niryuu/sni-each-converted huggingface dataset:
for the result records of all rows , most of them fuzzily match to nominated .
Output: most_eq { all_rows ; result ; nominated }
select the rows whose constructor record fuzzily matches to renault . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; constructor ; renault } } ; 4 }
select the row whose score record of all rows is maximum . the game record of this row is 5 .
Output: eq { hop { argmax { all_rows ; score } ; game } ; 5 }
select the rows whose location attendance record fuzzily matches to at & t center . the average of the location attendance record of these rows is 18797 .
Output: round_eq { avg { filter_eq { all_rows ; location attendance ; at & t center } ; location attendance } ; 18797 }
select the rows whose type record fuzzily matches to fraternity . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; type ; fraternity } } ; 4 }
select the rows whose city district record fuzzily matches to santiago . take the population 2007 census ( hab ) record of this row . select the rows whose city district record fuzzily matches to wanchaq . take the population 2007 census ( hab ) record of this row . the first record is 11753 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; city district ; santiago } ; population 2007 census ( hab ) } ; hop { filter_eq { all_rows ; city district ; wanchaq } ; population 2007 census ( hab ) } } ; 11753 }
select the row whose points record of all rows is maximum . the club record of this row is caernarfon rfc .
Output: eq { hop { argmax { all_rows ; points } ; club } ; caernarfon rfc }
select the rows whose circuit record is arbitrary . the number of such rows is 5 .
Output: eq { count { filter_all { all_rows ; circuit } } ; 5 }
select the row whose scoring average record of all rows is minimum . the year record of this row is 2007 .
Output: eq { hop { argmin { all_rows ; scoring average } ; year } ; 2007 }
the average of the central rate record of all rows is 3.4 .
Output: round_eq { avg { all_rows ; central rate } ; 3.4 }
select the rows whose city record fuzzily matches to budapest , hungary . there is only one such row in the table .
Output: only { filter_eq { all_rows ; city ; budapest , hungary } }
select the rows whose round record is equal to 1 . select the row whose weight record of these rows is maximum . the player name record of this row is felix jones .
Output: eq { hop { argmax { filter_eq { all_rows ; round ; 1 } ; weight } ; player name } ; felix jones }
the sum of the try bonus record of all rows is 44 .
Output: round_eq { sum { all_rows ; try bonus } ; 44 }
select the rows whose rd record is greater than or equal to 4 . select the row whose pick record of these rows is 2nd minimum . the player record of this row is matt butcher .
Output: eq { hop { nth_argmin { filter_greater_eq { all_rows ; rd ; 4 } ; pick ; 2 } ; player } ; matt butcher }
select the rows whose name of rivalry record fuzzily matches to auburn - tennessee football rivalry . take the games played record of this row . select the rows whose name of rivalry record fuzzily matches to auburn - clemson rivalry . take the games played record of this row . the first record is 3 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; name of rivalry ; auburn - tennessee football rivalry } ; games played } ; hop { filter_eq { all_rows ; name of rivalry ; auburn - clemson rivalry } ; games played } } ; 3 }
select the rows whose directed by record fuzzily matches to virgil fabian . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; directed by ; virgil fabian } } ; 7 }
select the rows whose result record fuzzily matches to re-elected . select the row whose first elected record of these rows is 1st maximum . the incumbent record of this row is donald d clancy .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; result ; re-elected } ; first elected ; 1 } ; incumbent } ; donald d clancy }
select the rows whose score in the final record fuzzily matches to w / o . there is only one such row in the table . the partnering record of this unqiue row is sorana cîrstea .
Output: and { only { filter_eq { all_rows ; score in the final ; w / o } } ; eq { hop { filter_eq { all_rows ; score in the final ; w / o } ; partnering } ; sorana cîrstea } }
select the row whose date record of all rows is 2nd minimum . the venue record of this row is bahrain national stadium , manama .
