niryuu/sni-each-converted huggingface dataset:
select the rows whose surface record fuzzily matches to hard . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; surface ; hard } } ; 4 }
select the rows whose diemaco model record fuzzily matches to lsw . take the barrel length record of this row . select the rows whose diemaco model record fuzzily matches to c8 . take the barrel length record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; diemaco model ; lsw } ; barrel length } ; hop { filter_eq { all_rows ; diemaco model ; c8 } ; barrel length } }
select the rows whose medal record fuzzily matches to silver . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; medal ; silver } } ; 6 }
select the rows whose high rebounds record fuzzily matches to chris bosh . the number of such rows is 9 .
Output: eq { count { filter_eq { all_rows ; high rebounds ; chris bosh } } ; 9 }
select the rows whose years as tallest record is less than 1902 . among these rows , select the rows whose height ft ( m ) record is less than 200 . the number of such rows is 2 .
Output: eq { count { filter_less { filter_less { all_rows ; years as tallest ; 1902 } ; height ft ( m ) ; 200 } } ; 2 }
select the rows whose label record fuzzily matches to astralwerks . take the date record of this row . select the rows whose label record fuzzily matches to capitol records . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; label ; astralwerks } ; date } ; hop { filter_eq { all_rows ; label ; capitol records } ; date } }
select the rows whose wood record fuzzily matches to green . there is only one such row in the table .
Output: only { filter_eq { all_rows ; wood ; green } }
select the rows whose partner record fuzzily matches to stephanie vogt . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; partner ; stephanie vogt } } ; 2 }
select the rows whose club record fuzzily matches to fk valmiera . take the position record of this row . select the rows whose club record fuzzily matches to fk auda kekava . take the position record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; club ; fk valmiera } ; position } ; hop { filter_eq { all_rows ; club ; fk auda kekava } ; position } }
select the rows whose player record fuzzily matches to jared borgetti . take the period record of this row . select the rows whose player record fuzzily matches to rafael figueroa . take the period record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; jared borgetti } ; period } ; hop { filter_eq { all_rows ; player ; rafael figueroa } ; period } }
for the start records of all rows , most of them are equal to 3 .
Output: most_eq { all_rows ; start ; 3 }
select the row whose attendance record of all rows is maximum . the game site record of this row is commerzbank - arena .
Output: eq { hop { argmax { all_rows ; attendance } ; game site } ; commerzbank - arena }
select the row whose runs record of all rows is maximum . the opponent record of this row is queensland .
Output: eq { hop { argmax { all_rows ; runs } ; opponent } ; queensland }
select the row whose population ( 2010 census ) record of all rows is 1st maximum . the district record of this row is tondo .
Output: eq { hop { nth_argmax { all_rows ; population ( 2010 census ) ; 1 } ; district } ; tondo }
for the high assists records of all rows , most of them fuzzily match to steve nash .
Output: most_eq { all_rows ; high assists ; steve nash }
for the date records of all rows , all of them fuzzily match to 28 august 1954 .
Output: all_eq { all_rows ; date ; 28 august 1954 }
the sum of the runs record of all rows is 1974 .
Output: round_eq { sum { all_rows ; runs } ; 1974 }
select the rows whose power ( kw ) record fuzzily matches to 10 kw . among these rows , select the rows whose frequency record is greater than 100.0 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_eq { all_rows ; power ( kw ) ; 10 kw } ; frequency ; 100.0 } } ; 2 }
the average of the population record of all rows is 28114713 .
Output: round_eq { avg { all_rows ; population } ; 28114713 }
select the rows whose director record fuzzily matches to shawn seet . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; director ; shawn seet } } ; 3 }
select the rows whose incumbent record fuzzily matches to noah m mason . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to sid simpson . 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 ; noah m mason } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; sid simpson } ; first elected } }
select the rows whose surface record fuzzily matches to clay . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; surface ; clay } } ; 3 }
select the rows whose home team record fuzzily matches to essendon . take the crowd record of this row . select the rows whose home team record fuzzily matches to footscray . take the crowd record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; home team ; essendon } ; crowd } ; hop { filter_eq { all_rows ; home team ; footscray } ; crowd } }
select the row whose position record of all rows is minimum . the venue record of this row is toronto , canada .
Output: eq { hop { argmin { all_rows ; position } ; venue } ; toronto , canada }
the sum of the score record of all rows is 1419 .
Output: round_eq { sum { all_rows ; score } ; 1419 }
for the object type records of all rows , most of them fuzzily match to spiral galaxy .
Output: most_eq { all_rows ; object type ; spiral galaxy }
select the rows whose category record fuzzily matches to iva . there is only one such row in the table .
Output: only { filter_eq { all_rows ; category ; iva } }
select the rows whose name record fuzzily matches to marc willers ( nzl ) . take the 1st run record of this row . select the rows whose name record fuzzily matches to mike day ( usa ) . take the 1st run record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; marc willers ( nzl ) } ; 1st run } ; hop { filter_eq { all_rows ; name ; mike day ( usa ) } ; 1st run } }
select the rows whose song record fuzzily matches to shady lady . take the points record of this row . select the rows whose song record fuzzily matches to peace will come . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; song ; shady lady } ; points } ; hop { filter_eq { all_rows ; song ; peace will come } ; points } }
select the row whose enrolment record of all rows is maximum . the school record of this row is pembroke school .
