScale . templateTitleSubject[0] is an American sporting goods manufacturer , based in Baltimore , Maryland .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the global templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] was about templateYValue[0] templateScale templateYLabel[3] dollars.H templateTitle[3] MH templateTitle[3] templateTitleSubject[0] is a leading global fashion company with strong values and a clear business concept . templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] constantly strives to have the best customer offering in each individual market – which includes giving customers the best price .
generated: This statistic shows the Revenue growth of the Under Armour Under worldwide from 2009 to 2019 .  In 2019 , the global Revenue growth of the Under Armour Under was about 1 billion growth dollars.H Under MH Under Armour is a leading global fashion company with strong values and a clear business concept .  Under Armour Under Under Armour constantly strives to have the best customer offering in each individual market – which includes giving customers the best price .

Example 372:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: U.S. : reported robbery cases 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Number', 'of', 'reported', 'cases']: ['282061', '320596', '332797', '328109', '322905', '345093', '355051', '354746', '369089', '408742', '443563', '447324', '449246', '417438', '401470', '414235', '420806', '422921', '408016', '409371', '447186', '497950', '535590', '580510', '618950', '659870', '672480', '687730', '639270']

gold: This graph shows the reported number of robbery cases in the United States from 1990 to 2018 . In 2018 an estimated 282,061 cases occurred nationwide .
gold_template: This graph shows the templateYLabel[1] templateYLabel[0] of templateTitle[2] templateYLabel[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] an estimated templateYValue[idxmax(X)] templateYLabel[2] occurred nationwide .

generated_template: This statistic show the templateYLabel[1] templateTitle[2] templateTitle[3] templateYLabel[2] between templateXValue[min] and templateXValue[max] .
generated: This statistic show the reported robbery cases between 1990 and 2018 .

Example 373:
titleEntities: {'Subject': ['Cincinnati Bengals', 'NFL'], 'Date': ['2001', '2018']}
title: Revenue of the Cincinnati Bengals ( NFL ) 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['380', '359', '355', '329', '296', '258', '250', '235', '236', '232', '222', '205', '194', '175', '171', '150', '141', '130']

gold: The statistic depicts the revenue of the Cincinnati Bengals , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Cincinnati Bengals was 380 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the Major League Baseball franchise amounted to templateYValue[idxmax(X)] templateScale templateYLabel[2] dollars.The templateTitleSubject[0] are owned by Robert Castellini , who bought the franchise for 270 templateScale templateYLabel[2] templateYLabel[3] in templateXValue[12] .
generated: The statistic depicts the Revenue of the Cincinnati Bengals from 2001 to 2018 .  In 2018 , the Revenue of the Major League Baseball franchise amounted to 380 million U.S. dollars.The Cincinnati Bengals are owned by Robert Castellini , who bought the franchise for 270 million U.S. dollars in 2006 .

Example 374:
titleEntities: {'Subject': ['Luxembourg'], 'Date': ['2024']}
title: Inflation rate in Luxembourg 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['1.93', '1.88', '1.95', '1.87', '1.74', '1.73', '2.02', '2.11', '0.04', '0.06', '0.7', '1.7', '2.89', '3.73', '2.8', '0.01', '4.09', '2.66', '2.96', '3.76', '3.24', '2.53', '2.06', '2.4', '3.78', '1.02', '0.97', '1.37', '1.56', '1.9', '2.2', '3.6', '3.2', '3.1', '3.7', '3.4', '1.4', '-0.1', '0.3', '4.09', '5.64']

gold: This statistic shows the average inflation rate in Luxembourg from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Luxembourg amounted to about 2.02 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Luxembourg from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Luxembourg amounted to about 2.02 % compared to the previous Year .

Example 375:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2005', '2018']}
title: U.S. motion picture/video production and distribution - revenue 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Estimated', 'revenue', 'in', 'billion', 'U.S.', 'dollars']: ['69.91', '64.41', '66.86', '64.43', '62.83', '64.5', '61.89', '59.63', '59.41', '55.83', '61.14', '61.91', '59.17', '56.83']

gold: The statistic above presents estimates of the annual aggregate revenue of the U.S. motion picture and video production and distribution industry from 2005 to 2018 . In 2018 , the industry generated an estimated total revenue of 69.91 billion U.S. dollars .
gold_template: The statistic above presents estimates of the annual aggregate templateYLabel[1] of the templateYLabel[3] templateTitle[1] picture and video templateTitle[3] and templateTitle[4] industry from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the industry generated an templateYLabel[0] total templateYLabel[1] of templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: The statistic above presents estimates of the annual aggregate templateYLabel[1] of the templateYLabel[3] templateTitle[1] publishing industry from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[3] templateTitle[1] templateTitle[2] generated an templateYLabel[0] total templateYLabel[1] of templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic above presents estimates of the annual aggregate revenue of the U.S. motion publishing industry from 2005 to 2018 .  In 2018 , U.S. motion picture/video generated an Estimated total revenue of 69.91 billion U.S. dollars .

Example 376:
titleEntities: {'Subject': ['Sporting Goods'], 'Date': ['2006', '2018']}
title: Dick 's Sporting Goods : gross profit 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Gross', 'profit', 'in', 'million', 'U.S.', 'dollars']: ['2437', '2489', '2366', '2183', '2087', '1944', '1837', '1595', '1449', '1217', '1184', '1158', '897']

gold: The timeline depicts the gross profit of Dick 's Sporting Goods from 2006 to 2018 . The gross profit of Dick 's Sporting Goods amounted to 2,437 million U.S. dollars in 2018 .
gold_template: The timeline depicts the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] templateTitleSubject[0] amounted to templateYValue[0] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateYLabel[1] were committed templateTitle[1] templateTitle[2] in the templateTitle[3] . While this figure has remained relatively steady since templateXValue[min] , it has been templatePositiveTrend since templateXValue[2] . Firearms in the templateTitle[3] Firearms in the templateTitle[3] have become a large part of American culture .
generated: In 2018 , 2437 million of profit were committed 's Sporting in the Goods .  While this figure has remained relatively steady since 2006 , it has been increasing since 2016 .  Firearms in the Goods Firearms in the Goods have become a large part of American culture .

Example 377:
titleEntities: {'Subject': ['Michigan'], 'Date': ['1990', '2018']}
title: Michigan - median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['60449', '57700', '57091', '54203', '52005', '48801', '50015', '48879', '46276', '45994', '49788', '49370', '48647', '45933', '42256', '45022', '42715', '45047', '45512', '46089', '41821', '38742', '39225', '36426', '35284', '32662', '32267', '32117', '29937']

gold: This statistic shows the median household income in Michigan from 1990 to 2018 . In 2018 , the median household income in Michigan amounted to 60,449 U.S. dollars .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: 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 378:
titleEntities: {'Subject': ['NFL'], 'Date': ['2006', '2019']}
title: Average Fan Cost Index of NFL teams 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Fan', 'Cost', 'Index', 'in', 'U.S.', 'dollars']: ['540.52', '536.04', '502.84', '480.89', '479.11', '459.73', '443.93', '427.42', '420.54', '412.64', '396.36', '367.31', '346.16']

gold: The statistic shows the average Fan Cost Index in the National Football League from 2006 to 2019 . The average Fan Cost Index was at 540.52 U.S. dollars in 2019 .
gold_template: The statistic shows the templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the National Football League from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] was at templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the British bank templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] bank reached templateYValue[max] templateScale U.S. dollars . templateTitleSubject[0] - additional information templateTitleSubject[0] Holdings plc is a British multinational bank and financial services organization based in London , United Kingdom .
generated: This statistic shows the Fan Cost of the British bank NFL from 2006 to 2019 .  In 2019 , the Fan Cost of the NFL bank reached 540.52 million U.S. dollars .  NFL - additional information NFL Holdings plc is a British multinational bank and financial services organization based in London , United Kingdom .

Example 379:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2005', '2017']}
title: Mexico : number of households 2005 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2010', '2005']
Y_Axis['Number', 'of', 'households', 'in', 'millions']: ['34.07', '32.9', '31.95', '28.16', '24.8']

gold: The statistic presents a timeline with the number of households in Mexico between 2005 and 2017 . In 2017 , there were more than 34 million households in Mexico , up from nearly 33 million households a year earlier .
gold_template: The statistic presents a timeline with the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , there were more than templateYValue[max] templateScale templateYLabel[1] in templateTitleSubject[0] , up from nearly templateYValue[1] templateScale templateYLabel[1] a templateXLabel[0] earlier .

generated_template: This statistic presents the templateYLabel[0] of templateYLabel[1] templateTitle[2] of templateTitle[3] templateTitle[4] in templateXValue[min] , witha forecast until templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale templateYLabel[1] templateTitle[4] were templateTitle[2] of templateTitle[3] . This templateYLabel[0] is projected to reach templateYValue[max] templateScale in templateXValue[idxmax(Y)] .
generated: This statistic presents the Number of households 2005 2017 in 2005 , witha forecast until 2017 .  In 2005 , 24.8 millions households 2017 were households of 2005 .  This Number is projected to reach 34.07 millions in 2017 .

Example 380:
titleEntities: {'Subject': ['Amazon Prime Day'], 'Date': ['2017', '2019']}
title: U.S. Amazon Prime Day conversion rate 2017 to 2019
X_Axis['Year']: ['2019', '2018', '2017']
Y_Axis['Conversion', 'rate']: ['13.5', '11.6', '11.2']

gold: During the Amazon Prime Day shopping event in July 2019 , the desktop conversion rate amounted to 13.5 percent , which represented a 16 percent growth from the previous year . Prime Day does not only drive conversion on Amazon but also on other retail platforms .
gold_template: During the templateTitleSubject[0] Day shopping event in templateXValue[idxmax(Y)] , the desktop templateYLabel[0] templateYLabel[1] amounted to templateYValue[max] templateScale , which represented a 16 templateScale growth from the previous templateXLabel[0] . templateTitleSubject[0] does not only drive templateYLabel[0] on templateTitleSubject[0] but also on other retail platforms .

generated_template: This statistic presents the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[0] to the first half of templateXValue[last] . In the most recently reported period , the chat app 's templateYLabel[1] amounted to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] , up from templateYValue[1] templateScale templateYLabel[3] templateYLabel[4] for the preceding fiscal templateXLabel[0] .
generated: This statistic presents the Conversion rate of Amazon Prime Day from 2019 to the first half of 2017 .  In the most recently reported period , the chat app 's rate amounted to 13.5 % rate , up from 11.6 % rate for the preceding fiscal Year .

Example 381:
titleEntities: {'Subject': ['Saudi Arabia'], 'Date': ['1984']}
title: Inflation in Saudi Arabia since 1984
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['2.09', '2.09', '2.12', '2.09', '2.23', '-1.05', '2.48', '-0.85', '2.03', '1.27', '2.2', '3.52', '2.88', '3.76', '3.78', '4.18', '6.08', '5.06', '1.9', '0.55', '0.27', '0.55', '0.14', '-1.22', '-1.08', '-2.11', '-0.39', '-0.26', '0.26', '5.25', '1.26', '1.28', '-0.98', '3.79', '-1.01', '1.17', '-0.35', '-2.39', '-3.12', '-2.31', '-0.68']

gold: The statistic shows the inflation rate in Saudi Arabia from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate amounted to 2.48 percent compared to the previous year . Oil production in Saudi Arabia Saudi Arabia 's economy relies heavily on production and export of oil and petroleum .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] amounted to templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . Oil production in templateTitleSubject[0] Saudi templateTitleSubject[0] 's economy relies heavily on production and export of oil and petroleum .

generated_template: 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[8] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Saudi Arabia from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Saudi Arabia amounted to about 2.03 % compared to the previous Year .

Example 382:
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 383:
titleEntities: {'Subject': ['North America'], 'Date': ['2007', '2015']}
title: Forecast : printer cartridge revenue in North America 2007 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['23616', '23628', '23886', '23695', '23348', '23081', '22992', '23767', '23813']

gold: The statistic shows a forecast for revenue from printer cartridges in North America between 2007 and 2015 . In 2012 , revenue of about 23.7 billion U.S. dollars are expected .
gold_template: The statistic shows a templateTitle[0] for templateYLabel[0] from templateTitle[1] cartridges in templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[3] , templateYLabel[0] of about templateYValue[3] templateScale templateYLabel[2] templateYLabel[3] are expected .

generated_template: templateTitle[2] spending in the National Football League ( templateTitleSubject[0] ) and its teams has templatePositiveTrend annually since templateXValue[min] , reaching templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] worldwide during the templateXValue[idxmax(Y)] 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 in the National Football League ( North America ) and its teams has increased annually since 2007 , reaching 23886 million U.S. dollars worldwide during the 2013 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 384:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2000', '2019']}
title: Inheritance tax : United Kingdom HMRC tax receipts 2000 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06', '2004/05', '2003/04', '2002/03', '2001/02', '2000/01']
Y_Axis['Inheritance', 'tax', 'receipts', 'in', 'billion', 'GBP']: ['5.36', '5.2', '4.8', '4.7', '3.8', '3.4', '3.1', '2.9', '2.7', '2.4', '2.8', '3.8', '3.5', '3.3', '2.9', '2.5', '2.4', '2.4', '2.2']

gold: This statistic shows the total United Kingdom ( UK ) HMRC inheritance tax receipts from fiscal year 2000/01 to fiscal year 2018/19 . Despite a dip in 2008/09 and 2009/10 the overall trend was one of increase . The peak was in 2018/19 at 5.36 billion British pounds ( GBP ) .
gold_template: This statistic shows the total templateTitleSubject[0] ( UK ) templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] templateXValue[0] . Despite a dip in templateXValue[10] and templateXValue[9] the overall trend was one of templatePositiveTrend . The peak was in templateXValue[0] at templateYValue[max] templateScale British pounds ( templateYLabel[4] ) .

generated_template: In templateXValue[0] templateYLabel[0] templateYLabel[1] templateTitle[5] in the templateTitleSubject[0] amounted to templateYValue[max] templateScale British pounds , which when compared with templateXValue[last] was a net templatePositiveTrend of templateDelta[18,0] templateScale pounds . The amount which workers in the templateTitleSubject[0] pay in templateYLabel[0] templateYLabel[1] is determined by how much they earn and falls into four templateYLabel[0] templateYLabel[1] bands . All workers in the templateTitleSubject[0] are entitled to earn a personal allowance of 12.5 thousand pounds before they are charged templateYLabel[0] templateYLabel[1] .
generated: In 2018/19 Inheritance tax in the United Kingdom amounted to 5.36 billion British pounds , which when compared with 2000/01 was a net increase of 3 billion pounds .  The amount which workers in the United Kingdom pay in Inheritance tax is determined by how much they earn and falls into four Inheritance tax bands .  All workers in the United Kingdom are entitled to earn a personal allowance of 12.5 thousand pounds before they are charged Inheritance tax .

Example 385:
titleEntities: {'Subject': ['LVMH Group'], 'Date': ['2008', '2019']}
title: LVMH Group 's R & D expenditure worldwide 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['R&D', 'expenditure', 'in', 'million', 'euros']: ['140', '130', '130', '111', '97', '79', '71', '68', '63', '46', '45', '43']

gold: This statistic highlights the trend in research and development ( R & D ) expenditure of the LVMH Group worldwide from 2008 to 2019 . In 2019 , LVMH Group 's global R & D expenditure amounted to about 140 million euros .
gold_template: This statistic highlights the trend in research and development ( templateTitleSubject[0] templateTitle[4] templateTitle[5] ) templateYLabel[1] of the templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's global templateTitleSubject[0] templateTitle[4] templateTitle[5] templateYLabel[1] amounted to about templateYValue[max] templateYValue[idxmax(X)] templateYLabel[3] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] of templateTitle[0] in the templateTitleSubject[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , in templateYLabel[1] templateYLabel[2] . In templateXValue[max] , templateTitle[1] templateYLabel[0] of templateTitle[0] in DR templateTitleSubject[0] totaled some templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] . Recently , worldwide templateYLabel[0] of templateTitle[0] has exceed consumption which has templateNegativeTrend prices for the mineral .
generated: This statistic shows the Group R&D of LVMH in the LVMH Group of from 2008 to 2019 , in expenditure million .  In 2019 , Group R&D of LVMH in DR LVMH Group totaled some 140 expenditure million .  Recently , worldwide R&D of LVMH has exceed consumption which has decreased prices for the mineral .

Example 386:
titleEntities: {'Subject': ['Panama'], 'Date': ['2024']}
title: Unemployment rate in Panama 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Unemployment', 'rate']: ['5.77', '5.77', '5.77', '5.8', '5.91', '6.11', '5.96', '6.13', '5.49', '5.05', '4.82']

gold: This statistic shows the unemployment rate in Panama from 2014 to 2018 , with projections up until 2024 . In 2018 , the unemployment rate in Panama was 5.96 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[6] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at templateYValue[min] templateScale .
generated: This statistic shows the Unemployment rate in Panama from 2014 to 2024 .  In 2018 , the Unemployment rate in Panama was at 4.82 % .

Example 387:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2008', '2018']}
title: Number of students in upper secondary education in Denmark 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'students']: ['148551', '150608', '149788', '148144', '147760', '148051', '144791', '140259', '132619', '122837', '118217']

gold: The statistic shows the number of students in upper secondary education in Denmark from 2008 to 2018 . The number increased from about 118 thousand upper secondary education students in 2008 to about 149 thousand students in 2018 .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[2] templateTitle[3] templateTitle[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templatePositiveTrend from about templateYValue[min] thousand templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[1] in templateXValue[idxmin(Y)] to about templateYValue[0] thousand templateYLabel[1] in templateXValue[max] .

generated_template: In templateXValue[max] , over templateYValue[max] templateScale of templateTitleSubject[0] 's templateYLabel[3] were templateYLabel[1] templateTitle[4] , a slight templateNegativeTrend from the templateXLabel[0] before . Of those 5.06 templateScale , some 785,000 were born in Germany , the most of any templateTitleSubject[0] member state . templateYLabel[1] templateYLabel[2] are the templateYLabel[2] of children excluding stillbirths ; a key figure that can provide insight to demographic analyses , such as population growth .
generated: In 2018 , over 150608 % of Denmark 's students were education , a slight decrease from the Year before .  Of those 5.06 % , some 785,000 were born in Germany , the most of any Denmark member state .  students students are the students of children excluding stillbirths ; a key figure that can provide insight to demographic analyses , such as population growth .

Example 388:
titleEntities: {'Subject': ['Summer Olympic Games'], 'Date': ['2016']}
title: Number of sports at the Summer Olympic Games 1896 to 2016
X_Axis['Year']: ['2016', '2012', '2008', '2004', '2000', '1996', '1992', '1988', '1984', '1980', '1976', '1972', '1968', '1964', '1960', '1956', '1952', '1948', '1936', '1932', '1928', '1924', '1920', '1912', '1908', '1906', '1904', '1900', '1896']
Y_Axis['Number', 'of', 'sports', 'played']: ['35', '32', '34', '34', '34', '31', '29', '27', '26', '23', '23', '23', '20', '21', '19', '18', '19', '20', '24', '18', '17', '20', '25', '17', '24', '13', '18', '20', '9']

gold: The statistic illustrates the number of sports at the Summer Olympic Games between 1896 and 2016 . In 1900 , 20 sporting events took place at the Summer Olympic Games .
gold_template: The statistic illustrates the templateYLabel[0] of templateYLabel[1] at the templateTitleSubject[0] Games between templateXValue[min] and templateXValue[max] . In templateXValue[27] , templateYValue[12] sporting events took place at the templateTitleSubject[0] Games .

generated_template: This statistic shows the templateYLabel[1] of the templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] templateTitle[3] templateTitle[4] were living below the templateYLabel[0] line in the templateTitle[5] . templateYLabel[0] is the state of one who lacks a certain amount of material possessions or money .
generated: This statistic shows the sports of the Olympic Games 1896 in the Summer Olympic Games from 1896 to 2016 .  In 2016 , 35 % 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 389:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2018']}
title: Urbanization in Denmark 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['87.87', '87.76', '87.64', '87.53', '87.41', '87.29', '87.14', '86.96', '86.8', '86.65', '86.49']

gold: This statistic shows the degree of urbanization in Denmark from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 87.87 percent of Denmark 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Denmark from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 87.87 % of Denmark 's total population lived in urban areas and cities .

Example 390:
titleEntities: {'Subject': ['Canada'], 'Date': ['2000', '2019']}
title: Number of births in Canada 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Number', 'of', 'births']: ['382533', '378848', '379675', '383579', '383315', '382281', '381607', '378840', '376951', '379373', '379290', '373695', '360916', '346082', '339270', '337762', '330523', '328155', '327107']

gold: In 2018 , there were an estimated 382,533 babies born in Canada . This is an increase from 327,107 births in the year 2001 . Births in Canada In 2018 , there were more male babies born than female babies , and overall births have been increasing since 2000 .
gold_template: In templateXValue[1] , there were an estimated templateYValue[0] babies born in templateTitleSubject[0] . This is an templatePositiveTrend from templateYValue[min] templateYLabel[1] in the templateXLabel[0] templateXValue[idxmin(Y)] . templateYLabel[1] in templateTitleSubject[0] In templateXValue[1] , there were more male babies born than female babies , and overall templateYLabel[1] have been templatePositiveTrend since templateTitleDate[min] .

generated_template: The total templateTitle[0] expenditure in templateTitleSubject[0] in templateXValue[max] accounted for approximately templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's Gross Domestic Product ( templateYLabel[1] ) that templateXLabel[0] . This was the result of the steepest templatePositiveTrend in the past ten years and was the first time templateTitle[0] templateTitle[1] as a share of the templateYLabel[1] exceeded templateYValue[max] templateScale . This share saw a continuous templatePositiveTrend over the past decade , indicating that as the templateYLabel[1] templatePositiveTrend , templateTitle[0] templateTitle[1] templatePositiveTrend at an even faster rate .
generated: The total Number expenditure in Canada 2019 accounted for approximately 382533 % of Canada 's Gross Domestic Product ( births ) that Year .  This was the result of the steepest increase in the past ten years and was the first time Number births as a share of the births exceeded 383579 % .  This share saw a continuous increase over the past decade , indicating that as the births increase , Number births increase at an even faster rate .

Example 391:
titleEntities: {'Subject': ['Indonesia'], 'Date': ['2006', '2019']}
title: Per capita poultry consumption in Indonesia 2006 to 2019
X_Axis['Year']: ['2025', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Consumption', 'per', 'capita', 'in', 'kilograms']: ['8.39', '7.57', '7.57', '7.68', '7.91', '7.05', '6.81', '6.54', '6.25', '6.07', '5.68', '5.27', '5.17', '5.08', '4.93']

gold: In 2019 , Indonesians consumed around 7.6 kilograms of poultry meat per capita . In 2025 , this was expected to increase to 8.4 kilograms per capita . Indonesia 's meat consumption had been increasing in the last few years , indicating improved economic prosperity for the population .
gold_template: In templateXValue[1] , Indonesians consumed around templateYValue[1] templateYLabel[3] of templateTitle[2] meat templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this was expected to templatePositiveTrend to templateYValue[max] templateYLabel[3] templateYLabel[1] templateYLabel[2] . templateTitleSubject[0] 's meat templateYLabel[0] had been templatePositiveTrend in the last few years , indicating improved economic prosperity for the population .

generated_template: In templateXValue[6] , 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 2014 , the poultry Consumption per capita in Indonesia was approximately 7.57 kilograms .  In 2025 , this was forecasted to reach around 8.39 kilograms per capita .  Indonesia 's poultry meat Consumption in 2019 was above the OECD average of that Year .

Example 392:
titleEntities: {'Subject': ['United States'], 'Date': ['2011']}
title: Geographic distance between grandparents and their grandchildren in the United States in 2011
X_Axis['Year']: ['10_miles_or_less', '11_-_50_miles', '51_-_100_miles', '101_-_150_miles', '151_-_200_miles', 'More_than_200_miles', 'Only_have_grandchildren_who_live_with_me', "Don't_know"]
Y_Axis['Percentage', 'of', 'respondents']: ['21', '17', '7', '4', '4', '43', '1', '2']

gold: This statistic shows the results of a survey among grandparents in the United States in 2011 on the geographic distance between themselves and their grandchildren . In 2011 , 43 percent of the respondents stated they live more than 200 miles away from their grandchildren , whereas 21 percent said they live 10 or less miles away from their grandchildren .
gold_template: This statistic shows the results of a survey among templateTitle[3] in the templateTitleSubject[0] in templateTitleDate[0] on the templateTitle[0] templateTitle[1] templateTitle[2] themselves and templateTitle[4] templateXValue[6] . In templateTitleDate[0] , templateYValue[max] templateScale of the templateYLabel[1] stated they templateXValue[6] templateXValue[5] 200 templateXValue[0] away from templateTitle[4] templateXValue[6] , whereas templateYValue[0] templateScale said they templateXValue[6] templateXValue[0] or templateXValue[0] away from templateTitle[4] templateXValue[6] .

generated_template: This statistic shows the templateTitle[0] templateYLabel[1] templateTitle[2] in the templateTitle[3] during the first quarter of templateTitleDate[0] . In the first quarter of templateTitleDate[0] , templateYValue[2] templateScale of templateYLabel[1] templateYLabel[2] made on tripology.com were for trips lengths of templateXValue[2] to templateXValue[2] templateXValue[0] .
generated: This statistic shows the Geographic respondents between in the grandparents during the first quarter of 2011 .  In the first quarter of 2011 , 7 percentage of respondents made on tripology.com were for trips lengths of 51 - 100 miles to 10 miles or less .

Example 393:
titleEntities: {'Subject': ['Yemen'], 'Date': ['2002', '2019']}
title: U.S. airstrikes in Yemen 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2002']
Y_Axis['Number', 'of', 'airstrikes']: ['8', '35', '125', '44', '23', '23', '26', '42', '10', '4', '2', '1']

gold: This statistic shows the number of U.S. airstrikes in Yemen from 2002 to 2019 . In 2018 , there were 35 United States airstrikes in Yemen .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , there were templateYValue[1] templateTitle[0] templateYLabel[1] in templateTitleSubject[0] .

generated_template: The templateTitleSubject[0] of templateTitleSubject[0] ( RBS ) is part of The 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 templateScale euros in templateYLabel[1] , the fourth highest in the United Kingdom . History of RBS Founded in Edinburgh in 1727 , the templateTitleSubject[0] of templateTitleSubject[0] is an amalgamation of hundreds of past banks .
generated: The Yemen of ( RBS ) is part of The Yemen of Group plc with Natwest and Ulster Yemen and consists of 67 thousand employees over 187 branches across the United Kingdom .  As of 2018 , RBS had over 830 million euros in airstrikes , the fourth highest in the United Kingdom .  History of RBS Founded in Edinburgh 1727 , the Yemen of is an amalgamation of hundreds past banks .