Output: eq { hop { nth_argmin { all_rows ; date ; 2 } ; venue } ; bahrain national stadium , manama }
select the rows whose goalsagainst record is greater than 300 . there is only one such row in the table . the season record of this unqiue row is 1997 - 98 .
Output: and { only { filter_greater { all_rows ; goalsagainst ; 300 } } ; eq { hop { filter_greater { all_rows ; goalsagainst ; 300 } ; season } ; 1997 - 98 } }
select the rows whose ngc number record fuzzily matches to 5823 . take the right ascension ( j2000 ) record of this row . select the rows whose ngc number record fuzzily matches to 5822 . take the right ascension ( j2000 ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; ngc number ; 5823 } ; right ascension ( j2000 ) } ; hop { filter_eq { all_rows ; ngc number ; 5822 } ; right ascension ( j2000 ) } }
for the outcome records of all rows , most of them fuzzily match to winner .
Output: most_eq { all_rows ; outcome ; winner }
select the rows whose mens doubles record fuzzily matches to vincent laigle svetoslav stoyanov . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; mens doubles ; vincent laigle svetoslav stoyanov } } ; 2 }
select the row whose series ep record of all rows is 2nd minimum . the netflix record of this row is s07e02 .
Output: eq { hop { nth_argmin { all_rows ; series ep ; 2 } ; netflix } ; s07e02 }
the average of the goals record of all rows is 120.8 .
Output: round_eq { avg { all_rows ; goals } ; 120.8 }
for the mean score records of all rows , most of them are greater than 621 .
Output: most_greater { all_rows ; mean score ; 621 }
select the row whose points record of all rows is maximum . the club record of this row is ud las palmas .
Output: eq { hop { argmax { all_rows ; points } ; club } ; ud las palmas }
select the rows whose crowd record is equal to 10000 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; crowd ; 10000 } } ; 3 }
select the rows whose venue record is arbitrary . the number of such rows is 6 .
Output: eq { count { filter_all { all_rows ; venue } } ; 6 }
select the rows whose gold record fuzzily matches to norbert sturny ( sui ) . take the year record of this row . select the rows whose gold record fuzzily matches to malcolm cooper ( gbr ) . take the year record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; gold ; norbert sturny ( sui ) } ; year } ; hop { filter_eq { all_rows ; gold ; malcolm cooper ( gbr ) } ; year } }
select the rows whose method record fuzzily matches to rear naked choke . there is only one such row in the table . the opponent record of this unqiue row is helio dipp .
Output: and { only { filter_eq { all_rows ; method ; rear naked choke } } ; eq { hop { filter_eq { all_rows ; method ; rear naked choke } ; opponent } ; helio dipp } }
for the final score records of all rows , most of them fuzzily match to l .
Output: most_eq { all_rows ; final score ; l }
select the rows whose party record fuzzily matches to independent . there is only one such row in the table . the candidate record of this unqiue row is peter law .
Output: and { only { filter_eq { all_rows ; party ; independent } } ; eq { hop { filter_eq { all_rows ; party ; independent } ; candidate } ; peter law } }
select the rows whose location record fuzzily matches to seoul , south korea . there is only one such row in the table . the event record of this unqiue row is hero 's 2005 in seoul .
Output: and { only { filter_eq { all_rows ; location ; seoul , south korea } } ; eq { hop { filter_eq { all_rows ; location ; seoul , south korea } ; event } ; hero 's 2005 in seoul } }
select the rows whose team record fuzzily matches to senators . take the overall record record of this row . select the rows whose team record fuzzily matches to golden knights . take the overall record record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; team ; senators } ; overall record } ; hop { filter_eq { all_rows ; team ; golden knights } ; overall record } }
select the row whose attendance record of all rows is 2nd maximum . the date record of this row is 23 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; 23 }
select the row whose attendance record of all rows is maximum . the opponent record of this row is green bay packers .
