niryuu/sni-each-converted huggingface dataset:
select the rows whose points record is greater than 50 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; points ; 50 } } ; 3 }
select the rows whose broadcast record fuzzily matches to espn2 . there is only one such row in the table . the visiting team record of this unqiue row is no 23 west virginia . the home team record of this unqiue row is marshall .
Output: and { only { filter_eq { all_rows ; broadcast ; espn2 } } ; and { eq { hop { filter_eq { all_rows ; broadcast ; espn2 } ; visiting team } ; no 23 west virginia } ; eq { hop { filter_eq { all_rows ; broadcast ; espn2 } ; home team } ; marshall } } }
select the rows whose agricultural panel record is equal to 0 . there is only one such row in the table . the party record of this unqiue row is independent .
Output: and { only { filter_eq { all_rows ; agricultural panel ; 0 } } ; eq { hop { filter_eq { all_rows ; agricultural panel ; 0 } ; party } ; independent } }
select the row whose championships record of all rows is 2nd maximum . the teams record of this row is new york nets .
Output: eq { hop { nth_argmax { all_rows ; championships ; 2 } ; teams } ; new york nets }
select the rows whose competition record fuzzily matches to 1994 fifa world cup qualification . select the row whose date record of these rows is minimum . the venue record of this row is estadio rigoberto lópez , managua , nicaragua .
Output: eq { hop { argmin { filter_eq { all_rows ; competition ; 1994 fifa world cup qualification } ; date } ; venue } ; estadio rigoberto lópez , managua , nicaragua }
select the rows whose entrant record fuzzily matches to machinist union racing team . the average of the finish record of these rows is 22 .
Output: round_eq { avg { filter_eq { all_rows ; entrant ; machinist union racing team } ; finish } ; 22 }
the sum of the uk viewers ( million ) record of all rows is 96.41 .
Output: round_eq { sum { all_rows ; uk viewers ( million ) } ; 96.41 }
select the rows whose country record fuzzily matches to australia . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; country ; australia } } ; 3 }
select the rows whose position record fuzzily matches to guard . there is only one such row in the table . the name record of this unqiue row is justin anderson .
Output: and { only { filter_eq { all_rows ; position ; guard } } ; eq { hop { filter_eq { all_rows ; position ; guard } ; name } ; justin anderson } }
select the row whose height record of all rows is maximum . the player record of this row is pape badiane .
Output: eq { hop { argmax { all_rows ; height } ; player } ; pape badiane }
select the rows whose chassis record fuzzily matches to cooper t53 . there is only one such row in the table . the year record of this unqiue row is 1961 .
Output: and { only { filter_eq { all_rows ; chassis ; cooper t53 } } ; eq { hop { filter_eq { all_rows ; chassis ; cooper t53 } ; year } ; 1961 } }
the average of the to par record of all rows is -2.19 .
Output: round_eq { avg { all_rows ; to par } ; -2.19 }
select the rows whose date record is less than or equal to february 5 . the sum of the attendance record of these rows is 37097 .
Output: round_eq { sum { filter_less_eq { all_rows ; date ; february 5 } ; attendance } ; 37097 }
select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is john conyers jr .
Output: eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; john conyers jr }
select the rows whose model record fuzzily matches to stls2f . take the frequency record of this row . select the rows whose model record fuzzily matches to stls2e . take the frequency record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; model ; stls2f } ; frequency } ; hop { filter_eq { all_rows ; model ; stls2e } ; frequency } }
select the rows whose attendance record is equal to 15000 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; attendance ; 15000 } } ; 2 }
select the row whose date record of all rows is 2nd maximum . the tournament record of this row is san jose , usa .
Output: eq { hop { nth_argmax { all_rows ; date ; 2 } ; tournament } ; san jose , usa }
select the row whose record record of all rows is 2nd maximum . the year record of this row is 1979 .
Output: eq { hop { nth_argmax { all_rows ; record ; 2 } ; year } ; 1979 }
the 2nd minimum date record of all rows is 2 - 95 . the interview subject record of the row with 2nd minimum date record is tim robbins .
Output: and { eq { nth_min { all_rows ; date ; 2 } ; 2 - 95 } ; eq { hop { nth_argmin { all_rows ; date ; 2 } ; interview subject } ; tim robbins } }
select the row whose first broadcast record of all rows is maximum . the episode record of this row is 3x08 .
Output: eq { hop { argmax { all_rows ; first broadcast } ; episode } ; 3x08 }
select the row whose crowd record of all rows is 2nd maximum . the venue record of this row is lake oval .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; venue } ; lake oval }
select the rows whose incumbent record fuzzily matches to adam smith . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to dave reichert . 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 ; adam smith } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; dave reichert } ; first elected } }
the average of the attendance record of all rows is 48976.33 .
Output: round_eq { avg { all_rows ; attendance } ; 48976.33 }
for the opponent records of all rows , all of them fuzzily match to new york islanders .
Output: all_eq { all_rows ; opponent ; new york islanders }
select the rows whose air force cross record fuzzily matches to lifesaving medal . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; air force cross ; lifesaving medal } } ; 2 }
select the rows whose label record fuzzily matches to mca . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; label ; mca } } ; 5 }
select the rows whose date record fuzzily matches to february . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; date ; february } } ; 6 }
select the rows whose competition record fuzzily matches to 2004 afc asian cup qualification . the 1st minimum date record of these rows is september 26 , 2003 .