Example 394:
titleEntities: {'Subject': ['Mexico'], 'Date': ['2024']}
title: Budget balance in Mexico in relation to gross domestic product ( GDP ) 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budget', 'balance', 'to', 'GDP', 'ratio']: ['-2.4', '-2.3', '-2.3', '-2.2', '-2.6', '-2.8', '-2.2', '-1.07', '-2.77', '-4', '-4.54']

gold: The statistic shows the budget balance in Mexico from 2014 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 . In 2018 , the state deficit of Mexico was at around 2.2 percent of the gross domestic product .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , the state deficit of templateTitleSubject[0] was at around 2.2 templateScale of the templateTitle[4] templateTitle[5] templateTitle[6] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] in templateTitle[3] to the templateTitle[4] templateTitle[5] templateTitle[6] ( templateYLabel[2] ) , with projections up until templateXValue[max] . In templateXValue[6] , the state deficit of templateTitleSubject[0] amounted to about 6.4 templateScale of templateTitle[4] templateTitle[5] templateTitle[6] .
generated: The statistic shows the Budget balance in Mexico from 2014 to 2018 in relation to the gross domestic product ( GDP ) , with projections up until 2024 .  In 2018 , the state deficit of Mexico amounted to about 6.4 % of gross domestic product .

Example 395:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000']}
title: Gross margin on furniture in U.S. wholesale 2000 to 2017
X_Axis['Year']: ['17', '16', '15', '14', '13', '12', '11', '10', '9', '8', '7', '6', '5', '4', '3', '2', '1', '0']
Y_Axis['Gross', 'margin', 'in', 'billion', 'U.S.', 'dollars']: ['27.92', '26.54', '25.0', '23.93', '22.73', '21.11', '19.2', '18.73', '15.81', '20.32', '21.17', '21.19', '19.09', '18.25', '17.61', '16.94', '15.49', '15.97']

gold: This timeline depicts the U.S. merchant wholesalers ' gross margin on furniture and home furnishings from 2000 to 2017 . In 2017 , the gross margin on furniture and home furnishings in U.S. wholesale was about 27.92 billion U.S. dollars .
gold_template: This timeline depicts the templateYLabel[3] merchant wholesalers ' templateYLabel[0] templateYLabel[1] on templateTitle[2] and home furnishings from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateYLabel[0] templateYLabel[1] on templateTitle[2] and home furnishings in templateYLabel[3] templateTitle[4] was about templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Basketball Association from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] franchise had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This graph depicts the Gross margin of the U.S. of the National Basketball Association from 0 to 17 .  In 17 , the U.S. franchise had an estimated margin of 27.92 billion U.S. dollars .

Example 396:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: Per capita consumption of butter in the U.S. 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'consumption', 'in', 'pounds']: ['5.8', '5.7', '5.7', '5.6', '5.5', '5.5', '5.5', '5.4', '4.9', '5.0', '5.0', '4.7', '4.7', '4.5', '4.5', '4.5', '4.4', '4.3', '4.5']

gold: This statistic shows the per capita consumption of butter in the United States from 2000 to 2018 . The U.S. per capita consumption of butter amounted to 5.8 pounds in 2018 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[3] amounted to templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[idxmax(Y)] .

generated_template: The timeline shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . According to the report , the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[4] templateTitle[5] amounted to approximately templateYValue[idxmax(X)] templateYLabel[3] in templateXValue[max] .
generated: 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 397:
titleEntities: {'Subject': ['Singapore'], 'Date': ['2005', '2018']}
title: Youth unemployment rate in Singapore 2005 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005']
Y_Axis['Youth', 'unemployment', 'rate']: ['4.2', '4.6', '4.1', '3.8', '6.3', '6.3', '6.5', '6.7', '7.1', '9.9', '9.2', '8.8', '8.8', '10.7']

gold: This statistic presents the unemployment rate for individuals aged 15 to 24 years in Singapore from 2005 to 2018 . In 2018 , approximately 4.2 percent of the labor force aged 15 to 24 years in Singapore were unemployed .
gold_template: This statistic presents the templateYLabel[1] templateYLabel[2] for individuals aged 15 to 24 years in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , approximately templateYValue[idxmax(X)] templateScale of the labor force aged 15 to 24 years in templateTitleSubject[0] were unemployed .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In its templateXValue[max] fiscal templateXLabel[0] , templateTitleSubject[0] paid out a templateYLabel[0] of approximately templateYValue[0] templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] .
generated: The statistic shows the Youth unemployment rate of Singapore from the fiscal Year of 2005 to the fiscal Year of 2018 .  In its 2018 fiscal Year , Singapore paid out a Youth of approximately 4.2 unemployment rate .

Example 398:
titleEntities: {'Subject': ['Worldwide'], 'Date': ['1990', '2017']}
title: Worldwide commercial space launches 1990 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995', '1990']
Y_Axis['Commercial', 'space', 'launches']: ['33', '21', '22', '23', '23', '20', '18', '23', '24', '28', '23', '21', '18', '35', '23', '15']

gold: This statistic represents worldwide commercial space launches from 1990 to 2017 . Globally , there were 33 commercial space launches in 2017 . The major nations conducting space launches include Russia , the United States and the member states of ESA .
gold_template: This statistic represents templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] from templateXValue[min] to templateXValue[max] . Globally , there were templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[max] . The major nations conducting templateYLabel[1] templateYLabel[2] include Russia , the country and the member states of ESA .

generated_template: The statistic shows the templateTitleSubject[0] templateYLabel[0] templateYLabel[1] due to earthquakes templateTitle[4] templateXValue[min] to templateXValue[max] . Around templateYValue[idxmax(X)] 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 Worldwide Commercial space due to earthquakes 1990 to 2017 .  Around 33 people died worldwide in 2017 as a result of earthquakes .  Earthquakes are typically caused by the movement of the earth crusts .

Example 399:
titleEntities: {'Subject': ['Denmark'], 'Date': ['2008', '2018']}
title: Number of divorces in Denmark 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Number', 'of', 'divorces']: ['14936', '15169', '17146', '16290', '19387', '18858', '15709', '14484', '14460', '14940', '14695']

gold: In 2017 and 2018 , most Danes were never married ; the number of never married inhabitants was around 2.8 million in the fourth quarter of 2018 . By contrast , among all Danes , the fewest were divorced . In general , the number of divorces between different sexes fluctuated in recent years , peaking in 2014 at about 19 thousand divorces .
gold_template: In templateXValue[1] and templateXValue[max] , most Danes were never married ; the templateYLabel[0] of never married inhabitants was around 2.8 templateScale in the fourth quarter of templateXValue[max] . By contrast , among all Danes , the fewest were divorced . In general , the templateYLabel[0] of templateYLabel[1] between different sexes fluctuated in recent years , peaking in templateXValue[4] at about templateYValue[max] thousand templateYLabel[1] .

generated_template: The templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] peaked in templateXValue[5] , when almost templateYValue[max] thousand couples got divorced . Since then the divorce templateYLabel[0] templateNegativeTrend until templateXValue[max] , when it again templatePositiveTrend and amounted to almost templateYValue[0] thousand templateYLabel[1] . A similar trend can be seen in the neighbor country Norway , where the templateYLabel[0] of templateYLabel[1] templateNegativeTrend for several years but templatePositiveTrend again in templateXValue[1] .
generated: The Number of divorces in Denmark peaked in 2013 , when almost 19387 thousand couples got divorced .  Since then the divorce Number decreased until 2018 , when it again increased and amounted to almost 14936 thousand divorces .  A similar trend can be seen in the neighbor country Norway , where the Number of divorces decreased for several years but increased again in 2017 .

Example 400:
titleEntities: {'Subject': ['Germany'], 'Date': ['2018']}
title: GDP of Germany 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['GDP', 'in', 'billion', 'euros']: ['3386.0', '3277.34', '3159.75', '3048.86', '2938.59', '2826.24', '2758.26', '2703.12', '2580.06', '2460.28', '2561.74', '2513.23']

gold: In 2018 , Germany 's gross domestic product ( GDP ) amounted to 3,386 billion euros . Germany is thus among the leading five countries in the world GDP ranking . Ze Germans are living large Germany 's economy is not only one of the largest worldwide , it is also the largest in Europe and predicted to be among the countries with the largest GDP by the year 2030 .
gold_template: In templateXValue[max] , templateTitleSubject[0] 's gross domestic product ( templateYLabel[0] ) amounted to templateYValue[max] templateScale templateYLabel[2] . templateTitleSubject[0] is thus among the leading five countries in the world templateYLabel[0] ranking . Ze Germans are living large templateTitleSubject[0] 's economy is not only one of the largest worldwide , it is also the largest in Europe and predicted to be among the countries with the largest templateYLabel[0] by the templateXLabel[0] 2030 .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[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] , templateTitleSubject[0] had a balance sheet templateYLabel[0] of around 208.9 templateScale templateYLabel[3] .
generated: This statistic shows the GDP billion of Germany from the fiscal Year of 2007 to the fiscal Year of 2018 .  In the fiscal Year of 2018 , Germany had a balance sheet GDP of around 208.9 billion euros .

Example 401:
titleEntities: {'Subject': ['European'], 'Date': ['2015', '2028']}
title: European Union-27 : poultry meat consumption volume forecast 2015 to 2028
X_Axis['Year']: ['2028', '2027', '2026', '2025', '2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Volume', 'in', 'thousand', 'metric', 'tons']: ['12182', '12111', '12041', '11976', '11921', '11869', '11817', '11751', '11690', '11664', '11861', '11606', '11667', '11102']

gold: Forecasts up until the year 2018 show that poultry meat consumption across the European Union is expected to increase to 11.86 million metric tons . In the following decade consumption will likely slow down , with the forecast up until 2028 remaining constant . By the end of the period in consideration , consumption will amount to an estimated 12.18 million metric tons .
gold_template: Forecasts up until the templateXLabel[0] templateXValue[10] show that templateTitle[2] templateTitle[3] templateTitle[4] across the templateTitleSubject[0] Union is expected to templatePositiveTrend to templateYValue[10] templateScale templateYLabel[2] templateYLabel[3] . In the following decade templateTitle[4] will likely slow down , with the templateTitle[6] up until templateXValue[max] remaining constant . By the end of the period in consideration , templateTitle[4] will amount to an estimated templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: In templateXValue[max] , the templateTitle[3] of templateTitle[0] and calves in the templateTitleSubject[0] was approximately templateYValue[min] templateScale , a slight templateNegativeTrend from the previous templateXLabel[0] . This was the lowest templateYLabel[0] for the entire period shown in this graph . Despite a small rebound in templateXValue[4] and templateXValue[3] this constitutes a slow long-term decline of herd sizes .
generated: In 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 402:
titleEntities: {'Subject': ['Great Britain'], 'Date': ['2003', '2013']}
title: Great Britain : Households that use WiFi to access the Internet 2003 to 2013
X_Axis['Year']: ['2013', '2011', '2009', '2007', '2005', '2003']
Y_Axis['Share', 'of', 'respondents']: ['96', '80', '54', '30', '6', '1']

gold: This survey presents the percentage of British households that use WiFi at home to access the Internet from 2003 to 2013 . In 2009 , 54 percent of respondents reported accessing the internet via WiFi , whereas in 2013 the share of respondents increased to 96 percent .
gold_template: This survey presents the templateScale of British templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] at home to templateTitle[6] the templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , templateYValue[2] templateScale of templateYLabel[1] reported accessing the templateTitle[7] via templateTitle[5] , whereas in templateXValue[max] the templateYLabel[0] of templateYLabel[1] templatePositiveTrend to templateYValue[idxmax(X)] templateScale .

generated_template: templateTitle[3] templateTitle[4] at templateTitle[5] templateTitle[6] had an average waiting time of 10 minutes . templateYValue[max] templateScale of templateYLabel[1] from a templateTitleDate[0] survey reported queuing approximately templateYValue[min] - 5 minutes . Only templateYValue[min] templateScale of templateYLabel[1] stated that they had to templateTitle[1] more than 30 minutes before being able to continue to their gate .
generated: that use at WiFi access had an average waiting time of 10 minutes .  96 % of respondents from a 2003 survey reported queuing approximately 1 - 5 minutes .  Only 1 % of respondents stated that they had to Britain more than 30 minutes before being able to continue their gate .

Example 403:
titleEntities: {'Subject': ['Austria'], 'Date': ['2018']}
title: Urbanization in Austria 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['58.3', '58.09', '57.91', '57.72', '57.53', '57.34', '57.15', '57.12', '57.4', '57.68', '57.97']

gold: This statistic shows the degree of urbanization in Austria from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 58.3 percent of Austria 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Austria from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 58.3 % of Austria 's total population lived in urban areas and cities .

Example 404:
titleEntities: {'Subject': ['France'], 'Date': ['2017']}
title: Distribution of women practicing nudism at the beach in France 2017 , by age
X_Axis['Year']: ['18_to_24_years', '25_to_34_years', '35_to_49_years', '50_to_59_years', '60_years_and_older']
Y_Axis['Share', 'of', 'women', 'surveyed']: ['6', '5', '9', '11', '13']

gold: This statistic indicates the share of French women who have already practiced naturism on the beach or in a nudist camp in 2017 , by age group . We can see that more than 10 percent of women aged 50 to 59 had already practiced nudism at the beach or in a naturist camp . Discover also the level of interest of the French for naturism .
gold_template: This statistic indicates the templateYLabel[0] of French templateYLabel[1] who have already practiced naturism on the templateTitle[4] or in a nudist camp in templateTitleDate[0] , templateTitle[7] templateTitle[8] group . We can see that more than 10 templateScale of templateYLabel[1] aged templateXValue[3] to templateXValue[3] had already practiced templateTitle[3] at the templateTitle[4] or in a naturist camp . Discover also the level of interest of the French for naturism .

generated_template: The figure shows the templateYLabel[2] generated in the global templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . By templateXValue[max] , the global templateYLabel[0] templateYLabel[1] was expected to generate templateYLabel[2] of around templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] . In that same templateXLabel[0] , templateYLabel[2] from the templateYLabel[0] merchandising templateYLabel[1] in North America was expected to reach 14.2 templateScale templateYLabel[4] templateYLabel[5]
generated: The figure shows the surveyed generated in the global Share women from 60 years and older to 50 to 59 years .  By 50 to 59 years , the global Share women was expected to generate surveyed of around 13 million surveyed .  In that same Year , surveyed from the Share merchandising women in North America was expected to reach 14.2 million surveyed . 

Example 405:
titleEntities: {'Subject': ['Dell'], 'Date': ['1996', '2019']}
title: Dell : Number of employees 1996 to 2019
X_Axis['Year']: ["'19", "'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96"]
Y_Axis['Number', 'of', 'employees', 'in', 'thousands']: ['157.0', '145.0', '138.0', '102.0', '98.3', '108.3', '111.3', '109.4', '103.3', '96.0', '78.9', '88.2', '90.5', '65.2', '55.2', '46.0', '39.1', '34.6', '40.0', '36.5', '24.4', '16.2', '10.35', '8.4']

gold: As of early 2019 , Dell 's employee count was 157 thousand . The majority , approximately 145 thousand , of these employees are full-time employees . 37 percent of Dell 's full-time employees are located in the company 's home market , the United States .
gold_template: As of early templateTitleDate[max] , templateTitleSubject[0] 's employee count was templateYValue[max] thousand . The majority , approximately templateYValue[1] thousand , of these templateYLabel[1] are full-time templateYLabel[1] . templateYValue[19] templateScale of templateTitleSubject[0] 's full-time templateYLabel[1] are located in the company 's home market , the country .

generated_template: As of templateXValue[max] , around templateYValue[idxmax(X)] people of templateTitle[5] in the templateTitle[4] templateTitle[6] templateTitle[7] to templateTitle[8] templateTitle[9] had been vaccinated against measles , mumps and rubella ( templateTitleSubject[0] ) . The templateTitleSubject[0] vaccine is widely used around the world and is recommended by the Centers for Disease Control and Prevention ( CDC ) for adults who do n't have immunity and all templateTitle[5] . Measles The number of cases of measles in the templateTitle[4] has templateNegativeTrend significantly since the introduction of the templateTitleSubject[0] templateTitle[1] .
generated: As of '99 , around 8.4 people of 2019 in the 2019 to had been vaccinated against measles , mumps and rubella ( Dell ) .  The Dell vaccine is widely used around the world and is recommended by the Centers for Disease Control and Prevention ( CDC ) for adults who do n't have immunity and all 2019 .  Measles The number of cases measles in the 2019 has decreased significantly since the introduction of the Dell Number .

Example 406:
titleEntities: {'Subject': ['Bhutan'], 'Date': ['2019']}
title: Youth unemployment rate in Bhutan in 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Youth', 'unemployment', 'rate']: ['9.69', '9.58', '9.57', '9.88', '9.97', '9', '9.25', '6.99', '8.93', '8.6', '12.46', '10.81', '11.2', '9.57', '9.41', '7.74', '5.82', '6.06', '6.26', '5.8', '4.89']

gold: The statistic shows the youth unemployment rate in Bhutan from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated youth unemployment rate in Bhutan was at 9.69 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .
generated: The statistic shows the Youth unemployment rate in Bhutan from 1999 and 2019 .  According to the source , the data are ILO estimates .  In 2019 , the estimated Youth unemployment rate in Bhutan was at 9.69 % .

Example 407:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Household income of black families in the U.S. 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Median', 'income', 'in', '2018', 'CPI-U-RS', 'adjusted', 'U.S.', 'dollars']: ['41361', '40324', '40339', '38178', '36689', '37232', '35641', '35203', '36195', '37319', '39054', '40196', '38963', '38828', '39151', '39607', '39661', '40902', '42348', '41192', '38212', '38269', '36649', '35880', '34503', '32721', '32210', '33103', '34068']

gold: This statistic shows the household income of black families in the United States from 1990 to 2018 . The median income in 2018 was at 41,361 U.S. dollars for black households .
gold_template: This statistic shows the templateTitle[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] in templateXValue[max] was at templateYValue[idxmax(X)] templateYLabel[5] templateYLabel[6] for templateTitle[2] households .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] templateTitle[3] templateTitle[4] were living below the templateYLabel[0] line in the templateTitle[5] . templateYLabel[0] is the state of one who lacks a certain amount of material possessions or money .
generated: This statistic shows the Median income of U.S. families in the U.S. from 1990 to 2018 .  In 2018 , 41361 % of U.S. families 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 408:
titleEntities: {'Subject': ['Denver Broncos', 'NFL'], 'Date': ['2002', '2019']}
title: Franchise value of the Denver Broncos ( NFL ) 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['3000', '2650', '2600', '2400', '1940', '1450', '1161', '1132', '1046', '1049', '1081', '1061', '994', '975', '907', '815', '683', '604']

gold: This graph depicts the franchise value of the Denver Broncos from the National Football League from 2002 to 2019 . In 2019 , the franchise value came to three billion U.S. dollars . The Denver Broncos are owned by the Pat Bowlen Trust , who bought the franchise for 78 million U.S. dollars in 1984 .
gold_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by the Pat Bowlen Trust , who bought the templateYLabel[0] for 78 templateScale templateYLabel[3] templateYLabel[4] in 1984 .

generated_template: This graph depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] of the National Football League from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] came to around templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] are owned by Jerry Jones who bought the templateYLabel[0] for 150 templateScale templateYLabel[3] templateYLabel[4] in 1989 .
generated: This graph depicts the Franchise value of the Denver Broncos of the National Football League from 2002 to 2019 .  In 2019 , the Franchise value came to around 3000 million U.S. dollars .  The Denver Broncos are owned by Jerry Jones who bought the Franchise for 150 million U.S. dollars in 1989 .

Example 409:
titleEntities: {'Subject': ['Aramark'], 'Date': ['2008', '2019']}
title: Facilities management industry - Aramark worldwide revenue 2008 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['16227.3', '15789.6', '14604.4', '14415.8', '14329.1', '14832.9', '13945.7', '13505.4', '13082.4', '12419.1', '12138.1', '13252.1']

gold: This statistic shows the annual total worldwide revenue of Aramark from 2008 to 2019 . In 2019 , Aramark had total revenues of over 16.2 billion U.S. dollars . The Aramark Corporation is an American foodservice , facilities , and clothing provider headquartered in Philadelphia , Pennsylvania .
gold_template: This statistic shows the annual total templateTitle[4] templateYLabel[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] had total revenues of over templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . The templateTitleSubject[0] Corporation is an American foodservice , templateTitle[0] , and clothing provider headquartered in Philadelphia , Pennsylvania .

generated_template: This statistic shows templateTitleSubject[0] 's templateTitle[1] annual templateYLabel[0] from templateXValue[min] to templateXValue[max] . As of templateXValue[max] , the daily deal website 's annual templateYLabel[0] amounted to 2.2 templateScale templateYLabel[2] templateYLabel[3] . The coupon portal had close to 43.6 templateScale active customers as the fourth quarter of templateXValue[max] .
generated: This statistic shows Aramark 's management annual Revenue from 2008 to 2019 .  As of 2019 , the daily deal website 's annual Revenue amounted to 2.2 million U.S. dollars .  The coupon portal had close to 43.6 million active customers as the fourth quarter of 2019 .

Example 410:
titleEntities: {'Subject': ['RIM/Blackberry'], 'Date': ['2004', '2019']}
title: Revenue of RIM/Blackberry worldwide 2004 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['904', '932', '1309', '2160', '3335', '6813', '11073', '18423', '19907', '14953', '11065', '6009', '3037', '2066', '1350', '595']

gold: In its 2019 fiscal year , Canadian company BlackBerry recorded revenues of less than one billion U.S. dollars for only the second time in the past 15 years . The company , previously known as Research in Motion ( RIM ) , has transitioned from developing BlackBerry brand smartphones to enterprise software and services involving the internet of things . Transitioning to the Enterprise of Things In the past decade , BlackBerry has seen a significant decline in their revenue figures and share of the global and U.S. smartphone market .
gold_template: In its templateXValue[max] fiscal templateXLabel[0] , Canadian company BlackBerry recorded revenues of less than templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] for only the second time in the past 15 years . The company , previously known as Research in Motion ( RIM ) , has transitioned from developing BlackBerry brand smartphones to enterprise software and services involving the internet of things . Transitioning to the Enterprise of Things In the past decade , BlackBerry has seen a significant decline in their templateYLabel[0] figures and share of the global and templateYLabel[2] smartphone market .

generated_template: This statistic represents templateTitleSubject[0] 's , formerly known as GSI Commerce , templateTitle[2] templateTitle[3] templateYLabel[0] from templateXValue[min] to templateXValue[max] , in templateScale templateYLabel[2] templateYLabel[3] . In templateXValue[11] , GSI Commerce reported a templateTitle[3] templateYLabel[0] of templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] before being acquired by templateTitleSubject[0] in 2011 .
generated: This statistic represents RIM/Blackberry 's , formerly known as GSI Commerce , worldwide 2004 Revenue from 2004 to 2019 , in million U.S. dollars .  In 2008 , GSI Commerce reported a 2004 Revenue of 19907 million U.S. dollars before being acquired by RIM/Blackberry in 2011 .

Example 411:
titleEntities: {'Subject': ['Bulgaria'], 'Date': ['2006', '2018']}
title: Number of arrivals in tourist accommodation Bulgaria 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'arrivals', 'in', 'millions']: ['7.8', '7.5', '7.2', '6.3', '5.9', '5.8', '5.5', '4.9', '4.3', '4.3', '5.0', '4.8', '4.3']

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Bulgaria from 2006 to 2018 . In 2018 , the number of arrivals in travel accommodation ( including both international and domestic tourists ) amounted to approximately 7.8 million .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] in travel templateTitle[3] ( including both international and domestic tourists ) amounted to approximately templateYValue[idxmax(X)] templateScale .

generated_template: 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 templatePositiveTrend over this period , from around templateYValue[9] templateScale in templateXValue[min] to approximately templateYValue[idxmax(X)] templateScale by templateXValue[idxmax(Y)] .
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 412:
titleEntities: {'Subject': ['Florida'], 'Date': ['2000', '2018']}
title: Per capita real GDP of Florida 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Per', 'capita', 'real', 'GDP', 'in', 'chained', '2012', 'U.S.', 'dollars']: ['43423', '42719', '42013', '41491', '40547', '40080', '39806', '40001', '40656', '40652', '43353', '45507', '45926', '45193', '43471', '42074', '41062', '40267', '40049']

gold: This statistic shows the per capita real Gross Domestic Product of Florida from 2000 to 2018 . In 2018 , the per capita real GDP of Florida stood at 43,423 chained 2012 U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[idxmax(X)] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] Gross Domestic Product of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] of templateTitleSubject[0] stood at templateYValue[idxmax(X)] templateYLabel[4] templateXValue[6] templateYLabel[6] templateYLabel[7] .
generated: This statistic shows the Per capita real Gross Domestic Product of Florida from 2000 to 2018 .  In 2018 , the Per capita real GDP of Florida stood at 43423 chained 2012 U.S. dollars .

Example 413:
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 displays the projected templateTitleSubject[0] m-commerce templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . According to the source , worldwide templateYLabel[0] templateYLabel[1] revenues amounted to templateYValue[1] templateScale templateYLabel[4] templateYLabel[5] in templateXValue[1] and are set to surpass templateYValue[max] templateScale templateYLabel[4] templateYLabel[5] in templateXValue[idxmax(Y)] .
generated: This statistic displays the projected Scotland m-commerce age population from 2014 to 2039 .  According to the source , worldwide Age revenues amounted to 44.3 billion Age in 2034 and are set to surpass 45.2 billion Age in 2039 .

