niryuu/sni-each-converted huggingface dataset:
for the games records of all rows , all of them are equal to 6 .
Output: all_eq { all_rows ; games ; 6 }
select the row whose game record of all rows is 2nd minimum . the date record of this row is march 6 .
Output: eq { hop { nth_argmin { all_rows ; game ; 2 } ; date } ; march 6 }
select the rows whose college record fuzzily matches to ohio state . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; college ; ohio state } } ; 3 }
for the commenced operations records of all rows , most of them are greater than 2000 .
Output: most_greater { all_rows ; commenced operations ; 2000 }
select the rows whose branding record fuzzily matches to mom 's radio 88.3 cebu . take the power ( kw ) record of this row . select the rows whose branding record fuzzily matches to mom 's radio 101.5 tacloban . take the power ( kw ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; branding ; mom 's radio 88.3 cebu } ; power ( kw ) } ; hop { filter_eq { all_rows ; branding ; mom 's radio 101.5 tacloban } ; power ( kw ) } }
select the row whose 1st week sales record of all rows is 2nd maximum . the artist record of this row is ti .
Output: eq { hop { nth_argmax { all_rows ; 1st week sales ; 2 } ; artist } ; ti }
select the rows whose spaceport record fuzzily matches to baikonur cosmodrome , kazakhstan . the sum of the flights record of these rows is 123 .
Output: round_eq { sum { filter_eq { all_rows ; spaceport ; baikonur cosmodrome , kazakhstan } ; flights } ; 123 }
select the rows whose tournament record fuzzily matches to mercedes . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; tournament ; mercedes } } ; 3 }
select the row whose total record of all rows is 2nd maximum . the league cup record of this row is 2 .
Output: eq { hop { nth_argmax { all_rows ; total ; 2 } ; league cup } ; 2 }
the average of the laps record of all rows is 259 .
Output: round_eq { avg { all_rows ; laps } ; 259 }
select the rows whose county record fuzzily matches to coconino . take the hancock record of this row . select the rows whose county record fuzzily matches to graham . take the hancock record of this row . the first record is greater than the second record . the hancock record of the first row is 1504 . the hancock record of the second row is 322 .
Output: and { greater { hop { filter_eq { all_rows ; county ; coconino } ; hancock } ; hop { filter_eq { all_rows ; county ; graham } ; hancock } } ; and { eq { hop { filter_eq { all_rows ; county ; coconino } ; hancock } ; 1504 } ; eq { hop { filter_eq { all_rows ; county ; graham } ; hancock } ; 322 } } }
the average of the score record of all rows is 22.8 .
Output: round_eq { avg { all_rows ; score } ; 22.8 }
the sum of the crowd record of all rows is 78,000 .
Output: round_eq { sum { all_rows ; crowd } ; 78,000 }
select the rows whose country record fuzzily matches to norway . there is only one such row in the table . the name record of this unqiue row is hålogaland bridge .
Output: and { only { filter_eq { all_rows ; country ; norway } } ; eq { hop { filter_eq { all_rows ; country ; norway } ; name } ; hålogaland bridge } }
select the row whose first elected record of all rows is minimum . the incumbent record of this row is henry garland dupré .
Output: eq { hop { argmin { all_rows ; first elected } ; incumbent } ; henry garland dupré }
select the rows whose engines record fuzzily matches to 2 , bergen diesel , brm - 8 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; engines ; 2 , bergen diesel , brm - 8 } } ; 3 }
select the rows whose position record fuzzily matches to running back . there is only one such row in the table . the player record of this unqiue row is tom moore .
Output: and { only { filter_eq { all_rows ; position ; running back } } ; eq { hop { filter_eq { all_rows ; position ; running back } ; player } ; tom moore } }
select the rows whose team record fuzzily matches to cd fas . take the goals conceded record of this row . select the rows whose team record fuzzily matches to cd arcense . take the goals conceded record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; team ; cd fas } ; goals conceded } ; hop { filter_eq { all_rows ; team ; cd arcense } ; goals conceded } }
select the rows whose joined record is greater than 1981 . the average of the enrollment record of these rows is 2767 .
Output: round_eq { avg { filter_greater { all_rows ; joined ; 1981 } ; enrollment } ; 2767 }
for the successor records of all rows , most of them fuzzily match to ( d ) .
Output: most_eq { all_rows ; successor ; ( d ) }
select the row whose distance ( km ) record of all rows is 2nd maximum . the station record of this row is higashi - ishinden .
Output: eq { hop { nth_argmax { all_rows ; distance ( km ) ; 2 } ; station } ; higashi - ishinden }
for the result records of all rows , most of them fuzzily match to w .
Output: most_eq { all_rows ; result ; w }
select the rows whose format record fuzzily matches to public broadcasting . there is only one such row in the table . the branding record of this unqiue row is cbc radio 2 .
Output: and { only { filter_eq { all_rows ; format ; public broadcasting } } ; eq { hop { filter_eq { all_rows ; format ; public broadcasting } ; branding } ; cbc radio 2 } }
for the wins records of all rows , most of them are greater than or equal to 3 .
Output: most_greater_eq { all_rows ; wins ; 3 }
select the row whose height record of all rows is minimum . the name record of this row is anthony brock .