Output: eq { nth_min { filter_eq { all_rows ; competition ; 2004 afc asian cup qualification } ; date ; 1 } ; september 26 , 2003 }
select the row whose event record of all rows is 3rd minimum . the circuit record of this row is wakefield park .
Output: eq { hop { nth_argmin { all_rows ; event ; 3 } ; circuit } ; wakefield park }
select the row whose total personnel record of all rows is maximum . the type of unit record of this row is heavy bombardment group .
Output: eq { hop { argmax { all_rows ; total personnel } ; type of unit } ; heavy bombardment group }
select the rows whose segment a record fuzzily matches to three wheeled vehicles . take the episode record of this row . select the rows whose segment a record fuzzily matches to air filters . take the episode record of this row . the first record is less than the second record . the episode record of the first row is 66 . the episode record of the second row is 70 .
Output: and { less { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; hop { filter_eq { all_rows ; segment a ; air filters } ; episode } } ; and { eq { hop { filter_eq { all_rows ; segment a ; three wheeled vehicles } ; episode } ; 66 } ; eq { hop { filter_eq { all_rows ; segment a ; air filters } ; episode } ; 70 } } }
the average of the pick record of all rows is 116.5 .
Output: round_eq { avg { all_rows ; pick } ; 116.5 }
select the rows whose venue record fuzzily matches to tahiti . there is only one such row in the table . the date record of this unqiue row is 11 may 1996 .
Output: and { only { filter_eq { all_rows ; venue ; tahiti } } ; eq { hop { filter_eq { all_rows ; venue ; tahiti } ; date } ; 11 may 1996 } }
for the first elected records of all rows , most of them are equal to 1982 .
Output: most_eq { all_rows ; first elected ; 1982 }
for the date records of all rows , all of them fuzzily match to 1 august .
Output: all_eq { all_rows ; date ; 1 august }
select the rows whose champions record fuzzily matches to jimmy connors . take the year record of this row . select the rows whose champions record fuzzily matches to john mcenroe . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; champions ; jimmy connors } ; year } ; hop { filter_eq { all_rows ; champions ; john mcenroe } ; year } }
select the rows whose date record fuzzily matches to july . among these rows , select the rows whose fastest lap record fuzzily matches to nigel mansell . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; date ; july } ; fastest lap ; nigel mansell } } ; 2 }
select the rows whose composer record fuzzily matches to alexander lunyov . there is only one such row in the table . the song record of this unqiue row is never let you go 2 .
Output: and { only { filter_eq { all_rows ; composer ; alexander lunyov } } ; eq { hop { filter_eq { all_rows ; composer ; alexander lunyov } ; song } ; never let you go 2 } }
select the rows whose country record fuzzily matches to united states . the average of the score record of these rows is 139.5 .
Output: round_eq { avg { filter_eq { all_rows ; country ; united states } ; score } ; 139.5 }
the average of the attendance record of all rows is 16325 .
Output: round_eq { avg { all_rows ; attendance } ; 16325 }
select the rows whose years for jazz record fuzzily matches to present . there is only one such row in the table . the player record of this unqiue row is jamaal tinsley .
Output: and { only { filter_eq { all_rows ; years for jazz ; present } } ; eq { hop { filter_eq { all_rows ; years for jazz ; present } ; player } ; jamaal tinsley } }
select the row whose no of barangays record of all rows is maximum . the city / municipality record of this row is binangonan .
Output: eq { hop { argmax { all_rows ; no of barangays } ; city / municipality } ; binangonan }
select the row whose uk viewers ( million ) record of all rows is 5th maximum . the title record of this row is episode 4 .
Output: eq { hop { nth_argmax { all_rows ; uk viewers ( million ) ; 5 } ; title } ; episode 4 }
select the row whose date record of all rows is minimum . the race name record of this row is vi glover trophy .
Output: eq { hop { argmin { all_rows ; date } ; race name } ; vi glover trophy }
select the rows whose gold record is equal to - . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; gold ; - } } ; 2 }
select the rows whose event record fuzzily matches to men 's . the average of the swim ( 1.5 km ) record of these rows is 18:41 .
Output: round_eq { avg { filter_eq { all_rows ; event ; men 's } ; swim ( 1.5 km ) } ; 18:41 }
select the rows whose result record fuzzily matches to won . for the distance records of these rows , most of them fuzzily match to 1400 m .
Output: most_eq { filter_eq { all_rows ; result ; won } ; distance ; 1400 m }
select the rows whose institution record fuzzily matches to baylor university . select the row whose enrollment record of these rows is maximum . the affiliation record of this row is private / baptist .
Output: eq { hop { argmax { filter_eq { all_rows ; institution ; baylor university } ; enrollment } ; affiliation } ; private / baptist }
select the rows whose country record fuzzily matches to united kingdom . select the row whose seats record of these rows is 1st maximum . the model record of this row is bae atp .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; country ; united kingdom } ; seats ; 1 } ; model } ; bae atp }
select the rows whose average annual rainfall ( mm ) record is less than 100 . select the row whose average annual runoff ( mm ) record of these rows is 1st maximum . the administrative region record of this row is i - tarapacá .