Example 414:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2019']}
title: Total value of international U.S. imports of goods and services 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Trillion', 'U.S.', 'dollars']: ['3.12', '3.13', '2.9', '2.72', '2.76', '2.87', '2.76', '2.76', '2.68', '2.35', '1.97', '2.55', '2.36', '2.22', '2.0', '1.77', '1.51', '1.4', '1.37', '1.45']

gold: The timeline shows the total value of international U.S. imports of goods and services from 2000 to 2019 . In 2019 , the total value of international U.S. imports of goods and services amounted to 3.1 trillion U.S. dollars .
gold_template: The timeline shows the templateTitle[0] templateTitle[1] of templateTitle[2] templateYLabel[1] templateTitle[4] of templateTitle[5] and templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] of templateTitle[2] templateYLabel[1] templateTitle[4] of templateTitle[5] and templateTitle[6] amounted to templateYValue[0] templateScale templateYLabel[1] templateYLabel[2] .

generated_template: This statistic shows the total templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateTitle[3] templateYLabel[2] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the total templateTitleSubject[0] templateYLabel[0] templateYLabel[1] templateTitle[3] templateYLabel[2] amounted to approximately templateYValue[idxmax(X)] templateYLabel[3] .
generated: This statistic shows the total U.S. Trillion dollars in the goods from 2000 to 2019 .  In 2019 , the total U.S. Trillion dollars amounted to approximately 3.12 dollars .

Example 415:
titleEntities: {'Subject': ['United Kingdom'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) per capita United Kingdom 2024 ( in U.S. dollars )
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['GDP', 'per', 'capita', 'in', 'U.S.', 'dollars']: ['45934.7', '44311.93', '42842.47', '41504.89', '40391.84', '41030.23', '42579.82', '39976.78', '40657.86', '44494.86', '47003.88', '42981.25', '42023.1', '41649.66', '39122.19', '38601.32', '47469.38', '50315.56', '44403.81', '41842.7', '40111.75', '34302.42', '29912.99', '27510.33', '28043.87', '28435.06', '28077.34', '26647.95', '24256.46', '23026.71', '21344.25', '19925.66', '22305.36', '21671.88', '20808.23', '17617.85', '17364.25', '14294.99', '11551.07', '9491.99', '8943.27']

gold: The statistic shows GDP per capita in the United Kingdom from 1984 to 2018 , with projections up until 2024 . In 2018 , GDP per capita in the United Kingdom was at around 42,579.82 US dollars . The same year , the total UK population amounted to about 64.6 million people .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitleSubject[0] was at around templateYValue[6] US templateYLabel[4] . The same templateXLabel[0] , the total UK population amounted to about 64.6 templateScale people .

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

Example 416:
titleEntities: {'Subject': ['Global'], 'Date': ['2013', '2030']}
title: Global energy commodity price index 2013 to 2030
X_Axis['Year']: ['2030', '2025', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Price', 'index', 'in', 'real', '2010', 'U.S.', 'dollars']: ['87.2', '79.1', '74.7', '73.3', '72.0', '74.3', '87.0', '68.1', '55.1', '65.0', '111.7', '120.1']

gold: This statistic shows a commodity price index of energy from 2013 through 2018 , and gives projections for 2030 . In 2018 , the index stood at around 87 real US dollars . Real 2010 US dollars reflect buying power in 2010 .
gold_template: This statistic shows a templateTitle[2] templateYLabel[0] templateYLabel[1] of templateTitle[1] from templateXValue[min] through templateXValue[6] , and gives projections for templateXValue[max] . In templateXValue[6] , the templateYLabel[1] stood at around templateYValue[0] templateYLabel[2] US templateYLabel[5] . templateYLabel[2] templateYLabel[3] US templateYLabel[5] reflect buying power in templateYLabel[3] .

generated_template: This statistic depicts the templateTitleSubject[0] annual templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[5] templateXValue[min] through templateXValue[max] . In templateXValue[6] , the templateTitleSubject[0] templateYLabel[0] templateTitle[2] templateTitle[3] stood at templateYValue[min] templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] templateYLabel[5] .
generated: This statistic depicts the Global annual energy commodity price 2013 through 2030 .  In 2018 , the Global Price commodity stood at 55.1 index real 2010 U.S. dollars .

Example 417:
titleEntities: {'Subject': ['Global'], 'Date': ['2012', '2022']}
title: Global ulcerative colitis market 2012 and 2022
X_Axis['Year']: ['2012', '2022']
Y_Axis['Market', 'value', 'in', 'billion', 'U.S.', 'dollars']: ['4.2', '6.6']

gold: This statistic displays the global ulcerative colitis market value in 2012 , and a forecast for 2022 . In 2012 , the ulcerative colitis market was valued at 4.2 billion U.S. dollars . Ulcerative colitis is a type of inflammatory bowel disease ( IBD ) which is characterized by the formation of ulcers in the colon .
gold_template: This statistic displays the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] in templateXValue[min] , and a forecast for templateXValue[max] . In templateXValue[min] , the templateTitle[1] templateTitle[2] templateYLabel[0] was valued at templateYValue[idxmin(X)] templateScale templateYLabel[3] templateYLabel[4] . templateTitle[1] templateTitle[2] is a type of inflammatory bowel disease ( IBD ) which is characterized by the formation of ulcers in the colon .

generated_template: The electric bike templateYLabel[0] is expected to reach almost templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] . This translates into a CAGR of between four and five templateScale over this period . Shaking off the bad rep Electric bicycles continue to enjoy a surge in popularity ; what was once seen as very unfashionable is now becoming a common sight , from rural areas to modern cities .
generated: The electric bike Market is expected to reach almost 6.6 billion U.S. dollars in 2022 .  This translates into a CAGR of between four and five billion over this period .  Shaking off the bad rep Electric bicycles continue to enjoy a surge in popularity ; what was once seen as very unfashionable is now becoming a common sight , from rural areas to modern cities .

Example 418:
titleEntities: {'Subject': ['Estonia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Estonia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['2.8', '2.8', '2.8', '2.8', '2.9', '3.2', '4.76', '5.75', '2.63', '1.85', '2.99']

gold: The statistic shows the growth in real GDP in Estonia from 2014 to 2018 , with projections up until 2024 . In 2018 , Estonia 's real gross domestic product grew by around 4.76 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Estonia from 2014 to 2018 , with projections up until 2024 .  In 2018 , Estonia 's real Gross domestic product increased by around 4.76 % compared to the previous Year .

Example 419:
titleEntities: {'Subject': ['Northern Ireland', 'UK'], 'Date': ['2013', '2019']}
title: Youth unemployment rate in Northern Ireland ( UK ) 2013 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Unemployment', 'rate']: ['7.6', '8.4', '12.6', '13.9', '19.5', '19.4', '20.4']

gold: This statistic shows the unemployment rate of young people ( aged 18 to 24 ) in Northern Ireland from 2013 to 2019 . At the start of this period the youth unemployment rate stood at over 20 percent , but by 2019 this had decreased to 7.6 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of young people ( aged 18 to 24 ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . At the start of this period the templateTitle[0] templateYLabel[0] templateYLabel[1] stood at over templateYValue[4] templateScale , but by templateXValue[max] this had templateNegativeTrend to templateYValue[idxmax(X)] templateScale .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] of the sports company templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] had a templateYLabel[0] templateYLabel[1] of templateYValue[last] templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] in templateXValue[max] .
generated: The statistic shows the Unemployment rate of the sports company Northern Ireland from 2013 to 2019 .  Northern Ireland had a Unemployment rate of 20.4 7.6 rate in 2019 .

Example 420:
titleEntities: {'Subject': ['Citigroup'], 'Date': ['2011', '2018']}
title: Total direct staff of Citigroup 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Number', 'of', 'direct', 'staff', 'in', 'thousands']: ['204', '209', '219', '231', '241', '251', '259', '266']

gold: This statistic shows the number of direct employees of Citigroup from 2011 to 2018 . In 2018 , the direct staff of Citigroup amounted to approximately 204,000 .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] employees of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] templateYLabel[2] of templateTitleSubject[0] amounted to approximately templateYValue[min] .

generated_template: The templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] ( templateTitleSubject[1] ) of templateTitleSubject[0] templateTitle[1] customers in templateTitleSubject[2] has fallen steadily since templateXValue[min] , reaching an estimated 14.8 templateYLabel[4] templateYLabel[2] templateYLabel[6] in templateXValue[max] . This is a templateNegativeTrend of 22.9 templateScale on the templateTitleSubject[1] generated in templateXValue[min] . templateNegativeTrend need for templateTitleSubject[0] templateTitle[1] The decline of templateTitleSubject[0] templateTitle[1] , as demonstrated by the templateNegativeTrend templateTitleSubject[1] and the diminishing penetration rate in countries such as the United Kingdom ( UK ) , can partially be attributed to the templatePositiveTrend speeds and reliability that comes with 3G and 4G templateTitleSubject[0] networks .
generated: The Number direct staff thousands ( Citigroup ) of Citigroup direct customers in Citigroup has fallen steadily since 2011 , reaching an estimated 14.8 thousands staff in 2018 .  This is a decrease of 22.9 thousands on the Citigroup generated in 2011 .  decreased need for Citigroup direct The decline of Citigroup direct , as demonstrated by the shrinking Citigroup and the diminishing penetration rate in countries such as the United Kingdom ( UK ) , can partially be attributed to the increased speeds and reliability that comes with 3G and 4G Citigroup networks .

Example 421:
titleEntities: {'Subject': ['Mattel'], 'Date': ['2019']}
title: Gross profit of toy manufacturer Mattel 2006 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Gross', 'profit', 'in', 'million', 'U.S.', 'dollars']: ['1980.78', '1798.68', '1824.57', '2546.69', '2806.36', '3001.02', '3478.88', '3409.2', '3145.83', '2954.97', '2714.7', '2684.41', '2777.3', '2611.79']

gold: This statistic shows the gross profit of the U.S. toy manufacturer Mattel worldwide from 2006 to 2019 . In 2019 , their gross profit came to around 1.98 billion U.S. dollars .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateYLabel[3] templateTitle[2] templateTitle[3] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . In templateXValue[max] , their templateYLabel[0] templateYLabel[1] came to around templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: In templateXValue[max] , the templateTitle[3] of templateTitle[0] and calves in the templateTitleSubject[0] was approximately templateYValue[min] templateScale , a slight templateNegativeTrend from the previous templateXLabel[0] . This was the lowest templateYLabel[0] for the entire period shown in this graph . Despite a small rebound in templateXValue[4] and templateXValue[3] this constitutes a slow long-term decline of herd sizes .
generated: In 2019 , the manufacturer of Gross and calves in the Mattel was approximately 1798.68 million , a slight decrease from the previous Year .  This was the lowest Gross for the entire period shown in this graph .  Despite a small rebound in 2015 and 2016 this constitutes a slow long-term decline of herd sizes .

Example 422:
titleEntities: {'Subject': ['Active Duty Navy'], 'Date': ['1995', '2018']}
title: Active Duty U.S. Navy personnel numbers from 1995 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995']
Y_Axis['Number', 'of', 'Navy', 'personnel']: ['325395', '319492', '320101', '323334', '321599', '319838', '314339', '320141', '323139', '324239', '326684', '332269', '345098', '357853', '367371', '429630']

gold: This graph shows the number of active duty U.S. Navy personnel from 1995 to 2018 . In 2018 , there were 325,395 active duty Navy members in the United States Department of Defense . In 2000 , there were 367,371 active duty members .
gold_template: This graph shows the templateYLabel[0] of templateTitleSubject[0] U.S. templateYLabel[1] templateYLabel[2] templateTitle[6] templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[idxmax(X)] templateTitleSubject[0] Navy members in the templateTitle[2] Department of Defense . In templateXValue[14] , there were templateYValue[14] templateTitleSubject[0] members .

generated_template: In templateXValue[max] , there were templateYValue[idxmax(X)] templateTitle[1] templateTitle[2] templateYLabel[1] across the templateTitle[4] . The FDIC , of Federal Deposit Insurance Corporation , is an agency that insures the banking system in the templateTitle[4] . The templateYLabel[0] of such registered templateYLabel[1] has been declining since templateXValue[min] , when it there were over 7,800 templateTitle[1] templateYLabel[1] in the country .
generated: In 2018 , there were 325395 Duty U.S. Navy across the personnel .  The FDIC , of Federal Deposit Insurance Corporation , is an agency that insures the banking system in the personnel .  The Number of such registered Navy has been declining since 1995 , when it there were over 7,800 Duty Navy in the country .

Example 423:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2002']}
title: Commodity prices of wheat in the United Kingdom ( UK ) 2002 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Price', 'per', 'tonne', 'in', 'GBP']: ['127.15', '143.06', '175.95', '179.26', '169.17', '123.76', '107.05', '137.87', '120.97', '78.88', '67.43', '80.3', '79.32', '65.02']

gold: This statistic shows the average price per tonne of wheat in the United Kingdom ( UK ) between 2002 and 2015 by year , according to published agricultural and commodity price figures . In 2012 , the price of wheat was 179.26 British Pound Sterling ( GBP ) per tonne .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] templateYLabel[2] of templateTitle[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) between templateXValue[min] and templateXValue[max] by templateXLabel[0] , according to published agricultural and templateTitle[0] templateYLabel[0] figures . In templateXValue[3] , the templateYLabel[0] of templateTitle[2] was templateYValue[max] British Pound Sterling ( templateYLabel[3] ) templateYLabel[1] templateYLabel[2] .

generated_template: In templateXValue[max] , the templateTitle[3] of templateTitle[0] and calves in the templateTitleSubject[0] was approximately templateYValue[min] templateScale , a slight templateNegativeTrend from the previous templateXLabel[0] . This was the lowest templateYLabel[0] for the entire period shown in this graph . Despite a small rebound in templateXValue[4] and templateXValue[3] this constitutes a slow long-term decline of herd sizes .
generated: In 2015 , the United of Commodity and calves in the United Kingdom was approximately 65.02 % , a slight decrease from the previous Year .  This was the lowest Price for the entire period shown in this graph .  Despite a small rebound in 2011 and 2012 this constitutes a slow long-term decline of herd sizes .

Example 424:
titleEntities: {'Subject': ['England'], 'Date': ['2019']}
title: Share of the population who gave to charity in England 2019 , by age
X_Axis['Year']: ['16_to_24', '25_to_34', '35_to_49', '50_to_64', '65_to_74', '75_and_over']
Y_Axis['Share', 'of', 'respondents']: ['59', '69', '76', '79', '82', '83']

gold: This statistic shows the share of the population who said they gave to charity in the last four weeks in 2018/19 , by age group . Proportionally , those aged 75 and more gave most to charity . At 59 percent , 16 to 24 year olds had the smallest proportion of charitable givers .
gold_template: This statistic shows the templateYLabel[0] of the templateTitle[1] templateTitle[2] said they templateTitle[3] to templateTitle[4] in the last four weeks in 2018/19 , templateTitle[7] templateTitle[8] group . Proportionally , those aged templateXValue[last] and more templateTitle[3] most to templateTitle[4] . At templateYValue[min] templateScale , templateXValue[0] to templateXValue[0] templateXLabel[0] olds had the smallest proportion of charitable givers .

generated_template: This statistic shows the results of a Statista survey conducted in the country in templateTitleDate[0] on tattoos and body modification . During the survey the templateYLabel[1] were asked how many templateTitle[2] they have . templateYValue[0] templateScale of the templateYLabel[1] said they only have templateXValue[0] piercing .
generated: This statistic shows the results of a Statista survey conducted in the country in 2019 on tattoos and body modification .  During the survey respondents were asked how many who they have .  59 % of the respondents said they only have 16 to 24 piercing .

Example 425:
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[0] reported templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateXValue[max] . This was a slight templatePositiveTrend compared to the previous templateXLabel[0] , but an templatePositiveTrend of 14 incidents compared to the low reported in templateXValue[4] . The Northern European island state is known for enforcing a strict templateTitle[1] safety policy in order to ensure the security of its residents and tourists in the country .
generated: There were 1867 reported road deaths in Romania 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 426:
titleEntities: {'Subject': ['USB', 'Germany'], 'Date': ['2004', '2018']}
title: Sales volume of USB flash drives in Germany 2004 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Sales', 'volume', 'in', 'millions']: ['12.88', '13.51', '16.17', '15.94', '15.46', '13.5', '15.85', '13.82', '11.78', '12.99', '13.0', '8.18', '5.0', '3.2', '2.03']

gold: USB flash drives experienced fluctuating sales numbers in recent years , with almost 12.9 million units sold in 2018 . Meanwhile , revenue generated amounted to 155 million euros in the same year , a decrease on the one before . Storage media USB flash drives revolutionized data storage for computer users everywhere when they emerged on the market , joining such well-known storage media as floppy discs , hard drives and optical storage units like CD-R and CD-RW discs .
gold_template: templateTitleSubject[0] templateTitle[3] templateTitle[4] experienced fluctuating templateYLabel[0] numbers in recent years , with almost templateYValue[0] templateScale units sold in templateXValue[max] . Meanwhile , revenue generated amounted to 155 templateScale euros in the same templateXLabel[0] , a templateNegativeTrend on the one before . Storage media templateTitleSubject[0] templateTitle[3] templateTitle[4] revolutionized data storage for computer users everywhere when they emerged on the market , joining such well-known storage media as floppy discs , hard templateTitle[4] and optical storage units like CD-R and CD-RW discs .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . According to the source , the templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] amounted to approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] in templateXValue[max] .
generated: This statistic shows the Sales volume of Sales volume in the flash drives 2004 to 2018 .  According to the source , the Sales volume of Sales volume amounted to approximately 12.88 millions in 2018 .

Example 427:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2009', '2018']}
title: Working age population in Vietnam 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Working', 'age', 'population', 'in', 'millions']: ['72.59', '71.89', '70.94', '69.74', '69.34', '68.69', '68.19', '67.38', '65.71', '64.44']

gold: In 2018 , the working age population in Vietnam amounted to approximately 72.59 million people . In that year , the labor participation rate was at 76.7 percent while the employment rate was at 75.2 percent .
gold_template: In templateXValue[max] , the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to approximately templateYValue[idxmax(X)] templateScale people . In that templateXLabel[0] , the labor participation rate was at 76.7 templateScale while the employment rate was at 75.2 templateScale .

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] ' templateYLabel[0] templateYLabel[1] amounted to around templateYValue[0] templateScale templateYLabel[3] . After spin-offs of other product segments , Koninklijke templateTitleSubject[0] N.V. today is a company focused on healthcare/medical technology .
generated: The statistic presents the Working age of Vietnam from 2009 to 2018 .  In 2018 , Vietnam ' Working age amounted to around 72.59 millions .  After spin-offs of other product segments , Koninklijke Vietnam N.V. today is a company focused on healthcare/medical technology .

Example 428:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2024']}
title: Total population of South Korea 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['52.91', '52.69', '52.48', '52.27', '52.06', '51.85', '51.64', '51.43', '51.22', '51.02', '50.75']

gold: The statistic shows the total population of South Korea from 2014 to 2018 , with projections up until 2024 . In 2018 , the total population of South Korea was about 51.64 million people . Population of South Korea South Korea , also called Republic of Korea , has one of the highest population densities worldwide , i.e .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was about templateYValue[6] templateScale people . templateTitle[1] of templateTitleSubject[0] South templateTitleSubject[0] , also called Republic of templateTitleSubject[0] , has one of the highest templateTitle[1] densities worldwide , i.e .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateScale templateYLabel[0] .
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 .

Example 429:
titleEntities: {'Subject': ['Iran'], 'Date': ['2024']}
title: Iran 's national debt in relation to gross domestic product 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Share', 'in', 'GDP']: ['30.26', '29.18', '28.24', '28.06', '28.79', '30.67', '32.18', '39.53', '47.47', '38.42', '11.82']

gold: This statistic shows the national debt of Iran in relation to gross domestic product ( GDP ) from 2014 to 2018 , with projections up until 2024 . In 2018 , Iran 's national debt amounted to 32.18 percent of gross domestic product .
gold_template: This statistic shows the templateTitle[2] templateTitle[3] of templateTitleSubject[0] in templateTitle[4] to templateTitle[5] templateTitle[6] templateTitle[7] ( templateYLabel[1] ) from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] amounted to templateYValue[6] templateScale of templateTitle[5] templateTitle[6] templateTitle[7] .

generated_template: The statistic shows the templateTitleSubject[0] Emirates ' ( 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] Emirates ' templateYLabel[0] templateTitle[4] rate amounted to approximately templateYValue[min] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the Iran Emirates ' ( UAE ) Iran 's national relation from 2014 to 2018 , with projections up until 2024 .  In 2018 , The Iran Emirates ' Share relation rate amounted to approximately 11.82 % GDP to the GDP Year .

Example 430:
titleEntities: {'Subject': ['Dubai'], 'Date': ['2006', '2026']}
title: Direct tourism contribution of Dubai to GDP of the UAE 2006 to 2026
X_Axis['Year']: ['2026', '2016', '2006']
Y_Axis['GDP', 'contribution', 'in', 'billion', 'U.S.', 'dollars']: ['20.9', '11.4', '4.0']

gold: This statistic described the direct tourism contribution of Dubai to the gross domestic product of the United Arab Emirates from 2006 to 2016 and a forecast for 2026 . The forecast of the direct tourism contribution of Dubai to the GDP of the United Arab Emirates for 2026 was approximately 20.9 billion U.S. dollars .
gold_template: This statistic described the templateTitle[0] templateTitle[1] templateYLabel[1] of templateTitleSubject[0] to the gross domestic product of the United Arab Emirates from templateXValue[min] to templateXValue[1] and a forecast for templateXValue[max] . The forecast of the templateTitle[0] templateTitle[1] templateYLabel[1] of templateTitleSubject[0] to the templateYLabel[0] of the United Arab Emirates for templateXValue[max] was approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: templateTitle[4] occurs when a number of private individuals each pays a small templateYLabel[0] of money to support a project . As of templateXValue[max] , this process had templateTitle[2] templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] templateTitle[5] . How does templateTitle[4] work ? There are three main types of templateTitle[4] : peer-to-peer lending , rewards and donation templateTitle[4] , and equity templateTitle[4] .
generated: GDP occurs when a number of private individuals each pays a small GDP of money to support a project .  As of 2026 , this process had contribution 20.9 billion U.S. UAE .  How does GDP work ? There are three main types of GDP : peer-to-peer lending , rewards and donation GDP , and equity GDP .

Example 431:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2006', '2018']}
title: Participants in hunting in the U.S. from 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'participants', 'in', 'millions']: ['15.69', '15.63', '15.47', '15.53', '14.85', '13.53', '14.71', '14.89', '14.01', '15.27', '13.98', '14.14', '15.1']

gold: This statistic shows the number of participants in hunting in the United States from 2006 to 2018 . In 2018 , the number of participants ( aged six years and older ) in hunting amounted to approximately 15.69 million .
gold_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] in the templateTitle[2] templateTitle[3] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] amounted to approximately templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitle[1] templateTitle[2] in the templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of templateYLabel[1] ( aged six years and older ) in templateTitle[1] templateTitle[2] amounted to approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Number of participants in 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 432:
titleEntities: {'Subject': ['England'], 'Date': ['2010', '2017']}
title: Total household waste in England 2010 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Kilograms', 'per', 'person', 'per', 'year']: ['403', '412', '406', '413', '402', '412', '421', '425']

gold: Household waste volumes per person in England remained at a similar level between 2010 and 2017 . Although there was an overall decrease during this period , the household volumes were still over 400 kilograms per person in 2017 . The region which generated the largest volume of residual waste per household was the North East of England , where an average of 601 kilograms of waste was generated per person in 2017/2018 .
gold_template: templateTitle[1] templateTitle[2] volumes templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] remained at a similar level between templateXValue[min] and templateXValue[max] . Although there was an overall templateNegativeTrend during this period , the templateTitle[1] volumes were still over 400 templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateXValue[max] . The region which generated the largest volume of residual templateTitle[2] templateYLabel[1] templateTitle[1] was the North East of templateTitleSubject[0] , where an average of 601 templateYLabel[0] of templateTitle[2] was generated templateYLabel[1] templateYLabel[2] in 2017/2018 .

generated_template: In templateXValue[max] , the templateTitle[1] templateYLabel[0] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] amounted to about templateYValue[idxmax(X)] 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 templateNegativeTrend to below templateYValue[0] templateScale 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 , all types included .  The Total realized Kilograms of England 2010 remained fairly steady throughout the years until 2014 , when it dropped to below 403 million 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 433:
titleEntities: {'Subject': ['Vietnam'], 'Date': ['2024']}
title: Inflation rate in Vietnam 2024 
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['4', '4', '3.9', '3.8', '3.75', '3.6', '3.54', '3.52', '2.67', '0.63', '4.09', '6.6', '9.1', '18.67', '9.21', '6.72', '23.12', '8.35', '7.5', '8.39', '7.89', '3.3', '4.08', '-0.31', '-1.77', '4.11', '8.11', '3.1', '5.59', '16.93', '9.49', '8.38', '37.71', '81.82', '36.03', '95.77', '374.35', '360.36', '453.54', '91.6', '64.9']

gold: In 2018 , the average inflation rate in Vietnam amounted to 3.54 percent compared to the previous year . After a severe drop below one percent in 2015 , Vietnam 's inflation seems to have stabilized again and is expected to level off at around four percent in the next few years . Vietnam 's economic struggles Around 2012 , Vietnam suffered the consequences of the global economic crisis and domestic economic mismanagement , which saw enterprises going bankrupt , inflation peaking at over nine percent , and gross domestic product slumping to a dramatic low .
gold_template: In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . After a severe drop below templateYValue[9] templateScale in templateXValue[9] , templateTitleSubject[0] 's templateYLabel[0] seems to have stabilized again and is expected to level off at around templateYValue[0] templateScale in the next few years . templateTitleSubject[0] 's economic struggles Around templateXValue[12] , templateTitleSubject[0] suffered the consequences of the global economic crisis and domestic economic mismanagement , which saw enterprises going bankrupt , templateYLabel[0] peaking at over templateYValue[12] templateScale , and gross domestic product slumping to a dramatic low .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Vietnam from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Vietnam amounted to about 3.54 % compared to the previous Year .

