niryuu/sni-each-converted huggingface dataset:
select the rows whose country record fuzzily matches to south africa . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; country ; south africa } } ; 4 }
select the rows whose team record fuzzily matches to oklahoma city . select the row whose date record of these rows is 2nd minimum . the location attendance record of this row is toyota center 15316 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; team ; oklahoma city } ; date ; 2 } ; location attendance } ; toyota center 15316 }
select the rows whose date record fuzzily matches to apr 2003 . the sum of the time record of these rows is 3:00 .
Output: round_eq { sum { filter_eq { all_rows ; date ; apr 2003 } ; time } ; 3:00 }
select the row whose height record of all rows is 2nd maximum . the player record of this row is argo meresaar ( c ) .
Output: eq { hop { nth_argmax { all_rows ; height ; 2 } ; player } ; argo meresaar ( c ) }
select the row whose total record of all rows is maximum . the nation record of this row is germany .
Output: eq { hop { argmax { all_rows ; total } ; nation } ; germany }
for the 1890 census records of all rows , most of them fuzzily match to na .
Output: most_eq { all_rows ; 1890 census ; na }
select the rows whose player record fuzzily matches to patrick collins . take the pick record of this row . select the rows whose player record fuzzily matches to sterling sharpe . take the pick record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; patrick collins } ; pick } ; hop { filter_eq { all_rows ; player ; sterling sharpe } ; pick } }
select the rows whose winner / nominee ( s ) record fuzzily matches to dev patel . the number of such rows is 1 .
Output: eq { count { filter_eq { all_rows ; winner / nominee ( s ) ; dev patel } } ; 1 }
for the result records of all rows , most of them fuzzily match to not nominated .
Output: most_eq { all_rows ; result ; not nominated }
select the rows whose candidate record fuzzily matches to kalevi kivistö . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; candidate ; kalevi kivistö } } ; 2 }
select the row whose % of votes khuzestan record of all rows is maximum . the candidates record of this row is mehdi karroubi .
Output: eq { hop { argmax { all_rows ; % of votes khuzestan } ; candidates } ; mehdi karroubi }
select the rows whose location in park record fuzzily matches to baja ridge . select the row whose year first opened record of these rows is 1st minimum . the current name record of this row is revolution .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; location in park ; baja ridge } ; year first opened ; 1 } ; current name } ; revolution }
for the weight records of all rows , most of them are less than 200 .
Output: most_less { all_rows ; weight ; 200 }
select the rows whose difference record is less than 0 . the number of such rows is 4 .
Output: eq { count { filter_less { all_rows ; difference ; 0 } } ; 4 }
select the rows whose description record fuzzily matches to heavy - lift helicopter . among these rows , select the rows whose total disk area record is greater than 8000 . the number of such rows is 1 .
Output: eq { count { filter_greater { filter_eq { all_rows ; description ; heavy - lift helicopter } ; total disk area ; 8000 } } ; 1 }
select the rows whose ceased to be duchess record fuzzily matches to husband 's execution . there is only one such row in the table . the name record of this unqiue row is louise marie adélaïde de bourbon .
Output: and { only { filter_eq { all_rows ; ceased to be duchess ; husband 's execution } } ; eq { hop { filter_eq { all_rows ; ceased to be duchess ; husband 's execution } ; name } ; louise marie adélaïde de bourbon } }
for the implementation records of all rows , most of them fuzzily match to software .
Output: most_eq { all_rows ; implementation ; software }
select the rows whose winner record fuzzily matches to team mad madison heights mi . there is only one such row in the table . the year record of this unqiue row is 1997 details .
Output: and { only { filter_eq { all_rows ; winner ; team mad madison heights mi } } ; eq { hop { filter_eq { all_rows ; winner ; team mad madison heights mi } ; year } ; 1997 details } }
for the first game records of all rows , most of them are equal to 1991 .
Output: most_eq { all_rows ; first game ; 1991 }
select the rows whose date record fuzzily matches to 12 june 1937 . for the crowd records of these rows , most of them are less than 20000 .
Output: most_less { filter_eq { all_rows ; date ; 12 june 1937 } ; crowd ; 20000 }
select the rows whose country record fuzzily matches to united states . for the events records of these rows , most of them are greater than 21 .
Output: most_greater { filter_eq { all_rows ; country ; united states } ; events ; 21 }
select the rows whose country of release record fuzzily matches to argentina . there is only one such row in the table . the title record of this unqiue row is demasiado candente .
Output: and { only { filter_eq { all_rows ; country of release ; argentina } } ; eq { hop { filter_eq { all_rows ; country of release ; argentina } ; title } ; demasiado candente } }
for the width records of all rows , all of them are equal to 96 .
Output: all_eq { all_rows ; width ; 96 }
select the row whose lost record of all rows is 2nd maximum . the club record of this row is tycroes rfc .
Output: eq { hop { nth_argmax { all_rows ; lost ; 2 } ; club } ; tycroes rfc }
select the row whose nor 1 record of all rows is 3rd maximum . the driver record of this row is jens klingmann .
Output: eq { hop { nth_argmax { all_rows ; nor 1 ; 3 } ; driver } ; jens klingmann }
select the rows whose first season of current spell in segunda división record is equal to 2013 . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; first season of current spell in segunda división ; 2013 } } ; 7 }
select the rows whose population ( 1991 ) record is greater than 15,000 . the number of such rows is 4 .