Output: eq { hop { argmax { all_rows ; attendance } ; opponent } ; green bay packers }
select the rows whose location ( city , state ) record fuzzily matches to wisconsin . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; location ( city , state ) ; wisconsin } } ; 4 }
select the rows whose tie no record fuzzily matches to replay . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; tie no ; replay } } ; 2 }
select the rows whose year record fuzzily matches to 1959 . take the rank record of this row . select the rows whose year record fuzzily matches to 1958 . take the rank record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; year ; 1959 } ; rank } ; hop { filter_eq { all_rows ; year ; 1958 } ; rank } }
select the rows whose primary conference record fuzzily matches to big east conference . there is only one such row in the table . the institution record of this unqiue row is georgetown university .
Output: and { only { filter_eq { all_rows ; primary conference ; big east conference } } ; eq { hop { filter_eq { all_rows ; primary conference ; big east conference } ; institution } ; georgetown university } }
select the rows whose score record fuzzily matches to 5-2 . there is only one such row in the table . the opponent record of this unqiue row is boston bruins .
Output: and { only { filter_eq { all_rows ; score ; 5-2 } } ; eq { hop { filter_eq { all_rows ; score ; 5-2 } ; opponent } ; boston bruins } }
select the rows whose result record is less than 20.3 . the number of such rows is 7 .
Output: eq { count { filter_less { all_rows ; result ; 20.3 } } ; 7 }
select the rows whose engine record fuzzily matches to 112 degree v4 ' big bang ' ( 2 - stroke ) . take the crankshaft record of this row . select the rows whose engine record fuzzily matches to 90 degree v4 ' twin pulse ' . take the crankshaft record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; engine ; 112 degree v4 ' big bang ' ( 2 - stroke ) } ; crankshaft } ; hop { filter_eq { all_rows ; engine ; 90 degree v4 ' twin pulse ' } ; crankshaft } }
select the rows whose display record is equal to 4.55 . there is only one such row in the table . the code name record of this unqiue row is aoba .
Output: and { only { filter_eq { all_rows ; display ; 4.55 } } ; eq { hop { filter_eq { all_rows ; display ; 4.55 } ; code name } ; aoba } }
select the rows whose place record does not match to t1 . the average of the to par record of these rows is 4 .
Output: round_eq { avg { filter_not_eq { all_rows ; place ; t1 } ; to par } ; 4 }
select the rows whose acquisition via record fuzzily matches to rookie draft . there is only one such row in the table . the name record of this unqiue row is reynel hugnatan .
Output: and { only { filter_eq { all_rows ; acquisition via ; rookie draft } } ; eq { hop { filter_eq { all_rows ; acquisition via ; rookie draft } ; name } ; reynel hugnatan } }
select the rows whose competition record fuzzily matches to world championships . the 2nd minimum year record of these rows is 2001 .
Output: eq { nth_min { filter_eq { all_rows ; competition ; world championships } ; year ; 2 } ; 2001 }
select the rows whose record record fuzzily matches to 4 - 0 . take the time record of this row . select the rows whose record record fuzzily matches to 5 - 0 . take the time record of this row . the first record is less than the second record . the time record of the first row is 1:43 . the time record of the second row is 5:00 .
Output: and { less { hop { filter_eq { all_rows ; record ; 4 - 0 } ; time } ; hop { filter_eq { all_rows ; record ; 5 - 0 } ; time } } ; and { eq { hop { filter_eq { all_rows ; record ; 4 - 0 } ; time } ; 1:43 } ; eq { hop { filter_eq { all_rows ; record ; 5 - 0 } ; time } ; 5:00 } } }
select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose score record fuzzily matches to 282 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; country ; united states } ; score ; 282 } } ; 2 }
for the extra records of all rows , most of them fuzzily match to team competition .
