niryuu/sni-each-converted huggingface dataset:
select the rows whose current affiliation record fuzzily matches to nbc affiliate owned by gannett company . there is only one such row in the table . the station record of this unqiue row is kyw - tv ( now wkyc - tv ) .
Output: and { only { filter_eq { all_rows ; current affiliation ; nbc affiliate owned by gannett company } } ; eq { hop { filter_eq { all_rows ; current affiliation ; nbc affiliate owned by gannett company } ; station } ; kyw - tv ( now wkyc - tv ) } }
select the rows whose result record fuzzily matches to lost . there is only one such row in the table . the date record of this unqiue row is october 7 , 1994 .
Output: and { only { filter_eq { all_rows ; result ; lost } } ; eq { hop { filter_eq { all_rows ; result ; lost } ; date } ; october 7 , 1994 } }
the average of the rating record of all rows is 0.6 .
Output: round_eq { avg { all_rows ; rating } ; 0.6 }
select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is s otis bland .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; s otis bland }
select the rows whose player record fuzzily matches to jeff urban . take the pick record of this row . select the rows whose player record fuzzily matches to chris george . take the pick record of this row . the first record is greater than the second record . the pick record of the first row is 41 . the pick record of the second row is 31 .
Output: and { greater { hop { filter_eq { all_rows ; player ; jeff urban } ; pick } ; hop { filter_eq { all_rows ; player ; chris george } ; pick } } ; and { eq { hop { filter_eq { all_rows ; player ; jeff urban } ; pick } ; 41 } ; eq { hop { filter_eq { all_rows ; player ; chris george } ; pick } ; 31 } } }
select the row whose location / attendance record of all rows is minimum . the date record of this row is july 2 .
Output: eq { hop { argmin { all_rows ; location / attendance } ; date } ; july 2 }
select the rows whose result record fuzzily matches to nominee . there is only one such row in the table . the english title record of this unqiue row is paradise now .
Output: and { only { filter_eq { all_rows ; result ; nominee } } ; eq { hop { filter_eq { all_rows ; result ; nominee } ; english title } ; paradise now } }
for the result records of all rows , most of them fuzzily match to icl india by 4 wickets .
Output: most_eq { all_rows ; result ; icl india by 4 wickets }
select the rows whose nation record is arbitrary . the number of such rows is 9 .
Output: eq { count { filter_all { all_rows ; nation } } ; 9 }
select the row whose pick record of all rows is minimum . the nhl team record of this row is hartford whalers .
Output: eq { hop { argmin { all_rows ; pick } ; nhl team } ; hartford whalers }
select the row whose pick record of all rows is 2nd maximum . the player record of this row is spencer wadsworth .
Output: eq { hop { nth_argmax { all_rows ; pick ; 2 } ; player } ; spencer wadsworth }
select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is overton brooks .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; overton brooks }
the average of the score record of all rows is 70.46 .
Output: round_eq { avg { all_rows ; score } ; 70.46 }
select the row whose year record of all rows is 4th minimum . the song title record of this row is hrudayam ekkadunnadi .
Output: eq { hop { nth_argmin { all_rows ; year ; 4 } ; song title } ; hrudayam ekkadunnadi }
select the row whose attendance record of all rows is minimum . the date record of this row is october 14 , 1962 .
Output: eq { hop { argmin { all_rows ; attendance } ; date } ; october 14 , 1962 }
for the manner records of all rows , most of them fuzzily match to sacked .
Output: most_eq { all_rows ; manner ; sacked }
for the introduction records of all rows , all of them fuzzily match to 12 december 1928 .
Output: all_eq { all_rows ; introduction ; 12 december 1928 }
select the rows whose series ep record is arbitrary . the number of such rows is 13 .
Output: eq { count { filter_all { all_rows ; series ep } } ; 13 }
select the rows whose capacity record is equal to 15042 . there is only one such row in the table . the home venue record of this unqiue row is sangju civic stadium .
Output: and { only { filter_eq { all_rows ; capacity ; 15042 } } ; eq { hop { filter_eq { all_rows ; capacity ; 15042 } ; home venue } ; sangju civic stadium } }
select the row whose episodes record of all rows is minimum . the character record of this row is aiden burn csi detective .
Output: eq { hop { argmin { all_rows ; episodes } ; character } ; aiden burn csi detective }
for the attendance records of all rows , most of them are greater than 50000 .
Output: most_greater { all_rows ; attendance ; 50000 }
select the rows whose money record is greater than 100000 . there is only one such row in the table . the player record of this unqiue row is ben crenshaw .
Output: and { only { filter_greater { all_rows ; money ; 100000 } } ; eq { hop { filter_greater { all_rows ; money ; 100000 } ; player } ; ben crenshaw } }
select the rows whose business intelligence record fuzzily matches to no . the number of such rows is 11 .
Output: eq { count { filter_eq { all_rows ; business intelligence ; no } } ; 11 }
select the rows whose album record fuzzily matches to lost souls . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; album ; lost souls } } ; 3 }
select the rows whose category record fuzzily matches to best series - drama . the minimum year record of these rows is 1994 .