Output: eq { count { filter_greater { all_rows ; population ( 1991 ) ; 15,000 } } ; 4 }
select the rows whose place record fuzzily matches to 3rd . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; place ; 3rd } } ; 5 }
the average of the home team score record of all rows is 10 .
Output: round_eq { avg { all_rows ; home team score } ; 10 }
select the row whose broadcast date record of all rows is 3rd minimum . the translation of title record of this row is hold out until you hit the wall .
Output: eq { hop { nth_argmin { all_rows ; broadcast date ; 3 } ; translation of title } ; hold out until you hit the wall }
select the rows whose runs record is greater than 300 . the number of such rows is 7 .
Output: eq { count { filter_greater { all_rows ; runs ; 300 } } ; 7 }
select the rows whose runner - up record fuzzily matches to corina morariu . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; runner - up ; corina morariu } } ; 2 }
select the rows whose ihsaa football class record fuzzily matches to aaaa . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; ihsaa football class ; aaaa } } ; 6 }
select the row whose televotes record of all rows is maximum . the performer record of this row is vlado janevski .
Output: eq { hop { argmax { all_rows ; televotes } ; performer } ; vlado janevski }
select the rows whose high points record fuzzily matches to nolan . the average of the high points record of these rows is 25.25 .
Output: round_eq { avg { filter_eq { all_rows ; high points ; nolan } ; high points } ; 25.25 }
select the row whose place record of all rows is minimum . the player record of this row is davis love iii .
Output: eq { hop { argmin { all_rows ; place } ; player } ; davis love iii }
the average of the score record of all rows is 136.82 .
Output: round_eq { avg { all_rows ; score } ; 136.82 }
select the rows whose year record is equal to 2010 . among these rows , select the rows whose recipient record fuzzily matches to the suite life on deck . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; year ; 2010 } ; recipient ; the suite life on deck } } ; 2 }
the average of the total record of all rows is 27.42 .
Output: round_eq { avg { all_rows ; total } ; 27.42 }
select the rows whose home team record fuzzily matches to geelong . take the home team score record of this row . select the rows whose home team record fuzzily matches to richmond . take the home team score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; home team ; geelong } ; home team score } ; hop { filter_eq { all_rows ; home team ; richmond } ; home team score } }
for the laps led records of all rows , most of them are equal to 0 .
Output: most_eq { all_rows ; laps led ; 0 }
select the rows whose timeslot record fuzzily matches to live 3p-6p . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; timeslot ; live 3p-6p } } ; 5 }
select the rows whose opponent record fuzzily matches to andrew chappelle . take the round record of this row . select the rows whose opponent record fuzzily matches to danny payne . take the round record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; andrew chappelle } ; round } ; hop { filter_eq { all_rows ; opponent ; danny payne } ; round } }
select the rows whose constituency record fuzzily matches to south west devon . for the electorate records of these rows , most of them are greater than 9000 .
Output: most_greater { filter_eq { all_rows ; constituency ; south west devon } ; electorate ; 9000 }
the 3rd maximum date record of all rows is september 18 , 2005 .
Output: eq { nth_max { all_rows ; date ; 3 } ; september 18 , 2005 }
the average of the score record of all rows is 2.3 .
Output: round_eq { avg { all_rows ; score } ; 2.3 }
select the rows whose performer record fuzzily matches to kaliopi . take the rank record of this row . select the rows whose performer record fuzzily matches to marjan necak . take the rank record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; performer ; kaliopi } ; rank } ; hop { filter_eq { all_rows ; performer ; marjan necak } ; rank } }
the sum of the prize ( k ) record of all rows is 1920 .
Output: round_eq { sum { all_rows ; prize ( k ) } ; 1920 }
select the rows whose governorate record fuzzily matches to piotrków governorate . take the population , in thousands , ( 1905 ) record of this row . select the rows whose governorate record fuzzily matches to płock governorate . take the population , in thousands , ( 1905 ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; governorate ; piotrków governorate } ; population , in thousands , ( 1905 ) } ; hop { filter_eq { all_rows ; governorate ; płock governorate } ; population , in thousands , ( 1905 ) } }
select the rows whose player record fuzzily matches to alvan adams . take the from record of this row . select the rows whose player record fuzzily matches to rafael addison . take the from record of this row . the second record is 11 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; player ; alvan adams } ; from } ; hop { filter_eq { all_rows ; player ; rafael addison } ; from } } ; -11 }
select the rows whose high assists record fuzzily matches to earl watson . the number of such rows is 13 .
Output: eq { count { filter_eq { all_rows ; high assists ; earl watson } } ; 13 }
select the rows whose episode record fuzzily matches to episode 4 . take the viewers ( millions ) record of this row . select the rows whose episode record fuzzily matches to episode 2 . take the viewers ( millions ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; episode ; episode 4 } ; viewers ( millions ) } ; hop { filter_eq { all_rows ; episode ; episode 2 } ; viewers ( millions ) } }
select the rows whose venue record fuzzily matches to central . among these rows , select the rows whose capacity record is greater than 5000 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_eq { all_rows ; venue ; central } ; capacity ; 5000 } } ; 2 }
the maximum weight record of all rows is 247 . the name record of the row with superlative weight record is matt kleinmann .