Output: eq { hop { argmax { all_rows ; enrolment } ; school } ; pembroke school }
select the rows whose result record fuzzily matches to re - elected . the number of such rows is 9 .
Output: eq { count { filter_eq { all_rows ; result ; re - elected } } ; 9 }
the average of the crowd record of all rows is 17128 .
Output: round_eq { avg { all_rows ; crowd } ; 17128 }
select the rows whose aspect record fuzzily matches to 4:3 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; aspect ; 4:3 } } ; 4 }
the average of the score record of all rows is 277 .
Output: round_eq { avg { all_rows ; score } ; 277 }
select the rows whose test record fuzzily matches to math . among these rows , select the rows whose number of students record is greater than 100000 . there is only one such row in the table .
Output: only { filter_greater { filter_eq { all_rows ; test ; math } ; number of students ; 100000 } }
the 2nd minimum discovered record of all rows is 1988 .
Output: eq { nth_min { all_rows ; discovered ; 2 } ; 1988 }
select the rows whose opponent record fuzzily matches to la salle . there is only one such row in the table . the year record of this unqiue row is 2013 .
Output: and { only { filter_eq { all_rows ; opponent ; la salle } } ; eq { hop { filter_eq { all_rows ; opponent ; la salle } ; year } ; 2013 } }
for the country records of all rows , most of them fuzzily match to japan .
Output: most_eq { all_rows ; country ; japan }
select the rows whose team record fuzzily matches to mg sport & racing ltd . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; team ; mg sport & racing ltd } } ; 2 }
the average of the altitude ( km ) record of all rows is 244.35 .
Output: round_eq { avg { all_rows ; altitude ( km ) } ; 244.35 }
select the row whose weeks on top record of all rows is 2nd maximum . the song record of this row is ( just like ) starting over .
Output: eq { hop { nth_argmax { all_rows ; weeks on top ; 2 } ; song } ; ( just like ) starting over }
select the rows whose player record is arbitrary . the number of such rows is 16 .
Output: eq { count { filter_all { all_rows ; player } } ; 16 }
select the row whose number in service record of all rows is maximum . the class record of this row is dc .
Output: eq { hop { argmax { all_rows ; number in service } ; class } ; dc }
select the rows whose team record fuzzily matches to simon . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; team ; simon } } ; 5 }
for the elevation ( m ) records of all rows , all of them are greater than 1000 .
Output: all_greater { all_rows ; elevation ( m ) ; 1000 }
the average of the attendance / g record of all rows is 11360 .
Output: round_eq { avg { all_rows ; attendance / g } ; 11360 }
select the rows whose game site record fuzzily matches to shea stadium . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; game site ; shea stadium } } ; 8 }
select the rows whose opponent record fuzzily matches to montreal maroons . take the march record of this row . select the rows whose opponent record fuzzily matches to boston bruins . take the march record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; opponent ; montreal maroons } ; march } ; hop { filter_eq { all_rows ; opponent ; boston bruins } ; march } }
the average of the rank record of all rows is 10th .
Output: round_eq { avg { all_rows ; rank } ; 10th }
select the rows whose former school record fuzzily matches to img academy . there is only one such row in the table . the name record of this unqiue row is jared swopshire .
Output: and { only { filter_eq { all_rows ; former school ; img academy } } ; eq { hop { filter_eq { all_rows ; former school ; img academy } ; name } ; jared swopshire } }
the average of the points record of all rows is 10 .
Output: round_eq { avg { all_rows ; points } ; 10 }
select the row whose number of seasons in superettan record of all rows is maximum . the club record of this row is assyriska ff .
Output: eq { hop { argmax { all_rows ; number of seasons in superettan } ; club } ; assyriska ff }
select the rows whose result record fuzzily matches to w . among these rows , select the rows whose opponent record fuzzily matches to san diego chargers . there is only one such row in the table . the date record of this unqiue row is september 17 , 1972 .
Output: and { only { filter_eq { filter_eq { all_rows ; result ; w } ; opponent ; san diego chargers } } ; eq { hop { filter_eq { filter_eq { all_rows ; result ; w } ; opponent ; san diego chargers } ; date } ; september 17 , 1972 } }
select the rows whose horse record fuzzily matches to colonel john . take the finished record of this row . select the rows whose horse record fuzzily matches to cowboy cal . take the finished record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; horse ; colonel john } ; finished } ; hop { filter_eq { all_rows ; horse ; cowboy cal } ; finished } }
select the rows whose home team score record is less than 15 . among these rows , select the rows whose crowd record is greater than 20000 . the number of such rows is 3 .
Output: eq { count { filter_greater { filter_less { all_rows ; home team score ; 15 } ; crowd ; 20000 } } ; 3 }
select the rows whose position record fuzzily matches to c . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; position ; c } } ; 2 }
select the rows whose directed by record fuzzily matches to debbie allen . there is only one such row in the table . the title record of this unqiue row is adjustment period .
Output: and { only { filter_eq { all_rows ; directed by ; debbie allen } } ; eq { hop { filter_eq { all_rows ; directed by ; debbie allen } ; title } ; adjustment period } }
for the cuts made records of all rows , all of them are greater than or equal to 1 .