Output: eq { min { filter_eq { all_rows ; category ; best series - drama } ; year } ; 1994 }
select the rows whose location record fuzzily matches to sheffield , england . among these rows , select the rows whose type record fuzzily matches to club trophy . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; location ; sheffield , england } ; type ; club trophy } } ; 2 }
select the rows whose airline record fuzzily matches to tam . take the rank record of this row . select the rows whose airline record fuzzily matches to gol . take the rank record of this row . the second record is 1 better larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; airline ; tam } ; rank } ; hop { filter_eq { all_rows ; airline ; gol } ; rank } } ; -1 better }
select the rows whose pitcher record fuzzily matches to toad ramsey . take the strikeouts record of this row . select the rows whose pitcher record fuzzily matches to old hoss radbourn . take the strikeouts record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; pitcher ; toad ramsey } ; strikeouts } ; hop { filter_eq { all_rows ; pitcher ; old hoss radbourn } ; strikeouts } }
for the candidates records of all rows , most of them fuzzily match to unopposed .
Output: most_eq { all_rows ; candidates ; unopposed }
select the rows whose nationality record does not match to ger . among these rows , select the rows whose points record is greater than 257.0 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_not_eq { all_rows ; nationality ; ger } ; points ; 257.0 } } ; 2 }
select the row whose date record of all rows is 2nd maximum . the opponent record of this row is maša zec peškirič .
Output: eq { hop { nth_argmax { all_rows ; date ; 2 } ; opponent } ; maša zec peškirič }
the average of the to par record of all rows is -4.64 .
Output: round_eq { avg { all_rows ; to par } ; -4.64 }
select the rows whose contestant record is arbitrary . the number of such rows is 19 .
Output: eq { count { filter_all { all_rows ; contestant } } ; 19 }
the average of the driver - matched db record of all rows is 0.07 .
Output: round_eq { avg { all_rows ; driver - matched db } ; 0.07 }
the sum of the points record of all rows is 2 .
Output: round_eq { sum { all_rows ; points } ; 2 }
for the team records of all rows , most of them fuzzily match to triple eight race engineering .
Output: most_eq { all_rows ; team ; triple eight race engineering }
select the rows whose official name record fuzzily matches to madawaska . take the area km 2 record of this row . select the rows whose official name record fuzzily matches to baker brook . take the area km 2 record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; official name ; madawaska } ; area km 2 } ; hop { filter_eq { all_rows ; official name ; baker brook } ; area km 2 } }
select the row whose time record of all rows is 2nd minimum . the event record of this row is gladiator challenge 87 : collision course .
Output: eq { hop { nth_argmin { all_rows ; time ; 2 } ; event } ; gladiator challenge 87 : collision course }
select the row whose attendance record of all rows is maximum . the date record of this row is april 22 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; april 22 }
select the rows whose result record is arbitrary . the number of such rows is 5 .
Output: eq { count { filter_all { all_rows ; result } } ; 5 }
select the rows whose date record fuzzily matches to may . there is only one such row in the table .
Output: only { filter_eq { all_rows ; date ; may } }
the sum of the laps record of all rows is 2021 .
Output: round_eq { sum { all_rows ; laps } ; 2021 }
select the rows whose winning driver record fuzzily matches to petri suvanto . select the row whose date record of these rows is 1st maximum . the circuit record of this row is road america .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; winning driver ; petri suvanto } ; date ; 1 } ; circuit } ; road america }
the average of the laps record of all rows is 200 .
Output: round_eq { avg { all_rows ; laps } ; 200 }
select the row whose crowd record of all rows is maximum . the venue record of this row is victoria park .
Output: eq { hop { argmax { all_rows ; crowd } ; venue } ; victoria park }
for the best jump records of all rows , most of them are greater than over 7 meters .
Output: most_greater { all_rows ; best jump ; over 7 meters }
select the rows whose venue record fuzzily matches to west germany . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; venue ; west germany } } ; 2 }
the average of the height ( m ) record of all rows is 261.3 .
Output: round_eq { avg { all_rows ; height ( m ) } ; 261.3 }
select the row whose population ( 2011 ) record of all rows is 2nd maximum . the urban settlement record of this row is ruma .
Output: eq { hop { nth_argmax { all_rows ; population ( 2011 ) ; 2 } ; urban settlement } ; ruma }
select the rows whose to par record is equal to -3 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; to par ; -3 } } ; 2 }
the 2nd maximum attendance record of all rows is 55400 .
Output: eq { nth_max { all_rows ; attendance ; 2 } ; 55400 }
select the rows whose venue record fuzzily matches to trent bridge . there is only one such row in the table . the date record of this unqiue row is 23 , 24 , 25 , 26 , 27 july 1998 .
Output: and { only { filter_eq { all_rows ; venue ; trent bridge } } ; eq { hop { filter_eq { all_rows ; venue ; trent bridge } ; date } ; 23 , 24 , 25 , 26 , 27 july 1998 } }
select the rows whose venue record fuzzily matches to austria . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; venue ; austria } } ; 3 }
select the rows whose entrant record fuzzily matches to jolly club spa . there is only one such row in the table . the engine record of this unqiue row is motori moderni tipo 615 - 90 1.5 v6 t .
Output: and { only { filter_eq { all_rows ; entrant ; jolly club spa } } ; eq { hop { filter_eq { all_rows ; entrant ; jolly club spa } ; engine } ; motori moderni tipo 615 - 90 1.5 v6 t } }
select the row whose sales ( billion ) record of all rows is maximum . the company record of this row is wal - mart stores .