Output: eq { hop { argmin { all_rows ; height } ; name } ; anthony brock }
select the rows whose season record fuzzily matches to 2010 . take the races record of this row . select the rows whose season record fuzzily matches to 2009 . take the races record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; season ; 2010 } ; races } ; hop { filter_eq { all_rows ; season ; 2009 } ; races } }
for the gold records of all rows , most of them are equal to 0 .
Output: most_eq { all_rows ; gold ; 0 }
select the rows whose constellation record fuzzily matches to hydra . there is only one such row in the table . the ngc number record of this unqiue row is 5078 .
Output: and { only { filter_eq { all_rows ; constellation ; hydra } } ; eq { hop { filter_eq { all_rows ; constellation ; hydra } ; ngc number } ; 5078 } }
for the poles records of all rows , most of them are equal to 0 .
Output: most_eq { all_rows ; poles ; 0 }
select the row whose yards record of all rows is maximum . the player record of this row is lonnie ford .
Output: eq { hop { argmax { all_rows ; yards } ; player } ; lonnie ford }
select the rows whose height record fuzzily matches to 6 - 4 . there is only one such row in the table . the name record of this unqiue row is stephanie murphy .
Output: and { only { filter_eq { all_rows ; height ; 6 - 4 } } ; eq { hop { filter_eq { all_rows ; height ; 6 - 4 } ; name } ; stephanie murphy } }
select the rows whose villages record is equal to 25 . there is only one such row in the table . the name record of this unqiue row is wuji town .
Output: and { only { filter_eq { all_rows ; villages ; 25 } } ; eq { hop { filter_eq { all_rows ; villages ; 25 } ; name } ; wuji town } }
select the rows whose airport record fuzzily matches to tenerife sur . take the 2012 record of this row . select the rows whose airport record fuzzily matches to kotoka international airport . take the 2012 record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; airport ; tenerife sur } ; 2012 } ; hop { filter_eq { all_rows ; airport ; kotoka international airport } ; 2012 } }
select the row whose location attendance record of all rows is maximum . the date record of this row is april 30 .
Output: eq { hop { argmax { all_rows ; location attendance } ; date } ; april 30 }
select the row whose area ( hectares ) record of all rows is maximum . the municipality record of this row is caramoran .
Output: eq { hop { argmax { all_rows ; area ( hectares ) } ; municipality } ; caramoran }
select the rows whose incumbent record fuzzily matches to robert l f sikes . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to donald ray matthews . take the first elected record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; incumbent ; robert l f sikes } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; donald ray matthews } ; first elected } }
select the rows whose wsu year ( s ) record fuzzily matches to 1959 - 62 . there is only one such row in the table . the name record of this unqiue row is gene wiley .
Output: and { only { filter_eq { all_rows ; wsu year ( s ) ; 1959 - 62 } } ; eq { hop { filter_eq { all_rows ; wsu year ( s ) ; 1959 - 62 } ; name } ; gene wiley } }
the sum of the attendance record of all rows is 120528 .
Output: round_eq { sum { all_rows ; attendance } ; 120528 }
select the rows whose location record fuzzily matches to winnipeg , canada . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; location ; winnipeg , canada } } ; 2 }
select the rows whose nation record fuzzily matches to united kingdom . take the silver record of this row . select the rows whose nation record fuzzily matches to finland . take the silver record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; nation ; united kingdom } ; silver } ; hop { filter_eq { all_rows ; nation ; finland } ; silver } }
select the rows whose title record fuzzily matches to empire . take the order record of this row . select the rows whose title record fuzzily matches to hollywood . take the order record of this row . the first record is less than the second record . the order record of the first row is 4 . the order record of the second row is 5 .
Output: and { less { hop { filter_eq { all_rows ; title ; empire } ; order } ; hop { filter_eq { all_rows ; title ; hollywood } ; order } } ; and { eq { hop { filter_eq { all_rows ; title ; empire } ; order } ; 4 } ; eq { hop { filter_eq { all_rows ; title ; hollywood } ; order } ; 5 } } }
the average of the crowd record of all rows is 16470 .
Output: round_eq { avg { all_rows ; crowd } ; 16470 }
select the rows whose party record fuzzily matches to democratic . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; party ; democratic } } ; 4 }
the sum of the points record of all rows is 1,357 .
Output: round_eq { sum { all_rows ; points } ; 1,357 }
select the row whose location attendance record of all rows is 1st maximum . the team record of this row is knicks .
Output: eq { hop { nth_argmax { all_rows ; location attendance ; 1 } ; team } ; knicks }
the average of the apparent magnitude record of all rows is 11.7 .
Output: round_eq { avg { all_rows ; apparent magnitude } ; 11.7 }
select the rows whose reason record fuzzily matches to death . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; reason ; death } } ; 3 }
select the row whose launched record of all rows is minimum . the ship name record of this row is brp pangasinan .
Output: eq { hop { argmin { all_rows ; launched } ; ship name } ; brp pangasinan }
select the rows whose joined record is equal to 1996 . among these rows , select the rows whose location record fuzzily matches to tennessee . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_eq { all_rows ; joined ; 1996 } ; location ; tennessee } } ; 4 }
select the rows whose round record is equal to 7 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; round ; 7 } } ; 2 }
for the original air date records of all rows , most of them are less than or equal to february 2005 .