Output: and { eq { max { all_rows ; weight } ; 247 } ; eq { hop { argmax { all_rows ; weight } ; name } ; matt kleinmann } }
for the platform ( s ) records of all rows , most of them fuzzily match to playstation 3 .
Output: most_eq { all_rows ; platform ( s ) ; playstation 3 }
select the rows whose party record fuzzily matches to democratic . there is only one such row in the table . the incumbent record of this unqiue row is robert cramer .
Output: and { only { filter_eq { all_rows ; party ; democratic } } ; eq { hop { filter_eq { all_rows ; party ; democratic } ; incumbent } ; robert cramer } }
select the rows whose bronze record is equal to 1 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; bronze ; 1 } } ; 4 }
select the row whose enrollment ( 2010 ) record of all rows is 2nd maximum . the school record of this row is heritage .
Output: eq { hop { nth_argmax { all_rows ; enrollment ( 2010 ) ; 2 } ; school } ; heritage }
select the rows whose first elected record fuzzily matches to 197 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; first elected ; 197 } } ; 3 }
select the rows whose party record is arbitrary . the number of such rows is 6 .
Output: eq { count { filter_all { all_rows ; party } } ; 6 }
the average of the total record of all rows is 19.71 .
Output: round_eq { avg { all_rows ; total } ; 19.71 }
select the rows whose us viewers ( in millions ) record is greater than 10 . the number of such rows is 2 .
Output: eq { count { filter_greater { all_rows ; us viewers ( in millions ) ; 10 } } ; 2 }
select the row whose us viewers ( million ) record of all rows is 3rd maximum . the title record of this row is blood and treasure .
Output: eq { hop { nth_argmax { all_rows ; us viewers ( million ) ; 3 } ; title } ; blood and treasure }
for the object type records of all rows , most of them fuzzily match to globular cluster .
Output: most_eq { all_rows ; object type ; globular cluster }
select the rows whose title record fuzzily matches to ( part . among these rows , select the rows whose directed by record fuzzily matches to tom tataranowicz . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; title ; ( part } ; directed by ; tom tataranowicz } } ; 3 }
select the rows whose school record fuzzily matches to emmanuel christian school . take the size record of this row . select the rows whose school record fuzzily matches to wabash high school . take the size record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; school ; emmanuel christian school } ; size } ; hop { filter_eq { all_rows ; school ; wabash high school } ; size } }
select the rows whose total record is equal to 24 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; total ; 24 } } ; 2 }
the average of the lost record of all rows is 7 .
Output: round_eq { avg { all_rows ; lost } ; 7 }
select the rows whose location record fuzzily matches to tagawa . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; location ; tagawa } } ; 2 }
select the row whose wickets record of all rows is 2nd maximum . the player record of this row is tim bresnan .
Output: eq { hop { nth_argmax { all_rows ; wickets ; 2 } ; player } ; tim bresnan }
select the rows whose date record fuzzily matches to april 18 . take the score record of this row . select the rows whose date record fuzzily matches to april 21 . take the score record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; date ; april 18 } ; score } ; hop { filter_eq { all_rows ; date ; april 21 } ; score } }
for the margin of victory records of all rows , most of them fuzzily match to 1 stroke .
Output: most_eq { all_rows ; margin of victory ; 1 stroke }
select the rows whose texas vs record fuzzily matches to missouri . take the current streak record of this row . select the rows whose texas vs record fuzzily matches to oklahoma . take the current streak record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; texas vs ; missouri } ; current streak } ; hop { filter_eq { all_rows ; texas vs ; oklahoma } ; current streak } }
the sum of the wins record of all rows is 286 .
Output: round_eq { sum { all_rows ; wins } ; 286 }
select the rows whose partner record fuzzily matches to fernando verdasco . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; partner ; fernando verdasco } } ; 7 }
select the rows whose opponents record fuzzily matches to ashleigh barty casey dellacqua . there is only one such row in the table . the year record of this unqiue row is 2013 . the championship record of this unqiue row is australian open .
Output: and { only { filter_eq { all_rows ; opponents ; ashleigh barty casey dellacqua } } ; and { eq { hop { filter_eq { all_rows ; opponents ; ashleigh barty casey dellacqua } ; year } ; 2013 } ; eq { hop { filter_eq { all_rows ; opponents ; ashleigh barty casey dellacqua } ; championship } ; australian open } } }
select the rows whose game site record fuzzily matches to shea stadium . among these rows , select the rows whose attendance record is greater than 63000 . the number of such rows is 4 .
Output: eq { count { filter_greater { filter_eq { all_rows ; game site ; shea stadium } ; attendance ; 63000 } } ; 4 }
for the engine records of all rows , most of them fuzzily match to audi 3.6 l turbo v8 .
Output: most_eq { all_rows ; engine ; audi 3.6 l turbo v8 }
select the rows whose engine record fuzzily matches to maserati straight - 6 . there is only one such row in the table .
Output: only { filter_eq { all_rows ; engine ; maserati straight - 6 } }
select the rows whose born / died record fuzzily matches to present . the minimum born / died record of these rows is 1928 - present .