Output: eq { hop { nth_argmax { filter_less { all_rows ; average annual rainfall ( mm ) ; 100 } ; average annual runoff ( mm ) ; 1 } ; administrative region } ; i - tarapacá }
select the rows whose founded record is greater than or equal to 1980 . the number of such rows is 3 .
Output: eq { count { filter_greater_eq { all_rows ; founded ; 1980 } } ; 3 }
for the league records of all rows , most of them fuzzily match to aviva premiership .
Output: most_eq { all_rows ; league ; aviva premiership }
select the rows whose driver record fuzzily matches to richard philippe . take the nor 1 record of this row . select the rows whose driver record fuzzily matches to stefano coletti . take the nor 1 record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; driver ; richard philippe } ; nor 1 } ; hop { filter_eq { all_rows ; driver ; stefano coletti } ; nor 1 } }
select the row whose date record of all rows is 1st minimum . the opponent in the final record of this row is monica niculescu .
Output: eq { hop { nth_argmin { all_rows ; date ; 1 } ; opponent in the final } ; monica niculescu }
select the rows whose to par record fuzzily matches to +5 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; to par ; +5 } } ; 3 }
select the rows whose incumbent record fuzzily matches to dale kildee . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to fred upton . 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 ; dale kildee } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; fred upton } ; first elected } }
select the row whose attendance record of all rows is maximum . the location record of this row is memorial stadium clemson , sc .
Output: eq { hop { argmax { all_rows ; attendance } ; location } ; memorial stadium clemson , sc }
select the row whose population served record of all rows is maximum . the location record of this row is marcala , hon .
Output: eq { hop { argmax { all_rows ; population served } ; location } ; marcala , hon }
select the rows whose event record fuzzily matches to ept barcelona open . take the date record of this row . select the rows whose event record fuzzily matches to 2008 european poker championships . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; event ; ept barcelona open } ; date } ; hop { filter_eq { all_rows ; event ; 2008 european poker championships } ; date } }
select the rows whose label record fuzzily matches to atlantic records . among these rows , select the rows whose region record fuzzily matches to united states . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_eq { all_rows ; label ; atlantic records } ; region ; united states } } ; 4 }
the average of the international tourism expenditure 2011 record of all rows is 45-48 billion .
Output: round_eq { avg { all_rows ; international tourism expenditure 2011 } ; 45-48 billion }
the sum of the total record of all rows is 119 .
Output: round_eq { sum { all_rows ; total } ; 119 }
select the rows whose ride record fuzzily matches to flight deck . take the rating record of this row . select the rows whose ride record fuzzily matches to woodstock express . take the rating record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; ride ; flight deck } ; rating } ; hop { filter_eq { all_rows ; ride ; woodstock express } ; rating } }
select the row whose population density ( / km 2 ) record of all rows is maximum . the administrative district record of this row is jaffna .
Output: eq { hop { argmax { all_rows ; population density ( / km 2 ) } ; administrative district } ; jaffna }
the average of the first downs record of all rows is 19.42 .
Output: round_eq { avg { all_rows ; first downs } ; 19.42 }
select the rows whose outcome of election record fuzzily matches to ndc opposition . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; outcome of election ; ndc opposition } } ; 2 }
select the rows whose days with frost record is equal to 0 . there is only one such row in the table . the city / town record of this unqiue row is a coruña .
Output: and { only { filter_eq { all_rows ; days with frost ; 0 } } ; eq { hop { filter_eq { all_rows ; days with frost ; 0 } ; city / town } ; a coruña } }
select the rows whose oberbayern a record fuzzily matches to bsc sendling . take the season record of this row . select the rows whose oberbayern a record fuzzily matches to fc oberau . take the season record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; oberbayern a ; bsc sendling } ; season } ; hop { filter_eq { all_rows ; oberbayern a ; fc oberau } ; season } }
select the rows whose class record fuzzily matches to i1 . take the no built record of this row . select the rows whose class record fuzzily matches to i2 . take the no built record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; class ; i1 } ; no built } ; hop { filter_eq { all_rows ; class ; i2 } ; no built } }
select the rows whose method record fuzzily matches to tko . there is only one such row in the table . the opponent record of this unqiue row is gegard mousasi .
Output: and { only { filter_eq { all_rows ; method ; tko } } ; eq { hop { filter_eq { all_rows ; method ; tko } ; opponent } ; gegard mousasi } }
select the rows whose launched record fuzzily matches to november 21 , 1964 . take the ceased operation record of this row . select the rows whose launched record fuzzily matches to december 12 , 1962 . take the ceased operation record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; launched ; november 21 , 1964 } ; ceased operation } ; hop { filter_eq { all_rows ; launched ; december 12 , 1962 } ; ceased operation } }
select the row whose frequency record of all rows is maximum . the format record of this row is classic rock / sports radio .
Output: eq { hop { argmax { all_rows ; frequency } ; format } ; classic rock / sports radio }
the average of the crowd record of all rows is 22250 .
Output: round_eq { avg { all_rows ; crowd } ; 22250 }
select the rows whose result record fuzzily matches to l . the 2nd minimum date record of these rows is october 11 , 1998 . the opponent record of the row with 2nd minimum date record is new england patriots .