Output: most_less_eq { all_rows ; original air date ; february 2005 }
select the rows whose maximum seating capacity record fuzzily matches to unknown . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; maximum seating capacity ; unknown } } ; 4 }
select the rows whose player record fuzzily matches to fedor dmitriev . take the year born record of this row . select the rows whose player record fuzzily matches to anton ponkrashov . take the year born record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; fedor dmitriev } ; year born } ; hop { filter_eq { all_rows ; player ; anton ponkrashov } ; year born } }
for the manner of departure records of all rows , most of them fuzzily match to sacked .
Output: most_eq { all_rows ; manner of departure ; sacked }
for the long records of all rows , most of them are less than 10 .
Output: most_less { all_rows ; long ; 10 }
for the nba draft records of all rows , most of them fuzzily match to has not yet declared for the nba draft .
Output: most_eq { all_rows ; nba draft ; has not yet declared for the nba draft }
select the rows whose sport record fuzzily matches to boxing . among these rows , select the rows whose event record fuzzily matches to men 's light flyweight . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; sport ; boxing } ; event ; men 's light flyweight } } ; 2 }
select the rows whose date record fuzzily matches to 29 november 2007 . take the attendance record of this row . select the rows whose date record fuzzily matches to 30 november 2007 . take the attendance record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; 29 november 2007 } ; attendance } ; hop { filter_eq { all_rows ; date ; 30 november 2007 } ; attendance } }
select the row whose points record of all rows is maximum . the series record of this row is formula renault uk .
Output: eq { hop { argmax { all_rows ; points } ; series } ; formula renault uk }
the average of the attendance record of all rows is 743 .
Output: round_eq { avg { all_rows ; attendance } ; 743 }
for the place records of all rows , most of them fuzzily match to t5 .
Output: most_eq { all_rows ; place ; t5 }
for the country records of all rows , most of them fuzzily match to united kingdom .
Output: most_eq { all_rows ; country ; united kingdom }
select the rows whose to par record is less than 0 . the number of such rows is 4 .
Output: eq { count { filter_less { all_rows ; to par ; 0 } } ; 4 }
select the rows whose padilla municipality record is greater than 10000 . there is only one such row in the table . the language record of this unqiue row is spanish .
Output: and { only { filter_greater { all_rows ; padilla municipality ; 10000 } } ; eq { hop { filter_greater { all_rows ; padilla municipality ; 10000 } ; language } ; spanish } }
the average of the joined record of all rows is 2005 .
Output: round_eq { avg { all_rows ; joined } ; 2005 }
select the row whose height feet / metres record of all rows is maximum . the name record of this row is dravska vrata .
Output: eq { hop { argmax { all_rows ; height feet / metres } ; name } ; dravska vrata }
select the row whose total elected 2001 record of all rows is 2nd maximum . the party record of this row is democratic party .
Output: eq { hop { nth_argmax { all_rows ; total elected 2001 ; 2 } ; party } ; democratic party }
the average of the points record of all rows is 14.56 .
Output: round_eq { avg { all_rows ; points } ; 14.56 }
select the row whose losses record of all rows is 2nd maximum . the club record of this row is club sestao .
Output: eq { hop { nth_argmax { all_rows ; losses ; 2 } ; club } ; club sestao }
select the rows whose year record fuzzily matches to 2011 . take the purse record of this row . select the rows whose year record fuzzily matches to 2005 . take the purse record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 2011 } ; purse } ; hop { filter_eq { all_rows ; year ; 2005 } ; purse } }
select the rows whose timeslot ( edt ) record fuzzily matches to monday - friday 10:00 pm . there is only one such row in the table . the season record of this unqiue row is 10 .
Output: and { only { filter_eq { all_rows ; timeslot ( edt ) ; monday - friday 10:00 pm } } ; eq { hop { filter_eq { all_rows ; timeslot ( edt ) ; monday - friday 10:00 pm } ; season } ; 10 } }
select the rows whose player record fuzzily matches to alfred binns . take the total dismissals record of this row . select the rows whose player record fuzzily matches to ivor mendonca . take the total dismissals record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; alfred binns } ; total dismissals } ; hop { filter_eq { all_rows ; player ; ivor mendonca } ; total dismissals } }
select the rows whose points record is less than 100 . the average of the points record of these rows is 71.4 .
Output: round_eq { avg { filter_less { all_rows ; points ; 100 } ; points } ; 71.4 }
select the rows whose founded record is less than 1800 . there is only one such row in the table . the institution record of this unqiue row is university of louisville .
Output: and { only { filter_less { all_rows ; founded ; 1800 } } ; eq { hop { filter_less { all_rows ; founded ; 1800 } ; institution } ; university of louisville } }
select the rows whose player record fuzzily matches to arnold palmer . take the score record of this row . select the rows whose player record fuzzily matches to kermit zarley . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; arnold palmer } ; score } ; hop { filter_eq { all_rows ; player ; kermit zarley } ; score } }
select the row whose episode count record of all rows is maximum . the character record of this row is professor leo dalton .
Output: eq { hop { argmax { all_rows ; episode count } ; character } ; professor leo dalton }
select the row whose position record of all rows is 7th minimum . the series record of this row is gp2 series .
Output: eq { hop { nth_argmin { all_rows ; position ; 7 } ; series } ; gp2 series }
the sum of the attendance record of all rows is 1135404 .