Output: eq { min { filter_eq { all_rows ; born / died ; present } ; born / died } ; 1928 - present }
select the rows whose country record fuzzily matches to france . among these rows , select the rows whose weeks at 1 record is equal to 8 . there is only one such row in the table . the number - one single ( s ) record of this unqiue row is n'importe quoi .
Output: and { only { filter_eq { filter_eq { all_rows ; country ; france } ; weeks at 1 ; 8 } } ; eq { hop { filter_eq { filter_eq { all_rows ; country ; france } ; weeks at 1 ; 8 } ; number - one single ( s ) } ; n'importe quoi } }
select the rows whose year record fuzzily matches to 2013 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; year ; 2013 } } ; 3 }
select the rows whose title record fuzzily matches to dimension twist . take the original air date record of this row . select the rows whose title record fuzzily matches to team impossible . take the original air date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; title ; dimension twist } ; original air date } ; hop { filter_eq { all_rows ; title ; team impossible } ; original air date } }
select the rows whose original west end performer record fuzzily matches to n / a . there is only one such row in the table . the character record of this unqiue row is ben weatherstaff .
Output: and { only { filter_eq { all_rows ; original west end performer ; n / a } } ; eq { hop { filter_eq { all_rows ; original west end performer ; n / a } ; character } ; ben weatherstaff } }
select the rows whose engine ( s ) record fuzzily matches to yamaha . there is only one such row in the table . the year record of this unqiue row is 1989 .
Output: and { only { filter_eq { all_rows ; engine ( s ) ; yamaha } } ; eq { hop { filter_eq { all_rows ; engine ( s ) ; yamaha } ; year } ; 1989 } }
select the rows whose year record is greater than or equal to 1966 . the sum of the points record of these rows is 15 .
Output: round_eq { sum { filter_greater_eq { all_rows ; year ; 1966 } ; points } ; 15 }
select the rows whose tournament record fuzzily matches to christchurch , new zealand . take the year record of this row . select the rows whose tournament record fuzzily matches to gstaad , switzerland . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; tournament ; christchurch , new zealand } ; year } ; hop { filter_eq { all_rows ; tournament ; gstaad , switzerland } ; year } }
the minimum pick record of all rows is 1 . the player record of the row with superlative pick record is candace parker .
Output: and { eq { min { all_rows ; pick } ; 1 } ; eq { hop { argmin { all_rows ; pick } ; player } ; candace parker } }
select the rows whose cfl team record fuzzily matches to ottawa renegades . take the pick record of this row . select the rows whose cfl team record fuzzily matches to bc lions . take the pick record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; cfl team ; ottawa renegades } ; pick } ; hop { filter_eq { all_rows ; cfl team ; bc lions } ; pick } }
select the row whose date record of all rows is maximum . the championship record of this row is newport , rhode island , us .
Output: eq { hop { argmax { all_rows ; date } ; championship } ; newport , rhode island , us }
select the rows whose year record fuzzily matches to 1931 . take the laps record of this row . select the rows whose year record fuzzily matches to 1930 . take the laps record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 1931 } ; laps } ; hop { filter_eq { all_rows ; year ; 1930 } ; laps } }
select the row whose goals scored record of all rows is 2nd maximum . the team record of this row is san salvador fc .
Output: eq { hop { nth_argmax { all_rows ; goals scored ; 2 } ; team } ; san salvador fc }
select the row whose attendance record of all rows is maximum . the date record of this row is 19th .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; 19th }
select the rows whose winning score record is less than 180 . there is only one such row in the table . the tournament record of this unqiue row is charles schwab cup championship .
Output: and { only { filter_less { all_rows ; winning score ; 180 } } ; eq { hop { filter_less { all_rows ; winning score ; 180 } ; tournament } ; charles schwab cup championship } }
the average of the catches record of all rows is 19.5 .
Output: round_eq { avg { all_rows ; catches } ; 19.5 }
select the rows whose image support record fuzzily matches to no . there is only one such row in the table . the format record of this unqiue row is plain text .
Output: and { only { filter_eq { all_rows ; image support ; no } } ; eq { hop { filter_eq { all_rows ; image support ; no } ; format } ; plain text } }
select the rows whose position record fuzzily matches to linebacker . there is only one such row in the table .
Output: only { filter_eq { all_rows ; position ; linebacker } }
select the rows whose year record fuzzily matches to 2000 . take the goals scored record of this row . select the rows whose year record fuzzily matches to 1999 . take the goals scored record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 2000 } ; goals scored } ; hop { filter_eq { all_rows ; year ; 1999 } ; goals scored } }
select the rows whose sunday record fuzzily matches to rylan clark . there is only one such row in the table .
Output: only { filter_eq { all_rows ; sunday ; rylan clark } }
select the rows whose exited record is less than day 16 . for the finished records of these rows , most of them are less than 9th .
Output: most_less { filter_less { all_rows ; exited ; day 16 } ; finished ; 9th }
select the row whose attendance record of all rows is maximum . the opponent record of this row is los angeles rams .
Output: eq { hop { argmax { all_rows ; attendance } ; opponent } ; los angeles rams }
select the row whose us viewers ( million ) record of all rows is maximum . the title record of this row is milfay .
Output: eq { hop { argmax { all_rows ; us viewers ( million ) } ; title } ; milfay }
select the row whose 1 usd = record of all rows is maximum . the currency record of this row is paraguayan guaraní ( pyg ) .