Output: eq { hop { argmax { all_rows ; sales ( billion ) } ; company } ; wal - mart stores }
the average of the attendance record of all rows is 36859 .
Output: round_eq { avg { all_rows ; attendance } ; 36859 }
select the rows whose player record fuzzily matches to mark fidler . take the goals record of this row . select the rows whose player record fuzzily matches to david sacco . take the goals record of this row . the first record is 3 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; player ; mark fidler } ; goals } ; hop { filter_eq { all_rows ; player ; david sacco } ; goals } } ; 3 }
select the rows whose won record is less than or equal to 5 . the sum of the points record of these rows is 75 .
Output: round_eq { sum { filter_less_eq { all_rows ; won ; 5 } ; points } ; 75 }
select the rows whose shooter record fuzzily matches to sergei pyzhianov . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; shooter ; sergei pyzhianov } } ; 2 }
select the row whose crowd record of all rows is maximum . the venue record of this row is vfl park .
Output: eq { hop { argmax { all_rows ; crowd } ; venue } ; vfl park }
select the row whose attendance record of all rows is maximum . the date record of this row is november 10 , 2002 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; november 10 , 2002 }
select the row whose % of global catholic pop record of all rows is 2nd maximum . the region record of this row is south asia .
Output: eq { hop { nth_argmax { all_rows ; % of global catholic pop ; 2 } ; region } ; south asia }
select the rows whose transfer window record fuzzily matches to winter . there is only one such row in the table . the name record of this unqiue row is burke .
Output: and { only { filter_eq { all_rows ; transfer window ; winter } } ; eq { hop { filter_eq { all_rows ; transfer window ; winter } ; name } ; burke } }
for the class records of all rows , most of them fuzzily match to cat a .
Output: most_eq { all_rows ; class ; cat a }
select the rows whose original air date record fuzzily matches to august . select the row whose us viewers ( million ) record of these rows is 2nd maximum . the title record of this row is a little death .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; original air date ; august } ; us viewers ( million ) ; 2 } ; title } ; a little death }
select the rows whose country record fuzzily matches to united states . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; country ; united states } } ; 8 }
select the rows whose result record fuzzily matches to new seat republican gain . there is only one such row in the table . the first elected record of this unqiue row is none ( district created ) .
Output: and { only { filter_eq { all_rows ; result ; new seat republican gain } } ; eq { hop { filter_eq { all_rows ; result ; new seat republican gain } ; first elected } ; none ( district created ) } }
select the row whose enrollment record of all rows is maximum . the institution record of this row is university of cincinnati .
Output: eq { hop { argmax { all_rows ; enrollment } ; institution } ; university of cincinnati }
select the row whose redshift ( km / s ) record of all rows is maximum . the name record of this row is ngc 1596 .
Output: eq { hop { argmax { all_rows ; redshift ( km / s ) } ; name } ; ngc 1596 }
select the rows whose rufus guest record fuzzily matches to jimmy carr . the number of such rows is 1 .
Output: eq { count { filter_eq { all_rows ; rufus guest ; jimmy carr } } ; 1 }
select the rows whose winner record fuzzily matches to brad jones . for the team records of these rows , all of them fuzzily match to brad jones racing .
Output: all_eq { filter_eq { all_rows ; winner ; brad jones } ; team ; brad jones racing }
for the of which currently forests , km square records of all rows , most of them are less than 1000 .
Output: most_less { all_rows ; of which currently forests , km square ; 1000 }
select the rows whose position record fuzzily matches to point guard . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; position ; point guard } } ; 2 }
the sum of the overall record of all rows is 503 .
Output: round_eq { sum { all_rows ; overall } ; 503 }
select the row whose wins record of all rows is maximum . the player record of this row is tom kite .
Output: eq { hop { argmax { all_rows ; wins } ; player } ; tom kite }
select the row whose gold record of all rows is maximum . the nation record of this row is canada ( can ) .
Output: eq { hop { argmax { all_rows ; gold } ; nation } ; canada ( can ) }
for the result records of all rows , most of them fuzzily match to loss .
Output: most_eq { all_rows ; result ; loss }
select the row whose enrollment record of all rows is maximum . the school record of this row is northview .
Output: eq { hop { argmax { all_rows ; enrollment } ; school } ; northview }
select the rows whose year record fuzzily matches to freshman . the average of the weight record of these rows is 215 .
Output: round_eq { avg { filter_eq { all_rows ; year ; freshman } ; weight } ; 215 }
select the rows whose artist record fuzzily matches to morena camilleri . the average of the place record of these rows is 3 .
Output: round_eq { avg { filter_eq { all_rows ; artist ; morena camilleri } ; place } ; 3 }
select the rows whose team record fuzzily matches to buck baker . there is only one such row in the table .
Output: only { filter_eq { all_rows ; team ; buck baker } }
select the row whose score record of all rows is 2nd maximum . the competition record of this row is 2007 afc asian cup qualification .
Output: eq { hop { nth_argmax { all_rows ; score ; 2 } ; competition } ; 2007 afc asian cup qualification }
select the row whose f bolt ( kgf ) record of all rows is maximum . the chambering record of this row is .454 casull .