Output: round_eq { sum { all_rows ; attendance } ; 1135404 }
select the rows whose h / a record fuzzily matches to a . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; h / a ; a } } ; 3 }
select the rows whose local name record fuzzily matches to det store korslaget . take the year premiered record of this row . select the rows whose local name record fuzzily matches to körslaget . take the year premiered record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; local name ; det store korslaget } ; year premiered } ; hop { filter_eq { all_rows ; local name ; körslaget } ; year premiered } }
select the rows whose high assists record fuzzily matches to delonte west . there is only one such row in the table .
Output: only { filter_eq { all_rows ; high assists ; delonte west } }
select the rows whose result record fuzzily matches to w . the 2nd minimum date record of these rows is november 28 , 1999 .
Output: eq { nth_min { filter_eq { all_rows ; result ; w } ; date ; 2 } ; november 28 , 1999 }
select the rows whose rank record fuzzily matches to chief justice . there is only one such row in the table . the name record of this unqiue row is federico hernández denton .
Output: and { only { filter_eq { all_rows ; rank ; chief justice } } ; eq { hop { filter_eq { all_rows ; rank ; chief justice } ; name } ; federico hernández denton } }
select the rows whose date record fuzzily matches to february . there is only one such row in the table . the event record of this unqiue row is indianapolis boat , sport & travel show .
Output: and { only { filter_eq { all_rows ; date ; february } } ; eq { hop { filter_eq { all_rows ; date ; february } ; event } ; indianapolis boat , sport & travel show } }
select the rows whose event record fuzzily matches to wimbledon . select the row whose aces record of these rows is 1st maximum . the player record of this row is john isner .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; event ; wimbledon } ; aces ; 1 } ; player } ; john isner }
the average of the attendance record of all rows is 48344 .
Output: round_eq { avg { all_rows ; attendance } ; 48344 }
select the row whose erp w record of all rows is 2nd maximum . the call sign record of this row is wriq .
Output: eq { hop { nth_argmax { all_rows ; erp w ; 2 } ; call sign } ; wriq }
select the rows whose current status record fuzzily matches to abc affiliate owned by hearst television . there is only one such row in the table . the station record of this unqiue row is wcvb - tv 1 .
Output: and { only { filter_eq { all_rows ; current status ; abc affiliate owned by hearst television } } ; eq { hop { filter_eq { all_rows ; current status ; abc affiliate owned by hearst television } ; station } ; wcvb - tv 1 } }
select the rows whose date record fuzzily matches to september . the average of the attendance record of these rows is 56,592 .
Output: round_eq { avg { filter_eq { all_rows ; date ; september } ; attendance } ; 56,592 }
for the location records of all rows , most of them fuzzily match to kabul .
Output: most_eq { all_rows ; location ; kabul }
select the rows whose year of election record fuzzily matches to 1951 . take the candidates elected record of this row . select the rows whose year of election record fuzzily matches to 1948 . take the candidates elected record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; year of election ; 1951 } ; candidates elected } ; hop { filter_eq { all_rows ; year of election ; 1948 } ; candidates elected } }
select the row whose location attendance record of all rows is maximum . the date record of this row is april 2 .
Output: eq { hop { argmax { all_rows ; location attendance } ; date } ; april 2 }
select the row whose capacity record of all rows is 2nd maximum . the home arena record of this row is bird arena .
Output: eq { hop { nth_argmax { all_rows ; capacity ; 2 } ; home arena } ; bird arena }
select the rows whose printer record fuzzily matches to avery dennison . for the face value records of these rows , all of them are greater than 35 .
Output: all_greater { filter_eq { all_rows ; printer ; avery dennison } ; face value ; 35 }
select the rows whose playoffs record fuzzily matches to champion . 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 ; playoffs ; champion } } ; eq { hop { filter_eq { all_rows ; playoffs ; champion } ; year } ; 1989 } }
select the rows whose high points record fuzzily matches to m williams . there is only one such row in the table . the date record of this unqiue row is may 2 .
Output: and { only { filter_eq { all_rows ; high points ; m williams } } ; eq { hop { filter_eq { all_rows ; high points ; m williams } ; date } ; may 2 } }
select the row whose first aired record of all rows is minimum . the entrepreneur ( s ) record of this row is james seddon .
Output: eq { hop { argmin { all_rows ; first aired } ; entrepreneur ( s ) } ; james seddon }
select the row whose attendance record of all rows is maximum . the year record of this row is 2008 . the date record of this row is sunday , november 16 .
Output: and { eq { hop { argmax { all_rows ; attendance } ; year } ; 2008 } ; eq { hop { argmax { all_rows ; attendance } ; date } ; sunday , november 16 } }
select the rows whose name record fuzzily matches to andrew . take the date record of this row . select the rows whose name record fuzzily matches to amanda . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; andrew } ; date } ; hop { filter_eq { all_rows ; name ; amanda } ; date } }
select the rows whose series record fuzzily matches to british formula three . take the points record of this row . select the rows whose series record fuzzily matches to formula bmw americas . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; series ; british formula three } ; points } ; hop { filter_eq { all_rows ; series ; formula bmw americas } ; points } }
select the rows whose location attendance record does not match to staples center . for the location attendance records of these rows , all of them are greater than 18500 .