Output: and { eq { nth_min { filter_eq { all_rows ; result ; l } ; date ; 2 } ; october 11 , 1998 } ; eq { hop { nth_argmin { filter_eq { all_rows ; result ; l } ; date ; 2 } ; opponent } ; new england patriots } }
select the rows whose cardinalatial order and title record fuzzily matches to bishop . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; cardinalatial order and title ; bishop } } ; 3 }
select the rows whose gender record fuzzily matches to male . for the status records of these rows , most of them fuzzily match to dead .
Output: most_eq { filter_eq { all_rows ; gender ; male } ; status ; dead }
select the row whose goals record of all rows is maximum . the player record of this row is oleg veretennikov .
Output: eq { hop { argmax { all_rows ; goals } ; player } ; oleg veretennikov }
select the row whose date record of all rows is 11th minimum . the field record of this row is cawley memorial stadium .
Output: eq { hop { nth_argmin { all_rows ; date ; 11 } ; field } ; cawley memorial stadium }
select the rows whose position record fuzzily matches to 8th . there is only one such row in the table . the season record of this unqiue row is 1946 - 47 .
Output: and { only { filter_eq { all_rows ; position ; 8th } } ; eq { hop { filter_eq { all_rows ; position ; 8th } ; season } ; 1946 - 47 } }
select the row whose cores record of all rows is 2nd maximum . the model record of this row is l3c .
Output: eq { hop { nth_argmax { all_rows ; cores ; 2 } ; model } ; l3c }
select the rows whose directed by record fuzzily matches to stephen kay . among these rows , select the rows whose written by record fuzzily matches to norman morrill . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; directed by ; stephen kay } ; written by ; norman morrill } } ; 2 }
select the row whose score record of all rows is maximum . the champion record of this row is toronto marlboros ( oha ) .
Output: eq { hop { argmax { all_rows ; score } ; champion } ; toronto marlboros ( oha ) }
select the rows whose member countries record fuzzily matches to france . take the gdp per capita ( us ) record of this row . select the rows whose member countries record fuzzily matches to west germany . take the gdp per capita ( us ) record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; member countries ; france } ; gdp per capita ( us ) } ; hop { filter_eq { all_rows ; member countries ; west germany } ; gdp per capita ( us ) } }
select the rows whose director record fuzzily matches to j clark mathis . there is only one such row in the table .
Output: only { filter_eq { all_rows ; director ; j clark mathis } }
for the owner records of all rows , most of them fuzzily match to greentrains .
Output: most_eq { all_rows ; owner ; greentrains }
select the rows whose attendance record is greater than 30000 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; attendance ; 30000 } } ; 3 }
select the row whose gold record of all rows is maximum . the nation record of this row is austria .
Output: eq { hop { argmax { all_rows ; gold } ; nation } ; austria }
the average of the of episodes record of all rows is 33.2 .
Output: round_eq { avg { all_rows ; of episodes } ; 33.2 }
select the rows whose year record is equal to 2013 . the sum of the result record of these rows is over 50 .
Output: round_eq { sum { filter_eq { all_rows ; year ; 2013 } ; result } ; over 50 }
the sum of the points record of all rows is 96 .
Output: round_eq { sum { all_rows ; points } ; 96 }
select the rows whose title record fuzzily matches to 49th . take the attendance record of this row . select the rows whose title record fuzzily matches to 48th . take the attendance record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; title ; 49th } ; attendance } ; hop { filter_eq { all_rows ; title ; 48th } ; attendance } }
select the rows whose season finale date record fuzzily matches to march . there is only one such row in the table . the season record of this unqiue row is 4 .
Output: and { only { filter_eq { all_rows ; season finale date ; march } } ; eq { hop { filter_eq { all_rows ; season finale date ; march } ; season } ; 4 } }
select the rows whose revenues record is greater than 10000 koku . there is only one such row in the table . the name record of this unqiue row is ōoka tadatomo ( 大岡忠與 ) .
Output: and { only { filter_greater { all_rows ; revenues ; 10000 koku } } ; eq { hop { filter_greater { all_rows ; revenues ; 10000 koku } ; name } ; ōoka tadatomo ( 大岡忠與 ) } }
select the rows whose recipient record fuzzily matches to debby ryan . there is only one such row in the table . the year record of this unqiue row is 2010 . the award record of this unqiue row is hollywood teen tv awards . the result record of this unqiue row is nominated .
Output: and { only { filter_eq { all_rows ; recipient ; debby ryan } } ; and { eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; year } ; 2010 } ; and { eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; award } ; hollywood teen tv awards } ; eq { hop { filter_eq { all_rows ; recipient ; debby ryan } ; result } ; nominated } } } }
select the row whose f bolt ( kgf ) record of all rows is maximum . the chambering record of this row is .50 bmg .
Output: eq { hop { argmax { all_rows ; f bolt ( kgf ) } ; chambering } ; .50 bmg }
select the rows whose won record is equal to 16 . there is only one such row in the table . the club record of this unqiue row is northampton saints ( sf ) .