Example 434:
titleEntities: {'Subject': ['Global'], 'Date': ['2016', '2022']}
title: Global smart augmented reality glasses revenue 2016 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017', '2016']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['19718.88', '15686.56', '10936.67', '7275.43', '4421.74', '409.67', '138.61']

gold: The statistic shows smart AR glasses revenue worldwide from 2016 to 2022 . Smart augmented reality glasses revenue reached 138.6 million U.S. dollars in 2016 and is forecast to amount to around 19.7 billion U.S. dollars by 2022 .
gold_template: The statistic shows templateTitle[1] AR templateTitle[4] templateYLabel[0] worldwide from templateXValue[min] to templateXValue[max] . templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] templateYLabel[0] reached templateYValue[min] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[idxmin(Y)] and is forecast to amount to around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] by templateXValue[idxmax(Y)] .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] Fitness , templateTitle[6] company from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the health templateTitle[1] chain generated a templateYLabel[0] of around templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . In templateXValue[idxmax(Y)] , the Minnesota ( USA ) based company operated 130 clubs .
generated: The statistic shows the Revenue of the Global Fitness , 2016 company from 2016 to 2022 .  In 2022 , the health smart chain generated a Revenue of around 19718.88 million U.S. dollars .  In 2022 , the Minnesota ( USA ) based company operated 130 clubs .

Example 435:
titleEntities: {'Subject': ['West Virginia'], 'Date': ['1990', '2018']}
title: West Virginia - Median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['50573', '45392', '44354', '42824', '39552', '40241', '43553', '41821', '42777', '40490', '37994', '42091', '38419', '36445', '33373', '32763', '29359', '29673', '29411', '29297', '26704', '27488', '25247', '24880', '23564', '22421', '20271', '23147', '22137']

gold: This statistic shows the median household income in West Virginia from 1990 to 2018 . In 2018 , the median household income in West Virginia amounted to 50,573 U.S. dollars .
gold_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[max] templateYValue[idxmax(X)] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: 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 436:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2009', '2019']}
title: Unemployment rate in the Netherlands 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Share', 'of', 'individuals']: ['4.3', '4.8', '5.9', '7.3', '8.6', '9', '8.9', '7.1', '6.1', '6.1', '5.5']

gold: In 2019 , the unemployment rate in the Netherlands was just over four percent . Unemployment peaked in 2013 and 2014 . At the height of the financial crisis , the annual unemployment rate in the country reached 8.9 and 9 percent respectively .
gold_template: In templateXValue[max] , the templateTitle[0] templateTitle[1] in the templateTitleSubject[0] was just over templateYValue[min] templateScale . templateTitle[0] peaked in templateXValue[6] and templateXValue[5] . At the height of the financial crisis , the annual templateTitle[0] templateTitle[1] in the country reached templateYValue[6] and templateYValue[4] templateScale respectively .

generated_template: The statistic shows the templateTitleSubject[0] of the templateYLabel[0] templateYLabel[1] since the birth of Christ . templateYValue[max] templateScale people were living on earth in templateXValue[idxmax(Y)] . Additional information on templateYLabel[0] templateYLabel[1] The global templateYLabel[1] has risen dramatically in the last 100 years from templateYValue[3] templateScale in templateXValue[3] to surpassing templateYValue[max] templateScale in 2011 .
generated: The statistic shows the Netherlands of the Share individuals since the birth of Christ .  9 billion people were living on earth in 2014 .  Additional information on Share individuals The global individuals has risen dramatically in the last 100 years from 7.3 million in 2016 to surpassing 9 million in 2011 .

Example 437:
titleEntities: {'Subject': ['Nicaragua'], 'Date': ['2024']}
title: Inflation rate in Nicaragua 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['5.04', '4.94', '4.79', '4.67', '4.19', '5.63', '4.97', '3.85', '3.52', '4', '6.04', '7.14', '7.19', '8.08', '5.46', '3.69', '19.83', '11.13', '9.14', '9.6', '8.47', '5.3', '3.75', '7.36', '11.55', '11.21', '13.05', '9.19', '11.65', '11.12', '3.7', '13.5', '21.9', '116.6', '3004.1', '7428.7', '4775.2', '13109.5', '885.2', '571.4', '141.3']

gold: This statistic shows the average inflation rate in Nicaragua from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Nicaragua amounted to about 4.97 percent compared to the previous year . Nicaragua 's economy Nicaragua 's inflation rate has been on the decline since 2011 , but it is expected to rise again in 2016 .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . templateTitleSubject[0] 's economy templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] has been on the decline since templateXValue[13] , but it is expected to rise again in templateXValue[8] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Nicaragua from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Nicaragua amounted to about 4.97 % compared to the previous Year .

Example 438:
titleEntities: {'Subject': ['Chile'], 'Date': ['2014', '2018']}
title: Chile : gender gap index 2014 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014']
Y_Axis['Index', 'score']: ['0.72', '0.7', '0.7', '0.7', '0.7']

gold: The graph presents the gender gap index in Chile from 2014 to 2018 . In 2018 , Chile scored 0.72 points , which shows a gender gap of approximately 28 percent ( women are 28 percent less likely than men to have equal opportunities ) . In 2018 , the gender gap in the area of political empowerment in Chile amounted to 69 percent .
gold_template: The graph presents the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] scored templateYValue[idxmax(X)] points , which shows a templateTitle[1] templateTitle[2] of approximately 28 templateScale ( women are 28 templateScale less likely than men to have equal opportunities ) . In templateXValue[idxmax(Y)] , the templateTitle[1] templateTitle[2] in the area of political empowerment in templateTitleSubject[0] amounted to 69 templateScale .

generated_template: The statistic shows the templateTitle[1] templateTitle[2] templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , templateTitleSubject[0] scored templateYValue[idxmax(X)] , which shows a templateTitle[1] templateTitle[2] of approximately 28 templateScale ( women are 28 templateScale less likely than men to have equal opportunities ) . That same templateXLabel[0] , the templateTitle[1] templateTitle[2] in the area of political empowerment in templateTitleSubject[0] amounted to 72 templateScale .
generated: The statistic shows the gender gap Index in Chile from 2014 to 2018 .  In 2015 , Chile scored 0.72 , which shows a gender gap of approximately 28 % ( women are 28 % less likely than men to have equal opportunities ) .  That same Year , the gender gap in the area of political empowerment in Chile amounted to 72 % .

Example 439:
titleEntities: {'Subject': ['Colombia'], 'Date': ['1990', '2018']}
title: District of Colombia - median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['85750', '83382', '70982', '70071', '68277', '60675', '65246', '55251', '56928', '53141', '55590', '50783', '48477', '44993', '43451', '45044', '39070', '41169', '41222', '38670', '33433', '31860', '31966', '30748', '30116', '27304', '30247', '29885', '27392']

gold: This statistic shows the median household income in the District of Colombia from 1990 to 2018 . In 2018 , the median household income in the District of Colombia amounted to 85,750 U.S. dollars .
gold_template: This statistic shows the templateTitle[2] templateYLabel[0] templateYLabel[1] in the templateTitle[0] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[2] templateYLabel[0] templateYLabel[1] in the templateTitle[0] of templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: 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 440:
titleEntities: {'Subject': ['Iraq'], 'Date': ['2024']}
title: Total population of Iraq 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['44.47', '43.35', '42.25', '41.18', '40.13', '39.12', '38.12', '37.14', '36.17', '35.21', '35.0']

gold: This statistic shows the total population of Iraq from 2014 to 2024 . In 2018 , the estimated total population of Iraq amounted to approximately 38.12 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the estimated templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateScale templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateScale templateYLabel[0] .
generated: This statistic shows the Total population of Iraq from 2014 to 2018 , with projections up until 2024 .  In 2018 , the Total population of Iraq amounted to approximately 38.12 millions Inhabitants .

Example 441:
titleEntities: {'Subject': ['Northern Ireland', 'UK'], 'Date': ['2000', '2019']}
title: Unemployment rate in Northern Ireland ( UK ) 2000 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Unemployment', 'rate']: ['2.7', '3.6', '4.6', '5.7', '6.1', '6.4', '7.5', '7.4', '7.2', '7.1', '6.4', '4.4', '3.9', '4.4', '4.6', '5', '5.6', '5.9', '6', '6.2']

gold: This statistic shows the unemployment rate in Northern Ireland from 2000 to 2019 . Unemployment in Northern Ireland peaked in 2013 when there were 7.5 percent of the population unemployed , compared with just 2.7 percent in the most recent reporting year of 2019 .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateYLabel[0] in templateTitleSubject[0] peaked in templateXValue[6] when there were templateYValue[max] templateScale of the population unemployed , compared with just templateYValue[min] templateYValue[idxmax(X)] in the most recent reporting templateXLabel[0] of templateXValue[idxmin(Y)] .

generated_template: The templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] in templateXValue[max] was templateYValue[idxmax(X)] templateScale , the lowest it has been since the mid-1970s . From templateXValue[min] until the templateXValue[11] financial crash the templateYLabel[0] templateYLabel[1] in the UK fluctuated between templateYValue[14] and templateYValue[11] templateScale , before it templatePositiveTrend suddenly in templateXValue[10] to templateYValue[6] templateScale . After peaking at templateYValue[max] templateScale in templateXValue[idxmax(Y)] , the templateYLabel[0] templateYLabel[1] gradually declined before returning to the levels seen in the early 2000s by templateXValue[4] .
generated: The Unemployment rate of the Northern Ireland in 2019 was 2.7 % , the lowest it has been since the mid-1970s .  From 2000 until the 2008 financial crash the Unemployment rate in the UK fluctuated between 4.6 and 4.4 percent , before it increased suddenly in 2009 to 7.5 % .  After peaking at 7.5 % in 2013 , the Unemployment rate gradually declined before returning to the levels seen in the early 2000s by 2015 .

Example 442:
titleEntities: {'Subject': ['Video'], 'Date': ['2015', '2022']}
title: Video analytics market revenues worldwide 2015 to 2022
X_Axis['Year']: ['2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015']
Y_Axis['Market', 'in', 'million', 'U.S.', 'dollars']: ['2997.8', '2692.7', '2347.1', '1998.4', '1665.5', '1405.1', '1137.7', '858.0']

gold: The statistic shows the size of the video analytics market worldwide , from 2015 to 2022 . In 2015 , revenues from video analytics reached 858 million U.S. dollars .
gold_template: The statistic shows the size of the templateTitleSubject[0] templateTitle[1] templateYLabel[0] templateTitle[4] , from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateTitle[3] from templateTitleSubject[0] templateTitle[1] reached templateYValue[min] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[3] templateYLabel[4] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale people accessed the templateYLabel[3] through their templateYLabel[1] templateYLabel[2] . In templateXValue[max] , this figure is projected to amount to templateYValue[idxmax(X)] templateScale templateYLabel[1] templateYLabel[2] templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Market of million dollars in Video from 2015 to 2022 .  In 2015 , 858.0 million people accessed the dollars through their million U.S. In 2022 , this figure is projected to amount 2997.8 million U.S. dollars .

Example 443:
titleEntities: {'Subject': ['American Customer Satisfaction'], 'Date': ['2007', '2019']}
title: American Customer Satisfaction Index : full-service restaurants in the U.S. 2007 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['ACSI', 'score']: ['81', '81', '78', '81', '82', '82', '81', '80', '82', '81', '84', '80', '81']

gold: This statistic shows the American Customer Satisfaction Index scores for full-service restaurants in the United States from 2007 to 2019 . In 2019 , the ACSI score for full-service restaurants in the U.S. was 81 .
gold_template: This statistic shows the templateTitleSubject[0] Satisfaction templateTitle[3] scores for templateTitle[4] templateTitle[5] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] for templateTitle[4] templateTitle[5] in the templateTitle[6] was templateYValue[idxmax(X)] .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] scores for templateTitleSubject[1] restaurants in the templateTitle[7] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] for templateTitleSubject[1] restaurants in the templateTitle[7] was templateYValue[min] templateYValue[idxmax(X)] down from templateYValue[last] templateYValue[idxmin(X)] previous templateXLabel[0] .
generated: This statistic shows the American Customer Satisfaction Customer Satisfaction Index scores for American Customer Satisfaction restaurants in the 2007 from to 2019 .  In 2019 , the ACSI for American Customer Satisfaction restaurants in the 2007 was 78 81 down from 81 previous Year .

Example 444:
titleEntities: {'Subject': ['Eastman Chemical'], 'Date': ['2008', '2018']}
title: Eastman Chemical 's revenue 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['10151', '9549', '9008', '9648', '9527', '9350', '8102', '7178', '5842', '4396', '5936']

gold: This statistic shows the revenues of Eastman Chemical from 2007 to 2018 . United States-based Eastman Chemical Company is engaged in the manufacture and sale of chemicals , fibers , and plastics . In 2018 , the company generated approximately 10.15 billion U.S. dollars of sales revenues .
gold_template: This statistic shows the revenues of templateTitleSubject[0] from 2007 to templateXValue[max] . United States-based templateTitleSubject[0] Company is engaged in the manufacture and sale of chemicals , fibers , and plastics . In templateXValue[max] , the company generated approximately templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] of sales revenues .

generated_template: In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] amounted to templateYValue[max] templateScale US templateYLabel[3] , up from templateYValue[1] templateScale templateYLabel[2] templateYLabel[3] in the previous fiscal templateXLabel[0] . The social network 's main source of income is digital advertising . templateTitleSubject[0] templateYLabel[0] and income – more information As a side effect from being the biggest social network worldwide , templateTitleSubject[0] is the leading platform for social media advertising and marketing with 94 templateScale of global marketers utilizing templateTitleSubject[0] in their marketing efforts .
generated: In 2018 , Eastman Chemical 's Revenue amounted to 10151 million US dollars , up from 9549 million U.S. dollars in the previous fiscal Year .  The social network 's main source of income is digital advertising .  Eastman Chemical Revenue and income – more information As a side effect from being the biggest social network worldwide , Eastman Chemical is the leading platform for social media advertising and marketing with 94 million of global marketers utilizing Eastman Chemical in their marketing efforts .

Example 445:
titleEntities: {'Subject': ['Sears Holdings'], 'Date': ['2009']}
title: Number of stores of Sears Holdings worldwide 2009 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Number', 'of', 'stores']: ['1002', '1430', '1672', '1725', '2429', '2548', '4010', '3949', '3862']

gold: This statistic depicts the total number of stores of Sears Holdings from 2009 to 2017 . In 2017 , Sears Holdings had a total of 1,002 stores worldwide . The company was founded in 2005 in Hoffman Estates , Illinois and is the tenth largest retailer by annual revenue in the United States .
gold_template: This statistic depicts the total templateYLabel[0] of templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] had a total of templateYValue[min] templateYValue[idxmax(X)] templateTitle[4] . The company was founded in 2005 in Hoffman Estates , Illinois and is the tenth largest retailer by annual revenue in the country .

generated_template: This statistic shows the templateYLabel[0] of specialized templateYLabel[1] for the templateTitle[2] sale of templateTitle[1] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from templateXValue[min] to templateXValue[max] . In templateXValue[max] , there were templateYValue[idxmax(X)] templateTitle[1] templateTitle[2] templateYLabel[1] in the templateTitleSubject[1] , an templatePositiveTrend of 44 templateYLabel[1] on the previous templateXLabel[0] .
generated: This statistic shows the Number of specialized stores for the Sears sale of stores in the Sears Holdings ( ) from 2009 to 2017 .  In 2017 , there were 1002 stores Sears in the Sears Holdings , an increase of 44 stores on the previous Year .

Example 446:
titleEntities: {'Subject': ['Colombia'], 'Date': ['2017']}
title: Colombia : poverty headcount ratio at 3.20 U.S. dollars a day 2005 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2005']
Y_Axis['Percentage', 'of', 'population']: ['10.8', '11.8', '11.9', '13.1', '14.2', '15.4', '16.3', '18.3', '20.5', '22.4', '23.4']

gold: In Colombia , the poverty rate has been decreasing throughout recent years . In 2017 , approximately 10.8 percent of Colombians were living on less than 3.20 U.S. dollars per day , down from 23.4 percent of the country 's population in 2005.Moreover , it was recently found that the incidence rate of poverty in Colombia is higher in families whose heads of household were women .
gold_template: In templateTitleSubject[0] , the templateTitle[1] rate has been templateNegativeTrend throughout recent years . In templateXValue[max] , approximately templateYValue[idxmax(X)] templateScale of Colombians were living on less than templateTitle[4] templateTitle[5] templateTitle[6] per templateTitle[7] , down from templateYValue[max] templateScale of the country 's templateYLabel[1] in 2005.Moreover , it was recently found that the incidence rate of templateTitle[1] in templateTitleSubject[0] is higher in families whose heads of household were women .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] is the average templateYLabel[0] of templateYLabel[1] templateYLabel[2] to one templateYLabel[4] while being of child-bearing age . templateTitleSubject[0] includes almost all countries south of the Sahara desert .
generated: This statistic shows the Colombia poverty in Colombia from 2005 to 2017 .  The Colombia poverty is the average Percentage of population to one population while being of child-bearing age .  Colombia includes almost all countries south of the Sahara desert .

Example 447:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2013', '2018']}
title: Retail sales of the vision care market in the U.S. 2013 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Retail', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['35725.0', '34782.0', '40357.8', '39767.0', '37595.7', '36236.2']

gold: This statistic depicts retail sales of the vision care market in the United States from 2013 to 2018 . In 2016 , the U.S. vision care market generated approximately 40.36 billion U.S. dollars , up from 39.77 billion U.S. dollars the previous year .
gold_template: This statistic depicts templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[2] , the templateYLabel[3] templateTitle[2] templateTitle[3] templateTitle[4] generated approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] , up from templateYValue[3] templateScale templateYLabel[3] templateYLabel[4] the previous templateXLabel[0] .

generated_template: templateTitleSubject[0] of templateTitleSubject[0] ( RBC ) has been templatePositiveTrend its templateYLabel[1] over the past few years . They stood at templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] , up from templateYValue[min] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmin(Y)] . How big is RBC ? The templateYLabel[1] given in this statistic make RBC the largest templateTitleSubject[0] in templateTitleSubject[0] , followed closely by Toronto-Dominion templateTitleSubject[0] ( TD templateTitleSubject[0] ) .
generated: U.S. of U.S. ( RBC ) has been increasing its sales over the past few years .  They stood at 40357.8 million U.S. dollars in 2016 , up from 34782.0 million U.S. dollars in 2017 .  How big is RBC ? The sales given in this statistic make RBC the largest U.S. in , followed closely by Toronto-Dominion U.S. ( TD U.S. ) .

Example 448:
titleEntities: {'Subject': ['UFC'], 'Date': ['2012', '2018']}
title: UFC : number of events 2012 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'UFC', 'events']: ['39', '39', '41', '41', '46', '33', '31']

gold: In 2018 , a total of 39 Ultimate Fighting Championship ( UFC ) events were hosted around the world featuring 474 fights . The highest live attendance in 2018 was at UFC Fight Night 136 – Hunt vs Oleinik with 22,603 ringside spectators and the highest attendance to date was at UFC 193 – Rousey vs Holm in 2015 with 56,214 attendees . Pay-Per-View In 2017 , the UFC was among the 10 most valuable sports business brands worldwide which is in part thanks to its ' Pay-Per-View ( PPV ) business model .
gold_template: In templateXValue[max] , a total of templateYValue[idxmax(X)] Ultimate Fighting Championship ( templateYLabel[1] ) templateYLabel[2] were hosted around the world featuring 474 fights . The highest live attendance in templateXValue[max] was at templateYLabel[1] Fight Night 136 – Hunt vs Oleinik with 22,603 ringside spectators and the highest attendance to date was at templateYLabel[1] 193 – Rousey vs Holm in templateXValue[3] with 56,214 attendees . Pay-Per-View In templateXValue[1] , the templateYLabel[1] was among the 10 most valuable sports business brands worldwide which is in part thanks to its ' Pay-Per-View ( PPV ) business model .

generated_template: The global templateTitle[3] templateTitle[4] templateYLabel[0] templateYLabel[1] was projected to reach templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[idxmax(Y)] . As a relative newcomer to the competitive consumer electronics landscape , templateTitle[3] templateTitle[4] have carved out a large space for itself , with templateTitle[5] shipments templateTitleSubject[0] to amount to 279 templateScale units by 2023 . templateTitle[3] templateTitle[4] As the name suggests , wearables are gadgets that can be worn .
generated: The global 2012 2018 Number UFC was projected to reach 46 million events in 2014 .  As a relative newcomer to the competitive consumer electronics landscape , 2012 2018 have carved out a large space for itself , with 2018 shipments UFC to amount 279 million units by 2023 .  2012 2018 As the name suggests , wearables are gadgets that can be worn .

Example 449:
titleEntities: {'Subject': ['United States'], 'Date': []}
title: Ratio of government expenditure to gross domestic product ( GDP ) in the United States
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Ratio', 'of', 'government', 'expenditure', 'to', 'GDP']: ['36.77', '36.75', '36.73', '36.51', '36.41', '36.19', '35.14', '35.25', '35.46', '35.15', '35.47']

gold: The statistic shows the ratio of government expenditure to gross domestic product ( GDP ) in the United States from 2014 to 2018 , with projections up until 2024 . In 2018 , government expenditure amounted to 35.14 percent of the gross domestic product . See the US GDP for further information .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in the templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] amounted to templateYValue[min] templateScale of the templateTitle[3] templateTitle[4] templateTitle[5] . See the US templateYLabel[3] for further information .

generated_template: The statistic shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] to templateTitle[3] templateTitle[4] templateTitle[5] ( templateYLabel[3] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale of the country 's templateTitle[3] templateTitle[4] templateTitle[5] .
generated: The statistic shows the Ratio of government expenditure to gross domestic product ( GDP ) in United States from 2014 to 2018 , with projections up until 2024 .  In 2018 , government expenditure in United States amounted to about 35.14 % of the country 's gross domestic product .

Example 450:
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 templatePositiveTrend compared to the previous templateXLabel[0] , but an templatePositiveTrend of 14 incidents compared to the low reported in templateXValue[4] . The Northern European island state is known for enforcing a strict templateTitle[1] safety policy in order to ensure the security of its residents and tourists in the country .
generated: There were 317 reported Number road in Croatia 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 451:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2017']}
title: Fatality rate per 100,000 drivers licensed in the U.S. 1990 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1995', '1990']
Y_Axis['Fatalities', 'per', '100,000', 'licensed', 'drivers']: ['16.48', '17.05', '16.27', '15.29', '15.5', '15.95', '15.33', '15.71', '16.16', '17.96', '20.05', '21.06', '21.7', '22.0', '23.68', '26.7']

gold: The timeline shows the fatality rate per 100,000 drivers licensed to operate a motor vehicle in the United States from 1990 to 2017 . The fatality rate stood at 16.5 deaths per 100,000 licensed drivers in 2017 .
gold_template: The timeline shows the templateTitle[0] templateTitle[1] templateYLabel[1] 100,000 templateYLabel[4] templateYLabel[3] to operate a motor vehicle in the templateTitle[6] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] stood at templateYValue[0] deaths templateYLabel[1] 100,000 templateYLabel[3] templateYLabel[4] in templateXValue[max] .

generated_template: In templateXValue[max] , the templateYLabel[1] of templateTitle[2] templateTitle[3] to the templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateScale . 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 templateNegativeTrend in recent years .
generated: In 2017 , the per of 100,000 to the U.S. amounted to 16.48 % .  Hospitals are a cornerstone of health services in any country and also contribute considerably to the economy .  Nevertheless , the number of hospitals in the U.S. has decreased in recent years .

Example 452:
titleEntities: {'Subject': ['Chicago White Sox'], 'Date': ['2002', '2019']}
title: Franchise value of the Chicago White Sox 2002 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002']
Y_Axis['Franchise', 'value', 'in', 'million', 'U.S.', 'dollars']: ['1600', '1500', '1350', '1050', '975', '695', '692', '600', '526', '466', '450', '443', '381', '315', '262', '248', '233', '223']

gold: This graph depicts the value of the Chicago White Sox franchise of Major League Baseball from 2002 to 2019 . In 2019 , the franchise had an estimated value of 1.6 billion U.S. dollars . The Chicago White Sox are owned by Jerry Reinsdorf , who bought the franchise for 20 million U.S. dollars in 1981 .
gold_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] Sox templateYLabel[0] of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . The templateTitleSubject[0] Sox are owned by Jerry Reinsdorf , who bought the templateYLabel[0] for 20 templateScale templateYLabel[3] templateYLabel[4] in 1981 .

generated_template: This graph depicts the templateYLabel[1] of the templateTitleSubject[0] franchise of Major League Baseball from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] had an estimated templateYLabel[1] of templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
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 .

Example 453:
titleEntities: {'Subject': ['WarnerMedia'], 'Date': ['2018']}
title: WarnerMedia television network revenue 2018
X_Axis['Year']: ['2018']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['10.58']

gold: This statistic contains data on the revenue that WarnerMedia generated with its TV network business in 2018 . In 2018 , the media giant generated 10.58 billion U.S. dollars with , among others , HBO , CNN and Cartoon Network . Due to the acquisition of Time Warner by AT & T and its subsequent renaming ( now WarnerMedia ) , results for previous years are not considered meaningful and as such were not reported by AT & T in 2018 .
gold_template: This statistic contains data on the templateYLabel[0] that templateTitleSubject[0] generated with its TV templateTitle[2] business in templateXValue[max] . In templateXValue[max] , the media giant generated templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] with , among others , HBO , CNN and Cartoon templateTitle[2] . Due to the acquisition of Time Warner by AT & T and its subsequent renaming ( now templateTitleSubject[0] ) , results for previous years are not considered meaningful and as such were not reported by AT & T in templateXValue[idxmax(Y)] .

generated_template: The timeline shows the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[2] a templateTitleSubject[0] subscription in the templateTitle[4] in templateXValue[min] and templateXValue[max] . In the presented time period , the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateTitle[2] templateTitleSubject[0] templatePositiveTrend from templateYValue[idxmin(X)] templateScale to templateYValue[idxmax(X)] templateScale .
generated: The timeline shows the Revenue of billion U.S. network a WarnerMedia subscription in the 2018 in and 2018 .  In the presented time period , the Revenue of billion U.S. network WarnerMedia increased from 10.58 billion to 10.58 billion .