Output: eq { hop { argmax { all_rows ; 1 usd
the average of the erp / power w record of all rows is 6012 .
Output: round_eq { avg { all_rows ; erp / power w } ; 6012 }
for the station type records of all rows , most of them fuzzily match to relay .
Output: most_eq { all_rows ; station type ; relay }
for the first used as team song records of all rows , most of them are less than 2000 .
Output: most_less { all_rows ; first used as team song ; 2000 }
select the row whose year record of all rows is minimum . the english title record of this row is demon skyscraper .
Output: eq { hop { argmin { all_rows ; year } ; english title } ; demon skyscraper }
for the t c ( k ) records of all rows , most of them are greater than 80 .
Output: most_greater { all_rows ; t c ( k ) ; 80 }
the maximum drawn record of all rows is 2 .
Output: eq { max { all_rows ; drawn } ; 2 }
select the row whose channel record of all rows is 2nd maximum . the channel name record of this row is tv tokyo / teleto ( tx ) .
Output: eq { hop { nth_argmax { all_rows ; channel ; 2 } ; channel name } ; tv tokyo / teleto ( tx ) }
the average of the wins record of all rows is 10.9 .
Output: round_eq { avg { all_rows ; wins } ; 10.9 }
the average of the crowd record of all rows is 20188 .
Output: round_eq { avg { all_rows ; crowd } ; 20188 }
select the rows whose nominating festival record fuzzily matches to prix uip valladolid . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; nominating festival ; prix uip valladolid } } ; 2 }
select the row whose 1980 record of all rows is 2nd minimum . the greek national account record of this row is public debt 8 ( billion ) .
Output: eq { hop { nth_argmin { all_rows ; 1980 ; 2 } ; greek national account } ; public debt 8 ( billion ) }
for the series ep records of all rows , all of them fuzzily match to 13 - .
Output: all_eq { all_rows ; series ep ; 13 - }
select the rows whose championship record fuzzily matches to australian open . take the score in the final record of this row . select the rows whose championship record fuzzily matches to wimbledon . take the score in the final record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; championship ; australian open } ; score in the final } ; hop { filter_eq { all_rows ; championship ; wimbledon } ; score in the final } }
for the high points records of all rows , most of them fuzzily match to kevin durant .
Output: most_eq { all_rows ; high points ; kevin durant }
select the row whose floors record of all rows is maximum . the name record of this row is at & t building .
Output: eq { hop { argmax { all_rows ; floors } ; name } ; at & t building }
select the rows whose game site record fuzzily matches to shea stadium . select the row whose attendance record of these rows is maximum . the date record of this row is 1983 - 10 - 16 .
Output: eq { hop { argmax { filter_eq { all_rows ; game site ; shea stadium } ; attendance } ; date } ; 1983 - 10 - 16 }
select the row whose population record of all rows is maximum . the municipality record of this row is malmö .
Output: eq { hop { argmax { all_rows ; population } ; municipality } ; malmö }
the average of the enrollment 08 - 09 record of all rows is 923.5 .
Output: round_eq { avg { all_rows ; enrollment 08 - 09 } ; 923.5 }
select the row whose championships record of all rows is maximum . the established record of this row is 1979 .
Output: eq { hop { argmax { all_rows ; championships } ; established } ; 1979 }
the average of the score record of all rows is 279 .
Output: round_eq { avg { all_rows ; score } ; 279 }
select the rows whose stadium record fuzzily matches to heinz field . there is only one such row in the table . the week record of this unqiue row is 4 .
Output: and { only { filter_eq { all_rows ; stadium ; heinz field } } ; eq { hop { filter_eq { all_rows ; stadium ; heinz field } ; week } ; 4 } }
for the high rebounds records of all rows , all of them fuzzily match to duncan .
Output: all_eq { all_rows ; high rebounds ; duncan }
for the jersey number ( s ) records of all rows , most of them are greater than 10 .
Output: most_greater { all_rows ; jersey number ( s ) ; 10 }
select the rows whose position record fuzzily matches to gk . there is only one such row in the table . the player record of this unqiue row is doug warren .
Output: and { only { filter_eq { all_rows ; position ; gk } } ; eq { hop { filter_eq { all_rows ; position ; gk } ; player } ; doug warren } }
the 1st minimum date record of all rows is march 22 .
Output: eq { nth_min { all_rows ; date ; 1 } ; march 22 }
select the row whose capacity record of all rows is 2nd maximum . the stadium record of this row is sydney sports ground .
Output: eq { hop { nth_argmax { all_rows ; capacity ; 2 } ; stadium } ; sydney sports ground }
select the row whose change ( 2010 to 2011 ) record of all rows is maximum . the country record of this row is uruguay .
Output: eq { hop { argmax { all_rows ; change ( 2010 to 2011 ) } ; country } ; uruguay }
select the rows whose date record is less than 1914 . the sum of the no built record of these rows is 910 .
Output: round_eq { sum { filter_less { all_rows ; date ; 1914 } ; no built } ; 910 }
select the row whose original air date record of all rows is 2nd maximum . the title record of this row is the western isles and shetland .