Output: all_greater_eq { all_rows ; cuts made ; 1 }
select the rows whose opponent in the final record fuzzily matches to ágnes szávay . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; opponent in the final ; ágnes szávay } } ; 2 }
select the row whose winners share record of all rows is maximum . the year record of this row is 2010 .
Output: eq { hop { argmax { all_rows ; winners share } ; year } ; 2010 }
select the rows whose total ( kg ) record is greater than 195.0 . the 1st minimum bodyweight record of these rows is 57.05 . the name record of the row with 1st minimum bodyweight record is rusmeris villar ( col ) .
Output: and { eq { nth_min { filter_greater { all_rows ; total ( kg ) ; 195.0 } ; bodyweight ; 1 } ; 57.05 } ; eq { hop { nth_argmin { filter_greater { all_rows ; total ( kg ) ; 195.0 } ; bodyweight ; 1 } ; name } ; rusmeris villar ( col ) } }
the sum of the number of reviews record of all rows is 83 .
Output: round_eq { sum { all_rows ; number of reviews } ; 83 }
select the rows whose destination record fuzzily matches to chennai . among these rows , select the rows whose frequency record fuzzily matches to daily . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_eq { all_rows ; destination ; chennai } ; frequency ; daily } } ; 4 }
select the rows whose weight ( kg ) record is equal to 53.5 . there is only one such row in the table . the race record of this unqiue row is sir rupert clarke stakes .
Output: and { only { filter_eq { all_rows ; weight ( kg ) ; 53.5 } } ; eq { hop { filter_eq { all_rows ; weight ( kg ) ; 53.5 } ; race } ; sir rupert clarke stakes } }
select the rows whose location record fuzzily matches to marquette , michigan . there is only one such row in the table . the institution record of this unqiue row is northern michigan university .
Output: and { only { filter_eq { all_rows ; location ; marquette , michigan } } ; eq { hop { filter_eq { all_rows ; location ; marquette , michigan } ; institution } ; northern michigan university } }
select the row whose purse record of all rows is maximum . the tournament record of this row is senior tournament players championship .
Output: eq { hop { argmax { all_rows ; purse } ; tournament } ; senior tournament players championship }
select the rows whose result record fuzzily matches to retired . there is only one such row in the table . the incumbent record of this unqiue row is james hamilton , jr .
Output: and { only { filter_eq { all_rows ; result ; retired } } ; eq { hop { filter_eq { all_rows ; result ; retired } ; incumbent } ; james hamilton , jr } }
select the rows whose bubbles record does not match to yes . there is only one such row in the table . the type record of this unqiue row is domnoderemovedfromdocument .
Output: and { only { filter_not_eq { all_rows ; bubbles ; yes } } ; eq { hop { filter_not_eq { all_rows ; bubbles ; yes } ; type } ; domnoderemovedfromdocument } }
select the row whose date of appointment record of all rows is 1st minimum . the replaced by record of this row is peter voets ( caretaker ) .
Output: eq { hop { nth_argmin { all_rows ; date of appointment ; 1 } ; replaced by } ; peter voets ( caretaker ) }
select the rows whose loser record fuzzily matches to kansas city chiefs . the average of the result record of these rows is 25.5 .
Output: round_eq { avg { filter_eq { all_rows ; loser ; kansas city chiefs } ; result } ; 25.5 }
select the rows whose year record fuzzily matches to 2000 . take the position record of this row . select the rows whose year record fuzzily matches to 2004 . take the position record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 2000 } ; position } ; hop { filter_eq { all_rows ; year ; 2004 } ; position } }
select the rows whose method record fuzzily matches to decision . there is only one such row in the table .
Output: only { filter_eq { all_rows ; method ; decision } }
select the rows whose country record fuzzily matches to japan . take the 1951 record of this row . select the rows whose country record fuzzily matches to scotland . take the 1951 record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; country ; japan } ; 1951 } ; hop { filter_eq { all_rows ; country ; scotland } ; 1951 } }
for the disease area records of all rows , most of them fuzzily match to cancer .
Output: most_eq { all_rows ; disease area ; cancer }
select the rows whose date record fuzzily matches to march . there is only one such row in the table . the goal record of this unqiue row is 3 .
Output: and { only { filter_eq { all_rows ; date ; march } } ; eq { hop { filter_eq { all_rows ; date ; march } ; goal } ; 3 } }
select the rows whose club record fuzzily matches to travnik . take the number of seasons in premier league a record of this row . select the rows whose club record fuzzily matches to zvijezda . take the number of seasons in premier league a record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; club ; travnik } ; number of seasons in premier league a } ; hop { filter_eq { all_rows ; club ; zvijezda } ; number of seasons in premier league a } }
select the rows whose total record is greater than 25 . there is only one such row in the table . the nation record of this unqiue row is east germany ( gdr ) .
Output: and { only { filter_greater { all_rows ; total ; 25 } } ; eq { hop { filter_greater { all_rows ; total ; 25 } ; nation } ; east germany ( gdr ) } }
select the rows whose model record fuzzily matches to short wing . there is only one such row in the table . the manufacturer record of this unqiue row is pipistrel .