Output: eq { hop { argmax { all_rows ; f bolt ( kgf ) } ; chambering } ; .454 casull }
select the rows whose nationality record fuzzily matches to alatri . there is only one such row in the table .
Output: only { filter_eq { all_rows ; nationality ; alatri } }
select the rows whose away team record fuzzily matches to south melbourne . for the crowd records of these rows , all of them are greater than 20000 .
Output: all_greater { filter_eq { all_rows ; away team ; south melbourne } ; crowd ; 20000 }
the average of the score record of all rows is 140 .
Output: round_eq { avg { all_rows ; score } ; 140 }
select the row whose time record of all rows is minimum . the rider record of this row is chas mortimer .
Output: eq { hop { argmin { all_rows ; time } ; rider } ; chas mortimer }
select the rows whose parent record fuzzily matches to the cheviot . the average of the height ( m ) record of these rows is 642 .
Output: round_eq { avg { filter_eq { all_rows ; parent ; the cheviot } ; height ( m ) } ; 642 }
for the country of origin records of all rows , most of them fuzzily match to united states .
Output: most_eq { all_rows ; country of origin ; united states }
the average of the crowd record of all rows is 21500 .
Output: round_eq { avg { all_rows ; crowd } ; 21500 }
the average of the days with frost record of all rows is 15.67 .
Output: round_eq { avg { all_rows ; days with frost } ; 15.67 }
select the row whose mccain % record of all rows is maximum . the county record of this row is eureka .
Output: eq { hop { argmax { all_rows ; mccain % } ; county } ; eureka }
select the row whose international tourism expenditure ( 2012 ) record of all rows is 1st maximum . the country record of this row is china .
Output: eq { hop { nth_argmax { all_rows ; international tourism expenditure ( 2012 ) ; 1 } ; country } ; china }
select the rows whose class record fuzzily matches to dx . take the number in service record of this row . select the rows whose class record fuzzily matches to dsc . take the number in service record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; class ; dx } ; number in service } ; hop { filter_eq { all_rows ; class ; dsc } ; number in service } }
select the rows whose escorts record is equal to - . there is only one such row in the table . the nato member record of this unqiue row is belgium .
Output: and { only { filter_eq { all_rows ; escorts ; - } } ; eq { hop { filter_eq { all_rows ; escorts ; - } ; nato member } ; belgium } }
select the rows whose event record fuzzily matches to 50 km . there is only one such row in the table . the year record of this unqiue row is 2011 . the competition record of this unqiue row is world championships .
Output: and { only { filter_eq { all_rows ; event ; 50 km } } ; and { eq { hop { filter_eq { all_rows ; event ; 50 km } ; year } ; 2011 } ; eq { hop { filter_eq { all_rows ; event ; 50 km } ; competition } ; world championships } } }
select the rows whose team ( league ) record fuzzily matches to london knights . there is only one such row in the table . the player record of this unqiue row is jim sandlak .
Output: and { only { filter_eq { all_rows ; team ( league ) ; london knights } } ; eq { hop { filter_eq { all_rows ; team ( league ) ; london knights } ; player } ; jim sandlak } }
select the rows whose result record fuzzily matches to 5 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; result ; 5 } } ; 2 }
the maximum final score record of all rows is w 41 - 31 . the opponent record of the row with superlative final score record is hamburg sea devils .
Output: and { eq { max { all_rows ; final score } ; w 41 - 31 } ; eq { hop { argmax { all_rows ; final score } ; opponent } ; hamburg sea devils } }
the average of the pick record of all rows is 88.4 .
Output: round_eq { avg { all_rows ; pick } ; 88.4 }
select the rows whose opponents record fuzzily matches to fulham . among these rows , select the rows whose result record fuzzily matches to 1-1 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; opponents ; fulham } ; result ; 1-1 } } ; 2 }
select the rows whose venue record fuzzily matches to western oval . there is only one such row in the table . the home team record of this unqiue row is footscray . the away team record of this unqiue row is essendon .
Output: and { only { filter_eq { all_rows ; venue ; western oval } } ; and { eq { hop { filter_eq { all_rows ; venue ; western oval } ; home team } ; footscray } ; eq { hop { filter_eq { all_rows ; venue ; western oval } ; away team } ; essendon } } }
for the length records of all rows , most of them are greater than 4:00 .
Output: most_greater { all_rows ; length ; 4:00 }
for the episodes records of all rows , most of them are greater than or equal to 40 .
Output: most_greater_eq { all_rows ; episodes ; 40 }
for the points records of all rows , most of them are greater than or equal to 10 .
Output: most_greater_eq { all_rows ; points ; 10 }
select the rows whose visitor record fuzzily matches to colorado . among these rows , select the rows whose attendance record is greater than 20000 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_eq { all_rows ; visitor ; colorado } ; attendance ; 20000 } } ; 2 }
for the high points records of all rows , most of them fuzzily match to brandon roy .
Output: most_eq { all_rows ; high points ; brandon roy }
select the rows whose venue record fuzzily matches to hampden park , glasgow , scotland . take the date record of this row . select the rows whose venue record fuzzily matches to varsity stadium , toronto , canada . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; venue ; hampden park , glasgow , scotland } ; date } ; hop { filter_eq { all_rows ; venue ; varsity stadium , toronto , canada } ; date } }
select the rows whose player record fuzzily matches to gene carr . take the pick record of this row . select the rows whose player record fuzzily matches to bernie doan . take the pick record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; gene carr } ; pick } ; hop { filter_eq { all_rows ; player ; bernie doan } ; pick } }
for the directed by records of all rows , all of them fuzzily match to don colliver .