Output: and { only { filter_eq { all_rows ; won ; 16 } } ; eq { hop { filter_eq { all_rows ; won ; 16 } ; club } ; northampton saints ( sf ) } }
select the rows whose sprints classification record fuzzily matches to no award . there is only one such row in the table . the stage record of this unqiue row is 1 .
Output: and { only { filter_eq { all_rows ; sprints classification ; no award } } ; eq { hop { filter_eq { all_rows ; sprints classification ; no award } ; stage } ; 1 } }
select the rows whose leading scorer record fuzzily matches to ashley stott . there is only one such row in the table . the year record of this unqiue row is 2008 - 09 .
Output: and { only { filter_eq { all_rows ; leading scorer ; ashley stott } } ; eq { hop { filter_eq { all_rows ; leading scorer ; ashley stott } ; year } ; 2008 - 09 } }
select the rows whose population ( in 2008 ) record is less than 1000 . there is only one such row in the table . the suburb record of this unqiue row is o'malley .
Output: and { only { filter_less { all_rows ; population ( in 2008 ) ; 1000 } } ; eq { hop { filter_less { all_rows ; population ( in 2008 ) ; 1000 } ; suburb } ; o'malley } }
select the row whose points record of all rows is maximum . the year record of this row is 1984 .
Output: eq { hop { argmax { all_rows ; points } ; year } ; 1984 }
select the rows whose opponent in the final record fuzzily matches to adriano panatta . there is only one such row in the table . the championship record of this unqiue row is bologna , italy .
Output: and { only { filter_eq { all_rows ; opponent in the final ; adriano panatta } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; adriano panatta } ; championship } ; bologna , italy } }
select the rows whose date record fuzzily matches to 16 june 1956 . the average of the crowd record of these rows is 24,570 .
Output: round_eq { avg { filter_eq { all_rows ; date ; 16 june 1956 } ; crowd } ; 24,570 }
the sum of the feet record of all rows is 2968 .
Output: round_eq { sum { all_rows ; feet } ; 2968 }
select the rows whose trailing party record fuzzily matches to indian national congress . among these rows , select the rows whose party won record fuzzily matches to praja socialist party . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; trailing party ; indian national congress } ; party won ; praja socialist party } } ; 3 }
select the rows whose affiliation record fuzzily matches to private . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; affiliation ; private } } ; 2 }
select the row whose bike ( 40 km ) record of all rows is 1st minimum . the athlete record of this row is olivier marceau .
Output: eq { hop { nth_argmin { all_rows ; bike ( 40 km ) ; 1 } ; athlete } ; olivier marceau }
the average of the margin of victory record of all rows is 4 .
Output: round_eq { avg { all_rows ; margin of victory } ; 4 }
select the rows whose pos record fuzzily matches to 2nd . there is only one such row in the table . the year record of this unqiue row is 2012 .
Output: and { only { filter_eq { all_rows ; pos ; 2nd } } ; eq { hop { filter_eq { all_rows ; pos ; 2nd } ; year } ; 2012 } }
select the rows whose navy record fuzzily matches to lithuanian naval force . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; navy ; lithuanian naval force } } ; 2 }
select the rows whose weight ( kg ) record is equal to 53 . there is only one such row in the table .
Output: only { filter_eq { all_rows ; weight ( kg ) ; 53 } }
select the rows whose nation record fuzzily matches to usa . the sum of the goals record of these rows is 74 .
Output: round_eq { sum { filter_eq { all_rows ; nation ; usa } ; goals } ; 74 }
select the rows whose class record fuzzily matches to a - 4 . take the quantity made record of this row . select the rows whose class record fuzzily matches to a - 3 . take the quantity made record of this row . the first record is equal to the second record .
Output: eq { hop { filter_eq { all_rows ; class ; a - 4 } ; quantity made } ; hop { filter_eq { all_rows ; class ; a - 3 } ; quantity made } }
select the rows whose opponent record fuzzily matches to new york giants . take the attendance record of this row . select the rows whose opponent record fuzzily matches to new york jets . take the attendance record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; new york giants } ; attendance } ; hop { filter_eq { all_rows ; opponent ; new york jets } ; attendance } }
select the rows whose opposing teams record fuzzily matches to new zealand . the sum of the against record of these rows is 18 .
Output: round_eq { sum { filter_eq { all_rows ; opposing teams ; new zealand } ; against } ; 18 }
select the row whose location / attendance record of all rows is 1st maximum . the opponent record of this row is new york liberty outdoor classic .
Output: eq { hop { nth_argmax { all_rows ; location / attendance ; 1 } ; opponent } ; new york liberty outdoor classic }
the maximum result record of all rows is kuwait won by 72 runs scorecard . the details record of the row with superlative result record is 2011 division seven .
Output: and { eq { max { all_rows ; result } ; kuwait won by 72 runs scorecard } ; eq { hop { argmax { all_rows ; result } ; details } ; 2011 division seven } }
select the rows whose format record fuzzily matches to sports . there is only one such row in the table . the station record of this unqiue row is wing - am .
Output: and { only { filter_eq { all_rows ; format ; sports } } ; eq { hop { filter_eq { all_rows ; format ; sports } ; station } ; wing - am } }
select the rows whose country record fuzzily matches to new zealand . there is only one such row in the table . the company record of this unqiue row is enernoc new zealand limited .