Output: eq { hop { nth_argmax { all_rows ; original air date ; 2 } ; title } ; the western isles and shetland }
select the rows whose high points record fuzzily matches to dwight howard . for the score records of these rows , most of them fuzzily match to w .
Output: most_eq { filter_eq { all_rows ; high points ; dwight howard } ; score ; w }
select the rows whose country record fuzzily matches to fiji . there is only one such row in the table . the player record of this unqiue row is vijay singh .
Output: and { only { filter_eq { all_rows ; country ; fiji } } ; eq { hop { filter_eq { all_rows ; country ; fiji } ; player } ; vijay singh } }
select the row whose class pos record of all rows is minimum . the year record of this row is 2013 .
Output: eq { hop { argmin { all_rows ; class pos } ; year } ; 2013 }
select the rows whose current status record fuzzily matches to operational . there is only one such row in the table .
Output: only { filter_eq { all_rows ; current status ; operational } }
the average of the seasons record of all rows is 15 .
Output: round_eq { avg { all_rows ; seasons } ; 15 }
select the rows whose year record is greater than or equal to 2002 . the sum of the laps record of these rows is 699 .
Output: round_eq { sum { filter_greater_eq { all_rows ; year ; 2002 } ; laps } ; 699 }
select the rows whose directed by record fuzzily matches to levie isaacks . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; directed by ; levie isaacks } } ; 5 }
select the row whose founded record of all rows is 1st minimum . the institution record of this row is erskine college . the location record of this row is due west , south carolina .
Output: and { eq { hop { nth_argmin { all_rows ; founded ; 1 } ; institution } ; erskine college } ; eq { hop { nth_argmin { all_rows ; founded ; 1 } ; location } ; due west , south carolina } }
select the row whose date record of all rows is 1st minimum . the title record of this row is the love letter .
Output: eq { hop { nth_argmin { all_rows ; date ; 1 } ; title } ; the love letter }
select the rows whose rank record fuzzily matches to 3 . take the silver record of this row . select the rows whose rank record fuzzily matches to 2 . take the silver record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; rank ; 3 } ; silver } ; hop { filter_eq { all_rows ; rank ; 2 } ; silver } }
select the row whose home team score record of all rows is 2nd maximum . the home team record of this row is hawthorn .
Output: eq { hop { nth_argmax { all_rows ; home team score ; 2 } ; home team } ; hawthorn }
the average of the epa rated combined fuel economy record of all rows is 100 mpg .
Output: round_eq { avg { all_rows ; epa rated combined fuel economy } ; 100 mpg }
select the rows whose away team record fuzzily matches to richmond . take the away team score record of this row . select the rows whose away team record fuzzily matches to collingwood . take the away team score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; away team ; richmond } ; away team score } ; hop { filter_eq { all_rows ; away team ; collingwood } ; away team score } }
for the population ( 2008 ) records of all rows , most of them are greater than 100000 .
Output: most_greater { all_rows ; population ( 2008 ) ; 100000 }
for the 2008 status records of all rows , all of them fuzzily match to re-election .
Output: all_eq { all_rows ; 2008 status ; re-election }
select the rows whose loss record is greater than 9 . there is only one such row in the table . the team record of this unqiue row is college of saint benilde .
Output: and { only { filter_greater { all_rows ; loss ; 9 } } ; eq { hop { filter_greater { all_rows ; loss ; 9 } ; team } ; college of saint benilde } }
for the decision records of all rows , most of them fuzzily match to theodore .
Output: most_eq { all_rows ; decision ; theodore }
select the rows whose location record fuzzily matches to georgia . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; location ; georgia } } ; 8 }
select the row whose original airdate record of all rows is maximum . the title record of this row is silent night .
Output: eq { hop { argmax { all_rows ; original airdate } ; title } ; silent night }
select the row whose pick record of all rows is minimum . the player record of this row is ed o'bannon .
Output: eq { hop { argmin { all_rows ; pick } ; player } ; ed o'bannon }
the 6th minimum rank record of all rows is 6 . the team record of the row with 6th minimum rank record is college of saint benilde . the loss record of the row with 6th minimum rank record is 4 .
Output: and { eq { nth_min { all_rows ; rank ; 6 } ; 6 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 6 } ; team } ; college of saint benilde } ; eq { hop { nth_argmin { all_rows ; rank ; 6 } ; loss } ; 4 } } }
select the rows whose type record fuzzily matches to 0 - 6 - 6 - 0 fairlie type . there is only one such row in the table . the name record of this unqiue row is caledon .
Output: and { only { filter_eq { all_rows ; type ; 0 - 6 - 6 - 0 fairlie type } } ; eq { hop { filter_eq { all_rows ; type ; 0 - 6 - 6 - 0 fairlie type } ; name } ; caledon } }
select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 60000 . there is only one such row in the table .
Output: only { filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 60000 } }
for the height records of all rows , most of them are greater than or equal to 6 ' 0 .
Output: most_greater_eq { all_rows ; height ; 6 ' 0 }
select the rows whose title record fuzzily matches to olympic . there is only one such row in the table .
Output: only { filter_eq { all_rows ; title ; olympic } }
select the rows whose points record is greater than or equal to 40 . for the difference records of these rows , all of them are greater than 40 .