Output: all_eq { all_rows ; directed by ; don colliver }
select the row whose original air date record of all rows is 1st maximum . the title record of this row is weeping willows .
Output: eq { hop { nth_argmax { all_rows ; original air date ; 1 } ; title } ; weeping willows }
select the row whose attendance record of all rows is maximum . the date record of this row is october 11 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; october 11 }
select the rows whose position record fuzzily matches to defensive end . select the row whose pick record of these rows is 2nd minimum . the player record of this row is leif larsen .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; position ; defensive end } ; pick ; 2 } ; player } ; leif larsen }
select the rows whose player record fuzzily matches to pascal chimbonda . take the total record of this row . select the rows whose player record fuzzily matches to robbie keane . take the total record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; pascal chimbonda } ; total } ; hop { filter_eq { all_rows ; player ; robbie keane } ; total } }
select the rows whose year record is greater than 2000 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; year ; 2000 } } ; 3 }
for the date records of all rows , all of them fuzzily match to 8 july 1933 .
Output: all_eq { all_rows ; date ; 8 july 1933 }
select the row whose mintage record of all rows is maximum . the design record of this row is diamonds .
Output: eq { hop { argmax { all_rows ; mintage } ; design } ; diamonds }
select the rows whose crew record fuzzily matches to none . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; crew ; none } } ; 8 }
the 3rd minimum year record of all rows is 2006 . the team record of the row with 3rd minimum year record is rollcentre racing .
Output: and { eq { nth_min { all_rows ; year ; 3 } ; 2006 } ; eq { hop { nth_argmin { all_rows ; year ; 3 } ; team } ; rollcentre racing } }
select the row whose entered service record of all rows is 3rd minimum . the locomotive record of this row is c503 .
Output: eq { hop { nth_argmin { all_rows ; entered service ; 3 } ; locomotive } ; c503 }
select the rows whose developer record fuzzily matches to cloud imperium games corporation . there is only one such row in the table . the title record of this unqiue row is star citizen .
Output: and { only { filter_eq { all_rows ; developer ; cloud imperium games corporation } } ; eq { hop { filter_eq { all_rows ; developer ; cloud imperium games corporation } ; title } ; star citizen } }
select the rows whose name record is arbitrary . the number of such rows is 18 .
Output: eq { count { filter_all { all_rows ; name } } ; 18 }
select the row whose ties record of all rows is maximum . the season record of this row is 2011 .
Output: eq { hop { argmax { all_rows ; ties } ; season } ; 2011 }
select the row whose score record of all rows is minimum . the player record of this row is tim simpson .
Output: eq { hop { argmin { all_rows ; score } ; player } ; tim simpson }
select the rows whose time / retired record is less than 10 secs . the number of such rows is 6 .
Output: eq { count { filter_less { all_rows ; time / retired ; 10 secs } } ; 6 }
the average of the score record of all rows is 34.5 .
Output: round_eq { avg { all_rows ; score } ; 34.5 }
the average of the rebounds record of all rows is 269 .
Output: round_eq { avg { all_rows ; rebounds } ; 269 }
select the rows whose position record fuzzily matches to guard . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; position ; guard } } ; 4 }
select the rows whose institution record fuzzily matches to minot state university . take the enrollment record of this row . select the rows whose institution record fuzzily matches to dakota state university . take the enrollment record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; institution ; minot state university } ; enrollment } ; hop { filter_eq { all_rows ; institution ; dakota state university } ; enrollment } }
select the rows whose college record fuzzily matches to arkansas state . there is only one such row in the table . the name record of this unqiue row is robert speer .
Output: and { only { filter_eq { all_rows ; college ; arkansas state } } ; eq { hop { filter_eq { all_rows ; college ; arkansas state } ; name } ; robert speer } }
select the row whose total record of all rows is 2nd maximum . the nation record of this row is soviet union .
Output: eq { hop { nth_argmax { all_rows ; total ; 2 } ; nation } ; soviet union }
select the rows whose result record fuzzily matches to won . there is only one such row in the table . the category record of this unqiue row is choice summer tv star : male .
Output: and { only { filter_eq { all_rows ; result ; won } } ; eq { hop { filter_eq { all_rows ; result ; won } ; category } ; choice summer tv star : male } }
select the row whose power notation record of all rows is 2nd maximum . the short scale record of this row is one billion a thousand million .
Output: eq { hop { nth_argmax { all_rows ; power notation ; 2 } ; short scale } ; one billion a thousand million }
select the rows whose result record is less than 10 . there is only one such row in the table . the date record of this unqiue row is sunday , september 27 .
Output: and { only { filter_less { all_rows ; result ; 10 } } ; eq { hop { filter_less { all_rows ; result ; 10 } ; date } ; sunday , september 27 } }
select the row whose points record of all rows is maximum . the name record of this row is tai babilonia / randy gardner .