Output: and { only { filter_eq { all_rows ; model ; short wing } } ; eq { hop { filter_eq { all_rows ; model ; short wing } ; manufacturer } ; pipistrel } }
select the rows whose women 's singles record fuzzily matches to xu huaiwen . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; women 's singles ; xu huaiwen } } ; 5 }
select the rows whose affiliation record fuzzily matches to public . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; affiliation ; public } } ; 8 }
select the rows whose away team score record is less than 10 . among these rows , select the rows whose crowd record is less than 40000 . the number of such rows is 3 .
Output: eq { count { filter_less { filter_less { all_rows ; away team score ; 10 } ; crowd ; 40000 } } ; 3 }
select the rows whose 2nd evening record fuzzily matches to n / a . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; 2nd evening ; n / a } } ; 3 }
select the rows whose champions record fuzzily matches to laura montalvo . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; champions ; laura montalvo } } ; 3 }
for the surface records of all rows , all of them fuzzily match to clay .
Output: all_eq { all_rows ; surface ; clay }
select the rows whose date record fuzzily matches to 12 july 1930 . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; date ; 12 july 1930 } } ; 6 }
select the row whose average record of all rows is maximum . the team record of this row is celtic .
Output: eq { hop { argmax { all_rows ; average } ; team } ; celtic }
select the rows whose mens singles record fuzzily matches to chen hong . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; mens singles ; chen hong } } ; 2 }
select the rows whose played record is equal to 0 . there is only one such row in the table . the club record of this unqiue row is penlan rfc .
Output: and { only { filter_eq { all_rows ; played ; 0 } } ; eq { hop { filter_eq { all_rows ; played ; 0 } ; club } ; penlan rfc } }
the average of the scored record of all rows is .23 .
Output: round_eq { avg { all_rows ; scored } ; .23 }
select the row whose caps record of all rows is maximum . the name record of this row is vaughan coveny .
Output: eq { hop { argmax { all_rows ; caps } ; name } ; vaughan coveny }
select the rows whose played record is equal to 20 . among these rows , select the rows whose drawn record is equal to 1 . the number of such rows is 5 .
Output: eq { count { filter_eq { filter_eq { all_rows ; played ; 20 } ; drawn ; 1 } } ; 5 }
select the rows whose player record fuzzily matches to stefan noesen . take the round record of this row . select the rows whose player record fuzzily matches to jean - gabriel pageau . take the round record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; stefan noesen } ; round } ; hop { filter_eq { all_rows ; player ; jean - gabriel pageau } ; round } }
select the rows whose kickoff record fuzzily matches to 6:00 pm . there is only one such row in the table . the date record of this unqiue row is sunday , april 28 .
Output: and { only { filter_eq { all_rows ; kickoff ; 6:00 pm } } ; eq { hop { filter_eq { all_rows ; kickoff ; 6:00 pm } ; date } ; sunday , april 28 } }
select the rows whose surface record fuzzily matches to carpet ( i ) . there is only one such row in the table . the tournament record of this unqiue row is opole .
Output: and { only { filter_eq { all_rows ; surface ; carpet ( i ) } } ; eq { hop { filter_eq { all_rows ; surface ; carpet ( i ) } ; tournament } ; opole } }
select the row whose roll record of all rows is minimum . the name record of this row is timatanga community school .
Output: eq { hop { argmin { all_rows ; roll } ; name } ; timatanga community school }
select the rows whose date record fuzzily matches to 11 march 1981 . there is only one such row in the table .
Output: only { filter_eq { all_rows ; date ; 11 march 1981 } }
select the row whose sunlight hours record of all rows is maximum . the city / town record of this row is pontevedra .
Output: eq { hop { argmax { all_rows ; sunlight hours } ; city / town } ; pontevedra }
the average of the attendance record of all rows is 43174 .
Output: round_eq { avg { all_rows ; attendance } ; 43174 }
select the row whose goals record of all rows is maximum . the player record of this row is dario vidošić .
Output: eq { hop { argmax { all_rows ; goals } ; player } ; dario vidošić }
the average of the against record of all rows is 21 .
Output: round_eq { avg { all_rows ; against } ; 21 }
select the rows whose result record fuzzily matches to re - elected . among these rows , select the rows whose first elected record is equal to 1902 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; result ; re - elected } ; first elected ; 1902 } } ; 2 }
for the transfer window records of all rows , most of them fuzzily match to summer .
Output: most_eq { all_rows ; transfer window ; summer }
select the rows whose district record fuzzily matches to guna . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; district ; guna } } ; 2 }
select the row whose total dismissals record of all rows is maximum . the no record of this row is 13 .
Output: eq { hop { argmax { all_rows ; total dismissals } ; no } ; 13 }
select the rows whose callsign record fuzzily matches to dwll . take the power record of this row . select the rows whose callsign record fuzzily matches to dyku . take the power record of this row . the first record is 10 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; callsign ; dwll } ; power } ; hop { filter_eq { all_rows ; callsign ; dyku } ; power } } ; 10 }
select the rows whose top division titles record is equal to 1 . there is only one such row in the table . the team record of this unqiue row is deportivo coopsol .
Output: and { only { filter_eq { all_rows ; top division titles ; 1 } } ; eq { hop { filter_eq { all_rows ; top division titles ; 1 } ; team } ; deportivo coopsol } }
select the row whose titles record of all rows is maximum . the city record of this row is budapest .