Output: and { only { filter_eq { all_rows ; country ; new zealand } } ; eq { hop { filter_eq { all_rows ; country ; new zealand } ; company } ; enernoc new zealand limited } }
for the elevation ( m ) records of all rows , most of them are greater than 2000 .
Output: most_greater { all_rows ; elevation ( m ) ; 2000 }
select the row whose year record of all rows is 2nd maximum . the host record of this row is marty reid .
Output: eq { hop { nth_argmax { all_rows ; year ; 2 } ; host } ; marty reid }
select the rows whose dance record fuzzily matches to lyrical jazz . take the week record of this row . select the rows whose dance record fuzzily matches to jive . take the week record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; dance ; lyrical jazz } ; week } ; hop { filter_eq { all_rows ; dance ; jive } ; week } }
select the rows whose category record fuzzily matches to favorite music group . there is only one such row in the table . the year record of this unqiue row is 2011 .
Output: and { only { filter_eq { all_rows ; category ; favorite music group } } ; eq { hop { filter_eq { all_rows ; category ; favorite music group } ; year } ; 2011 } }
select the rows whose championship record is greater than or equal to 5 . among these rows , select the rows whose fa cup record is equal to 1 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_greater_eq { all_rows ; championship ; 5 } ; fa cup ; 1 } } ; 2 }
select the rows whose gp - gs record is equal to 14 . there is only one such row in the table . the season record of this unqiue row is 2009 .
Output: and { only { filter_eq { all_rows ; gp - gs ; 14 } } ; eq { hop { filter_eq { all_rows ; gp - gs ; 14 } ; season } ; 2009 } }
select the row whose score record of all rows is maximum . the team 1 record of this row is ogc nice ( d1 ) .
Output: eq { hop { argmax { all_rows ; score } ; team 1 } ; ogc nice ( d1 ) }
select the rows whose name record fuzzily matches to arrowtown school . take the decile record of this row . select the rows whose name record fuzzily matches to glenorchy school . take the decile record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; arrowtown school } ; decile } ; hop { filter_eq { all_rows ; name ; glenorchy school } ; decile } }
for the first elected records of all rows , most of them are less than 1920 .
Output: most_less { all_rows ; first elected ; 1920 }
for the nominated work records of all rows , most of them fuzzily match to 71 : into the fire .
Output: most_eq { all_rows ; nominated work ; 71 : into the fire }
select the rows whose location attendance record fuzzily matches to wachovia center . the sum of the location attendance record of these rows is 89767 .
Output: round_eq { sum { filter_eq { all_rows ; location attendance ; wachovia center } ; location attendance } ; 89767 }
select the row whose attendance record of all rows is 1st maximum . the acc team record of this row is 4 duke .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; acc team } ; 4 duke }
select the row whose 2011 gdp ( ppp ) billions of usd record of all rows is maximum . the country record of this row is iran .
Output: eq { hop { argmax { all_rows ; 2011 gdp ( ppp ) billions of usd } ; country } ; iran }
select the rows whose city record fuzzily matches to paris . take the population m ( luz ) record of this row . select the rows whose city record fuzzily matches to london . take the population m ( luz ) record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; city ; paris } ; population m ( luz ) } ; hop { filter_eq { all_rows ; city ; london } ; population m ( luz ) } }
select the row whose margin record of all rows is 2nd maximum . the club record of this row is geelong .
Output: eq { hop { nth_argmax { all_rows ; margin ; 2 } ; club } ; geelong }
select the rows whose host location record fuzzily matches to plainview , tx . there is only one such row in the table . the year record of this unqiue row is 1952 .
Output: and { only { filter_eq { all_rows ; host location ; plainview , tx } } ; eq { hop { filter_eq { all_rows ; host location ; plainview , tx } ; year } ; 1952 } }
select the rows whose position record is greater than 0 . the number of such rows is 6 .
Output: eq { count { filter_greater { all_rows ; position ; 0 } } ; 6 }
select the row whose length record of all rows is maximum . the circuit record of this row is donington park .
Output: eq { hop { argmax { all_rows ; length } ; circuit } ; donington park }
select the rows whose frequency mhz record is arbitrary . the number of such rows is 5 .
Output: eq { count { filter_all { all_rows ; frequency mhz } } ; 5 }
select the rows whose no party preference record fuzzily matches to 22.6 % . there is only one such row in the table . the city record of this unqiue row is lemon grove .
Output: and { only { filter_eq { all_rows ; no party preference ; 22.6 % } } ; eq { hop { filter_eq { all_rows ; no party preference ; 22.6 % } ; city } ; lemon grove } }
select the rows whose crowd record is greater than 25000 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; crowd ; 25000 } } ; 3 }
select the rows whose year record is less than 1960 . among these rows , select the rows whose laps record is equal to 200 . the number of such rows is 5 .
Output: eq { count { filter_eq { filter_less { all_rows ; year ; 1960 } ; laps ; 200 } } ; 5 }
select the rows whose events record is greater than 10 . among these rows , select the rows whose top - 10 record is equal to 2 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_greater { all_rows ; events ; 10 } ; top - 10 ; 2 } } ; 2 }
select the row whose caps record of all rows is maximum . the player record of this row is nick rimando .