Output: eq { hop { argmax { all_rows ; points } ; name } ; tai babilonia / randy gardner }
select the rows whose tournament record fuzzily matches to the open championship . take the events record of this row . select the rows whose tournament record fuzzily matches to pga championship . take the events record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; tournament ; the open championship } ; events } ; hop { filter_eq { all_rows ; tournament ; pga championship } ; events } }
select the row whose shot % record of all rows is minimum . the skip record of this row is anna kubešková .
Output: eq { hop { argmin { all_rows ; shot % } ; skip } ; anna kubešková }
the average of the matches record of all rows is 4.1 .
Output: round_eq { avg { all_rows ; matches } ; 4.1 }
select the rows whose city / state record fuzzily matches to phillip island , victoria . among these rows , select the rows whose team record fuzzily matches to volvo racing . there is only one such row in the table .
Output: only { filter_eq { filter_eq { all_rows ; city / state ; phillip island , victoria } ; team ; volvo racing } }
select the rows whose opponent record fuzzily matches to leicester city . there is only one such row in the table . the date record of this unqiue row is 30 january 2000 .
Output: and { only { filter_eq { all_rows ; opponent ; leicester city } } ; eq { hop { filter_eq { all_rows ; opponent ; leicester city } ; date } ; 30 january 2000 } }
select the row whose imports record of all rows is maximum . the country record of this row is china .
Output: eq { hop { argmax { all_rows ; imports } ; country } ; china }
select the row whose total ( kg ) record of all rows is 2nd maximum . the name record of this row is pedro stetsiuk ( arg ) .
Output: eq { hop { nth_argmax { all_rows ; total ( kg ) ; 2 } ; name } ; pedro stetsiuk ( arg ) }
select the row whose time record of all rows is 2nd maximum . the country record of this row is slovenia .
Output: eq { hop { nth_argmax { all_rows ; time ; 2 } ; country } ; slovenia }
select the rows whose opposing teams record fuzzily matches to japan . take the against record of this row . select the rows whose opposing teams record fuzzily matches to usa . 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 ; japan } ; against } ; hop { filter_eq { all_rows ; opposing teams ; usa } ; against } }
the average of the enrollment record of all rows is 476 .
Output: round_eq { avg { all_rows ; enrollment } ; 476 }
select the row whose population ( 2011 ) record of all rows is 2nd maximum . the settlement record of this row is aleksandrovo .
Output: eq { hop { nth_argmax { all_rows ; population ( 2011 ) ; 2 } ; settlement } ; aleksandrovo }
select the rows whose snatch record is greater than 150 . select the row whose bodyweight record of these rows is maximum . the name record of this row is herbys márquez ( ven ) .
Output: eq { hop { argmax { filter_greater { all_rows ; snatch ; 150 } ; bodyweight } ; name } ; herbys márquez ( ven ) }
for the res records of all rows , most of them fuzzily match to win .
Output: most_eq { all_rows ; res ; win }
the average of the attendance record of all rows is 20240 .
Output: round_eq { avg { all_rows ; attendance } ; 20240 }
select the rows whose time / retired record fuzzily matches to accident . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; time / retired ; accident } } ; 2 }
select the rows whose college record fuzzily matches to hastings college . there is only one such row in the table . the player record of this unqiue row is adam kossack .
Output: and { only { filter_eq { all_rows ; college ; hastings college } } ; eq { hop { filter_eq { all_rows ; college ; hastings college } ; player } ; adam kossack } }
select the rows whose location record fuzzily matches to fleetcenter . the minimum date record of these rows is fri mar 1 .
Output: eq { min { filter_eq { all_rows ; location ; fleetcenter } ; date } ; fri mar 1 }
select the row whose winnings record of all rows is maximum . the year record of this row is 2006 .
Output: eq { hop { argmax { all_rows ; winnings } ; year } ; 2006 }
the average of the time record of all rows is 3:09 .
Output: round_eq { avg { all_rows ; time } ; 3:09 }
for the place records of all rows , most of them are greater than 10 .
Output: most_greater { all_rows ; place ; 10 }
select the rows whose audition city record fuzzily matches to são paulo . there is only one such row in the table . the guest fourth judge record of this unqiue row is peninha .
Output: and { only { filter_eq { all_rows ; audition city ; são paulo } } ; eq { hop { filter_eq { all_rows ; audition city ; são paulo } ; guest fourth judge } ; peninha } }
select the row whose height ( cm ) record of all rows is 2nd maximum . the name record of this row is al iafrate .
Output: eq { hop { nth_argmax { all_rows ; height ( cm ) ; 2 } ; name } ; al iafrate }
select the row whose matches record of all rows is 2nd maximum . the goalkeeper record of this row is joel rodríguez .
Output: eq { hop { nth_argmax { all_rows ; matches ; 2 } ; goalkeeper } ; joel rodríguez }
for the position records of all rows , most of them are less than or equal to 15 .
Output: most_less_eq { all_rows ; position ; 15 }
select the rows whose team record fuzzily matches to aermacchi . there is only one such row in the table .
Output: only { filter_eq { all_rows ; team ; aermacchi } }
select the rows whose date record fuzzily matches to october . among these rows , select the rows whose result record fuzzily matches to l . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; date ; october } ; result ; l } } ; 3 }
the sum of the wickets record of all rows is 90 .