Output: all_greater { filter_not_eq { all_rows ; location attendance ; staples center } ; location attendance ; 18500 }
select the rows whose manufacturer record fuzzily matches to oldsmobile . there is only one such row in the table . the driver record of this unqiue row is steve grissom .
Output: and { only { filter_eq { all_rows ; manufacturer ; oldsmobile } } ; eq { hop { filter_eq { all_rows ; manufacturer ; oldsmobile } ; driver } ; steve grissom } }
select the rows whose laid down record fuzzily matches to july 1933 . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; laid down ; july 1933 } } ; 5 }
select the rows whose socialist labor ticket record fuzzily matches to ( none ) . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; socialist labor ticket ; ( none ) } } ; 2 }
select the rows whose surface record fuzzily matches to hard . among these rows , select the rows whose opponent in the final record fuzzily matches to todd martin . there is only one such row in the table . the date record of this unqiue row is 28 february 1993 .
Output: and { only { filter_eq { filter_eq { all_rows ; surface ; hard } ; opponent in the final ; todd martin } } ; eq { hop { filter_eq { filter_eq { all_rows ; surface ; hard } ; opponent in the final ; todd martin } ; date } ; 28 february 1993 } }
for the date records of all rows , all of them fuzzily match to 31 july 1954 .
Output: all_eq { all_rows ; date ; 31 july 1954 }
select the rows whose town ships record is equal to 18 . there is only one such row in the table . the state / region record of this unqiue row is kachin state .
Output: and { only { filter_eq { all_rows ; town ships ; 18 } } ; eq { hop { filter_eq { all_rows ; town ships ; 18 } ; state / region } ; kachin state } }
select the rows whose position record fuzzily matches to forward-center . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; position ; forward-center } } ; 2 }
select the row whose starts record of all rows is maximum . the year record of this row is 2001 .
Output: eq { hop { argmax { all_rows ; starts } ; year } ; 2001 }
select the rows whose name record fuzzily matches to kara lynn joyce . take the time record of this row . select the rows whose name record fuzzily matches to aliaksandra herasimenia . take the time record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; kara lynn joyce } ; time } ; hop { filter_eq { all_rows ; name ; aliaksandra herasimenia } ; time } }
select the rows whose years in orlando record fuzzily matches to present . there is only one such row in the table . the player record of this unqiue row is victor oladipo .
Output: and { only { filter_eq { all_rows ; years in orlando ; present } } ; eq { hop { filter_eq { all_rows ; years in orlando ; present } ; player } ; victor oladipo } }
for the win % records of all rows , most of them are greater than 0 .
Output: most_greater { all_rows ; win % ; 0 }
select the rows whose country record fuzzily matches to luxembourg . there is only one such row in the table . the season record of this unqiue row is 1978 - 79 .
Output: and { only { filter_eq { all_rows ; country ; luxembourg } } ; eq { hop { filter_eq { all_rows ; country ; luxembourg } ; season } ; 1978 - 79 } }
select the rows whose television record fuzzily matches to espn2 . the sum of the attendance record of these rows is 44912 .
Output: round_eq { sum { filter_eq { all_rows ; television ; espn2 } ; attendance } ; 44912 }
select the row whose weight ( kg ) record of all rows is minimum . the race record of this row is cox plate .
Output: eq { hop { argmin { all_rows ; weight ( kg ) } ; race } ; cox plate }
select the rows whose date record fuzzily matches to 7 march 2008 . take the attendance record of this row . select the rows whose date record fuzzily matches to 8 march 2008 . take the attendance record of this row . the first record is less than the second record . the attendance record of the first row is 17225 . the attendance record of the second row is 18279 .
Output: and { less { hop { filter_eq { all_rows ; date ; 7 march 2008 } ; attendance } ; hop { filter_eq { all_rows ; date ; 8 march 2008 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; 7 march 2008 } ; attendance } ; 17225 } ; eq { hop { filter_eq { all_rows ; date ; 8 march 2008 } ; attendance } ; 18279 } } }
select the rows whose issue price record is equal to 45.95 . there is only one such row in the table . the year record of this unqiue row is 2007 . the theme record of this unqiue row is trumpeter swan .
Output: and { only { filter_eq { all_rows ; issue price ; 45.95 } } ; and { eq { hop { filter_eq { all_rows ; issue price ; 45.95 } ; year } ; 2007 } ; eq { hop { filter_eq { all_rows ; issue price ; 45.95 } ; theme } ; trumpeter swan } } }
the average of the crowd record of all rows is 16000 .
Output: round_eq { avg { all_rows ; crowd } ; 16000 }
select the row whose number of seasons in top division record of all rows is maximum . the club record of this row is sarajevo b , c .
Output: eq { hop { argmax { all_rows ; number of seasons in top division } ; club } ; sarajevo b , c }
select the rows whose original album record fuzzily matches to previously unreleased . among these rows , select the rows whose time record is greater than 4:00 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_eq { all_rows ; original album ; previously unreleased } ; time ; 4:00 } } ; 2 }
select the rows whose nationality record fuzzily matches to serbia . there is only one such row in the table . the player record of this unqiue row is aleksandar radojeviä ‡ .
Output: and { only { filter_eq { all_rows ; nationality ; serbia } } ; eq { hop { filter_eq { all_rows ; nationality ; serbia } ; player } ; aleksandar radojeviä ‡ } }
the average of the population record of all rows is 3289 .