Output: eq { hop { argmax { all_rows ; caps } ; player } ; nick rimando }
select the row whose rank record of all rows is minimum . the candidate 's name record of this row is michael mackay .
Output: eq { hop { argmin { all_rows ; rank } ; candidate 's name } ; michael mackay }
the sum of the score record of all rows is 14 .
Output: round_eq { sum { all_rows ; score } ; 14 }
select the row whose laps record of all rows is minimum . the rider record of this row is carlos checa .
Output: eq { hop { argmin { all_rows ; laps } ; rider } ; carlos checa }
select the rows whose polyunsaturated fat record fuzzily matches to 3 % . there is only one such row in the table . the total fat record of this unqiue row is 94 g .
Output: and { only { filter_eq { all_rows ; polyunsaturated fat ; 3 % } } ; eq { hop { filter_eq { all_rows ; polyunsaturated fat ; 3 % } ; total fat } ; 94 g } }
select the rows whose power record fuzzily matches to 20 kw . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; power ; 20 kw } } ; 3 }
select the rows whose home team record fuzzily matches to fitzroy . take the home team score record of this row . select the rows whose home team record fuzzily matches to collingwood . take the home team score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; home team score } ; hop { filter_eq { all_rows ; home team ; collingwood } ; home team score } }
select the rows whose result record fuzzily matches to w . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; result ; w } } ; 8 }
select the row whose crowd record of all rows is maximum . the date record of this row is friday , 1 august .
Output: eq { hop { argmax { all_rows ; crowd } ; date } ; friday , 1 august }
select the rows whose final position / round record does not match to runner-up . there is only one such row in the table . the competition record of this unqiue row is fa cup .
Output: and { only { filter_not_eq { all_rows ; final position / round ; runner-up } } ; eq { hop { filter_not_eq { all_rows ; final position / round ; runner-up } ; competition } ; fa cup } }
select the rows whose wheels record is equal to 5 . there is only one such row in the table . the class record of this unqiue row is 0 - 6 - 0t .
Output: and { only { filter_eq { all_rows ; wheels ; 5 } } ; eq { hop { filter_eq { all_rows ; wheels ; 5 } ; class } ; 0 - 6 - 0t } }
select the rows whose new adherents per year record is greater than 0 . for the growth rate records of these rows , most of them are greater than 1.00 % .
Output: most_greater { filter_greater { all_rows ; new adherents per year ; 0 } ; growth rate ; 1.00 % }
for the ends records of all rows , all of them are equal to 2008 .
Output: all_eq { all_rows ; ends ; 2008 }
select the rows whose division record fuzzily matches to on hiatus . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; division ; on hiatus } } ; 2 }
select the row whose year record of all rows is maximum . the driver record of this row is toni teittinen .
Output: eq { hop { argmax { all_rows ; year } ; driver } ; toni teittinen }
select the rows whose state record fuzzily matches to jammu & kashmir . the maximum total capacity ( mw ) record of these rows is 330 . the power plant record of the row with superlative total capacity ( mw ) record is kishenganga .
Output: and { eq { max { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) } ; 330 } ; eq { hop { argmax { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) } ; power plant } ; kishenganga } }
select the rows whose location attendance record fuzzily matches to skydome . for the high points records of these rows , most of them fuzzily match to damon stoudamire .
Output: most_eq { filter_eq { all_rows ; location attendance ; skydome } ; high points ; damon stoudamire }
the 1st maximum carpet record of all rows is 1 - 0 .
Output: eq { nth_max { all_rows ; carpet ; 1 } ; 1 - 0 }
for the chassis records of all rows , most of them fuzzily match to bmw sauber .
Output: most_eq { all_rows ; chassis ; bmw sauber }
select the rows whose opponent in the final record fuzzily matches to sania mirza . there is only one such row in the table . the championship record of this unqiue row is hyderabad , india .
Output: and { only { filter_eq { all_rows ; opponent in the final ; sania mirza } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; sania mirza } ; championship } ; hyderabad , india } }
select the rows whose reason for change record fuzzily matches to contested election . there is only one such row in the table . the vacator record of this unqiue row is josã m gallegos ( d ) .
Output: and { only { filter_eq { all_rows ; reason for change ; contested election } } ; eq { hop { filter_eq { all_rows ; reason for change ; contested election } ; vacator } ; josã m gallegos ( d ) } }
select the rows whose week record fuzzily matches to 8 . take the attendance record of this row . select the rows whose week record fuzzily matches to 10 . take the attendance record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; week ; 8 } ; attendance } ; hop { filter_eq { all_rows ; week ; 10 } ; attendance } }
the average of the normal total record of all rows is 88 .
Output: round_eq { avg { all_rows ; normal total } ; 88 }
select the row whose us viewers ( million ) record of all rows is 2nd maximum . the - record of this row is 1 .
Output: eq { hop { nth_argmax { all_rows ; us viewers ( million ) ; 2 } ; - } ; 1 }
select the row whose first elected record of all rows is 4th minimum . the incumbent record of this row is robert aderholt .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 4 } ; incumbent } ; robert aderholt }
select the rows whose result record fuzzily matches to win . the sum of the cowboys points record of these rows is 154 .