Output: eq { hop { argmax { all_rows ; titles } ; city } ; budapest }
select the rows whose opponent record fuzzily matches to anastasia pavlyuchenkova . there is only one such row in the table . the date record of this unqiue row is may 4 , 2013 .
Output: and { only { filter_eq { all_rows ; opponent ; anastasia pavlyuchenkova } } ; eq { hop { filter_eq { all_rows ; opponent ; anastasia pavlyuchenkova } ; date } ; may 4 , 2013 } }
select the row whose pick record of all rows is 3rd minimum . the player record of this row is dale hackbart .
Output: eq { hop { nth_argmin { all_rows ; pick ; 3 } ; player } ; dale hackbart }
select the rows whose crowd record is greater than 20000 . there is only one such row in the table . the venue record of this unqiue row is arden street oval .
Output: and { only { filter_greater { all_rows ; crowd ; 20000 } } ; eq { hop { filter_greater { all_rows ; crowd ; 20000 } ; venue } ; arden street oval } }
select the row whose finish record of all rows is 2nd minimum . the player record of this row is jeff sluman .
Output: eq { hop { nth_argmin { all_rows ; finish ; 2 } ; player } ; jeff sluman }
select the rows whose written by record fuzzily matches to chris collins . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; written by ; chris collins } } ; 8 }
for the day / boarding records of all rows , most of them fuzzily match to day & boarding .
Output: most_eq { all_rows ; day / boarding ; day & boarding }
the average of the gold record of all rows is 3.7 .
Output: round_eq { avg { all_rows ; gold } ; 3.7 }
select the row whose total record of all rows is maximum . the year record of this row is 2010 .
Output: eq { hop { argmax { all_rows ; total } ; year } ; 2010 }
select the row whose crime record of all rows is 2nd maximum . the character record of this row is shane casey .
Output: eq { hop { nth_argmax { all_rows ; crime ; 2 } ; character } ; shane casey }
for the high points records of all rows , most of them fuzzily match to jalen rose .
Output: most_eq { all_rows ; high points ; jalen rose }
select the row whose date record of all rows is minimum . the location attendance record of this row is energysolutions arena 18732 .
Output: eq { hop { argmin { all_rows ; date } ; location attendance } ; energysolutions arena 18732 }
select the rows whose date record fuzzily matches to sun dec 24 . take the result record of this row . select the rows whose date record fuzzily matches to sun dec 17 . take the result record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; sun dec 24 } ; result } ; hop { filter_eq { all_rows ; date ; sun dec 17 } ; result } }
select the row whose gross record of all rows is 5th maximum . the title record of this row is the color purple . the director record of this row is steven spielberg .
Output: and { eq { hop { nth_argmax { all_rows ; gross ; 5 } ; title } ; the color purple } ; eq { hop { nth_argmax { all_rows ; gross ; 5 } ; director } ; steven spielberg } }
the average of the capacity record of all rows is 1732 .
Output: round_eq { avg { all_rows ; capacity } ; 1732 }
select the row whose win % record of all rows is maximum . the matches record of this row is 6 .
Output: eq { hop { argmax { all_rows ; win % } ; matches } ; 6 }
select the rows whose event record fuzzily matches to 400 m h . the average of the notes record of these rows is 54.54 seconds .
Output: round_eq { avg { filter_eq { all_rows ; event ; 400 m h } ; notes } ; 54.54 seconds }
the sum of the wins record of all rows is 18 .
Output: round_eq { sum { all_rows ; wins } ; 18 }
select the rows whose flag record fuzzily matches to united kingdom . the average of the tonnage ( grt ) record of these rows is 5983.8 .
Output: round_eq { avg { filter_eq { all_rows ; flag ; united kingdom } ; tonnage ( grt ) } ; 5983.8 }
select the rows whose name of the country record fuzzily matches to serbia ( srb ) . there is only one such row in the table . the years in world group record of this unqiue row is 3 ( 2 - 3 ) .
Output: and { only { filter_eq { all_rows ; name of the country ; serbia ( srb ) } } ; eq { hop { filter_eq { all_rows ; name of the country ; serbia ( srb ) } ; years in world group } ; 3 ( 2 - 3 ) } }
select the rows whose player record fuzzily matches to ed furgol . take the money record of this row . select the rows whose player record fuzzily matches to lloyd mangrum . take the money record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; ed furgol } ; money } ; hop { filter_eq { all_rows ; player ; lloyd mangrum } ; money } }
the sum of the gold record of all rows is 26 .
Output: round_eq { sum { all_rows ; gold } ; 26 }
the average of the enrollment record of all rows is 20944 .
Output: round_eq { avg { all_rows ; enrollment } ; 20944 }
for the overall records of all rows , most of them are greater than 100 .
Output: most_greater { all_rows ; overall ; 100 }
the average of the entrants record of all rows is 399 .
Output: round_eq { avg { all_rows ; entrants } ; 399 }
for the played records of all rows , all of them are equal to 34 .
Output: all_eq { all_rows ; played ; 34 }
select the row whose laps record of all rows is 2nd minimum . the driver record of this row is dan gurney .