Output: most_eq { all_rows ; extra ; team competition }
select the rows whose television service record fuzzily matches to telemarket . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; television service ; telemarket } } ; 2 }
select the rows whose ties record is equal to 1 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; ties ; 1 } } ; 3 }
select the row whose attendance record of all rows is 2nd maximum . the week record of this row is 8 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; week } ; 8 }
select the rows whose points record is greater than 10 . among these rows , select the rows whose drawn record is equal to 2 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_greater { all_rows ; points ; 10 } ; drawn ; 2 } } ; 2 }
select the rows whose apps record is equal to 24 . there is only one such row in the table . the season record of this unqiue row is 2005 / 06 .
Output: and { only { filter_eq { all_rows ; apps ; 24 } } ; eq { hop { filter_eq { all_rows ; apps ; 24 } ; season } ; 2005 / 06 } }
select the row whose top 10 record of all rows is maximum . the year record of this row is 2009 .
Output: eq { hop { argmax { all_rows ; top 10 } ; year } ; 2009 }
for the hand guards records of all rows , most of them fuzzily match to m4 .
Output: most_eq { all_rows ; hand guards ; m4 }
select the rows whose round record fuzzily matches to 1st . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; round ; 1st } } ; 2 }
select the rows whose score record is equal to 70 . among these rows , select the rows whose country record fuzzily matches to denmark . there is only one such row in the table . the player record of this unqiue row is søren kjeldsen .
Output: and { only { filter_eq { filter_eq { all_rows ; score ; 70 } ; country ; denmark } } ; eq { hop { filter_eq { filter_eq { all_rows ; score ; 70 } ; country ; denmark } ; player } ; søren kjeldsen } }
the 3rd minimum pick record of all rows is 3 . the player record of the row with 3rd minimum pick record is candice wiggins . the school / club team record of the row with 3rd minimum pick record is stanford .
Output: and { eq { nth_min { all_rows ; pick ; 3 } ; 3 } ; and { eq { hop { nth_argmin { all_rows ; pick ; 3 } ; player } ; candice wiggins } ; eq { hop { nth_argmin { all_rows ; pick ; 3 } ; school / club team } ; stanford } } }
select the rows whose name record fuzzily matches to dave farmer . take the round record of this row . select the rows whose name record fuzzily matches to don parrish . take the round record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; dave farmer } ; round } ; hop { filter_eq { all_rows ; name ; don parrish } ; round } }
the average of the total record of all rows is 154.3245 .
Output: round_eq { avg { all_rows ; total } ; 154.3245 }
select the rows whose opposition record fuzzily matches to surrey . take the score record of this row . select the rows whose opposition record fuzzily matches to somerset . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opposition ; surrey } ; score } ; hop { filter_eq { all_rows ; opposition ; somerset } ; score } }
select the rows whose 2004 record fuzzily matches to w . there is only one such row in the table . the tournament record of this unqiue row is french open .
Output: and { only { filter_eq { all_rows ; 2004 ; w } } ; eq { hop { filter_eq { all_rows ; 2004 ; w } ; tournament } ; french open } }
select the rows whose date record fuzzily matches to 22 october . take the score record of this row . select the rows whose date record fuzzily matches to 24 october . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; 22 october } ; score } ; hop { filter_eq { all_rows ; date ; 24 october } ; score } }
select the row whose attendance record of all rows is maximum . the date record of this row is september 28 , 1986 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; september 28 , 1986 }
select the rows whose country record fuzzily matches to fra . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; country ; fra } } ; 3 }
for the points records of all rows , most of them are less than 90 .
Output: most_less { all_rows ; points ; 90 }
the average of the result record of all rows is 24.69 .
Output: round_eq { avg { all_rows ; result } ; 24.69 }
select the rows whose institution record is arbitrary . the number of such rows is 17 .
Output: eq { count { filter_all { all_rows ; institution } } ; 17 }
the sum of the enrollment record of all rows is 6339 .