Output: round_eq { sum { filter_eq { all_rows ; result ; win } ; cowboys points } ; 154 }
select the rows whose location record fuzzily matches to ga . among these rows , select the rows whose champion record fuzzily matches to emory . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; location ; ga } ; champion ; emory } } ; 2 }
select the row whose result record of all rows is 3rd maximum . the opponent record of this row is arizona cardinals .
Output: eq { hop { nth_argmax { all_rows ; result ; 3 } ; opponent } ; arizona cardinals }
select the row whose strike rate record of all rows is maximum . the player record of this row is paul harris .
Output: eq { hop { argmax { all_rows ; strike rate } ; player } ; paul harris }
select the rows whose individual wins record is equal to 4 . there is only one such row in the table . the nation record of this unqiue row is england .
Output: and { only { filter_eq { all_rows ; individual wins ; 4 } } ; eq { hop { filter_eq { all_rows ; individual wins ; 4 } ; nation } ; england } }
select the rows whose result record fuzzily matches to retired jacksonian hold . there is only one such row in the table . the incumbent record of this unqiue row is john floyd .
Output: and { only { filter_eq { all_rows ; result ; retired jacksonian hold } } ; eq { hop { filter_eq { all_rows ; result ; retired jacksonian hold } ; incumbent } ; john floyd } }
select the rows whose opponent record fuzzily matches to east fife . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; opponent ; east fife } } ; 2 }
select the rows whose high assists record fuzzily matches to mario chalmers . the average of the high assists record of these rows is 8.33 .
Output: round_eq { avg { filter_eq { all_rows ; high assists ; mario chalmers } ; high assists } ; 8.33 }
select the rows whose competition record fuzzily matches to world junior championships . take the position record of this row . select the rows whose competition record fuzzily matches to world championships . take the position record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; competition ; world junior championships } ; position } ; hop { filter_eq { all_rows ; competition ; world championships } ; position } }
the sum of the crowd record of all rows is 77,000 .
Output: round_eq { sum { all_rows ; crowd } ; 77,000 }
select the row whose votes record of all rows is maximum . the candidate 's name record of this row is scott simms .
Output: eq { hop { argmax { all_rows ; votes } ; candidate 's name } ; scott simms }
select the row whose originalairdate record of all rows is 2nd minimum . the title record of this row is a bright and shiny future .
Output: eq { hop { nth_argmin { all_rows ; originalairdate ; 2 } ; title } ; a bright and shiny future }
the sum of the crowd record of all rows is 121000 .
Output: round_eq { sum { all_rows ; crowd } ; 121000 }
select the row whose home team score record of all rows is maximum . the home team record of this row is carlton .
Output: eq { hop { argmax { all_rows ; home team score } ; home team } ; carlton }
select the row whose attendance record of all rows is 3rd minimum . the date record of this row is april 23 .
Output: eq { hop { nth_argmin { all_rows ; attendance ; 3 } ; date } ; april 23 }
select the rows whose definition record fuzzily matches to civil parish . select the row whose population record of these rows is 1st maximum . the town record of this row is ripon .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; definition ; civil parish } ; population ; 1 } ; town } ; ripon }
select the rows whose top 5 record is not equal to 0 . the number of such rows is 4 .
Output: eq { count { filter_not_eq { all_rows ; top 5 ; 0 } } ; 4 }
select the rows whose college name record fuzzily matches to government thiruvarur medical college . take the estd record of this row . select the rows whose college name record fuzzily matches to government theni medical college . take the estd record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; college name ; government thiruvarur medical college } ; estd } ; hop { filter_eq { all_rows ; college name ; government theni medical college } ; estd } }
select the row whose year record of all rows is minimum . the title record of this row is face / off .
Output: eq { hop { argmin { all_rows ; year } ; title } ; face / off }
select the rows whose record fuzzily matches to thailand . take the total freshwater withdrawal record of this row . select the rows whose record fuzzily matches to ecuador . take the total freshwater withdrawal record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; ; thailand } ; total freshwater withdrawal } ; hop { filter_eq { all_rows ; ; ecuador } ; total freshwater withdrawal } }
select the rows whose call sign record fuzzily matches to w207af . take the fcc info record of this row . select the rows whose call sign record fuzzily matches to w211ae . take the fcc info record of this row . the first record fuzzily matches to the second record . the fcc info record of the first row is fcc . the fcc info record of the second row is fcc .
Output: and { eq { hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info } ; hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info } } ; and { eq { hop { filter_eq { all_rows ; call sign ; w207af } ; fcc info } ; fcc } ; eq { hop { filter_eq { all_rows ; call sign ; w211ae } ; fcc info } ; fcc } } }
for the location records of all rows , most of them fuzzily match to bayamón , puerto rico .
Output: most_eq { all_rows ; location ; bayamón , puerto rico }
select the rows whose place record is less than or equal to 3 . the average of the score record of these rows is 68 .
Output: round_eq { avg { filter_less_eq { all_rows ; place ; 3 } ; score } ; 68 }
select the rows whose us viewers ( in millions ) record is equal to 3.69 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; us viewers ( in millions ) ; 3.69 } } ; 2 }
for the date records of all rows , all of them fuzzily match to 23 may 1936 .
Output: all_eq { all_rows ; date ; 23 may 1936 }