Output: all_greater { filter_greater_eq { all_rows ; points ; 40 } ; difference ; 40 }
select the rows whose races record is equal to 16 . there is only one such row in the table . the year record of this unqiue row is 2012 . the riders record of this unqiue row is alex de angelis .
Output: and { only { filter_eq { all_rows ; races ; 16 } } ; and { eq { hop { filter_eq { all_rows ; races ; 16 } ; year } ; 2012 } ; eq { hop { filter_eq { all_rows ; races ; 16 } ; riders } ; alex de angelis } } }
select the rows whose name record is arbitrary . the number of such rows is 13 .
Output: eq { count { filter_all { all_rows ; name } } ; 13 }
select the rows whose country record fuzzily matches to united states . the number of such rows is 10 .
Output: eq { count { filter_eq { all_rows ; country ; united states } } ; 10 }
select the rows whose games played record is greater than 100 . there is only one such row in the table . the rival record of this unqiue row is georgia .
Output: and { only { filter_greater { all_rows ; games played ; 100 } } ; eq { hop { filter_greater { all_rows ; games played ; 100 } ; rival } ; georgia } }
select the rows whose player record fuzzily matches to craig kieswetter . take the runs record of this row . select the rows whose player record fuzzily matches to zander de bruyn . take the runs record of this row . the first record is 7 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; player ; craig kieswetter } ; runs } ; hop { filter_eq { all_rows ; player ; zander de bruyn } ; runs } } ; 7 }
select the rows whose location attendance record fuzzily matches to target center . there is only one such row in the table . the date record of this unqiue row is march 3 .
Output: and { only { filter_eq { all_rows ; location attendance ; target center } } ; eq { hop { filter_eq { all_rows ; location attendance ; target center } ; date } ; march 3 } }
select the rows whose country record fuzzily matches to ger . there is only one such row in the table . the name record of this unqiue row is bopp .
Output: and { only { filter_eq { all_rows ; country ; ger } } ; eq { hop { filter_eq { all_rows ; country ; ger } ; name } ; bopp } }
select the rows whose winner record fuzzily matches to thomas wassberg . take the fis nordic world ski championships record of this row . select the rows whose winner record fuzzily matches to gunde svan . take the fis nordic world ski championships record of this row . the second record is 9 larger than the first record . the fis nordic world ski championships record of the first row is 1980 . the fis nordic world ski championships record of the second row is 1989 .
Output: and { eq { diff { hop { filter_eq { all_rows ; winner ; thomas wassberg } ; fis nordic world ski championships } ; hop { filter_eq { all_rows ; winner ; gunde svan } ; fis nordic world ski championships } } ; -9 } ; and { eq { hop { filter_eq { all_rows ; winner ; thomas wassberg } ; fis nordic world ski championships } ; 1980 } ; eq { hop { filter_eq { all_rows ; winner ; gunde svan } ; fis nordic world ski championships } ; 1989 } } }
select the rows whose constructor record fuzzily matches to brm . select the row whose time / retired record of these rows is 3rd minimum . the driver record of this row is lucien bianchi .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; constructor ; brm } ; time / retired ; 3 } ; driver } ; lucien bianchi }
select the row whose fatalities record of all rows is maximum . the name record of this row is 1990 manjil - rudbar earthquake .
Output: eq { hop { argmax { all_rows ; fatalities } ; name } ; 1990 manjil - rudbar earthquake }
select the rows whose away team score record is less than 20 . among these rows , select the rows whose crowd record is greater than 20000 . the number of such rows is 3 .
Output: eq { count { filter_greater { filter_less { all_rows ; away team score ; 20 } ; crowd ; 20000 } } ; 3 }
select the rows whose outcome of election record fuzzily matches to minority in parliament . among these rows , select the rows whose number of pnc votes record is greater than 200000 . there is only one such row in the table . the election record of this unqiue row is 2000 .
Output: and { only { filter_greater { filter_eq { all_rows ; outcome of election ; minority in parliament } ; number of pnc votes ; 200000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; outcome of election ; minority in parliament } ; number of pnc votes ; 200000 } ; election } ; 2000 } }
select the rows whose competition record fuzzily matches to 2004 afc asian cup qualification . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; competition ; 2004 afc asian cup qualification } } ; 3 }
select the rows whose opponent record fuzzily matches to los angeles rams . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; opponent ; los angeles rams } } ; 2 }
select the rows whose college record fuzzily matches to southern illinois . there is only one such row in the table . the name record of this unqiue row is damon jones .
Output: and { only { filter_eq { all_rows ; college ; southern illinois } } ; eq { hop { filter_eq { all_rows ; college ; southern illinois } ; name } ; damon jones } }
select the rows whose points record is equal to 0 . there is only one such row in the table . the team record of this unqiue row is sírio .
Output: and { only { filter_eq { all_rows ; points ; 0 } } ; eq { hop { filter_eq { all_rows ; points ; 0 } ; team } ; sírio } }
select the rows whose location / attendance record fuzzily matches to the omni . among these rows , select the rows whose score record fuzzily matches to l . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; location / attendance ; the omni } ; score ; l } } ; 2 }
select the rows whose location record fuzzily matches to st pete times forum . select the row whose game record of these rows is 1st minimum . the attendance record of this row is 16104 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; location ; st pete times forum } ; game ; 1 } ; attendance } ; 16104 }
select the rows whose winner record fuzzily matches to bobby clarke . among these rows , select the rows whose goals record is equal to 51 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; winner ; bobby clarke } ; goals ; 51 } } ; 2 }
select the row whose to par record of all rows is 2nd minimum . the player record of this row is jim colbert .