Output: eq { hop { nth_argmin { all_rows ; laps ; 2 } ; driver } ; dan gurney }
select the rows whose theme record fuzzily matches to great blue heron . take the issue price record of this row . select the rows whose theme record fuzzily matches to common eider . take the issue price record of this row . the first record is 2 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; theme ; great blue heron } ; issue price } ; hop { filter_eq { all_rows ; theme ; common eider } ; issue price } } ; 2 }
select the rows whose location record fuzzily matches to philips arena . select the row whose attendance record of these rows is maximum . the date record of this row is december 30 .
Output: eq { hop { argmax { filter_eq { all_rows ; location ; philips arena } ; attendance } ; date } ; december 30 }
select the rows whose city record fuzzily matches to san bernardino . take the year record of this row . select the rows whose city record fuzzily matches to san juan . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; city ; san bernardino } ; year } ; hop { filter_eq { all_rows ; city ; san juan } ; year } }
select the rows whose density record is equal to 33 . there is only one such row in the table . the english name record of this unqiue row is luobei county .
Output: and { only { filter_eq { all_rows ; density ; 33 } } ; eq { hop { filter_eq { all_rows ; density ; 33 } ; english name } ; luobei county } }
select the row whose enrollment 08 - 09 record of all rows is maximum . the school record of this row is twin lakes .
Output: eq { hop { argmax { all_rows ; enrollment 08 - 09 } ; school } ; twin lakes }
select the row whose f bolt ( kgf ) record of all rows is 2nd maximum . the chambering record of this row is .300 lapua magnum .
Output: eq { hop { nth_argmax { all_rows ; f bolt ( kgf ) ; 2 } ; chambering } ; .300 lapua magnum }
for the original air date records of all rows , all of them fuzzily match to april 20 , 2009 .
Output: all_eq { all_rows ; original air date ; april 20 , 2009 }
select the rows whose name record fuzzily matches to happy planet index . take the rank record of this row . select the rows whose name record fuzzily matches to environmental performance index . take the rank record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; happy planet index } ; rank } ; hop { filter_eq { all_rows ; name ; environmental performance index } ; rank } }
select the rows whose season outcome record fuzzily matches to won div ii state championship . there is only one such row in the table . the team record of this unqiue row is indians .
Output: and { only { filter_eq { all_rows ; season outcome ; won div ii state championship } } ; eq { hop { filter_eq { all_rows ; season outcome ; won div ii state championship } ; team } ; indians } }
select the rows whose points record is equal to 130 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; points ; 130 } } ; 2 }
select the row whose 2005 record of all rows is maximum . the tournament record of this row is year end ranking .
Output: eq { hop { argmax { all_rows ; 2005 } ; tournament } ; year end ranking }
select the rows whose constructor record fuzzily matches to ferrari . the maximum date record of these rows is 18 july .
Output: eq { max { filter_eq { all_rows ; constructor ; ferrari } ; date } ; 18 july }
select the row whose points record of all rows is 2nd maximum . the name record of this row is angelika buck / erich buck .
Output: eq { hop { nth_argmax { all_rows ; points ; 2 } ; name } ; angelika buck / erich buck }
for the capacity records of all rows , most of them are greater than 10,000 .
Output: most_greater { all_rows ; capacity ; 10,000 }
for the date records of all rows , all of them fuzzily match to 9 june 1928 .
Output: all_eq { all_rows ; date ; 9 june 1928 }
for the surface records of all rows , most of them fuzzily match to hard .
Output: most_eq { all_rows ; surface ; hard }
select the rows whose surface record fuzzily matches to grass . there is only one such row in the table . the outcome record of this unqiue row is winner .
Output: and { only { filter_eq { all_rows ; surface ; grass } } ; eq { hop { filter_eq { all_rows ; surface ; grass } ; outcome } ; winner } }
select the row whose winter olympics record of all rows is 2nd maximum . the winner record of this row is thomas wassberg .
Output: eq { hop { nth_argmax { all_rows ; winter olympics ; 2 } ; winner } ; thomas wassberg }
select the rows whose result record fuzzily matches to lost re - election republican gain . there is only one such row in the table . the district record of this unqiue row is illinois 21 .
Output: and { only { filter_eq { all_rows ; result ; lost re - election republican gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election republican gain } ; district } ; illinois 21 } }
select the rows whose chassis - engine record fuzzily matches to porsche 962 c . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; chassis - engine ; porsche 962 c } } ; 8 }
select the rows whose pole position record fuzzily matches to ayrton senna . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; pole position ; ayrton senna } } ; 8 }
select the rows whose mlb draft record fuzzily matches to 1st round . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; mlb draft ; 1st round } } ; 2 }
for the nationality records of all rows , most of them fuzzily match to rome .
Output: most_eq { all_rows ; nationality ; rome }
select the rows whose outcome record fuzzily matches to winner . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; outcome ; winner } } ; 4 }
select the rows whose publisher record fuzzily matches to walker . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; publisher ; walker } } ; 6 }
select the rows whose elevation record is greater than 12000 feet . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; elevation ; 12000 feet } } ; 3 }
the average of the caps record of all rows is 14.83 .
Output: round_eq { avg { all_rows ; caps } ; 14.83 }
select the row whose goals against record of all rows is 1st maximum . the team record of this row is ashton athletic .