Output: round_eq { sum { all_rows ; wickets } ; 90 }
select the rows whose date record fuzzily matches to november . among these rows , select the rows whose result record fuzzily matches to l . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; date ; november } ; result ; l } } ; 2 }
select the rows whose call sign record fuzzily matches to cimo - fm . take the frequency record of this row . select the rows whose call sign record fuzzily matches to cfak - fm . take the frequency record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; call sign ; cimo - fm } ; frequency } ; hop { filter_eq { all_rows ; call sign ; cfak - fm } ; frequency } }
select the rows whose player record fuzzily matches to adam gilchrist . take the matches record of this row . select the rows whose player record fuzzily matches to steve rixon . take the matches record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; adam gilchrist } ; matches } ; hop { filter_eq { all_rows ; player ; steve rixon } ; matches } }
select the rows whose winner record fuzzily matches to edvald boasson hagen . the minimum stage record of these rows is 4 .
Output: eq { min { filter_eq { all_rows ; winner ; edvald boasson hagen } ; stage } ; 4 }
select the rows whose date record fuzzily matches to oct . among these rows , select the rows whose opponent record fuzzily matches to new england patriots . there is only one such row in the table .
Output: only { filter_eq { filter_eq { all_rows ; date ; oct } ; opponent ; new england patriots } }
the sum of the score record of all rows is 16 .
Output: round_eq { sum { all_rows ; score } ; 16 }
select the rows whose competition record is equal to 2014 . there is only one such row in the table . the venue record of this unqiue row is prince mohamed bin fahd stadium , dammam .
Output: and { only { filter_eq { all_rows ; competition ; 2014 } } ; eq { hop { filter_eq { all_rows ; competition ; 2014 } ; venue } ; prince mohamed bin fahd stadium , dammam } }
for the single day net gross records of all rows , most of them are greater than 25 , 00 , 00000 .
Output: most_greater { all_rows ; single day net gross ; 25 , 00 , 00000 }
select the rows whose date record fuzzily matches to 1987 . the average of the winning score record of these rows is 305.5 .
Output: round_eq { avg { filter_eq { all_rows ; date ; 1987 } ; winning score } ; 305.5 }
for the format records of all rows , most of them fuzzily match to lp .
Output: most_eq { all_rows ; format ; lp }
select the row whose crowd record of all rows is 1st maximum . the venue record of this row is vfl park .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; vfl park }
select the row whose s risk - weighted asset , million record of all rows is maximum . the institution record of this row is allied irish banks .
Output: eq { hop { argmax { all_rows ; s risk - weighted asset , million } ; institution } ; allied irish banks }
the maximum saturated fat record of all rows is 52 g ( 55 % ) . the record of the row with superlative saturated fat record is suet .
Output: and { eq { max { all_rows ; saturated fat } ; 52 g ( 55 % ) } ; eq { hop { argmax { all_rows ; saturated fat } ; } ; suet } }
select the rows whose title record fuzzily matches to road to the north pole . take the original air date record of this row . select the rows whose title record fuzzily matches to road to europe . take the original air date record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; title ; road to the north pole } ; original air date } ; hop { filter_eq { all_rows ; title ; road to europe } ; original air date } }
for the performer 1 records of all rows , all of them fuzzily match to wayne brady .
Output: all_eq { all_rows ; performer 1 ; wayne brady }
select the rows whose race winner record fuzzily matches to carlos pace . take the date record of this row . select the rows whose race winner record fuzzily matches to niki lauda . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; race winner ; carlos pace } ; date } ; hop { filter_eq { all_rows ; race winner ; niki lauda } ; date } }
the average of the games record of all rows is 70 .
Output: round_eq { avg { all_rows ; games } ; 70 }
select the rows whose general classification record fuzzily matches to igor antón . among these rows , select the rows whose winner record fuzzily matches to markus fothen . there is only one such row in the table . the stage record of this unqiue row is 5 .
Output: and { only { filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen } } ; eq { hop { filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen } ; stage } ; 5 } }
select the row whose pick record of all rows is minimum . the player record of this row is patrick kabongo .
Output: eq { hop { argmin { all_rows ; pick } ; player } ; patrick kabongo }
select the row whose attendance record of all rows is 2nd minimum . the date record of this row is january 2 , 2005 .
Output: eq { hop { nth_argmin { all_rows ; attendance ; 2 } ; date } ; january 2 , 2005 }
the average of the time record of all rows is 2:08.00.0 .
Output: round_eq { avg { all_rows ; time } ; 2:08.00.0 }
select the row whose attendance record of all rows is 2nd minimum . the date record of this row is december 16 , 1962 .
Output: eq { hop { nth_argmin { all_rows ; attendance ; 2 } ; date } ; december 16 , 1962 }
select the rows whose site / stadium record fuzzily matches to goodwin field . there is only one such row in the table . the date record of this unqiue row is may 25 . the opponent record of this unqiue row is cal state fullerton .
Output: and { only { filter_eq { all_rows ; site / stadium ; goodwin field } } ; and { eq { hop { filter_eq { all_rows ; site / stadium ; goodwin field } ; date } ; may 25 } ; eq { hop { filter_eq { all_rows ; site / stadium ; goodwin field } ; opponent } ; cal state fullerton } } }
select the rows whose surface record fuzzily matches to grass . among these rows , select the rows whose outcome record fuzzily matches to winner . there is only one such row in the table . the tournament record of this unqiue row is newport , rhode island , usa .