Output: round_eq { avg { all_rows ; population } ; 3289 }
select the rows whose position record fuzzily matches to quarterback . there is only one such row in the table . the name record of this unqiue row is bob gagliano .
Output: and { only { filter_eq { all_rows ; position ; quarterback } } ; eq { hop { filter_eq { all_rows ; position ; quarterback } ; name } ; bob gagliano } }
select the row whose total record of all rows is maximum . the nation record of this row is soviet union .
Output: eq { hop { argmax { all_rows ; total } ; nation } ; soviet union }
select the rows whose location attendance record fuzzily matches to pepsi center . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; location attendance ; pepsi center } } ; 7 }
select the row whose year record of all rows is maximum . the chassis record of this row is ags jh25b ags jh27 .
Output: eq { hop { argmax { all_rows ; year } ; chassis } ; ags jh25b ags jh27 }
select the rows whose player record fuzzily matches to lionel cann . take the runs record of this row . select the rows whose player record fuzzily matches to dean minors . take the runs record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; lionel cann } ; runs } ; hop { filter_eq { all_rows ; player ; dean minors } ; runs } }
select the rows whose crowd record is greater than 20000 . among these rows , select the rows whose away team score record is equal to 13.11 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_greater { all_rows ; crowd ; 20000 } ; away team score ; 13.11 } } ; 2 }
select the row whose time record of all rows is minimum . the year record of this row is 2006 .
Output: eq { hop { argmin { all_rows ; time } ; year } ; 2006 }
select the rows whose opponent record fuzzily matches to st louis blues . there is only one such row in the table .
Output: only { filter_eq { all_rows ; opponent ; st louis blues } }
select the rows whose position record fuzzily matches to punter . there is only one such row in the table . the name record of this unqiue row is adam podlesh .
Output: and { only { filter_eq { all_rows ; position ; punter } } ; eq { hop { filter_eq { all_rows ; position ; punter } ; name } ; adam podlesh } }
for the national team records of all rows , all of them fuzzily match to brazil .
Output: all_eq { all_rows ; national team ; brazil }
for the owner ( s ) records of all rows , most of them fuzzily match to private owner .
Output: most_eq { all_rows ; owner ( s ) ; private owner }
select the rows whose county record fuzzily matches to clark . take the casinos record of this row . select the rows whose county record fuzzily matches to south lake tahoe . take the casinos record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; county ; clark } ; casinos } ; hop { filter_eq { all_rows ; county ; south lake tahoe } ; casinos } }
the sum of the score record of all rows is 8 .
Output: round_eq { sum { all_rows ; score } ; 8 }
the average of the attendance record of all rows is 66000 .
Output: round_eq { avg { all_rows ; attendance } ; 66000 }
for the open cup records of all rows , most of them fuzzily match to did not qualify .
Output: most_eq { all_rows ; open cup ; did not qualify }
the 1st maximum crowd record of all rows is 45848 . the venue record of the row with 1st maximum crowd record is mcg .
Output: and { eq { nth_max { all_rows ; crowd ; 1 } ; 45848 } ; eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; mcg } }
select the rows whose venue record fuzzily matches to toowoomba . there is only one such row in the table . the date record of this unqiue row is 25 may 1988 .
Output: and { only { filter_eq { all_rows ; venue ; toowoomba } } ; eq { hop { filter_eq { all_rows ; venue ; toowoomba } ; date } ; 25 may 1988 } }
select the rows whose built record fuzzily matches to 19th . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; built ; 19th } } ; 4 }
select the rows whose bronze record is equal to 1 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; bronze ; 1 } } ; 3 }
for the played records of all rows , most of them are equal to 38 .
Output: most_eq { all_rows ; played ; 38 }
select the rows whose opponents record fuzzily matches to anne hobbs andrew castle . take the year record of this row . select the rows whose opponents record fuzzily matches to gretchen magers kelly jones . take the year record of this row . the second record is 1 year larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; opponents ; anne hobbs andrew castle } ; year } ; hop { filter_eq { all_rows ; opponents ; gretchen magers kelly jones } ; year } } ; -1 year }
select the rows whose company record fuzzily matches to royal dutch shell . take the market value ( billion ) record of this row . select the rows whose company record fuzzily matches to american international group . take the market value ( billion ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; company ; royal dutch shell } ; market value ( billion ) } ; hop { filter_eq { all_rows ; company ; american international group } ; market value ( billion ) } }
for the nationality records of all rows , all of them fuzzily match to united states .
Output: all_eq { all_rows ; nationality ; united states }
select the row whose 13.5 - inch / 1400lb record of all rows is maximum . the ship record of this row is könig .
Output: eq { hop { argmax { all_rows ; 13.5 - inch / 1400lb } ; ship } ; könig }
the average of the height record of all rows is 2.01 .
Output: round_eq { avg { all_rows ; height } ; 2.01 }
select the rows whose opposing teams record fuzzily matches to france . take the against record of this row . select the rows whose opposing teams record fuzzily matches to wales . take the against record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opposing teams ; france } ; against } ; hop { filter_eq { all_rows ; opposing teams ; wales } ; against } }
the average of the average record of all rows is 25.2 .