Example 454:
titleEntities: {'Subject': ['Average'], 'Date': ['2009']}
title: Average global hotel rates from 2009 to 2015
X_Axis['Year']: ['2009', '2010', '2011', '2012', '2013', '2014', '2015']
Y_Axis['Average', 'hotel', 'rate', 'in', 'U.S.', 'dollars']: ['155', '162', '174', '171', '171', '174', '179']

gold: This statistic shows average global hotel rates from 2009 to 2015 . In 2013 , the average global hotel rate was 171 U.S. dollars . This figure was forecasted to increase to 174 U.S. dollars in 2014 and again to 179 dollars in 2015 .
gold_template: This statistic shows templateYLabel[0] templateTitle[1] templateYLabel[1] templateTitle[3] templateTitle[4] templateXValue[min] to templateXValue[max] . In templateXValue[4] , the templateYLabel[0] templateTitle[1] templateYLabel[1] templateYLabel[2] was templateYValue[3] templateYLabel[3] templateYLabel[4] . This figure was forecasted to templatePositiveTrend to templateYValue[2] templateYLabel[3] templateYLabel[4] in templateXValue[5] and again to templateYValue[idxmax(X)] templateYLabel[4] in templateXValue[idxmax(Y)] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] worldwide from templateXValue[0] to templateXValue[last] . During the marketing templateXLabel[0] templateXValue[last] , templateTitleSubject[0] templateTitle[1] templateYLabel[0] amounted to about templateYValue[last] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the global Average worldwide from 2009 to 2015 .  During the marketing Year 2015 , Average global amounted to about 179 % rate U.S. .

Example 455:
titleEntities: {'Subject': ['Algeria'], 'Date': ['2019']}
title: Unemployment rate in Algeria 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['12.35', '12.15', '12', '10.2', '11.21', '10.21', '9.82', '10.97', '9.96', '9.96', '10.16', '11.33', '13.79', '12.27', '15.27', '17.65', '23.72', '25.9', '27.3', '29.77', '28.45']

gold: This statistic shows the unemployment rate in Algeria from 1998 to 2019 . In 2019 , the unemployment rate in Algeria was 12.35 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from 1998 to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[idxmax(X)] templateScale .

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

Example 456:
titleEntities: {'Subject': ['Kenya'], 'Date': ['2024']}
title: Total population of Kenya 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['56.43', '54.96', '53.52', '52.11', '50.72', '49.36', '48.03', '46.73', '45.45', '44.2', '43.0']

gold: This statistic shows the total population of Kenya from 2014 to 2024 . In 2018 , the total population of Kenya was estimated at approximately 48.03 million inhabitants .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] was estimated at approximately templateYValue[6] templateScale templateYLabel[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateScale templateYLabel[0] .
generated: This statistic shows the Total population of Kenya from 2014 to 2018 , with projections up until 2024 .  In 2018 , the Total population of Kenya amounted to approximately 48.03 millions Inhabitants .

Example 457:
titleEntities: {'Subject': ['Qatar'], 'Date': ['2018']}
title: Urbanization in Qatar 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['99.14', '99.08', '99.02', '98.95', '98.87', '98.79', '98.7', '98.6', '98.5', '98.34', '98.14']

gold: This statistic shows the degree of urbanization in Qatar from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 99.14 percent of Qatar 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Qatar from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 99.14 % of Qatar 's total population lived in urban areas and cities .

Example 458:
titleEntities: {'Subject': ['Michigan'], 'Date': ['2000', '2018']}
title: Michigan - real GDP 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Real', 'GDP', 'in', 'billion', 'U.S.', 'dollars']: ['468.39', '456.0', '448.71', '440.31', '430.5', '424.32', '418.86', '411.47', '400.94', '380.09', '416.7', '441.15', '443.31', '450.75', '444.2', '443.79', '435.25', '423.62', '438.28']

gold: This statistic shows the development of Michigan 's real GDP from 2000 to 2018 . In 2018 , the real GDP of Michigan was 468.39 billion U.S. dollars .
gold_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of templateTitleSubject[0] was about templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the development of Michigan 's Real GDP from 2000 to 2018 .  In 2018 , the GDP of Michigan was about 468.39 billion U.S. dollars .

Example 459:
titleEntities: {'Subject': ['Kuwait'], 'Date': ['2018']}
title: Urbanization in Kuwait 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['100', '100', '100', '100', '100', '100', '100', '100', '100', '100', '100']

gold: This statistic shows the degree of urbanization in Kuwait from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 100 percent of Kuwait 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Kuwait from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 100 % of Kuwait 's total population lived in urban areas and cities .

Example 460:
titleEntities: {'Subject': ['New Jersey'], 'Date': ['2000', '2018']}
title: New Jersey - poverty rate 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Percentage', 'of', 'population']: ['9.5', '10', '10.4', '10.8', '11.1', '11.4', '10.8', '10.4', '10.3', '9.4', '8.7', '8.6', '8.7', '8.7', '8.5', '8.4', '7.5', '7.9', '7.9']

gold: This statistic shows the poverty rate in New Jersey from 2000 to 2018 . For instance , 9.5 percent of New Jersey 's population lived below the poverty line in 2018
gold_template: This statistic shows the templateTitle[2] templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . For instance , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[1] lived below the templateTitle[2] line in templateXValue[max]

generated_template: This statistic shows the templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , about templateYValue[idxmax(X)] templateScale 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 percentage of New Jersey 's population lived below the Jersey line .

Example 461:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: U.S. preterm birth rate 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2000', '1990']
Y_Axis['Percentage', 'of', 'all', 'births']: ['10.02', '9.93', '9.85', '9.63', '9.57', '9.62', '9.76', '9.81', '9.98', '10.07', '10.36', '10.44', '12.8', '12.73', '11.64', '10.62']

gold: This statistic depicts the percentage of births that were preterm births in the United States from 1990 to 2018 . In 1990 , some 10.6 percent of all births in the United States were preterm births . A preterm birth means that a child was delivered after less than 37 weeks of gestation .
gold_template: This statistic depicts the templateScale of templateYLabel[2] that were templateTitle[1] templateYLabel[2] in the templateTitle[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , some templateYValue[last] templateScale of templateYLabel[1] templateYLabel[2] in the templateTitle[0] were templateTitle[1] templateYLabel[2] . A templateTitle[1] templateTitle[2] means that a child was delivered after less than 37 weeks of gestation .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] in the templateTitle[5] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] stood at around templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Percentage all of birth rate 1990 in the 2018 between 1990 and 2018 .  In 2018 , the Percentage all of U.S. birth rate 1990 stood at around 10.02 percentage .

Example 462:
titleEntities: {'Subject': ['Groupon'], 'Date': ['2009', '2019']}
title: Groupon : annual net income 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Net', 'income', 'in', 'million', 'U.S.', 'dollars']: ['-11.7', '1.99', '26.63', '-183.32', '33.68', '-63.92', '-88.95', '-51.03', '-297.76', '-413.39', '-1.34']

gold: The statistic above shows the annual net income of Groupon from 2008 to 2019 . In 2019 , the coupon site accumulated a net loss of more than 11.6 million dollars , an decline from the previous year 's net income of two million US dollars .
gold_template: The statistic above shows the templateTitle[1] templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from 2008 to templateXValue[max] . In templateXValue[max] , the coupon site accumulated a templateYLabel[0] loss of more than 11.6 templateScale templateYLabel[4] , an decline from the previous templateXLabel[0] 's templateYLabel[0] templateYLabel[1] of templateYValue[1] templateScale US templateYLabel[4] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of The templateTitleSubject[0] templateTitle[3] templateTitleSubject[0] worldwide from templateXValue[min] to templateXValue[max] . Fast food chain templateTitleSubject[0] templateTitle[3] had a templateYLabel[0] templateYLabel[1] of approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] in templateXValue[max] . This shows a 70 templateScale templateNegativeTrend over previous templateXLabel[0] templateTitle[3] total amounting to templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Net income of The Groupon income worldwide from 2009 to 2019 .  Fast food chain Groupon income had a Net income of approximately -11.7 million U.S. dollars in 2019 .  This shows a 70 million decrease over previous Year income total amounting to 33.68 million U.S. dollars .

Example 463:
titleEntities: {'Subject': ['Kazakhstan'], 'Date': ['2018']}
title: Urbanization in Kazakhstan 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Share', 'of', 'urban', 'population', 'in', 'total', 'population']: ['57.43', '57.34', '57.26', '57.19', '57.12', '57.05', '56.97', '56.9', '56.83', '56.76', '56.68']

gold: This statistic shows the degree of urbanization in Kazakhstan from 2008 to 2018 . Urbanization means the share of urban population in the total population of a country . In 2018 , 57.43 percent of Kazakhstan 's total population lived in urban areas and cities .
gold_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .

generated_template: This statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . templateTitle[0] means the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateYLabel[3] templateYLabel[2] of a country . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] 's templateYLabel[3] templateYLabel[2] lived in templateYLabel[1] areas and cities .
generated: This statistic shows the degree of Urbanization in Kazakhstan from 2008 to 2018 .  Urbanization means the Share of urban population in the total population of a country .  In 2018 , 57.43 % of Kazakhstan 's total population lived in urban areas and cities .

Example 464:
titleEntities: {'Subject': ['BP'], 'Date': ['2010', '2018']}
title: BP 's revenue - Upstream segment 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['56399', '45440', '33188', '43235', '65424', '70374', '72225', '75754', '66266']

gold: This statistic shows the revenue of the BP Upstream segment from 2010 to 2018 . In 2018 , BP Upstream reported some 56.4 billion U.S. dollars of revenue . BP is a globally operating oil and gas company , and the eighth largest company generally worldwide as of 2017 , measured by revenue .
gold_template: This statistic shows the templateYLabel[0] of the templateTitleSubject[0] templateTitle[3] templateTitle[4] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] templateTitle[3] reported some templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] of templateYLabel[0] . templateTitleSubject[0] is a globally operating oil and gas company , and the eighth largest company generally worldwide as of templateXValue[1] , measured by templateYLabel[0] .

generated_template: templateTitle[2] spending on the National Football League ( templateTitleSubject[0] ) and its teams has templatePositiveTrend annually since templateXValue[min] , reaching templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] worldwide during the templateXValue[idxmax(Y)] 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 2011 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 465:
titleEntities: {'Subject': ['Finland'], 'Date': ['2007', '2017']}
title: Number of hospitals in Finland 2007 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007']
Y_Axis['Number', 'of', 'hospitals']: ['247', '262', '268', '258', '259', '263', '275', '280', '298', '320', '325']

gold: The number of hospitals in Finland was down at the lowest point of the observed period in 2017 , when there were 247 hospitals . At the beginning of the observed period , in 2007 , the number of hospitals amounted to 325 .
gold_template: The templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] was down at the lowest point of the observed period in templateXValue[max] , when there were templateYValue[idxmax(X)] templateYLabel[1] . At the beginning of the observed period , in templateXValue[min] , the templateYLabel[0] of templateYLabel[1] amounted to templateYValue[idxmin(X)] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] had reached about templateYValue[idxmax(X)] years.Demographic development in templateTitleSubject[0] – additional information templateYLabel[0] templateYLabel[1] at templateYLabel[2] refers to the average number of years a group of people born in the same templateXLabel[0] would live , assuming constant mortality rates . The country with the highest templateYLabel[0] templateYLabel[1] at templateYLabel[2] was Japan , while templateTitleSubject[0] had reached a templateYLabel[0] templateYLabel[1] above global average .
generated: The statistic shows the Number hospitals at in Finland from 2007 to 2017 .  In 2017 , the average Number hospitals at in Finland had reached about 247 years.Demographic development in Finland – additional information Number hospitals at 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 Number hospitals at was Japan , while Finland had reached a Number hospitals above global average .

Example 466:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2018']}
title: Number of pubs in the United Kingdom ( UK ) 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '48', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Number', 'of', 'pubs', 'in', 'thousands']: ['47.6', '48.35', '50.3', '50.8', '51.9', '52.5', '53.8', '54.7', '55.4', '52.5', '54.8', '56.8', '58.2', '58.6', '59.0', '59.4', '60.1', '60.7', '60.8']

gold: How many pubs are there in the UK ? There were approximately 47,600 pubs operating in the United Kingdom in 2018 . This represented a decrease of approximately 7,200 pubs in the last ten years , and over 13,200 pubs since 2000 . Pubs in decline Several factors have been suggested for the decline in pubs in the UK .
gold_template: How many templateYLabel[1] are there in the templateTitleSubject[1] ? There were approximately templateYValue[min] templateYLabel[1] operating in the templateTitleSubject[0] in templateXValue[max] templateXValue[idxmin(Y)] This represented a templateNegativeTrend of approximately 7,200 templateYLabel[1] in the last ten years , and over 13,200 templateYLabel[1] since templateXValue[last] . templateYLabel[1] in decline Several factors have been suggested for the decline in templateYLabel[1] in the templateTitleSubject[1] .

generated_template: This statistic shows the total templateTitleSubject[0] ( UK ) templateTitle[4] templateYLabel[0] templateYLabel[1] templateYLabel[2] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] templateXValue[0] . The overall trend was one of templatePositiveTrend , seeing a peak in templateXValue[0] with 27.99 templateScale British pounds and a total rise of over 5 templateScale British pounds throughout this period .
generated: This statistic shows the total United Kingdom ( UK ) Number pubs thousands from fiscal Year 2000 to fiscal Year 2018 .  The overall trend was one of increasing , seeing a peak in 2018 with 27.99 thousands British pounds and a total rise of over 5 thousands British pounds throughout this period .

Example 467:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Household income of white families in the U.S. 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Median', 'income', 'in', '2018', 'CPI-U-RS', 'adjusted', 'U.S.', 'dollars']: ['70642', '69851', '66440', '65133', '62453', '63588', '60979', '60526', '61361', '62374', '63378', '65089', '63892', '63900', '63627', '63832', '64084', '62773', '63609', '63654', '62480', '60548', '59128', '58184', '56297', '55914', '55842', '55568', '56917']

gold: This statistic shows the household income of white families in the U.S. from 1990 to 2018 . The median income in 2018 was at 70,642 U.S. dollars for white , non-Hispanic families . The median household income of the United States can be accessed here .
gold_template: This statistic shows the templateTitle[0] templateYLabel[1] of templateTitle[2] templateTitle[3] in the templateYLabel[5] from templateXValue[min] to templateXValue[max] . The templateYLabel[0] templateYLabel[1] in templateXValue[max] was at templateYValue[idxmax(X)] templateYLabel[5] templateYLabel[6] for templateTitle[2] , non-Hispanic templateTitle[3] . The templateYLabel[0] templateTitle[0] templateYLabel[1] of the templateTitle[4] can be accessed here .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateTitle[3] templateTitle[4] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of templateTitleSubject[0] templateTitle[3] templateTitle[4] were living below the templateYLabel[0] line in the templateTitle[5] . templateYLabel[0] is the state of one who lacks a certain amount of material possessions or money .
generated: This statistic shows the Median income of U.S. families in the U.S. from 1990 to 2018 .  In 2018 , 70642 % of U.S. families 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 468:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010', '2018']}
title: Average annual food away-from-home expenditures of U.S. households 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Expenditure', 'in', 'U.S.', 'dollars']: ['3459', '3365', '3154', '3008', '2787', '2625', '2678', '2620', '2505']

gold: This timeline depicts the average annual food away-from-home expenditure of United States households from 2010 to 2018 . In 2018 , average food away-from-home expenditure of U.S. households amounted to about 3,459 U.S. dollars .
gold_template: This timeline depicts the templateTitle[0] templateTitle[1] templateTitle[2] templateTitle[3] templateYLabel[0] of templateTitle[5] templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[2] templateTitle[3] templateYLabel[0] of templateYLabel[1] templateTitle[6] amounted to about templateYValue[idxmax(X)] templateYLabel[1] templateYLabel[2] .

generated_template: This statistic depicts the templateTitle[0] templateYLabel[0] of L'Oreal templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , L'Oreal 's templateTitle[0] templateYLabel[0] templateTitle[3] amounted to about templateYValue[max] templateScale templateYLabel[2] . L'Oreal is the world 's largest cosmetics and beauty company , concentrating on hair color , skin care , sun protection , make-up , perfumes , and hair care .
generated: This statistic depicts the Average Expenditure of L'Oreal away-from-home from 2010 to 2018 .  In 2018 , L'Oreal 's Average Expenditure away-from-home amounted to about 3459 million dollars .  L'Oreal is the world 's largest cosmetics and beauty company , concentrating on hair color , skin care , sun protection , make-up perfumes , and hair care .

Example 469:
titleEntities: {'Subject': ['Turkey'], 'Date': []}
title: Ratio of government expenditure in relation to gross domestic product ( GDP ) in Turkey
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Budgetary', 'balance', 'in', 'relation', 'to', 'the', 'gross', 'domestic', 'product']: ['35.66', '35.68', '35.64', '35.61', '35.17', '34.81', '34.61', '33.62', '35.08', '33.37', '33.23']

gold: This statistic shows the ratio of government expenditure to gross domestic product ( GDP ) in Turkey from 2014 to 2018 , with projections up until 2024 . In 2018 , the ratio in relation to the GDP in Turkey was at approximately 34.61 percent .
gold_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] to templateYLabel[3] templateYLabel[4] templateYLabel[5] ( templateTitle[7] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] in templateYLabel[2] to the templateTitle[7] in templateTitleSubject[0] was at approximately templateYValue[6] templateScale .

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

Example 470:
titleEntities: {'Subject': ['El Salvador'], 'Date': ['2024']}
title: Inflation rate in El Salvador 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['1', '1', '1.1', '1.1', '1.06', '0.89', '1.09', '1.01', '0.6', '-0.73', '1.14', '0.76', '1.73', '5.13', '1.18', '0.54', '7.26', '4.58', '4.04', '4.69', '4.45', '2.12', '1.87', '3.75', '2.27', '0.51', '2.55', '4.49', '9.79', '10.03', '10.58', '18.51', '11.22', '14.41', '28.29', '17.65', '19.77', '24.85', '31.95', '22.32', '11.71']

gold: This statistic shows the average inflation rate in El Salvador from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in El Salvador amounted to about 1.09 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

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

Example 471:
titleEntities: {'Subject': ['France'], 'Date': ['2013', '2013']}
title: Distribution of young people according to the age of their first kiss in France 2013
X_Axis['Year']: ['Under_11_years_old', '11_to_12_years_old', '13_to_14_years_old', '15_years_old', '16_years_old', '17_years_old_and_over', 'Is_not_pronounced']
Y_Axis['Share', 'of', 'respondents']: ['16', '12', '31', '16', '10', '13', '2']

gold: In 2013 , it appears that the majority of French teenagers were in middle school when they had their first kiss . Love appears to be an important area of life at a young age , with more than 50 percent of young French people stating that love relationships were important for them . First love experiences Even though new technologies and smartphones may have changed the way teenagers live their love life , it seems that the age for first love and sex experiences has not really changed over the years .
gold_template: In templateTitleDate[0] , it appears that the majority of French teenagers were in middle school when they had templateTitle[5] templateTitle[6] templateTitle[7] . Love appears to be an important area of life at a templateTitle[1] templateTitle[4] , with more than 50 templateScale of templateTitle[1] French templateTitle[2] stating that love relationships were important for them . templateTitle[6] love experiences Even though new technologies and smartphones may have changed the way teenagers live templateTitle[5] love life , it seems that the templateTitle[4] for templateTitle[6] love and sex experiences has templateXValue[last] really changed templateXValue[5] the templateXValue[0] .

generated_template: This statistic gives information on the templateTitle[1] templateTitle[2] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] as of 2019 . During this period of time , it was found that templateYValue[max] templateScale of templateTitle[3] templateTitle[4] in the Latin American country were aged between 25 and 34 templateXValue[0] .
generated: This statistic gives information on the young people of according age in France as of 2019 .  During this period of time , it was found that 31 % of according age in the Latin American country were aged between 25 and 34 Under 11 years old .

Example 472:
titleEntities: {'Subject': ['National Football League'], 'Date': ['2001', '2018']}
title: National Football League : operating income of the Dallas Cowboys 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Operating', 'income', 'in', 'million', 'U.S.', 'dollars']: ['420.0', '365.0', '350.0', '300.0', '270.0', '245.7', '250.5', '226.7', '119.0', '143.3', '9.2', '30.6', '4.3', '37.1', '54.3', '37.5', '52.3', '75.0']

gold: The statistic depicts the operating income of the Dallas Cowboys , a franchise of the National Football League , from 2001 to 2018 . In the 2018 season , the operating income of the Dallas Cowboys was at 420 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[5] templateTitle[6] , a franchise of the templateTitleSubject[0] League , from templateXValue[min] to templateXValue[max] . In the templateXValue[max] season , the templateYLabel[0] templateYLabel[1] of the templateTitle[5] templateTitle[6] was at templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: The statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Yankees from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] Yankees franchise amounted to templateYValue[0] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic depicts the Operating income of the National Football League Yankees from 2001 to 2018 .  In 2018 , the Operating income of the National Football League Yankees franchise amounted to 420.0 million U.S. dollars .

Example 473:
titleEntities: {'Subject': ['Nigeria'], 'Date': ['2024']}
title: Inflation rate in Nigeria 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['11', '11.14', '11.4', '11.31', '11.73', '11.32', '12.09', '16.5', '15.7', '9.01', '8.05', '8.5', '12.23', '10.83', '13.74', '12.54', '11.58', '5.4', '8.22', '17.86', '15']

gold: Nigeria 's inflation has been higher than the average for African and Sub-Saharan countries for years now , and even exceeded 16 percent in 2017 – and a real , significant decrease is nowhere in sight . The bigger problem is its unsteadiness , however : An inflation rate that is bouncing all over the place , like this one , is usually a sign of a struggling economy , causing prices to fluctuate , and unemployment and poverty to increase . Nigeria 's economy - a so-called `` mixed economy '' , which means the market economy is at least in part regulated by the state – is not entirely in bad shape , though .
gold_template: templateTitleSubject[0] 's templateYLabel[0] has been higher than the average for African and Sub-Saharan countries for years now , and even exceeded templateYValue[8] templateScale in templateXValue[7] – and a real , significant templateNegativeTrend is nowhere in sight . The bigger problem is its unsteadiness , however : An templateYLabel[0] templateYLabel[1] that is bouncing all over the place , like this one , is usually a sign of a struggling economy , causing prices to fluctuate , and unemployment and poverty to templatePositiveTrend . templateTitleSubject[0] 's economy - a so-called `` mixed economy '' , which means the market economy is at least in part regulated by the state – is not entirely in bad shape , though .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Nigeria from 2004 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Nigeria amounted to about 12.09 % compared to the previous Year .

Example 474:
titleEntities: {'Subject': ['Online Great Britain'], 'Date': ['2019', '2019']}
title: Medicine : Online purchasing in Great Britain 2019 , by demographic
X_Axis['Year']: ['Men', 'Women', '16-24', '25-34', '35-44', '45-54', '55-64', '65+']
Y_Axis['Share', 'of', 'respondents']: ['15', '18', '14', '16', '26', '16', '15', '12']

gold: This statistic displays the share of individuals in Great Britain who purchased medicine online in 2019 , by age and gender . Purchasing online was most common among individuals within the 35 to 44 age demographic , at 26 percent of respondents .
gold_template: This statistic displays the templateYLabel[0] of individuals in templateTitleSubject[0] who purchased templateTitle[0] templateTitleSubject[0] in templateTitleDate[0] , templateTitle[6] age and gender . templateTitle[2] templateTitleSubject[0] was most common among individuals within the 35 to 44 age templateTitle[7] , at templateYValue[max] templateScale of templateYLabel[1] .

generated_template: In templateTitleDate[0] , templateYValue[1] templateScale of templateXValue[1] and around a quarter of templateXValue[0] had purchased templateTitle[0] or templateTitle[1] templateTitle[2] in the previous 12 months in templateTitleSubject[0] . templateTitle[3] templateTitle[2] was most common among the 25 to 34-year-old templateTitle[8] , with templateYValue[max] templateScale of individuals within this group having made purchases in templateTitleDate[0] . European templateTitle[2] templateTitle[3] compared Overall , 29 templateScale of British adults bought templateTitle[1] templateTitle[2] in templateTitleDate[0] .
generated: In 2019 , 18 % of Women and around a quarter of Men had purchased Medicine or Online purchasing in the previous 12 months in Online Great Britain .  Great purchasing was most common among the 25 to 34-year-old demographic , with 26 % of individuals within this group having made purchases in 2019 .  European purchasing Great compared Overall , 29 % of British adults bought Online purchasing in 2019 .

Example 475:
titleEntities: {'Subject': ['Netherlands'], 'Date': ['2006', '2018']}
title: Number of road deaths in the Netherlands 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'fatalities']: ['678', '613', '629', '621', '570', '570', '650', '661', '640', '720', '750', '791', '811']

gold: In 2018 , 678 people were killed on roads in the Netherlands . Between 2006 and 2018 , road traffic fatalities had seen a net decline of 16 percent , with the peak recorded at the beginning of the reporting period in 2006 . The Dutch Government is dedicated to making its roads even safer in the future and aims to bring the number of road deaths down to below 500 by 2020 .
gold_template: In templateXValue[max] , templateYValue[idxmax(X)] people were killed on roads in the templateTitleSubject[0] . Between templateXValue[min] and templateXValue[max] , templateTitle[1] traffic templateYLabel[1] had seen a net decline of 16 templateScale , with the peak recorded at the beginning of the reporting period in templateXValue[min] . The Dutch Government is dedicated to making its roads even safer in the future and aims to bring the templateYLabel[0] of templateTitle[1] templateTitle[2] down to below 500 by 2020 .

generated_template: There were templateYValue[0] reported templateTitle[1] templateTitle[2] in templateTitleSubject[0] in templateXValue[max] . This was a slight templatePositiveTrend compared to the previous templateXLabel[0] , but an templatePositiveTrend of 14 incidents compared to the low reported in templateXValue[4] . The Northern European island state is known for enforcing a strict templateTitle[1] safety policy in order to ensure the security of its residents and tourists in the country .
generated: There were 678 reported road deaths in Netherlands 2018 .  This was a slight increase compared to the previous Year , but an increase of 14 incidents compared to the low reported in 2014 .  The Northern European island state is known for enforcing a strict road safety policy in order to ensure the security of its residents and tourists in the country .