Output: eq { hop { nth_argmin { all_rows ; to par ; 2 } ; player } ; jim colbert }
for the label records of all rows , most of them fuzzily match to alfa records .
Output: most_eq { all_rows ; label ; alfa records }
select the rows whose nickname record fuzzily matches to roadrunners . there is only one such row in the table . the institution record of this unqiue row is california state university , bakersfield .
Output: and { only { filter_eq { all_rows ; nickname ; roadrunners } } ; eq { hop { filter_eq { all_rows ; nickname ; roadrunners } ; institution } ; california state university , bakersfield } }
select the rows whose total record is greater than 30 . among these rows , select the rows whose style record fuzzily matches to jive . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_greater { all_rows ; total ; 30 } ; style ; jive } } ; 2 }
select the row whose event record of all rows is minimum . the event record of this row is 1995 antholz .
Output: eq { hop { argmin { all_rows ; event } ; event } ; 1995 antholz }
select the row whose points record of all rows is maximum . the artist record of this row is niamh kavanagh .
Output: eq { hop { argmax { all_rows ; points } ; artist } ; niamh kavanagh }
select the row whose lions points record of all rows is maximum . the opponent record of this row is minnesota vikings .
Output: eq { hop { argmax { all_rows ; lions points } ; opponent } ; minnesota vikings }
select the rows whose points record is greater than 0 . the number of such rows is 2 .
Output: eq { count { filter_greater { all_rows ; points ; 0 } } ; 2 }
the maximum losses record of all rows is 4 . the quarterback record of the row with superlative losses record is dave krieg .
Output: and { eq { max { all_rows ; losses } ; 4 } ; eq { hop { argmax { all_rows ; losses } ; quarterback } ; dave krieg } }
select the rows whose team 2 record fuzzily matches to mp algiers . take the 1st leg record of this row . select the rows whose team 2 record fuzzily matches to fortior mahajanga . take the 1st leg record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; team 2 ; mp algiers } ; 1st leg } ; hop { filter_eq { all_rows ; team 2 ; fortior mahajanga } ; 1st leg } }
select the rows whose release date record fuzzily matches to january 2011 . the average of the release price ( usd ) record of these rows is 507.71 .
Output: round_eq { avg { filter_eq { all_rows ; release date ; january 2011 } ; release price ( usd ) } ; 507.71 }
select the rows whose location record fuzzily matches to clarksville . take the year joined record of this row . select the rows whose location record fuzzily matches to ramsey . take the year joined record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; location ; clarksville } ; year joined } ; hop { filter_eq { all_rows ; location ; ramsey } ; year joined } }
the average of the score record of all rows is 281 .
Output: round_eq { avg { all_rows ; score } ; 281 }
select the rows whose country record fuzzily matches to united states . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; country ; united states } } ; 3 }
select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is william j driver .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; william j driver }
select the rows whose year record fuzzily matches to 2009 . take the finish position record of this row . select the rows whose year record fuzzily matches to 2008 . take the finish position record of this row . the second record is 4 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; year ; 2009 } ; finish position } ; hop { filter_eq { all_rows ; year ; 2008 } ; finish position } } ; -4 }
select the rows whose years record fuzzily matches to 9 - 15 . there is only one such row in the table . the name record of this unqiue row is gore high school .
Output: and { only { filter_eq { all_rows ; years ; 9 - 15 } } ; eq { hop { filter_eq { all_rows ; years ; 9 - 15 } ; name } ; gore high school } }
the average of the races record of all rows is 14.4 .
Output: round_eq { avg { all_rows ; races } ; 14.4 }
select the rows whose category record fuzzily matches to newcomer . there is only one such row in the table . the year record of this unqiue row is 2008 . the result record of this unqiue row is won .
Output: and { only { filter_eq { all_rows ; category ; newcomer } } ; and { eq { hop { filter_eq { all_rows ; category ; newcomer } ; year } ; 2008 } ; eq { hop { filter_eq { all_rows ; category ; newcomer } ; result } ; won } } }
select the rows whose directed by record fuzzily matches to turi meyer . there is only one such row in the table . the - record of this unqiue row is 6 . the title record of this unqiue row is harvest .
Output: and { only { filter_eq { all_rows ; directed by ; turi meyer } } ; and { eq { hop { filter_eq { all_rows ; directed by ; turi meyer } ; - } ; 6 } ; eq { hop { filter_eq { all_rows ; directed by ; turi meyer } ; title } ; harvest } } }
select the row whose year record of all rows is 2nd minimum . the perpetrator record of this row is wagner , ernst august , 38 .
Output: eq { hop { nth_argmin { all_rows ; year ; 2 } ; perpetrator } ; wagner , ernst august , 38 }
select the rows whose estd record is less than 1900 . there is only one such row in the table . the college name record of this unqiue row is madras medical college and research institute .
Output: and { only { filter_less { all_rows ; estd ; 1900 } } ; eq { hop { filter_less { all_rows ; estd ; 1900 } ; college name } ; madras medical college and research institute } }