Output: round_eq { avg { all_rows ; average } ; 25.2 }
select the rows whose written by record fuzzily matches to jonathan shapiro . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; written by ; jonathan shapiro } } ; 2 }
select the rows whose opponent record fuzzily matches to los angeles rams . the average of the attendance record of these rows is 68,437 .
Output: round_eq { avg { filter_eq { all_rows ; opponent ; los angeles rams } ; attendance } ; 68,437 }
select the row whose final placing record of all rows is minimum . the series record of this row is porsche carrera cup germany .
Output: eq { hop { argmin { all_rows ; final placing } ; series } ; porsche carrera cup germany }
select the rows whose visitor record fuzzily matches to philadelphia . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; visitor ; philadelphia } } ; 6 }
select the row whose frequency mhz record of all rows is 2nd maximum . the call sign record of this row is w294aj .
Output: eq { hop { nth_argmax { all_rows ; frequency mhz ; 2 } ; call sign } ; w294aj }
select the rows whose opponent team record fuzzily matches to latvia . take the outcome record of this row . select the rows whose opponent team record fuzzily matches to iceland . take the outcome record of this row . the first record fuzzily matches to the second record . the outcome record of the first row is winner . the outcome record of the second row is winner .
Output: and { eq { hop { filter_eq { all_rows ; opponent team ; latvia } ; outcome } ; hop { filter_eq { all_rows ; opponent team ; iceland } ; outcome } } ; and { eq { hop { filter_eq { all_rows ; opponent team ; latvia } ; outcome } ; winner } ; eq { hop { filter_eq { all_rows ; opponent team ; iceland } ; outcome } ; winner } } }
select the row whose attendance record of all rows is maximum . the tie no record of this row is 51 .
Output: eq { hop { argmax { all_rows ; attendance } ; tie no } ; 51 }
select the rows whose year record is less than 2010 . the number of such rows is 3 .
Output: eq { count { filter_less { all_rows ; year ; 2010 } } ; 3 }
select the rows whose year record is equal to 1978 . the sum of the result record of these rows is 62 .
Output: round_eq { sum { filter_eq { all_rows ; year ; 1978 } ; result } ; 62 }
for the monounsaturated fat records of all rows , most of them are greater than or equal to 20 g .
Output: most_greater_eq { all_rows ; monounsaturated fat ; 20 g }
select the rows whose opponent record fuzzily matches to ottawa senators . take the february record of this row . select the rows whose opponent record fuzzily matches to nashville predators . take the february record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; opponent ; ottawa senators } ; february } ; hop { filter_eq { all_rows ; opponent ; nashville predators } ; february } }
select the rows whose year record is equal to 2008 . there is only one such row in the table . the tournament record of this unqiue row is vietnam open grand prix .
Output: and { only { filter_eq { all_rows ; year ; 2008 } } ; eq { hop { filter_eq { all_rows ; year ; 2008 } ; tournament } ; vietnam open grand prix } }
select the rows whose venue record fuzzily matches to randwick . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; venue ; randwick } } ; 5 }
select the row whose uncut run time record of all rows is 2nd maximum . the title record of this row is addio zio tom .
Output: eq { hop { nth_argmax { all_rows ; uncut run time ; 2 } ; title } ; addio zio tom }
the average of the overall record of all rows is 176.09 .
Output: round_eq { avg { all_rows ; overall } ; 176.09 }
select the rows whose top - 25 record is equal to 6 . there is only one such row in the table . the tournament record of this unqiue row is masters tournament .
Output: and { only { filter_eq { all_rows ; top - 25 ; 6 } } ; eq { hop { filter_eq { all_rows ; top - 25 ; 6 } ; tournament } ; masters tournament } }
select the row whose games record of all rows is maximum . the player record of this row is kevin anderson .
Output: eq { hop { argmax { all_rows ; games } ; player } ; kevin anderson }
for the venue records of all rows , most of them fuzzily match to estadio cuscatlán , san salvador , el salvador .
Output: most_eq { all_rows ; venue ; estadio cuscatlán , san salvador , el salvador }
select the rows whose races record is less than 10 . there is only one such row in the table . the drivers record of this unqiue row is ben hanley .
Output: and { only { filter_less { all_rows ; races ; 10 } } ; eq { hop { filter_less { all_rows ; races ; 10 } ; drivers } ; ben hanley } }
select the row whose orbital period record of all rows is minimum . the planet record of this row is alpha centauri bb .
Output: eq { hop { argmin { all_rows ; orbital period } ; planet } ; alpha centauri bb }
select the rows whose manner of departure record fuzzily matches to resigned . there is only one such row in the table . the outgoing manager record of this unqiue row is miguel brindisi .
Output: and { only { filter_eq { all_rows ; manner of departure ; resigned } } ; eq { hop { filter_eq { all_rows ; manner of departure ; resigned } ; outgoing manager } ; miguel brindisi } }
select the rows whose 1953 record fuzzily matches to nathan karp . take the total record of this row . select the rows whose 1953 record fuzzily matches to david l weiss . take the total record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; 1953 ; nathan karp } ; total } ; hop { filter_eq { all_rows ; 1953 ; david l weiss } ; total } }
the average of the downstream rate record of all rows is 14 .
Output: round_eq { avg { all_rows ; downstream rate } ; 14 }
select the rows whose music director record fuzzily matches to dharan . there is only one such row in the table . the song title record of this unqiue row is anti pettukundhuna .