Example 476:
titleEntities: {'Subject': ['Balfour Beatty Group'], 'Date': ['2011', '2018']}
title: Balfour Beatty Group 's average number of employees 2011 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Average', 'number', 'of', 'employees']: ['26000', '28000', '22450', '23316', '39751', '41221', '50304', '50301']

gold: Balfour Beatty was employer to some 26,000 people in 2018 . The United Kingdom based heavy construction company let go 2,000 employees between 2017 and 2018 , whilst the overall decline in the period of consideration was even more prominent . Overall , employment figures have fallen by nearly 50 percent since a high of 50,304 was reported in 2012 .
gold_template: templateTitleSubject[0] was employer to some templateYValue[0] people in templateXValue[max] . The United Kingdom based heavy construction company let go 2,000 templateYLabel[2] between templateXValue[1] and templateXValue[max] , whilst the overall decline in the period of consideration was even more prominent . Overall , employment figures have fallen by nearly 50 templateScale since a high of templateYValue[max] was reported in templateXValue[idxmax(Y)] .

generated_template: The templateYLabel[0] of the templateTitleSubject[0] luxury brand templateTitle[4] templateTitle[5] has templatePositiveTrend twofold over the period surveyed , templatePositiveTrend from roughly templateYValue[min] templateScale templateYLabel[2] in templateXValue[idxmin(Y)] to templateYValue[max] templateScale templateYLabel[2] in the templateXLabel[0] templateXValue[idxmax(Y)] . Despite the steady templatePositiveTrend in templateYLabel[0] during the period considered , the templateTitle[3] reported a net loss of approximately 25 templateScale templateYLabel[2] in templateXValue[max] . Worldwide recognition Founded in 1978 in Milan , templateTitle[4] templateTitle[5] is one of the leading international templateTitle[2] design houses .
generated: The Average of the Balfour Beatty Group luxury brand average number has increased twofold over the period surveyed , increasing from roughly 22450 % employees in 2016 to 50304 % employees in the Year 2012 .  Despite the steady increase in Average during the period considered , the 's reported a net loss of approximately 25 million employees in 2018 .  Worldwide recognition Founded in 1978 Milan , average number is one of the leading international Group design houses .

Example 477:
titleEntities: {'Subject': ['Cintas'], 'Date': ['2012', '2019']}
title: Cintas - annual revenue 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['6.89', '6.48', '5.32', '4.8', '4.37', '4.09', '3.88', '3.76']

gold: This statistic depicts the annual revenue of Cintas Corporation between the fiscal year of 2012 and the fiscal year of 2019 . For the fiscal year of 2019 , the Cincinnati-based specialized facility services company reported an annual revenue of just under 6.9 billion U.S. dollars .
gold_template: This statistic depicts the templateTitle[1] templateYLabel[0] of templateTitleSubject[0] Corporation between the fiscal templateXLabel[0] of templateXValue[min] and the fiscal templateXLabel[0] of templateXValue[max] . For the fiscal templateXLabel[0] of templateXValue[max] , the Cincinnati-based specialized facility services company reported an templateTitle[1] templateYLabel[0] of just under templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of International Workplace Group ( templateTitleSubject[0] ) , formerly Regus , templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] generated a templateYLabel[0] of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] templateTitle[2] . In templateXValue[2] , Regus changed its holding company to templateTitleSubject[0] but hold the Regus name as their brand .
generated: This statistic shows the Revenue of International Workplace Group ( Cintas ) , formerly Regus , revenue from 2012 to 2019 .  In 2019 , Cintas generated a Revenue of 6.89 billion U.S. dollars revenue .  In 2017 , Regus changed its holding company to Cintas but hold the Regus name as their brand .

Example 478:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2002', '2017']}
title: U.S. wholesale sales of beer and wine 2002 to 2017
X_Axis['Year']: ['17', '16', '15', '14', '13', '12', '11', '10', '09', '08', '07', '06', '05', '04', '03', '02']
Y_Axis['Sales', 'in', 'billion', 'U.S.', 'dollars']: ['166.31', '161.16', '152.57', '147.34', '145.86', '136.77', '129.43', '122.9', '120.76', '121.58', '115.57', '108.56', '103.91', '96.25', '91.23', '87.56']

gold: The timeline shows the beer , wine , and distilled alcoholic beverages sales of merchant wholesalers in the United States from 2002 to 2017 . In 2017 , the beer , wine , and distilled alcoholic beverages sales of U.S. merchant wholesalers amounted to about 166.31 billion U.S. dollars . Alcohol in the United States During the recession , alcohol-related industries were some of the few seeing continued growth , proving alcohol to be a frequent purchase for many Americans .
gold_template: The timeline shows the templateTitle[3] , templateTitle[4] , and distilled alcoholic beverages templateYLabel[0] of merchant wholesalers in the templateTitle[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the templateTitle[3] , templateTitle[4] , and distilled alcoholic beverages templateYLabel[0] of templateYLabel[2] merchant wholesalers amounted to about templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] . Alcohol in the templateTitle[0] During the recession , alcohol-related industries were some of the few seeing continued growth , proving alcohol to be a frequent purchase for many Americans .

generated_template: This statistic represents the templateTitle[0] templateYLabel[0] of templateTitle[2] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitle[0] templateTitle[2] templateYLabel[0] came to templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic represents the U.S. Sales of in the wholesale from 02 to 17 .  In 17 , U.S. sales came to 166.31 billion U.S. dollars .

Example 479:
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 presents the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] templateYLabel[2] templateYLabel[3] templateTitle[2] from templateXValue[min] to templateXValue[max] . For the 52 weeks ended on 1 , templateXValue[max] , there were approximately templateYValue[idxmax(X)] templateYValue[idxmax(X)] templateTitleSubject[0] cardholders all over the world .
generated: This statistic presents the Average estimated of Christmas U.S amount U.S. Christmas from 2006 to 2011 .  For the 52 weeks ended on 1 , 2011 there were approximately 712 Christmas U.S cardholders all over the world .

Example 480:
titleEntities: {'Subject': ['Advance Publications'], 'Date': ['2006', '2014']}
title: Advance Publications ' revenue 2006 to 2014
X_Axis['Year']: ['2006', '2007', '2008', '2009', '2011', '2012', '2013', '2014']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['7.14', '7.97', '7.36', '7.16', '6.55', '6.78', '6.56', '8.0']

gold: The timeline shows estimated data on the revenue of the American media corporation Advance Publications , Inc. from 2006 to 2014 . Advance Publications is a privately-owned , unlisted company and owns such brands as Reddit - a social news and entertainment website and Conde Nast - a magazine publisher . Its 2006 revenue is estimated to have amounted to 7.14 billion US dollars .
gold_template: The timeline shows estimated data on the templateYLabel[0] of the American media corporation templateTitleSubject[0] , Inc. from templateXValue[min] to templateXValue[max] . templateTitleSubject[0] is a privately-owned , unlisted company and owns such brands as Reddit - a social news and entertainment website and Conde Nast - a magazine publisher . Its templateXValue[min] templateYLabel[0] is estimated to have amounted to templateYValue[idxmin(X)] templateYValue[idxmin(X)] US templateYLabel[3] .

generated_template: This statistic shows the templateYLabel[0] of International Workplace Group ( templateTitleSubject[0] ) , formerly Regus , templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] generated a templateYLabel[0] of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] templateTitle[2] . In templateXValue[2] , Regus changed its holding company to templateTitleSubject[0] but hold the Regus name as their brand .
generated: This statistic shows the Revenue of International Workplace Group ( Advance Publications ) , formerly Regus , ' from 2006 to 2014 .  In 2014 , Advance Publications generated a Revenue of 8.0 billion U.S. dollars ' .  In 2008 , Regus changed its holding company to Advance Publications but hold the Regus name as their brand .

Example 481:
titleEntities: {'Subject': ['Nike'], 'Date': ['2016', '2020']}
title: Global brand value of Nike from 2016 to 2020
X_Axis['Year']: ['2020', '2019', '2018', '2017', '2016']
Y_Axis['Brand', 'value', 'in', 'million', 'U.S.', 'dollars']: ['34792', '32421', '28030', '31762', '28041']

gold: In 2020 , the Nike brand was valued at approximately 34.8 billion U.S. dollars , which was an increase of over two billion U.S. dollars from 2019 . Nike 's popularity Nike 's footwear segment was the source of the most revenue for the company in 2019 , netting over 24 billion U.S. dollars that year . Among U.S. consumers , Nike was the most popular sports shoe , ahead of its main competitors Adidas .
gold_template: In templateXValue[max] , the templateTitleSubject[0] templateYLabel[0] was valued at approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] , which was an templatePositiveTrend of over two templateScale templateYLabel[3] templateYLabel[4] templateTitle[4] templateXValue[1] . templateTitleSubject[0] 's popularity templateTitleSubject[0] 's footwear segment was the source of the most revenue for the company in templateXValue[1] , netting over 24 templateScale templateYLabel[3] templateYLabel[4] that templateXLabel[0] . Among templateYLabel[3] consumers , templateTitleSubject[0] was the most popular sports shoe , ahead of its main competitors Adidas .

generated_template: As per recent data exploring the number of people employed by Hewlett Packard Enterprise ( previously a part of Hewlett-Packard Company ) worldwide between templateXValue[min] and templateXValue[max] , in templateXValue[max] the company had templateYValue[idxmax(X)] thousand templateYLabel[0] .
generated: As per recent data exploring the number of people employed by Hewlett Packard Enterprise ( previously a part of Hewlett-Packard Company ) worldwide between 2016 and 2020 , in 2020 the company had 34792 thousand Brand .

Example 482:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2008']}
title: Stationery retail sales turnover in the United Kingdom ( UK ) 2008 to 207
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Turnover', 'in', 'million', 'GBP']: ['4784', '4892', '4372', '4304', '4620', '4397', '4432', '4025', '4423', '4446']

gold: This statistic shows the total annual turnover of the retail sales of stationery and drawing materials and miscellaneous printed matter in the United Kingdom , from 2008 to 2017 . In 2017 , turnover from stationery and drawing material retail sales reached 4.78 billion British pounds which was the highest point of turnover over the nine year period .
gold_template: This statistic shows the total annual templateYLabel[0] of the templateTitle[1] templateTitle[2] of templateTitle[0] and drawing materials and miscellaneous printed matter in the templateTitleSubject[0] , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] from templateTitle[0] and drawing material templateTitle[1] templateTitle[2] reached templateYValue[0] templateScale British pounds which was the highest point of templateYLabel[0] over the nine templateXLabel[0] period .

generated_template: In templateXValue[min] , approximately templateYValue[idxmin(X)] templateScale 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[idxmax(X)] templateScale . Due to the implementation of new broadcasting resolution technologies , such as the 4K UHD , and to the templatePositiveTrend affordability of high-end televisions , this figure could templatePositiveTrend in the coming years .
generated: In 2008 , approximately 4446 million Turnover in United Kingdom disposed of one television or more .  In 2017 , the Stationery of French Turnover owning at least one television amounted to 4784 million .  Due to the implementation of new broadcasting resolution technologies , such as the 4K UHD , and to the increased affordability of high-end televisions , this figure could increase in the coming years .

Example 483:
titleEntities: {'Subject': ['Norway'], 'Date': ['2018', '2024']}
title: Forecast of smartphone user numbers in Norway 2018 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018']
Y_Axis['Smartphone', 'users', 'in', 'millions']: ['5.19', '5.15', '5.11', '5.0', '4.89', '4.77', '4.64']

gold: This statistic displays the development in smartphone user numbers in Norway in 2018 with a forecast from 2019 to 2024 . In 2018 , the number of smartphone users amounted to 4.64 million . In the same year , smartphone penetration rate was at 86.95 percent .
gold_template: This statistic displays the development in templateYLabel[0] templateTitle[2] templateTitle[3] in templateTitleSubject[0] in templateXValue[min] with a templateTitle[0] from templateXValue[5] to templateXValue[max] . In templateXValue[min] , the number of templateYLabel[0] templateYLabel[1] amounted to templateYValue[idxmin(X)] templateScale . In the same templateXLabel[0] , templateYLabel[0] penetration rate was at 86.95 templateScale .

generated_template: This statistic depicts the templateTitle[1] networking reach in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . In templateXValue[last] , templateYValue[min] templateScale of the population in the country accessed templateTitle[1] templateTitle[2] . In templateXValue[4] , this templateYLabel[0] is projected to reach templateYValue[4] templateScale .
generated: This statistic depicts the smartphone networking reach in Norway from 2018 to 2024 .  In 2018 , 4.64 millions of the population in the country accessed smartphone user .  In 2020 , this Smartphone is projected to reach 4.89 millions .

Example 484:
titleEntities: {'Subject': ['Haiti'], 'Date': ['2018']}
title: Infant mortality rate in Haiti 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Deaths', 'per', '1,000', 'live', 'births']: ['49.5', '50.8', '52.1', '53.3', '54.5', '55.7', '56.8', '57.9', '85.6', '60.2', '61.4']

gold: The statistic shows the infant mortality rate in Haiti from 2008 to 2018 . In 2018 , the infant mortality rate in Haiti was at about 49.5 deaths per 1,000 live births .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in templateTitleSubject[0] was at about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateTitle[2] in the templateTitleSubject[0] was at about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] 1,000 templateYLabel[3] templateYLabel[4] .
generated: The statistic shows the Infant mortality rate in the Haiti from 2008 to 2018 .  In 2018 , the Infant mortality rate in the Haiti was at about 49.5 Deaths per 1,000 live births .

Example 485:
titleEntities: {'Subject': ['Gannett'], 'Date': ['2013', '2018']}
title: Gannett 's revenue 2013 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013']
Y_Axis['Revenue', 'in', 'billion', 'U.S.', 'dollars']: ['2.92', '3.15', '3.05', '2.89', '3.17', '3.32']

gold: This statistic presents Gannett Company 's annual revenue from 2013 to 2018 . In 2018 , the publisher of USA Today generated a total revenue of 2.92 billion U.S. dollars .
gold_template: This statistic presents templateTitleSubject[0] Company templateTitle[1] annual templateYLabel[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the publisher of USA Today generated a total templateYLabel[0] of templateYValue[idxmax(X)] templateScale templateYLabel[2] templateYLabel[3] .

generated_template: This statistic shows the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] size templateTitle[5] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitleSubject[0] templateTitle[1] templateTitle[2] templateTitle[3] templateTitle[4] had a size of templateYValue[idxmin(X)] templateScale templateYLabel[2] templateYLabel[3] .
generated: This statistic shows the Gannett 's revenue 2013 2018 size from 2013 to 2018 .  In 2013 , the Gannett 's revenue 2013 2018 had a size of 3.32 billion U.S. dollars .

Example 486:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2020', '2024']}
title: Forecast on U.S. petroleum refinery end-use market output 2020 to 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020']
Y_Axis['Change', 'from', 'year', 'to', 'year']: ['2', '0.5', '0.5', '0.8', '2.2']

gold: This statistic displays a forecast of the petroleum and refinery end-use market output in the United States from 2020 to 2024 . Through 2020 , the petroleum and refinery end-use market output is expected to increase by 2.2 percent . U.S. petroleum refinery market It is projected that the growth of output from the U.S. petroleum refinery end-use market will slow , from a rate of 2.2 percent in 2020 to 0.5 percent in 2023 , and grow again to 2.2 percent in 2024 .
gold_template: This statistic displays a templateTitle[0] of the templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] in the templateTitle[1] templateYLabel[1] templateXValue[min] to templateXValue[max] . Through templateXValue[min] , the templateTitle[2] and templateTitle[3] templateTitle[4] templateTitle[5] templateTitle[6] is expected to templatePositiveTrend by templateYValue[idxmin(X)] templateScale . templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[5] It is projected that the growth of templateTitle[6] templateYLabel[1] the templateTitleSubject[0] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] will slow , templateYLabel[1] a rate of templateYValue[max] templateScale in templateXValue[idxmax(Y)] to templateYValue[min] templateScale in templateXValue[idxmin(Y)] , and grow again to templateYValue[idxmin(X)] templateScale in templateXValue[max] .

generated_template: This statistic 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 templatePositiveTrend by templateYValue[max] templateScale . Appreciation of the templateTitleSubject[0] dollar is likely to reduce costs and templatePositiveTrend production in the chemical industry .
generated: This statistic displays a Forecast of the petroleum and refinery end-use from output in the U.S. year 2020 to 2024 .  year 2022 to 2023 , the petroleum and refinery end-use from output is expected to increase by 2.2 % .  Appreciation of the U.S. dollar is likely to reduce costs and increase production in the chemical industry .

Example 487:
titleEntities: {'Subject': ['Spanish'], 'Date': ['2008', '2018']}
title: Chocolate and cocoa products consumption in Spanish households 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Total', 'consumption', 'in', 'million', 'kilograms']: ['164.9', '162.4', '164.1', '165.5', '163.6', '165.3', '158.7', '151.5', '150.54', '147.44', '143.6']

gold: Chocolate has inspired uncountable desserts in many countries across the globe and throughout history . This delicacy originating in Mesoamerica is the favorite sweet of many places including Spain , which imported this delight to Europe during the country 's explorations in the Americas . Spain 's sweet tooth has been on the rise in recent years , with a peak in chocolate and cocoa product consumption amounting to 165.5 million kilograms in 2013 .
gold_template: templateTitle[0] has inspired uncountable desserts in many countries across the globe and throughout history . This delicacy originating in Mesoamerica is the favorite sweet of many places including Spain , which imported this delight to Europe during the country 's explorations in the Americas . Spain 's sweet tooth has been on the rise in recent years , with a peak in templateTitle[0] and templateTitle[1] product templateYLabel[1] amounting to templateYValue[max] templateScale templateYLabel[3] in templateXValue[5] .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the average templateYLabel[0] templateYLabel[1] at templateYLabel[2] in templateTitleSubject[0] had reached about templateYValue[idxmax(X)] years.Demographic development in templateTitleSubject[0] – additional information templateYLabel[0] templateYLabel[1] at templateYLabel[2] refers to the average number of years a group of people born in the same templateXLabel[0] would live , assuming constant mortality rates . The country with the highest templateYLabel[0] templateYLabel[1] at templateYLabel[2] was Japan , while templateTitleSubject[0] had reached a templateYLabel[0] templateYLabel[1] above global average .
generated: The statistic shows the Total consumption at million in Spanish from 2008 to 2018 .  In 2018 , the average Total consumption at million in Spanish had reached about 164.9 years.Demographic development in Spanish – additional information Total consumption at million 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 Total consumption at million was Japan , while Spanish had reached a Total consumption above global average .

Example 488:
titleEntities: {'Subject': ['Burundi'], 'Date': ['2024']}
title: Inflation rate in Burundi 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Inflation', 'rate', 'compared', 'to', 'previous', 'year']: ['8.97', '8.97', '8.97', '8.97', '8.97', '7.32', '1.24', '16.64', '5.53', '5.55', '4.42', '7.94', '18.18', '9.58', '6.5', '10.56', '24.41', '8.41', '2.74', '13.25', '8.18', '10.57', '-1.26', '7.87', '25.52', '3.52', '12.47', '31.06', '26.42', '19.36', '14.71', '9.71', '5.33', '9.01', '6.99', '11.67', '4.49', '7.11', '1.67', '3.82', '14.3']

gold: This statistic shows the average inflation rate in Burundi from 1984 to 2017 , with projections up until 2024 . In 2017 , the average inflation rate in Burundi amounted to about 16.64 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[7] , with projections up until templateXValue[max] . In templateXValue[7] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[7] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . The templateYLabel[0] templateYLabel[1] is calculated using the price templatePositiveTrend of a defined product basket . This product basket contains products and services , on which the average consumer spends money throughout the templateXLabel[0] .
generated: The statistic shows the average Inflation rate in Burundi from 1984 to 2018 , with projections up until 2024 .  The Inflation rate is calculated using the price increase of a defined product basket .  This product basket contains products and services , on which the average consumer spends money throughout the Year .

Example 489:
titleEntities: {'Subject': ['United Kingdom', 'UK'], 'Date': ['2000', '2019']}
title: Public sector expenditure as a share of GDP in the United Kingdom ( UK ) 2000 to 2019
X_Axis['Year']: ['18/19', '17/18', '16/17', '15/16', '14/15', '13/14', '12/13', '11/12', '10/11', '09/10', '08/09', '07/08', '06/07', '05/06', '04/05', '03/04', '02/03', '01/02', '00/01']
Y_Axis['Expenditure', 'as', 'share', 'of', 'GDP']: ['34.1', '34.4', '34.8', '35.7', '36.4', '37.3', '38.3', '39.1', '39.7', '39.4', '36.9', '34.9', '34.5', '34.7', '34.9', '34', '32.8', '32.1', '31.8']

gold: This statistic shows total public sector current expenditure as a share of GDP in the United Kingdom ( UK ) from 2000/01 to 2018/19 . During this period public sector spending fluctuated , peaking in 2010/11 at 39.7 percent of GDP .
gold_template: This statistic shows total templateTitle[0] templateTitle[1] current templateYLabel[0] as a templateYLabel[1] of templateYLabel[2] in the templateTitleSubject[0] ( templateTitleSubject[1] ) from 2000/01 to 2018/19 . During this period templateTitle[0] templateTitle[1] spending fluctuated , peaking in 2010/11 at templateYValue[max] templateScale of templateYLabel[2] .

generated_template: This statistic shows the total templateTitleSubject[0] ( UK ) templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] from fiscal templateXLabel[0] templateXValue[last] to fiscal templateXLabel[0] 2018/19 . There were some fluctuations during this period , the largest of which was from 2008/09 to 2009/10 when the revenue went from a peak of templateYValue[10] templateScale British pounds ( templateYLabel[5] ) to templateYValue[9] templateScale templateYLabel[5] in the next fiscal templateXLabel[0] .
generated: This statistic shows the total United Kingdom ( UK ) expenditure share GDP from fiscal Year 00/01 to fiscal Year 2018/19 .  There were some fluctuations during this period , the largest of which was from 2008/09 to 2009/10 when the revenue went from a peak of 36.9 billion British pounds ( GDP ) to 39.4 billion GDP in the next fiscal Year .

Example 490:
titleEntities: {'Subject': ['Luxembourg'], 'Date': ['2006', '2018']}
title: Number of arrivals in tourist accommodation Luxembourg 2006 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012_', '2011', '2010', '2009', '2008', '2007', '2006']
Y_Axis['Number', 'of', 'arrivals', 'in', 'thousands']: ['1139.0', '1156.0', '1161.8', '1196.1', '1142.9', '1044.3', '1021.7', '935.0', '854.72', '907.53', '936.65', '979.21', '967.88']

gold: This statistic shows the number of tourist arrivals at accommodation establishments in Luxembourg from 2006 to 2018 . There were around 1.1 million arrivals at accommodation establishments in Luxembourg in 2018 .
gold_template: This statistic shows the templateYLabel[0] of templateTitle[2] templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] from templateXValue[last] to templateXValue[0] . There were around templateYValue[0] templateScale templateYLabel[1] at templateTitle[3] establishments in templateTitleSubject[0] in templateXValue[0] .

generated_template: This statistic shows the templateTitle[2] earnings before interest and tax ( templateTitle[3] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] had a templateTitle[2] templateTitle[3] of approximately templateYValue[0] templateScale templateYLabel[1] , representing an templatePositiveTrend over the previous templateXLabel[0] .
generated: This statistic shows the tourist earnings before interest and tax ( accommodation ) of Luxembourg from 2006 to 2018 .  In 2018 , Luxembourg had a tourist accommodation of approximately 1139.0 thousands arrivals , representing an increase over the previous Year .

Example 491:
titleEntities: {'Subject': ['Iberdrola'], 'Date': ['2009', '2018']}
title: Iberdrola - revenue 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Revenue', 'in', 'million', 'euros']: ['35075.9', '31263.26', '28759.15', '31418.69', '30032.27', '31077.11', '34201.0', '31648.0', '30431.0', '25891.9']

gold: This statistic represents Iberdrola 's global revenue between the fiscal year of 2009 and the fiscal year of 2018 . The Spain-based multinational electric utility company with headquarters in Bilbao generated around 35 billion euros in revenue in the fiscal year of 2018 .
gold_template: This statistic represents templateTitleSubject[0] 's global templateYLabel[0] between the fiscal templateXLabel[0] of templateXValue[min] and the fiscal templateXLabel[0] of templateXValue[max] . The Spain-based multinational electric utility company with headquarters in Bilbao generated around templateYValue[max] templateScale templateYLabel[2] in templateYLabel[0] in the fiscal templateXLabel[0] of templateXValue[idxmax(Y)] .

generated_template: templateTitleSubject[0] has managed to templatePositiveTrend its annual templateYLabel[0] from templateXValue[1] by three templateScale , meaning that annual templateYLabel[0] surpassed templateYValue[max] templateScale templateYLabel[2] in the templateXValue[idxmax(Y)] fiscal templateXLabel[0] . Restructure and redirection templateYLabel[0] templatePositiveTrend in templateXValue[max] at templateTitleSubject[0] in spite of a struggling power and gas division , which will be spun off by the end of 2020 . In fact , templateYLabel[0] from this division templateNegativeTrend from around 18 templateScale templateYLabel[2] in templateXValue[1] to about 17.66 templateScale templateYLabel[2] in templateXValue[max] .
generated: Iberdrola has managed to increase its annual Revenue from 2017 by three million , meaning that annual Revenue surpassed 35075.9 million euros in the 2018 fiscal Year .  Restructure and redirection Revenue increased in 2018 at Iberdrola in spite of a struggling power and gas division , which will be spun off by the end of 2020 .  In fact , Revenue from this division fell from around 18 million euros in 2017 to about 17.66 million euros in 2018 .