Output: round_eq { sum { all_rows ; enrollment } ; 6339 }
select the rows whose result record fuzzily matches to bottom 3 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; result ; bottom 3 } } ; 2 }
for the high points records of all rows , most of them fuzzily match to paul .
Output: most_eq { all_rows ; high points ; paul }
the average of the field goals record of all rows is 113 .
Output: round_eq { avg { all_rows ; field goals } ; 113 }
select the rows whose date successor seated record fuzzily matches to march . there is only one such row in the table . the district record of this unqiue row is kentucky 2nd .
Output: and { only { filter_eq { all_rows ; date successor seated ; march } } ; eq { hop { filter_eq { all_rows ; date successor seated ; march } ; district } ; kentucky 2nd } }
select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the year record of this unqiue row is 1994 . the championship record of this unqiue row is rome .
Output: and { only { filter_eq { all_rows ; surface ; clay } } ; and { eq { hop { filter_eq { all_rows ; surface ; clay } ; year } ; 1994 } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; championship } ; rome } } }
the maximum length of retirement record of all rows is 6030 days . the president record of the row with superlative length of retirement record is venkataraman , ramaswamy ramaswamy venkataraman .
Output: and { eq { max { all_rows ; length of retirement } ; 6030 days } ; eq { hop { argmax { all_rows ; length of retirement } ; president } ; venkataraman , ramaswamy ramaswamy venkataraman } }
the average of the days with frost record of all rows is 18.4 .
Output: round_eq { avg { all_rows ; days with frost } ; 18.4 }
select the rows whose name record fuzzily matches to gergely kiss . take the date of birth record of this row . select the rows whose name record fuzzily matches to tibor benedek . take the date of birth record of this row . the first record is 5 years larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; name ; gergely kiss } ; date of birth } ; hop { filter_eq { all_rows ; name ; tibor benedek } ; date of birth } } ; 5 years }
select the rows whose top - 5 record is equal to 1 . there is only one such row in the table .
Output: only { filter_eq { all_rows ; top - 5 ; 1 } }
select the row whose erp w record of all rows is maximum . the frequency mhz record of this row is 105.1 fm .
Output: eq { hop { argmax { all_rows ; erp w } ; frequency mhz } ; 105.1 fm }
for the nationality records of all rows , most of them fuzzily match to canada .
Output: most_eq { all_rows ; nationality ; canada }
select the rows whose gymnast record fuzzily matches to kor . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; gymnast ; kor } } ; 2 }
select the row whose population record of all rows is maximum . the english name record of this row is luobei county .
Output: eq { hop { argmax { all_rows ; population } ; english name } ; luobei county }
select the row whose us viewers ( in millions ) record of all rows is maximum . the title record of this row is crazy for you .
Output: eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; title } ; crazy for you }
select the rows whose home team score record is greater than 10 . among these rows , select the rows whose crowd record is less than 20000 . the number of such rows is 3 .
Output: eq { count { filter_less { filter_greater { all_rows ; home team score ; 10 } ; crowd ; 20000 } } ; 3 }
select the rows whose opponent record fuzzily matches to iowa . take the date record of this row . select the rows whose opponent record fuzzily matches to 4 michigan . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; opponent ; iowa } ; date } ; hop { filter_eq { all_rows ; opponent ; 4 michigan } ; date } }
select the rows whose player record fuzzily matches to tom watson . take the year ( s ) won record of this row . select the rows whose player record fuzzily matches to retief goosen . take the year ( s ) won record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; tom watson } ; year ( s ) won } ; hop { filter_eq { all_rows ; player ; retief goosen } ; year ( s ) won } }
select the rows whose rank record is equal to 20 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; rank ; 20 } } ; 2 }
select the rows whose joined record is equal to 2007 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; joined ; 2007 } } ; 2 }
select the rows whose episode count record is greater than 80 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; episode count ; 80 } } ; 3 }
select the rows whose original air date ( uk ) record fuzzily matches to july . there is only one such row in the table . the episode title record of this unqiue row is night games .