Output: and { only { filter_eq { filter_eq { all_rows ; surface ; grass } ; outcome ; winner } } ; eq { hop { filter_eq { filter_eq { all_rows ; surface ; grass } ; outcome ; winner } ; tournament } ; newport , rhode island , usa } }
for the nationality records of all rows , all of them fuzzily match to canada .
Output: all_eq { all_rows ; nationality ; canada }
select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is mcg .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; mcg }
select the rows whose school type record fuzzily matches to public . among these rows , select the rows whose joined acc record fuzzily matches to 197 . there is only one such row in the table . the institution record of this unqiue row is georgia tech .
Output: and { only { filter_eq { filter_eq { all_rows ; school type ; public } ; joined acc ; 197 } } ; eq { hop { filter_eq { filter_eq { all_rows ; school type ; public } ; joined acc ; 197 } ; institution } ; georgia tech } }
select the rows whose tournament record fuzzily matches to masters tournament . take the top - 25 record of this row . select the rows whose tournament record fuzzily matches to pga championship . take the top - 25 record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; tournament ; masters tournament } ; top - 25 } ; hop { filter_eq { all_rows ; tournament ; pga championship } ; top - 25 } }
select the rows whose lost record is equal to 0 . there is only one such row in the table . the team record of this unqiue row is paulistano .
Output: and { only { filter_eq { all_rows ; lost ; 0 } } ; eq { hop { filter_eq { all_rows ; lost ; 0 } ; team } ; paulistano } }
select the rows whose player record is arbitrary . the number of such rows is 11 .
Output: eq { count { filter_all { all_rows ; player } } ; 11 }
select the row whose % of popular vote record of all rows is minimum . the election record of this row is 1984 .
Output: eq { hop { argmin { all_rows ; % of popular vote } ; election } ; 1984 }
select the rows whose candidates record fuzzily matches to jay le fevre ( r ) 69.5 % john f killgrew ( d ) 30.5 % . take the first elected record of this row . select the rows whose candidates record fuzzily matches to hadwen c fuller ( r ) 54.3 % frank a emma ( d ) 45.7 % . take the first elected record of this row . the first record is less than the second record . the first elected record of the first row is 1942 . the first elected record of the second row is 1943 .
Output: and { less { hop { filter_eq { all_rows ; candidates ; jay le fevre ( r ) 69.5 % john f killgrew ( d ) 30.5 % } ; first elected } ; hop { filter_eq { all_rows ; candidates ; hadwen c fuller ( r ) 54.3 % frank a emma ( d ) 45.7 % } ; first elected } } ; and { eq { hop { filter_eq { all_rows ; candidates ; jay le fevre ( r ) 69.5 % john f killgrew ( d ) 30.5 % } ; first elected } ; 1942 } ; eq { hop { filter_eq { all_rows ; candidates ; hadwen c fuller ( r ) 54.3 % frank a emma ( d ) 45.7 % } ; first elected } ; 1943 } } }
the average of the floors record of all rows is 21.43 .
Output: round_eq { avg { all_rows ; floors } ; 21.43 }
for the faxing records of all rows , most of them fuzzily match to no .
Output: most_eq { all_rows ; faxing ; no }
select the rows whose opponent record fuzzily matches to vancouver canucks . take the points record of this row . select the rows whose opponent record fuzzily matches to washington capitals . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; vancouver canucks } ; points } ; hop { filter_eq { all_rows ; opponent ; washington capitals } ; points } }
select the rows whose directed by record fuzzily matches to jeremy podeswa . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; directed by ; jeremy podeswa } } ; 2 }
select the rows whose date record fuzzily matches to 10 march 1984 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; date ; 10 march 1984 } } ; 3 }
the average of the weight record of all rows is 250.5 .
Output: round_eq { avg { all_rows ; weight } ; 250.5 }
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 }
for the round records of all rows , most of them are equal to 1 .
Output: most_eq { all_rows ; round ; 1 }
select the rows whose position in 2004 record fuzzily matches to first league . there is only one such row in the table . the team record of this unqiue row is lokomotiv .
Output: and { only { filter_eq { all_rows ; position in 2004 ; first league } } ; eq { hop { filter_eq { all_rows ; position in 2004 ; first league } ; team } ; lokomotiv } }
select the rows whose locale record fuzzily matches to canada . take the ends won record of this row . select the rows whose locale record fuzzily matches to scotland . take the ends won record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; locale ; canada } ; ends won } ; hop { filter_eq { all_rows ; locale ; scotland } ; ends won } }
for the score records of all rows , most of them are less than 5 .
Output: most_less { all_rows ; score ; 5 }
select the rows whose nationality record fuzzily matches to united states . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; nationality ; united states } } ; 5 }
select the rows whose school / club team record fuzzily matches to duke . there is only one such row in the table . the player record of this unqiue row is antonio lang .
Output: and { only { filter_eq { all_rows ; school / club team ; duke } } ; eq { hop { filter_eq { all_rows ; school / club team ; duke } ; player } ; antonio lang } }
select the rows whose type of game record fuzzily matches to friendly . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; type of game ; friendly } } ; 5 }
select the row whose viewership record of all rows is maximum . the original title record of this row is testing times .
Output: eq { hop { argmax { all_rows ; viewership } ; original title } ; testing times }