Example 492:
titleEntities: {'Subject': ['Nissan', 'Europe'], 'Date': ['2003', '2018']}
title: Nissan car sales in Europe 2003 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003']
Y_Axis['Number', 'of', 'units', 'sold']: ['487017', '560415', '547343', '554046', '477703', '421134', '436169', '470004', '411084', '372096', '338169', '313437', '332150', '387325', '409717', '409511']

gold: This statistic shows the number of cars sold by Nissan in Europe between 2003 and 2018 . European sales of the Nissan cars rose from 400 thousand units sold in 2003 to over 560 thousand units sold by 2017 . In 2018 , there were 487 thousand units of Nissan cars sold in Europe .
gold_template: This statistic shows the templateYLabel[0] of cars templateYLabel[2] by templateTitleSubject[0] in templateTitleSubject[1] between templateXValue[min] and templateXValue[max] . European templateTitle[2] of the templateTitleSubject[0] cars templatePositiveTrend from 400 thousand templateYLabel[1] templateYLabel[2] in templateXValue[min] to over templateYValue[max] thousand templateYLabel[1] templateYLabel[2] by templateXValue[idxmax(Y)] . In templateXValue[max] , there were templateYValue[0] thousand templateYLabel[1] of templateTitleSubject[0] cars templateYLabel[2] in templateTitleSubject[1] .

generated_template: In templateXValue[max] , there were templateYValue[idxmax(X)] templateTitle[1] templateTitle[2] templateYLabel[1] across the templateTitle[4] . The FDIC , of Federal Deposit Insurance Corporation , is an agency that insures the banking system in the templateTitle[4] . The templateYLabel[0] of such registered templateYLabel[1] has been declining since templateXValue[min] , when it there were over 7,800 templateTitle[1] templateYLabel[1] in the country .
generated: In 2018 , there were 487017 car sales units across the 2003 .  The FDIC , of Federal Deposit Insurance Corporation , is an agency that insures the banking system in the 2003 .  The Number of such registered units has been declining since 2003 , when it there were over 7,800 car units in the country .

Example 493:
titleEntities: {'Subject': ['Annual'], 'Date': ['2010', '2018']}
title: Annual growth in average global hotel rates 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Year-over-year', 'growth', 'in', 'average', 'hotel', 'rates']: ['3.7', '2.5', '2.5', '2.6', '1.8', '0', '-1.5', '7.4', '4.7']

gold: This statistic shows annual growth in average global hotel rates from 2010 to 2018 . Global hotel rates were forecasted to increase by 3.7 percent in 2018 . The average daily rate of the hotel industry in the Americas reached around 123.37 U.S. dollars in 2016 .
gold_template: This statistic shows templateTitleSubject[0] templateYLabel[1] in templateYLabel[2] templateTitle[3] templateYLabel[3] templateYLabel[4] from templateXValue[min] to templateXValue[max] . templateTitle[3] templateYLabel[3] templateYLabel[4] were forecasted to templatePositiveTrend by templateYValue[idxmax(X)] templateScale in templateXValue[max] . The templateYLabel[2] daily rate of the templateYLabel[3] industry in the Americas reached around 123.37 U.S. dollars in templateXValue[2] .

generated_template: The templateYLabel[0] templateYLabel[1] of templateTitle[2] templateTitle[3] templateTitle[4] photovoltaics has seen an overall templatePositiveTrend since templateXValue[min] , amounting to templateYValue[0] templateScale in templateXValue[max] . However , this was a minor templateNegativeTrend 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 Year-over-year growth of average global hotel photovoltaics has seen an overall increase since 2010 , amounting to 3.7 % in 2018 .  However , this was a minor decrease global the previous Year and significantly lower when compared to the Year-over-year factors of other renewable sources .  This can be explained by the lack of consistency in the number of sunny days recorded .

Example 494:
titleEntities: {'Subject': ['Tampa Bay Buccaneers', 'NFL'], 'Date': ['2001', '2018']}
title: Revenue of the Tampa Bay Buccaneers ( NFL ) 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['400', '383', '367', '341', '313', '275', '267', '258', '245', '246', '241', '224', '205', '203', '195', '175', '168', '151']

gold: The statistic depicts the revenue of the Tampa Bay Buccaneers , a franchise of the National Football League , from 2001 to 2018 . In 2018 , the revenue of the Tampa Bay Buccaneers was 400 million U.S. dollars .
gold_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] Buccaneers , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] Buccaneers was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .

generated_template: The statistic depicts the templateYLabel[0] of the templateTitleSubject[0] , a franchise of the National Football League , from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitleSubject[0] was templateYValue[max] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .
generated: The statistic depicts the Revenue of the Tampa Bay Buccaneers , a franchise of the National Football League , from 2001 to 2018 .  In 2018 , the Revenue of the Tampa Bay Buccaneers was 400 U.S. dollars .

Example 495:
titleEntities: {'Subject': ['Afghanistan'], 'Date': ['2018']}
title: Population growth in Afghanistan 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Population', 'growth', 'compared', 'to', 'previous', 'year']: ['2.38', '2.55', '2.78', '3.08', '3.36', '3.49', '3.41', '3.14', '2.75', '2.4', '2.27']

gold: This timeline shows the population growth in Afghanistan from 2008 to 2018 . In 2018 , Afghanistan 's population grew by an estimated 2.38 percent compared to the previous year . See Afghanistan 's population figures for comparison .
gold_template: This timeline shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] templatePositiveTrend by an estimated templateYValue[idxmax(X)] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] . See templateTitleSubject[0] 's templateYLabel[0] figures for comparison .

generated_template: The statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the templateScale of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the total templateYLabel[2] in templateTitleSubject[0] lived in cities . templateTitleSubject[0] 's rural exodus templateTitleSubject[0] is one of the largest countries in the world regarding land area , second behind Russia .
generated: The statistic shows the degree of Population in Afghanistan from 2008 to 2018 and details the percentage of the entire compared , living in growth areas .  In 2018 , 2.38 % of the total compared in Afghanistan lived in cities .  Afghanistan 's rural exodus Afghanistan is one of the largest countries in the world regarding land area , second behind Russia .

Example 496:
titleEntities: {'Subject': ['Philippines'], 'Date': ['2017']}
title: Philippines social media user penetration 2017 to 2023
X_Axis['Year']: ['2023', '2022', '2021', '2020', '2019', '2018', '2017']
Y_Axis['Share', 'of', 'population']: ['55', '54', '54', '53', '52', '51', '49']

gold: The social media penetration in the Philippines was at 49 percent in 2017 , amounting to about 54 million people using a social network in the Philippines as of 2018 . Considering that the number of internet users in the Philippines was at just under 70 million in that year , the social media penetration was projected to increase to 55 percent of the population by 2023 . Social media in the Philippines The Philippines are an archipelagic country , which poses logistical problems for social interaction and communication between residents from the various islands .
gold_template: The templateTitle[1] templateTitle[2] templateTitle[4] in the templateTitleSubject[0] was at templateYValue[min] templateScale in templateXValue[idxmin(Y)] , amounting to about templateYValue[1] templateScale people using a templateTitle[1] network in the templateTitleSubject[0] as of templateXValue[5] . Considering that the number of internet users in the templateTitleSubject[0] was at just under 70 templateScale in that templateXLabel[0] , the templateTitle[1] templateTitle[2] templateTitle[4] was projected to templatePositiveTrend to templateYValue[max] templateScale of the templateYLabel[1] by templateXValue[idxmax(Y)] . templateTitle[1] templateTitle[2] in the templateTitleSubject[0] The templateTitleSubject[0] are an archipelagic country , which poses logistical problems for templateTitle[1] interaction and communication between residents from the various islands .

generated_template: This statistic gives information on the templateTitle[1] templateTitle[3] rate in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , templateYValue[idxmin(X)] templateScale of the Indonesian templateYLabel[1] were using the templateTitle[1] . In templateXValue[max] , this figure is projected to grow to templateYValue[idxmax(X)] templateScale .
generated: This statistic gives information on the social user rate in Philippines from 2017 to 2023 .  In 2017 , 49 % of the Indonesian population were using the social .  In 2023 , this figure is projected to grow 55 % .

Example 497:
titleEntities: {'Subject': ['USA'], 'Date': ['1990', '2018']}
title: USA - reported burglary rate 1990 to 2018
X_Axis['Year']: ["'18", "'17", "'16", "'15", "'14", "'13", "'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92", "'91", "'90"]
Y_Axis['Rate', 'per', '100,000', 'population']: ['376.0', '429.7', '468.9', '494.7', '537.2', '610.5', '672.2', '701.3', '701.0', '717.7', '733.0', '726.1', '733.1', '726.9', '730.3', '741.0', '747.0', '740.8', '728.8', '770.4', '863.0', '919.6', '944.8', '987.1', '1042.0', '1099.2', '1168.2', '1252.0', '1235.9']

gold: This graph shows the reported burglary rate in the United States from 1990 to 2018 . In 2018 , the nationwide rate was 376 cases per 100,000 of the population .
gold_template: This graph shows the templateTitle[1] templateTitle[2] templateYLabel[0] in the country from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the nationwide templateYLabel[0] was templateYValue[min] cases templateYLabel[1] 100,000 of the templateYLabel[3] .

generated_template: This graph shows the templateTitle[1] templateTitle[2] and non-negligent templateTitle[4] templateYLabel[0] in the country from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , the nationwide templateYLabel[0] was templateYValue[0] cases templateYLabel[1] 100,000 of the templateYLabel[3] .
generated: This graph shows the reported burglary and non-negligent 1990 Rate in the country from 1990 to 2018 .  In 2018 , the nationwide Rate was 376.0 cases per 100,000 of the population .

Example 498:
titleEntities: {'Subject': ['U.S.'], 'Date': ['1990', '2018']}
title: Cotton price received by U.S. farmers 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1995', '1990']
Y_Axis['Price', 'per', 'pound', 'in', 'U.S.', 'cents']: ['86.85', '84.48', '64.7', '61.49', '74.9', '77.23', '79.5', '88.02', '70.95', '49.15', '60.79', '49.96', '47.53', '42.69', '54.3', '51.65', '33.63', '38.86', '49.81', '77.21', '64.83']

gold: This statistic shows the average cotton price per pound as received by U.S. farmers from 1990 to 2018 . In the 1990 calendar year , a U.S. cotton farmer received an average price of 64.83 cents per one pound of upland cotton .
gold_template: This statistic shows the average templateTitle[0] templateYLabel[0] templateYLabel[1] templateYLabel[2] as templateTitle[2] templateTitle[3] templateYLabel[3] templateTitle[5] from templateXValue[min] to templateXValue[max] . In the templateXValue[min] calendar templateXLabel[0] , a templateYLabel[3] templateTitle[0] farmer templateTitle[2] an average templateYLabel[0] of templateYValue[idxmin(X)] templateYLabel[4] templateYLabel[1] one templateYLabel[2] of upland templateTitle[0] .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] templateYLabel[0] templateYLabel[1] templateYLabel[2] in the templateTitle[4] from templateXValue[min] to templateXValue[max] . In calendar templateXLabel[0] templateXValue[1] , the average templateTitle[1] templateYLabel[0] templateYLabel[1] one templateYLabel[2] of templateTitle[0] templateTitle[2] was about templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the Cotton price received per pound in the U.S. from 1990 to 2018 .  In calendar Year 2017 , the average price per one pound of Cotton received was about 86.85 U.S. cents .

Example 499:
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: The statistic shows the templateYLabel[0] of templateYLabel[1] in templateTitleSubject[0] due to templateTitle[0] between templateXLabel[0] templateXValue[min] and templateXValue[max] . In templateXValue[max] , templateYValue[idxmax(X)] people were killed by terrorists in templateTitleSubject[0] . templateTitleSubject[0] suffered from 1,294 terrorist attacks that templateXLabel[0] .
generated: The statistic shows the Number of deaths in Spain due to Number between Year 2006 and 2018 .  In 2018 , 474523 people were killed by terrorists in Spain .  Spain suffered from 1,294 terrorist attacks that Year .

Example 500:
titleEntities: {'Subject': ['Winter Olympic Games'], 'Date': ['2014', '2014']}
title: Number of participants Winter Olympic Games 2014
X_Axis['Year']: ['2014_Sochi', '2010_Vancouver', '2006_Torino', '2002_Salt_Lake_City', '1998_Nagano', '1994_Lillehammer', '1992_Albertville', '1988_Calgary', '1984_Sarajevo', '1980_Lake_Placid', '1976_Innsbruck', '1972_Sapporo', '1968_Grenoble', '1964_Innsbruck', '1960_Squaw_Valley', "1956_Cortina_d'Ampezzo", '1952_Oslo', '1948_St._Moritz', '1936_Garmisch-Partenkirchen', '1932_Lake_Placid', '1928_St._Moritz', '1924_Chamonix']
Y_Axis['Number', 'of', 'participants']: ['2800', '2536', '2494', '2402', '2180', '1738', '1801', '1424', '1273', '1072', '1129', '1008', '1160', '1094', '665', '821', '694', '668', '668', '252', '461', '292']

gold: The statistic shows the number of participants in the Winter Olympic Games from 1924 to 2014 . At the first Olympic Winter Games in Chamonix in 1924 , 292 athletes participated . This figure grew to 2,536 participating athletes from 82 nations during the 2010 Vancouver Winter Olympics .
gold_template: The statistic shows the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] Games from templateXValue[last] to templateXValue[0] . At the first templateTitleSubject[0] Games in templateXValue[last] in templateXValue[last] , templateYValue[last] athletes participated . This figure templatePositiveTrend to templateYValue[1] participating athletes from 82 nations during the templateXValue[1] Winter Olympics .

generated_template: This statistic shows the templateTitle[0] of templateTitle[1] templateTitle[2] in templateTitleSubject[0] in selected years from templateXValue[min] to templateXValue[max] . In templateXValue[min] , German templateTitle[1] templateTitle[2] had a templateTitle[1] templateTitle[0] of roughly templateYValue[idxmin(X)] templateScale templateYLabel[1] . templateYValue[10] years later , templateTitle[0] figures amounted to less than templateYValue[8] templateScale templateYLabel[1] .
generated: This statistic shows the Number of participants Winter in Winter Olympic Games selected years from 1924 Chamonix to 2014 Sochi .  In 1924 Chamonix , German participants Winter had a participants Number of roughly 292 million participants .  1129 years later , Number figures amounted to less than 1273 thousand participants .

Example 501:
titleEntities: {'Subject': ['California'], 'Date': ['2000', '2018']}
title: California - real GDP 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Real', 'GDP', 'in', 'billion', 'U.S.', 'dollars']: ['2677.94', '2587.57', '2498.84', '2426.14', '2309.93', '2220.87', '2144.5', '2091.59', '2058.14', '2026.49', '2111.14', '2103.62', '2072.18', '1990.14', '1902.32', '1825.42', '1743.65', '1702.78', '1709.94']

gold: This statistic shows the development of California 's real GDP from 2000 to 2018 . In 2018 , the real GDP of California was 2.67 trillion U.S. dollars .
gold_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] was 2.67 templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the development of templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[1] of templateTitleSubject[0] was about templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic shows the development of California 's Real GDP from 2000 to 2018 .  In 2018 , the GDP of California was about 2677.94 billion U.S. dollars .

Example 502:
titleEntities: {'Subject': ['South Korea'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) in South Korea 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990', '1989', '1988', '1987', '1986', '1985', '1984']
Y_Axis['Gross', 'domestic', 'product', 'in', 'billion', 'U.S.', 'dollars']: ['1988.11', '1884.06', '1789.96', '1702.14', '1626.55', '1629.53', '1720.49', '1623.9', '1500.48', '1465.77', '1484.32', '1305.61', '1222.81', '1202.46', '1094.5', '901.94', '1002.22', '1122.68', '1011.8', '898.14', '764.88', '680.52', '609.02', '533.05', '561.63', '485.25', '374.24', '557.5', '598.1', '556.13', '455.61', '386.3', '350.05', '325.73', '279.35', '243.53', '196.97', '146.13', '115.54', '100.27', '96.6']

gold: The statistic shows gross domestic product ( GDP ) of South Korea from 1984 to 2018 , with projections up until 2024 . GDP or gross domestic product is the sum of all goods and services produced in a country in a year ; it is a strong indicator of economic strength . In 2018 , South Korea 's GDP was around 1.72 trillion U.S. dollars .
gold_template: The statistic shows templateYLabel[0] templateYLabel[1] templateYLabel[2] ( templateTitle[3] ) of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . templateTitle[3] or templateYLabel[0] templateYLabel[1] templateYLabel[2] is the sum of all goods and services produced in a country in a templateXLabel[0] ; it is a strong indicator of economic strength . In templateXValue[6] , templateTitleSubject[0] 's templateTitle[3] was around templateYValue[6] templateScale templateYLabel[4] templateYLabel[5] .

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

Example 503:
titleEntities: {'Subject': ['Iraq'], 'Date': ['2018']}
title: U.S. petroleum imports from Iraq 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Imports', 'in', 'thousand', 'barrels', 'per', 'day']: ['521', '604', '424', '229', '369', '341', '476', '459', '415', '450', '627', '484', '553', '531', '656', '481', '459', '795', '620']

gold: This statistic represents U.S. petroleum imports from Iraq between 2000 and 2018 . In 2018 , the United States imported an average of approximately 521,000 barrels of petroleum per day from the Middle Eastern country .
gold_template: This statistic represents templateTitle[0] templateTitle[1] templateYLabel[0] templateTitle[3] templateTitleSubject[0] between templateXValue[min] and templateXValue[max] . In templateXValue[max] , the templateTitle[0] imported an average of approximately templateYValue[0] templateYLabel[2] of templateTitle[1] templateYLabel[3] templateYLabel[4] templateTitle[3] the Middle Eastern country .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] of templateTitle[2] at templateTitle[3] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . Throughout this period there has been a clear trend of templateTitle[2] having children later in life , with the templateTitle[0] templateYLabel[1] of templateTitle[2] in the UK templatePositiveTrend from templateYValue[idxmin(X)] in templateXValue[idxmin(Y)] to templateYValue[idxmax(X)] by templateXValue[idxmax(Y)] .
generated: This statistic shows the Imports thousand of imports at from in the Iraq from 2000 to 2018 .  Throughout this period there has been a clear trend of imports having children later in life , with the U.S. thousand of imports in the UK increasing from 620 in 2015 to 521 by 2001 .

Example 504:
titleEntities: {'Subject': ['Cree'], 'Date': ['2015', '2019']}
title: Cree 's revenue 2015 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015']
Y_Axis['Net', 'revenue', 'in', 'million', 'U.S.', 'dollars']: ['1080.0', '924.9', '771.5', '727.5', '726.0']

gold: This statistic represents Cree 's revenue from the fiscal year of 2015 to the fiscal year of 2019 . In the fiscal year of 2019 , the LED technology company reported revenue of about 1.08 billion U.S. dollars .
gold_template: This statistic represents templateTitleSubject[0] templateTitle[1] templateYLabel[1] from the fiscal templateXLabel[0] of templateXValue[min] to the fiscal templateXLabel[0] of templateXValue[max] . In the fiscal templateXLabel[0] of templateXValue[max] , the LED technology company reported templateYLabel[1] of about templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] .

generated_template: This statistic presents the annual templateTitle[2] templateYLabel[1] of mobile messaging platform templateTitleSubject[0] . In templateXValue[max] , the chat app had generated 1.03 templateScale templateYLabel[3] templateYLabel[4] in templateTitle[2] losses , a slight improvement from 1.56 templateScale templateYLabel[3] templateYLabel[4] in losses in the preceding templateXLabel[0] .
generated: This 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 the preceding Year .

Example 505:
titleEntities: {'Subject': ['Malaysia'], 'Date': ['2018']}
title: Population growth in Malaysia 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Population', 'growth', 'compared', 'to', 'previous', 'year']: ['1.35', '1.36', '1.36', '1.34', '1.34', '1.37', '1.45', '1.56', '1.69', '1.82', '1.91']

gold: This statistic shows the population growth in Malaysia from 2008 to 2018 . In 2018 , Malaysia 's population increased by approximately 1.35 percent compared to the previous year .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] 's templateYLabel[0] templatePositiveTrend by approximately templateYValue[idxmax(X)] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the degree of templateTitle[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] and details the templateScale of the entire templateYLabel[2] , living in templateYLabel[1] areas . In templateXValue[max] , templateYValue[idxmax(X)] templateScale of the total templateYLabel[2] in templateTitleSubject[0] lived in cities . templateTitleSubject[0] 's rural exodus templateTitleSubject[0] is one of the largest countries in the world regarding land area , second behind Russia .
generated: The statistic shows the degree of Population in Malaysia from 2008 to 2018 and details the percentage of the entire compared , living in growth areas .  In 2018 , 1.35 % of the total compared in Malaysia lived in cities .  Malaysia 's rural exodus Malaysia is one of the largest countries in the world regarding land area , second behind Russia .

Example 506:
titleEntities: {'Subject': ['Nepal'], 'Date': ['2024']}
title: Total population of Nepal 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['30.36', '29.97', '29.59', '29.2', '28.83', '28.46', '28.09', '27.63', '27.26', '27.02', '26.91']

gold: This statistic represents the total population of Nepal from 2014 to 2015 , with projections up until 2024 . In 2018 , the estimated total population of Nepal amounted to around 28.09 million people .
gold_template: This statistic represents the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[9] , with projections up until templateXValue[max] . In templateXValue[6] , the estimated templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateScale people .

generated_template: This statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to approximately templateYValue[6] templateScale templateYLabel[0] .
generated: This statistic shows the Total population of Nepal from 2014 to 2018 , with projections up until 2024 .  In 2018 , the Total population of Nepal amounted to approximately 28.09 millions Inhabitants .

Example 507:
titleEntities: {'Subject': ['Minnesota Wilds'], 'Date': ['2005', '2019']}
title: Minnesota Wilds ' revenue 2005 to 2019
X_Axis['Year']: ['2018/19', '2017/18', '2016/17', '2015/16', '2014/15', '2013/14', '2012/13', '2011/12', '2010/11', '2009/10', '2008/09', '2007/08', '2006/07', '2005/06']
Y_Axis['Revenue', 'in', 'million', 'U.S.', 'dollars']: ['149', '142', '139', '136', '125', '111', '81', '99', '97', '92', '95', '94', '78', '71']

gold: This graph depicts the annual National Hockey League revenue of the Minnesota Wild from the 2005/06 season to the 2018/19 season . The revenue of the Minnesota Wild amounted to 149 million U.S. dollars in the 2018/19 season .
gold_template: This graph depicts the annual National Hockey League templateYLabel[0] of the templateTitleSubject[0] Wild from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] Wild amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .

generated_template: The statistic shows the templateYLabel[0] of the templateTitleSubject[0] from the templateXValue[last] season to the templateXValue[0] season . The templateYLabel[0] of the templateTitleSubject[0] amounted to templateYValue[max] templateScale templateYLabel[2] templateYLabel[3] in the templateXValue[idxmax(Y)] season .
generated: The statistic shows the Revenue of the Minnesota Wilds from the 2005/06 season to the 2018/19 season .  The Revenue of the Minnesota Wilds amounted to 149 million U.S. dollars in the 2018/19 season .

Example 508:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2000', '2018']}
title: New issue volume of U.S. asset-backed securities 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2010', '2005', '2000']
Y_Axis['Volume', 'in', 'billion', 'U.S.', 'dollars']: ['517', '550', '325', '333', '393', '126', '474', '240']

gold: This statistic presents the new issue volume of the asset-backed securities of the United States from 2000 to 2018 . In 2018 , the new issue volume of the asset-backed securities of the United States was 517 billion U.S. dollars .
gold_template: This statistic presents the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitle[4] templateTitle[5] of the templateTitle[3] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] templateYLabel[0] of the templateTitle[4] templateTitle[5] of the templateTitle[3] was templateYValue[0] templateYValue[idxmax(X)] templateYLabel[2] templateYLabel[3] .

generated_template: This statistic presents the templateYLabel[0] of templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[1] was approximately 0.99 templateScale 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 presents the Volume of 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 0.99 billion U.S. dollars .  The volume U.S. is an informal network of banks , brokers dealers and financial institutions which are linked electronically .

Example 509:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2011', '2017']}
title: Retail sales of the frame market for eyewear in the U.S. 2011 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011']
Y_Axis['Retail', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['9732.2', '9710.0', '9627.0', '9153.0', '8871.0', '8475.0', '8213.0']

gold: This statistic depicts the retail sales of the frame market for eyewear in the United States from 2011 to 2017 . In 2017 , the U.S. frame market for eyewear generated about 9.73 billion U.S. dollars in retail sales .
gold_template: This statistic depicts the templateYLabel[0] templateYLabel[1] of the templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] in the templateTitle[6] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[3] templateTitle[2] templateTitle[3] templateTitle[4] templateTitle[5] generated about templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] in templateYLabel[0] templateYLabel[1] .

generated_template: This statistic illustrates the templateYLabel[0] templateYLabel[1] of templateTitle[1] templateTitle[2] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateYLabel[0] templateTitle[1] templateTitle[2] reached approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: This statistic illustrates the Retail sales of frame sales in the U.S. from 2011 to 2017 .  In 2017 , Retail sales frame reached approximately 9732.2 million U.S. dollars .

Example 510:
titleEntities: {'Subject': ['Nepal'], 'Date': ['2008', '2018']}
title: Population density in Nepal 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['Inhabitants', 'per', 'square', 'kilometer']: ['195.94', '192.73', '190.17', '188.46', '187.7', '187.78', '188.28', '188.64', '188.44', '187.54', '186.02']

gold: The statistic shows the population density in Nepal from 2008 to 2018 . In 2018 , the population density in Nepal amounted to about 195.94 inhabitants per square kilometer .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[idxmax(X)] templateYLabel[0] templateYLabel[1] templateYLabel[2] templateYLabel[3] .

generated_template: 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 templateScale 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 511:
titleEntities: {'Subject': ['Vending'], 'Date': ['2010']}
title: Vending machines : sales volume of vended products 2010
X_Axis['Year']: ['1999', '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009', '2010']
Y_Axis['Vended', 'volume', '(in', 'billion', 'U.S.', 'dollars)']: ['36.6', '38.7', '41.0', '41.1', '42.2', '44.2', '46.0', '46.8', '47.5', '45.6', '42.9', '42.2']

gold: This graph depicts the total sales volume of products sold through vending machines in the U.S. from 1999 to 2010 . In 1999 , the sales volume was 36.6 billion U.S. dollars .
gold_template: This graph depicts the total templateTitle[2] templateYLabel[1] of templateTitle[5] sold through templateTitleSubject[0] templateTitle[1] in the templateYLabel[4] from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[2] templateYLabel[1] was templateYValue[idxmin(X)] templateScale templateYLabel[4] dollars .

generated_template: This statistic gives information on the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] in templateTitleDate[0] , broken down templateTitle[5] templateTitle[6] group . In templateTitleDate[0] , more than templateYValue[2] templateScale of Brazilian templateYLabel[1] templateYLabel[2] were between 18 and 19 years old .
generated: This statistic gives information on the Vended of volume (in in Vending 2010 , broken down products 2010 group .  In 2010 , more than 41.0 billion of Brazilian volume (in were between 18 and 19 years old .