Output: eq { hop { nth_argmax { all_rows ; goals against ; 1 } ; team } ; ashton athletic }
select the row whose 1910 census record of all rows is 3rd maximum . the city record of this row is fuzan .
Output: eq { hop { nth_argmax { all_rows ; 1910 census ; 3 } ; city } ; fuzan }
the average of the crowd record of all rows is 24662 .
Output: round_eq { avg { all_rows ; crowd } ; 24662 }
select the rows whose home team score record is greater than 5 . for the crowd records of these rows , most of them are greater than 20000 .
Output: most_greater { filter_greater { all_rows ; home team score ; 5 } ; crowd ; 20000 }
select the rows whose segment d record fuzzily matches to part 2 . there is only one such row in the table . the series ep record of this unqiue row is 4 - 04 .
Output: and { only { filter_eq { all_rows ; segment d ; part 2 } } ; eq { hop { filter_eq { all_rows ; segment d ; part 2 } ; series ep } ; 4 - 04 } }
for the party records of all rows , all of them fuzzily match to democratic .
Output: all_eq { all_rows ; party ; democratic }
for the tv records of all rows , most of them fuzzily match to nbc .
Output: most_eq { all_rows ; tv ; nbc }
select the row whose average record of all rows is 2nd maximum . the team record of this row is river plate .
Output: eq { hop { nth_argmax { all_rows ; average ; 2 } ; team } ; river plate }
select the rows whose pba team record fuzzily matches to formula shel . select the row whose pick record of these rows is 2nd minimum . the player record of this row is arnold rodriguez .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; pba team ; formula shel } ; pick ; 2 } ; player } ; arnold rodriguez }
select the rows whose 2nd place team record fuzzily matches to nitehawks . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; 2nd place team ; nitehawks } } ; 2 }
select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is carlton .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; carlton }
select the rows whose novelty record fuzzily matches to gen et sp nov . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; novelty ; gen et sp nov } } ; 4 }
select the rows whose skipper record fuzzily matches to andy forbes . take the combined elapsed time record of this row . select the rows whose skipper record fuzzily matches to stuart jackson . take the combined elapsed time record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; skipper ; andy forbes } ; combined elapsed time } ; hop { filter_eq { all_rows ; skipper ; stuart jackson } ; combined elapsed time } }
select the rows whose output record fuzzily matches to 15 - 30w . there is only one such row in the table . the model record of this unqiue row is schmidt .
Output: and { only { filter_eq { all_rows ; output ; 15 - 30w } } ; eq { hop { filter_eq { all_rows ; output ; 15 - 30w } ; model } ; schmidt } }
select the row whose w record of all rows is maximum . the skip ( club ) record of this row is brad gushue ( bally haly ) .
Output: eq { hop { argmax { all_rows ; w } ; skip ( club ) } ; brad gushue ( bally haly ) }
select the rows whose visitor record fuzzily matches to minnesota . among these rows , select the rows whose attendance record is greater than 15000 . the number of such rows is 5 .
Output: eq { count { filter_greater { filter_eq { all_rows ; visitor ; minnesota } ; attendance ; 15000 } } ; 5 }
select the row whose date record of all rows is maximum . the venue record of this row is alexandria stadium , alexandria .
Output: eq { hop { argmax { all_rows ; date } ; venue } ; alexandria stadium , alexandria }
the sum of the wins record of all rows is 2 .
Output: round_eq { sum { all_rows ; wins } ; 2 }
select the rows whose position record fuzzily matches to goalkeeper . there is only one such row in the table . the player record of this unqiue row is paul robinson .
Output: and { only { filter_eq { all_rows ; position ; goalkeeper } } ; eq { hop { filter_eq { all_rows ; position ; goalkeeper } ; player } ; paul robinson } }
select the rows whose score record fuzzily matches to w . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; score ; w } } ; 2 }
the average of the score record of all rows is 96.5 .
Output: round_eq { avg { all_rows ; score } ; 96.5 }
select the rows whose agg score record fuzzily matches to 0 - 2 . there is only one such row in the table . the team 1 record of this unqiue row is ilisiakos .
Output: and { only { filter_eq { all_rows ; agg score ; 0 - 2 } } ; eq { hop { filter_eq { all_rows ; agg score ; 0 - 2 } ; team 1 } ; ilisiakos } }
the average of the attendance record of all rows is 46966 .
Output: round_eq { avg { all_rows ; attendance } ; 46966 }
the average of the horsepowers record of all rows is 7031 .
Output: round_eq { avg { all_rows ; horsepowers } ; 7031 }
select the row whose metacritic record of all rows is maximum . the game title record of this row is sly 2 : band of thieves .
Output: eq { hop { argmax { all_rows ; metacritic } ; game title } ; sly 2 : band of thieves }
select the rows whose team record fuzzily matches to team penske . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; team ; team penske } } ; 5 }
select the row whose bronze record of all rows is minimum . the nation record of this row is commonwealth of independent states ( 1992 only ) .
Output: eq { hop { argmin { all_rows ; bronze } ; nation } ; commonwealth of independent states ( 1992 only ) }
for the capacity ( mw ) records of all rows , most of them are greater than 20 .
Output: most_greater { all_rows ; capacity ( mw ) ; 20 }
select the row whose date record of all rows is 13th minimum . the race record of this row is italian grand prix .