Output: and { only { filter_eq { all_rows ; music director ; dharan } } ; eq { hop { filter_eq { all_rows ; music director ; dharan } ; song title } ; anti pettukundhuna } }
the average of the away team score record of all rows is 11.36 .
Output: round_eq { avg { all_rows ; away team score } ; 11.36 }
select the rows whose name record is arbitrary . the number of such rows is 12 .
Output: eq { count { filter_all { all_rows ; name } } ; 12 }
select the rows whose couple record fuzzily matches to jason & edyta . take the score record of this row . select the rows whose couple record fuzzily matches to kristi & mark . take the score record of this row . the first record is less than the second record . the score record of the first row is 23 ( 8 , 7 , 8 ) . the score record of the second row is 27 ( 9 , 9 , 9 ) .
Output: and { less { hop { filter_eq { all_rows ; couple ; jason & edyta } ; score } ; hop { filter_eq { all_rows ; couple ; kristi & mark } ; score } } ; and { eq { hop { filter_eq { all_rows ; couple ; jason & edyta } ; score } ; 23 ( 8 , 7 , 8 ) } ; eq { hop { filter_eq { all_rows ; couple ; kristi & mark } ; score } ; 27 ( 9 , 9 , 9 ) } } }
select the rows whose opponent record fuzzily matches to christian wellisch . take the round record of this row . select the rows whose opponent record fuzzily matches to pat harmon . take the round record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; christian wellisch } ; round } ; hop { filter_eq { all_rows ; opponent ; pat harmon } ; round } }
select the rows whose hometown record fuzzily matches to swakopmund . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; hometown ; swakopmund } } ; 2 }
for the decision records of all rows , most of them fuzzily match to lundqvist .
Output: most_eq { all_rows ; decision ; lundqvist }
select the rows whose tries against record is equal to 6 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; tries against ; 6 } } ; 2 }
select the rows whose constructor record fuzzily matches to mclaren - mercedes . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; constructor ; mclaren - mercedes } } ; 2 }
select the rows whose rushing yards record is less than 1000 . there is only one such row in the table . the year record of this unqiue row is 1989 .
Output: and { only { filter_less { all_rows ; rushing yards ; 1000 } } ; eq { hop { filter_less { all_rows ; rushing yards ; 1000 } ; year } ; 1989 } }
for the video records of all rows , most of them fuzzily match to 480i .
Output: most_eq { all_rows ; video ; 480i }
the average of the goals record of all rows is 1.4 .
Output: round_eq { avg { all_rows ; goals } ; 1.4 }
for the tyres records of all rows , most of them fuzzily match to g .
Output: most_eq { all_rows ; tyres ; g }
the 3rd minimum rank record of all rows is 3 . the nation record of the row with 3rd minimum rank record is japan ( jpn ) . the silver record of the row with 3rd minimum rank record is 1 .
Output: and { eq { nth_min { all_rows ; rank ; 3 } ; 3 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 3 } ; nation } ; japan ( jpn ) } ; eq { hop { nth_argmin { all_rows ; rank ; 3 } ; silver } ; 1 } } }
select the rows whose first elected record fuzzily matches to 193 . among these rows , select the rows whose candidates record fuzzily matches to unopposed . the number of such rows is 1 .
Output: eq { count { filter_eq { filter_eq { all_rows ; first elected ; 193 } ; candidates ; unopposed } } ; 1 }
select the rows whose date record is greater than or equal to december 3 , 1989 . among these rows , select the rows whose attendance record is greater than 10000 . the number of such rows is 3 .
Output: eq { count { filter_greater { filter_greater_eq { all_rows ; date ; december 3 , 1989 } ; attendance ; 10000 } } ; 3 }
select the rows whose no record is greater than or equal to 8 . the sum of the us viewers ( millions ) record of these rows is 2.20 .
Output: round_eq { sum { filter_greater_eq { all_rows ; no ; 8 } ; us viewers ( millions ) } ; 2.20 }
for the hk viewers records of all rows , most of them are greater than 2 .
Output: most_greater { all_rows ; hk viewers ; 2 }
select the row whose money raised , 2q record of all rows is 3rd maximum . the candidate record of this row is john edwards .
Output: eq { hop { nth_argmax { all_rows ; money raised , 2q ; 3 } ; candidate } ; john edwards }
the sum of the against record of all rows is 121 .
Output: round_eq { sum { all_rows ; against } ; 121 }
select the rows whose native american record fuzzily matches to 0.0 % . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; native american ; 0.0 % } } ; 4 }
select the row whose highest record of all rows is maximum . the team record of this row is st johnstone .
Output: eq { hop { argmax { all_rows ; highest } ; team } ; st johnstone }
select the rows whose resolution record fuzzily matches to committed suicide . there is only one such row in the table . the name record of this unqiue row is josé manuel balmaceda .
Output: and { only { filter_eq { all_rows ; resolution ; committed suicide } } ; eq { hop { filter_eq { all_rows ; resolution ; committed suicide } ; name } ; josé manuel balmaceda } }
select the row whose duration record of all rows is 2nd maximum . the song record of this row is sólo cristo .
Output: eq { hop { nth_argmax { all_rows ; duration ; 2 } ; song } ; sólo cristo }
for the year joined records of all rows , most of them are equal to 1998 .
Output: most_eq { all_rows ; year joined ; 1998 }