Example 512:
titleEntities: {'Subject': ['Ohio'], 'Date': ['1990', '2018']}
title: Ohio - Median household income 1990 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999', '1998', '1997', '1996', '1995', '1994', '1993', '1992', '1991', '1990']
Y_Axis['Household', 'income', 'in', 'current', 'U.S.', 'dollars']: ['61633', '59768', '53985', '53301', '49644', '46398', '44375', '44648', '45886', '45879', '46934', '49099', '45900', '44203', '43055', '43520', '42684', '41785', '42962', '39489', '38925', '36134', '34070', '34941', '31855', '31285', '31404', '29790', '30013']

gold: This statistic shows the median household income in Ohio from 1990 to 2018 . In 2018 , the median household income in Ohio amounted to 61,633 U.S. dollars .
gold_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .

generated_template: This statistic shows the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitle[1] templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to templateYValue[idxmax(X)] templateYLabel[3] templateYLabel[4] .
generated: 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 513:
titleEntities: {'Subject': ['LEGO Group'], 'Date': ['2009', '2018']}
title: LEGO Group operating profit 2009 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Operating', 'profit', 'in', 'million', 'Euros']: ['1440.6', '1391.21', '1674.3', '1645.35', '1302.45', '1117.43', '1019.49', '762.19', '667.1', '389.5']

gold: This statistic shows the operating profit of the LEGO Group from 2009 to 2018 . In 2015 , the LEGO Group 's operating profit amounted to approximately 1.65 billion euros .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[3] , the templateTitleSubject[0] 's templateYLabel[0] templateYLabel[1] amounted to approximately templateYValue[3] templateScale templateYLabel[3] .

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , templateTitleSubject[0] ' templateYLabel[0] templateYLabel[1] amounted to around templateYValue[0] templateScale templateYLabel[3] . After spin-offs of other product segments , Koninklijke templateTitleSubject[0] N.V. today is a company focused on healthcare/medical technology .
generated: The statistic presents the Operating profit of LEGO Group from 2009 to 2018 .  In 2018 , LEGO Group ' Operating profit amounted to around 1440.6 million Euros .  After spin-offs of other product segments , Koninklijke LEGO Group N.V. today is a company focused on healthcare/medical technology .

Example 514:
titleEntities: {'Subject': ['China'], 'Date': ['2000', '2018']}
title: Direct investment position of the U.S. in China 2000 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000']
Y_Axis['Direct', 'investments', 'in', 'billion', 'U.S.', 'dollars']: ['116.52', '107.56', '97.29', '92.15', '82.24', '60.45', '54.51', '53.66', '59.0', '54.07', '53.93', '29.71', '26.46', '19.02', '17.62', '11.26', '10.57', '12.08', '11.14']

gold: This statistic shows the direct investment position of the United States in China from 2000 to 2018 , on a historical-cost basis . In 2018 , the U.S. investments made in China were valued at approximately 117 billion U.S. dollars . Direct investment position of the United States - additional information Foreign direct investment ( FDI ) , simply put , is an investment of one company into another company located in a different country .
gold_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitleSubject[0] were valued at approximately templateYValue[max] templateScale templateYLabel[3] templateYLabel[4] . templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] - additional information Foreign templateYLabel[0] templateTitle[1] ( FDI ) , simply put , is an templateTitle[1] of one company into another company located in a different country .

generated_template: This statistic shows the templateYLabel[0] templateTitle[1] templateTitle[2] of the templateTitle[3] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] , on a historical-cost basis . In templateXValue[max] , the templateYLabel[3] templateYLabel[1] made in templateTitleSubject[0] were valued at approximately templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] . templateYLabel[3] templateYLabel[0] templateTitle[1] abroad is defined as ownership by a templateYLabel[3] investor of at least templateYValue[12] templateScale of a foreign business .
generated: This statistic shows the Direct investment position of the U.S. in China from 2000 to 2018 , on a historical-cost basis .  In 2018 , the U.S. investments made in China were valued at approximately 116.52 billion U.S. dollars .  U.S. Direct investment abroad is defined as ownership by a U.S. investor of at least 26.46 billion of a foreign business .

Example 515:
titleEntities: {'Subject': ['Banco Santander'], 'Date': ['2012', '2019']}
title: Banco Santander : customer numbers globally 2012 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012']
Y_Axis['Number', 'of', 'customers', 'in', 'millions']: ['145.0', '144.0', '133.0', '125.0', '121.0', '117.0', '106.6', '102.1']

gold: Between 2018 and 2019 , the Banco Santander Group increased by one million customers worldwide . In 2019 , the Spanish giant had approximately196 thousand employees , across nearly12 thousand branches looking after the needs of its customers globally . As of 2019 , Banco Santander 's largest contributor to the company 's profit was Europe .
gold_template: Between templateXValue[1] and templateXValue[max] , the templateTitleSubject[0] Group templatePositiveTrend by one templateScale templateYLabel[1] worldwide . In templateXValue[max] , the Spanish giant had approximately196 thousand employees , across nearly12 thousand branches looking after the needs of its templateYLabel[1] templateTitle[4] . As of templateXValue[max] , templateTitleSubject[0] 's largest contributor to the company 's profit was Europe .

generated_template: The statistic presents the templateYLabel[0] of templateTitle[0] templateYLabel[1] in the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[1] , templateTitle[0] templateTitleSubject[0] Championship , which took place in various cities across France , Germany , and Spain , had a templateYLabel[0] templateYLabel[1] of templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] , down from templateYValue[max] templateScale a templateXLabel[0] earlier .
generated: The statistic presents the Number of Banco customers in the Banco Santander from 2012 to 2019 .  In 2018 , Banco Banco Santander Championship , which took place in various cities across France , Germany and Spain , had a Number customers of 145.0 millions , down from 145.0 millions a Year earlier .

Example 516:
titleEntities: {'Subject': ['Guatemala'], 'Date': ['2019']}
title: Unemployment rate in Guatemala 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['2.75', '2.73', '2.68', '2.83', '2.51', '2.72', '3.02', '2.77', '4.13', '3.5', '3.31', '2.84', '2.8', '2.89', '2.99', '2.97', '2.81', '2.85', '2.78', '2.9', '2.92']

gold: This statistic shows the unemployment rate in Guatemala from 1999 to 2019 . In 2019 , the unemployment rate in Guatemala was 2.75 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was templateYValue[idxmax(X)] templateScale .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in Guatemala from 1999 to 2019 .  In 2019 , the Unemployment rate in Guatemala was at approximately 2.75 % .

Example 517:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2001', '2018']}
title: Unemployment in U.S. motion picture and recording industries 2001 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001']
Y_Axis['Unemployment', 'rate']: ['5.5', '6.2', '7.3', '5.6', '7.1', '9.3', '12.9', '10.7', '13', '13.8', '9', '6.9', '5.9', '8.5', '8.7', '11.2', '10.3', '9.2']

gold: The statistic above presents the yearly unemployment rate for the U.S. motion picture and sound recording industry from 2001 to 2018 . In this industry , 5.5 percent of all private wage and salary workers were unemployed in 2018 .
gold_template: The statistic above presents the yearly templateYLabel[0] templateYLabel[1] for the templateTitleSubject[0] templateTitle[2] templateTitle[3] and sound templateTitle[4] industry from templateXValue[min] to templateXValue[max] . In this industry , templateYValue[idxmax(X)] templateScale of all private wage and salary workers were unemployed in templateXValue[idxmin(Y)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in U.S. from 2001 to 2018 .  In 2018 , the Unemployment rate in U.S. was at approximately 5.5 % .

Example 518:
titleEntities: {'Subject': ['El Salvador'], 'Date': ['2019']}
title: Unemployment rate in El Salvador 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008', '2007', '2006', '2005', '2004', '2003', '2002', '2001', '2000', '1999']
Y_Axis['Unemployment', 'rate']: ['4.35', '4.39', '4.39', '4.42', '4', '4.16', '3.69', '3.85', '4.3', '4.89', '7.33', '5.88', '6.41', '6.57', '7.22', '6.05', '6.26', '5.73', '6.96', '6.96', '6.68']

gold: This statistic shows the unemployment rate in El Salvador from 1999 to 2019 . In 2019 , the unemployment rate in El Salvador amounted to approximately 4.35 percent .
gold_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to approximately templateYValue[0] templateYValue[idxmax(X)] .

generated_template: This statistic shows the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] was at approximately templateYValue[idxmax(X)] templateScale .
generated: This statistic shows the Unemployment rate in El Salvador from 1999 to 2019 .  In 2019 , the Unemployment rate in El Salvador was at approximately 4.35 % .

Example 519:
titleEntities: {'Subject': ['Canada'], 'Date': ['2024']}
title: Total population in Canada 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['Inhabitants', 'in', 'millions']: ['39.22', '38.87', '38.52', '38.17', '37.81', '37.46', '36.99', '36.49', '36.05', '35.68', '35.39']

gold: The statistic shows the total population in Canada from 2014 to 2018 , with projections up until 2024 . In 2018 , the total population in Canada amounted to about 36.99 million inhabitants . Population of Canada Canada ranks second among the largest countries in the world in terms of area size , right behind Russia , despite having a relatively low total population .
gold_template: The statistic shows the templateTitle[0] templateTitle[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[0] . templateTitle[1] of templateTitleSubject[0] ranks second among the largest countries in the world in terms of area size , right behind Russia , despite having a relatively low templateTitle[0] templateTitle[1] .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the templateTitle[0] templateTitle[1] of templateTitleSubject[0] amounted to around templateYValue[6] templateScale templateYLabel[0] . See the figures for the templateTitle[1] of South Korea for comparison .
generated: The 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 around 36.99 millions Inhabitants .  See the figures for the population of South Korea for comparison .

Example 520:
titleEntities: {'Subject': ['Norway'], 'Date': ['2009', '2019']}
title: Population in Norway 2009 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009']
Y_Axis['Inhabitants', 'in', 'millions']: ['5.33', '5.3', '5.26', '5.21', '5.17', '5.12', '5.05', '4.99', '4.92', '4.86', '4.8']

gold: This statistic shows the population in Norway over the years from 2009 to 2019 . In 2009 , the population of Norway was around 4.8 million people . In 2019 , the number of inhabitants increased to around 5.33 million .
gold_template: This statistic shows the templateTitle[0] in templateTitleSubject[0] over the years from templateXValue[min] to templateXValue[max] . In templateXValue[min] , the templateTitle[0] of templateTitleSubject[0] was around templateYValue[idxmin(X)] templateScale people . In templateXValue[max] , the number of templateYLabel[0] templatePositiveTrend to around templateYValue[idxmax(X)] templateScale .

generated_template: The statistic shows the templateTitle[0] templateTitle[1] of the templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . In the course of this ten-year period , the templateTitle[1] of the municipality The templateTitleSubject[0] templatePositiveTrend by over 55,000 templateYLabel[1] . In templateXValue[min] , The templateTitleSubject[0] had almost 482,000 templateYLabel[1] .
generated: The statistic shows the Population Norway of the Norway from 2009 to 2019 .  In the course of this ten-year period , the Norway of the municipality Norway increased by over 55,000 millions .  In 2009 , The Norway had almost 482,000 millions .

Example 521:
titleEntities: {'Subject': ['Bolivia'], 'Date': ['2024']}
title: Inflation rate in Bolivia 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', '4.48', '4.29', '3.97', '3.09', '1.73', '2.27', '2.82', '3.63', '4.06', '5.77', '5.74', '4.52', '9.88', '2.5', '3.35', '14.01', '6.66', '4.3', '5.4', '4.44', '3.34', '0.92', '1.6', '4.6', '2.16', '7.67', '4.71', '12.43', '10.19', '7.88', '8.53', '12.96', '21.44', '17.12', '15.17', '16', '14.58', '273.35', '11749.63', '1281.35']

gold: This statistic shows the average inflation rate in Bolivia from 1984 to 2018 , with projections up until 2024 . In 2018 , the average inflation rate in Bolivia amounted to about 2.27 percent compared to the previous year .
gold_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: This statistic shows the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , the average templateYLabel[0] templateYLabel[1] in templateTitleSubject[0] amounted to about templateYValue[6] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: This statistic shows the average Inflation rate in Bolivia from 1984 to 2018 , with projections up until 2024 .  In 2018 , the average Inflation rate in Bolivia amounted to about 2.27 % compared to the previous Year .

Example 522:
titleEntities: {'Subject': ['Spain'], 'Date': ['2010', '2018']}
title: External net migration in Spain 2010 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Net', 'migration']: ['334158', '163272', '87422', '-1761', '-94976', '-251531', '-142552', '-37698', '-42675']

gold: This statistic displays the external net migration figures in Spain from 2010 to 2018 . During the first years of the period under consideration , the net migration balance with countries other than Spain resulted in negative figures . This trend , however , terminated in 2016 , year in which Spain received more newcomers than people leaving the country .
gold_template: This statistic displays the templateTitle[0] templateYLabel[0] templateYLabel[1] figures in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . During the first years of the period under consideration , the templateYLabel[0] templateYLabel[1] balance with countries other than templateTitleSubject[0] resulted in negative figures . This trend , however , terminated in templateXValue[2] , templateXLabel[0] in which templateTitleSubject[0] received more newcomers than people leaving the country .

generated_template: The statistic presents the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[1] templateTitle[2] , a franchise of the National Football League , templateTitle[4] templateXValue[min] to templateXValue[max] . In the templateXValue[max] season , the templateYLabel[0] templateYLabel[1] of the templateTitleSubject[1] templateTitle[2] were at templateYValue[idxmax(X)] templateScale templateYLabel[3] templateYLabel[4] .
generated: The statistic presents the Net migration of the Spain migration , a franchise of the National Football League , 2010 to 2018 .  In the 2018 season , the Net migration of the Spain migration were at 334158 % migration .

Example 523:
titleEntities: {'Subject': ['United States'], 'Date': ['1987', '2012']}
title: Number of billionaires in the United States 1987 to 2012
X_Axis['Year']: ["'12", "'11", "'10", "'09", "'08", "'07", "'06", "'05", "'04", "'03", "'02", "'01", "'00", "'99", "'98", "'97", "'96", "'95", "'94", "'93", "'92", "'91", "'90", "'89", "'88", "'87"]
Y_Axis['Number', 'of', 'billionaires']: ['425', '413', '403', '359', '469', '415', '371', '341', '274', '222', '243', '272', '49', '49', '70', '60', '149', '129', '120', '108', '101', '96', '99', '55', '68', '41']

gold: This graph shows the number of billionaires in the United States from 1987 to 2012 . The number of billionaires in the U.S. has increased tenfold during this time , from 41 billionaires in 1987 to 425 billionaires in 2012 .
gold_template: This graph shows the templateYLabel[0] of templateYLabel[1] in the templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . The templateYLabel[0] of templateYLabel[1] in the templateTitle[3] has templatePositiveTrend tenfold during this time , from templateYValue[min] templateYLabel[1] in templateTitleDate[min] to templateYValue[0] templateYLabel[1] in templateTitleDate[max] .

generated_template: The statistic shows the total templateYLabel[0] templateTitle[1] of templateTitle[2] templateTitle[3] templateTitle[4] located in templateTitleSubject[0] from templateTitleDate[min] to templateTitleDate[max] . In templateTitleDate[max] , templateYLabel[1] templateTitle[0] , templateTitle[1] , and templateTitle[2] templateTitle[3] templateTitle[4] amounted to about templateYValue[0] templateScale 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 templateNegativeTrend 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: The statistic shows the total Number billionaires of United States 1987 located in United States from 1987 to 2012 .  In 2012 , billionaires Number , billionaires and United States 1987 amounted to about 425 million billionaires dollars.Additional information on the billionaires and gifts market in the USThe statistic above shows how 1987 in the Number , billionaires and United sector fell following the market crash in 2008 , as did retail in the 2012 overall .  A comparison of Number 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 524:
titleEntities: {'Subject': ['Norway'], 'Date': ['2008', '2018']}
title: Gross domestic product ( GDP ) at current prices in Norway 2008 to 2018
X_Axis['Year']: ['2018', '2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010', '2009', '2008']
Y_Axis['GDP', 'in', 'billion', 'NOK']: ['3531', '3295', '3098', '3111', '3141', '3071', '2964', '2793', '2591', '2428', '2607']

gold: This statistic shows the gross domestic product ( GDP ) in Norway from 2008 to 2018 . The Norwegian GDP has grown significantly in this time period from 2,607 billion Norwegian kroner in 2008 to 3,531 billion kroner in 2018 .
gold_template: This statistic shows the templateTitle[0] templateTitle[1] templateTitle[2] ( templateYLabel[0] ) in templateTitleSubject[0] from templateXValue[min] to templateXValue[max] . The Norwegian templateYLabel[0] has grown significantly in this time period from templateYValue[idxmin(X)] templateScale Norwegian kroner in templateXValue[min] to templateYValue[idxmax(X)] templateScale kroner in templateXValue[idxmax(Y)] .

generated_template: The templateYLabel[0] templateYLabel[1] of templateTitle[0] templateTitle[1] manufactured in the templateTitleSubject[1] was approximately 40 thousand tons in templateXValue[max] , an templatePositiveTrend 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 templateNegativeTrend by just under eighty templateScale , 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 GDP billion of Gross domestic manufactured in the Norway was approximately 40 thousand tons in 2018 , an increase of over eight thousand tons from 2017 .  This constitutes a recovery of the production billion , which collapsed between 2008 and 2011 falling by just under eighty billion , before it stagnated in the early 2010s .  A stagnating market ? The corresponding developments in GDP values give the same picture as above .

Example 525:
titleEntities: {'Subject': ['Spotify'], 'Date': ['2012', '2015']}
title: Spotify artist royalty payments 2012 to 2015
X_Axis['Year']: ['2015', '2014', '2013', '2012']
Y_Axis['Costs', 'in', 'million', 'euros']: ['1633.29', '882.46', '602.92', '376.39']

gold: This statistic presents the cost of Spotify 's royalty fees and distribution costs payable to music artists and license holders from 2012 to 2015 . In the most recently reported year , the streaming platform paid out 1.62 billion euros in royalty fees . Further information With more than 100 million monthly active users worldwide , Spotify is one of the most popular music streaming platforms .
gold_template: This statistic presents the cost of templateTitleSubject[0] 's templateTitle[2] fees and distribution templateYLabel[0] payable to music artists and license holders from templateXValue[min] to templateXValue[max] . In the most recently reported templateXLabel[0] , the streaming platform paid out 1.62 templateScale templateYLabel[2] in templateTitle[2] fees . Further information With more than 100 templateScale monthly active users worldwide , templateTitleSubject[0] is one of the most popular music streaming platforms .

generated_template: The statistic presents the templateYLabel[0] of templateYLabel[1] templateYLabel[2] in the templateTitle[3] from templateXValue[min] to templateXValue[max] . A declining tendency could be observed as the templateYLabel[0] of templateYLabel[1] templateYLabel[2] templateNegativeTrend from templateYValue[idxmin(X)] in templateXValue[idxmax(Y)] to 6,146 in templateXValue[max] .
generated: The statistic presents the Costs of million euros in the payments from 2012 to 2015 .  A declining tendency could be observed as the Costs of million euros fell from 376.39 in 2015 to 6,146 in 2015 .

Example 526:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2010', '2017']}
title: Retail sales of the lens market for eyewear in the U.S. 2010 to 2017
X_Axis['Year']: ['2017', '2016', '2015', '2014', '2013', '2012', '2011', '2010']
Y_Axis['Retail', 'sales', 'in', 'million', 'U.S.', 'dollars']: ['12658.9', '12820.3', '12622.3', '11692.7', '11137.5', '10410.0', '9551.4', '9438.2']

gold: This statistic depicts the retail sales of the lens market for eyewear in the United States from 2010 to 2017 . In 2017 , the U.S. lens market for eyewear generated about 12.7 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[0] templateScale templateYLabel[3] templateYLabel[4] in templateYLabel[0] templateYLabel[1] .

generated_template: In templateXValue[max] , the templateTitle[1] templateYLabel[0] of templateTitle[3] templateTitle[4] in templateTitleSubject[0] amounted to about templateYValue[idxmax(X)] 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 templateNegativeTrend to below templateYValue[0] templateScale 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 sales Retail of market for in U.S. amounted to about 12658.9 sales million U.S. , all types included .  The Retail realized of market for remained fairly steady throughout the years until 2014 , when it dropped to below 12658.9 million U.S. market for The Retail serves as an indicator for a variety of different selling prices on the for market , gathering all Retail ranges of market wines purchased in U.S. .

Example 527:
titleEntities: {'Subject': ['Malawi'], 'Date': ['2019']}
title: Youth unemployment rate in Malawi 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']: ['7.07', '7.16', '7.21', '7.57', '7.66', '7.75', '7.82', '8.2', '8.47', '8.4', '8.55', '8.23', '8.63', '9.63', '10.73', '11.18', '11.4', '11.58', '11.73', '11.85', '11.87']

gold: The statistic shows the youth unemployment rate in Malawi from 1999 and 2019 . According to the source , the data are ILO estimates . In 2019 , the estimated youth unemployment rate in Malawi was at 7.07 percent .
gold_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .

generated_template: The statistic shows the templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] from templateXValue[min] and templateXValue[max] . According to the source , the data are ILO estimates . In templateXValue[max] , the estimated templateYLabel[0] templateYLabel[1] templateYLabel[2] in templateTitleSubject[0] was at templateYValue[idxmax(X)] templateScale .
generated: The statistic shows the Youth unemployment rate in Malawi from 1999 and 2019 .  According to the source , the data are ILO estimates .  In 2019 , the estimated Youth unemployment rate in Malawi was at 7.07 % .

Example 528:
titleEntities: {'Subject': ['ETFs U.S.'], 'Date': ['2002', '2018']}
title: Total net assets of ETFs in the U.S. 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['TNA', 'in', 'billion', 'U.S.', 'dollars']: ['3371', '3401', '2524', '2101', '1975', '1675', '1337', '1048', '992', '777', '531', '608', '423', '301', '228', '151', '102']

gold: The statistic presents the total net assets of Exchange Traded Funds in the United States from 2002 to 2018 . The total net assets of the U.S. ETFs amounted to approximately 3.4 trillion U.S. dollars in 2018 .
gold_template: The statistic presents the templateTitle[0] templateTitle[1] templateTitle[2] of Exchange Traded Funds in the templateTitle[4] from templateXValue[min] to templateXValue[max] . The templateTitle[0] templateTitle[1] templateTitle[2] of the templateYLabel[2] templateTitleSubject[0] amounted to approximately templateYValue[0] templateScale templateYLabel[2] templateYLabel[3] in templateXValue[max] .

generated_template: In templateXValue[max] , there were templateYValue[idxmax(X)] templateTitle[1] templateTitle[2] templateYLabel[1] in the templateTitle[4] . The FDIC , of Federal Deposit Insurance Corporation , is an agency that insures the banking system in the templateTitle[4] . The templateYLabel[0] of such registered templateYLabel[1] has been declining since templateXValue[min] , when it there were over 7,800 templateTitle[1] templateYLabel[1] in the country .
generated: In 2018 , there were 3371 net assets billion in the U.S. The FDIC , of Federal Deposit Insurance Corporation , is an agency that insures the banking system in the U.S. The TNA of such registered billion has been declining since 2002 , when it there were over 7,800 net billion in the country .

Example 529:
titleEntities: {'Subject': ['U.S.'], 'Date': ['2015', '2019']}
title: Zinc production U.S. 2015 to 2019
X_Axis['Year']: ['2019', '2018', '2017', '2016', '2015']
Y_Axis['Production', 'in', 'thousand', 'metric', 'tons']: ['780', '824', '774', '805', '825']

gold: The statistic represents the mine production of zinc in the United States from 2015 to 2019 . In 2019 , the U.S. produced some 780,000 metric tons of zinc .
gold_template: The statistic represents the mine templateYLabel[0] of templateTitle[0] in the templateTitle[2] from templateXValue[min] to templateXValue[max] . In templateXValue[max] , the templateTitleSubject[0] produced some templateYValue[0] templateYLabel[2] templateYLabel[3] of templateTitle[0] .

generated_template: This statistic depicts 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 templatePositiveTrend by templateYValue[max] templateScale . Appreciation of the templateTitleSubject[0] dollar is likely to reduce costs and templatePositiveTrend production in the chemical industry .
generated: This statistic depicts a Zinc of the U.S. and 2015 2019 thousand in the production metric 2015 to 2019 .  metric 2017 to 2018 , the U.S. and 2015 2019 thousand is expected to increase by 825 thousand .  Appreciation of the U.S. dollar is likely to reduce costs and increase production in the chemical industry .

Example 530:
titleEntities: {'Subject': ['Latvia'], 'Date': ['2024']}
title: Gross domestic product ( GDP ) growth rate in Latvia 2024
X_Axis['Year']: ['2024', '2023', '2022', '2021', '2020', '2019', '2018', '2017', '2016', '2015', '2014']
Y_Axis['GDP', 'growth', 'compared', 'to', 'previous', 'year']: ['3.01', '3.02', '3.02', '2.9', '2.79', '2.84', '4.77', '4.65', '2.06', '2.97', '1.86']

gold: The statistic shows the growth in real GDP in Latvia from 2014 to 2018 , with projections up until 2024 . In 2018 , Latvia 's real gross domestic product grew by around 4.77 percent compared to the previous year .
gold_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] templatePositiveTrend by around templateYValue[max] templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .

generated_template: The statistic shows the templateYLabel[1] in real templateYLabel[0] in templateTitleSubject[0] from between templateXValue[min] to templateXValue[6] , with projections up until templateXValue[max] . In templateXValue[6] , templateTitleSubject[0] 's real templateTitle[0] templateTitle[1] templateTitle[2] had templateNegativeTrend by around 2.48 templateScale templateYLabel[2] to the templateYLabel[3] templateXLabel[0] .
generated: The statistic shows the growth in real GDP in Latvia from between 2014 to 2018 , with projections up until 2024 .  In 2018 , Latvia 's real Gross domestic product had decreased by around 2.48 % compared to the previous Year .