Output: eq { hop { nth_argmin { all_rows ; date ; 13 } ; race } ; italian grand prix }
select the rows whose high rebounds record fuzzily matches to tim duncan . the average of the high rebounds record of these rows is 11.5 .
Output: round_eq { avg { filter_eq { all_rows ; high rebounds ; tim duncan } ; high rebounds } ; 11.5 }
select the row whose rank record of all rows is minimum . the athlete record of this row is alan campbell .
Output: eq { hop { argmin { all_rows ; rank } ; athlete } ; alan campbell }
select the rows whose incumbent record fuzzily matches to rené louis derouen . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to bolivar e kemp . take the first elected record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; incumbent ; rené louis derouen } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; bolivar e kemp } ; first elected } }
select the rows whose founding university record fuzzily matches to city college of new york . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; founding university ; city college of new york } } ; 2 }
select the rows whose hometown record fuzzily matches to santiago de los caballeros . among these rows , select the rows whose age record is less than 19 . there is only one such row in the table . the contestant record of this unqiue row is valerie chardonnens vargas .
Output: and { only { filter_less { filter_eq { all_rows ; hometown ; santiago de los caballeros } ; age ; 19 } } ; eq { hop { filter_less { filter_eq { all_rows ; hometown ; santiago de los caballeros } ; age ; 19 } ; contestant } ; valerie chardonnens vargas } }
select the rows whose gold record is greater than 100 . there is only one such row in the table . the province record of this unqiue row is bangkok .
Output: and { only { filter_greater { all_rows ; gold ; 100 } } ; eq { hop { filter_greater { all_rows ; gold ; 100 } ; province } ; bangkok } }
for the matches records of all rows , most of them are equal to 4 .
Output: most_eq { all_rows ; matches ; 4 }
select the row whose pts record of all rows is 4th maximum . the year record of this row is 1986 .
Output: eq { hop { nth_argmax { all_rows ; pts ; 4 } ; year } ; 1986 }
the average of the goals record of all rows is 278.8 .
Output: round_eq { avg { all_rows ; goals } ; 278.8 }
for the result records of all rows , most of them are less than or equal to 20th .
Output: most_less_eq { all_rows ; result ; 20th }
select the rows whose meet record fuzzily matches to 1979 pan american games . there is only one such row in the table . the event record of this unqiue row is 200 m freestyle .
Output: and { only { filter_eq { all_rows ; meet ; 1979 pan american games } } ; eq { hop { filter_eq { all_rows ; meet ; 1979 pan american games } ; event } ; 200 m freestyle } }
select the rows whose artist record fuzzily matches to ann breen . take the points record of this row . select the rows whose artist record fuzzily matches to fran meen . take the points record of this row . the first record is 14 larger than the second record . the song record of the first row is oh , darling . the song record of the second row is say that you love me .
Output: and { eq { diff { hop { filter_eq { all_rows ; artist ; ann breen } ; points } ; hop { filter_eq { all_rows ; artist ; fran meen } ; points } } ; 14 } ; and { eq { hop { filter_eq { all_rows ; artist ; ann breen } ; song } ; oh , darling } ; eq { hop { filter_eq { all_rows ; artist ; fran meen } ; song } ; say that you love me } } }
select the rows whose round of 16 record fuzzily matches to did not advance . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; round of 16 ; did not advance } } ; 2 }
select the rows whose archbishop record fuzzily matches to jean baptiste lamy . take the born record of this row . select the rows whose archbishop record fuzzily matches to jean baptiste salpointe . take the born record of this row . the first record is less than the second record . the born record of the first row is october 11 , 1814 . the born record of the second row is february 22 , 1825 .
Output: and { less { hop { filter_eq { all_rows ; archbishop ; jean baptiste lamy } ; born } ; hop { filter_eq { all_rows ; archbishop ; jean baptiste salpointe } ; born } } ; and { eq { hop { filter_eq { all_rows ; archbishop ; jean baptiste lamy } ; born } ; october 11 , 1814 } ; eq { hop { filter_eq { all_rows ; archbishop ; jean baptiste salpointe } ; born } ; february 22 , 1825 } } }
select the rows whose label record fuzzily matches to alfa records . among these rows , select the rows whose format record fuzzily matches to cd . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_eq { all_rows ; label ; alfa records } ; format ; cd } } ; 4 }
select the row whose year born record of all rows is minimum . the player record of this row is goran jagodnik .
Output: eq { hop { argmin { all_rows ; year born } ; player } ; goran jagodnik }
select the rows whose october 20 , 2008 record fuzzily matches to 2010 . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; october 20 , 2008 ; 2010 } } ; 6 }
select the rows whose venue record fuzzily matches to away . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; venue ; away } } ; 4 }
the average of the to par record of all rows is -2.54 .
Output: round_eq { avg { all_rows ; to par } ; -2.54 }
select the rows whose college record fuzzily matches to mississippi . there is only one such row in the table . the player record of this unqiue row is allen brown .
Output: and { only { filter_eq { all_rows ; college ; mississippi } } ; eq { hop { filter_eq { all_rows ; college ; mississippi } ; player } ; allen brown } }
for the competition records of all rows , most of them fuzzily match to league .
Output: most_eq { all_rows ; competition ; league }