Output: and { only { filter_eq { all_rows ; original air date ( uk ) ; july } } ; eq { hop { filter_eq { all_rows ; original air date ( uk ) ; july } ; episode title } ; night games } }
select the rows whose starring record fuzzily matches to warren mitchell as alf garnett . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; starring ; warren mitchell as alf garnett } } ; 3 }
select the rows whose directed by record fuzzily matches to jonathan frakes . among these rows , select the rows whose written by record fuzzily matches to linda mcgibney . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; directed by ; jonathan frakes } ; written by ; linda mcgibney } } ; 2 }
select the row whose enrollment ihsaa class record of all rows is maximum . the school record of this row is penn .
Output: eq { hop { argmax { all_rows ; enrollment ihsaa class } ; school } ; penn }
select the row whose score record of all rows is maximum . the date record of this row is 23 may 2002 .
Output: eq { hop { argmax { all_rows ; score } ; date } ; 23 may 2002 }
for the season premiere date records of all rows , all of them fuzzily match to 20 .
Output: all_eq { all_rows ; season premiere date ; 20 }
the 1st minimum round record of all rows is 1 . the player record of the row with 1st minimum round record is john hummer .
Output: and { eq { nth_min { all_rows ; round ; 1 } ; 1 } ; eq { hop { nth_argmin { all_rows ; round ; 1 } ; player } ; john hummer } }
for the played records of all rows , most of them are equal to 114 .
Output: most_eq { all_rows ; played ; 114 }
select the rows whose construction record fuzzily matches to plastic . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; construction ; plastic } } ; 4 }
the average of the attendance record of all rows is 43583 .
Output: round_eq { avg { all_rows ; attendance } ; 43583 }
select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose score record is greater than 67 . the number of such rows is 5 .
Output: eq { count { filter_greater { filter_eq { all_rows ; country ; united states } ; score ; 67 } } ; 5 }
select the rows whose manner of departure record fuzzily matches to mutual consent . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; manner of departure ; mutual consent } } ; 3 }
select the row whose total record of all rows is 2nd maximum . the name record of this row is michael doyle .
Output: eq { hop { nth_argmax { all_rows ; total ; 2 } ; name } ; michael doyle }
select the row whose enrollment record of all rows is maximum . the school record of this row is belleville east high school .
Output: eq { hop { argmax { all_rows ; enrollment } ; school } ; belleville east high school }
the average of the average ratings record of all rows is 14.57 .
Output: round_eq { avg { all_rows ; average ratings } ; 14.57 }
select the row whose goals record of all rows is minimum . the player record of this row is mehmet yılmaz .
Output: eq { hop { argmin { all_rows ; goals } ; player } ; mehmet yılmaz }
select the rows whose visitor record fuzzily matches to winnipeg . take the date record of this row . select the rows whose visitor record fuzzily matches to chicago . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; visitor ; winnipeg } ; date } ; hop { filter_eq { all_rows ; visitor ; chicago } ; date } }
select the rows whose score record fuzzily matches to ( ot ) . there is only one such row in the table . the team record of this unqiue row is milwaukee .
Output: and { only { filter_eq { all_rows ; score ; ( ot ) } } ; eq { hop { filter_eq { all_rows ; score ; ( ot ) } ; team } ; milwaukee } }
select the rows whose tournament record fuzzily matches to commonwealth games . take the result record of this row . select the rows whose tournament record fuzzily matches to african championships . take the result record of this row . the first record is less than the second record . the year record of the first row is 2006 . the year record of the second row is 2006 .
Output: and { less { hop { filter_eq { all_rows ; tournament ; commonwealth games } ; result } ; hop { filter_eq { all_rows ; tournament ; african championships } ; result } } ; and { eq { hop { filter_eq { all_rows ; tournament ; commonwealth games } ; year } ; 2006 } ; eq { hop { filter_eq { all_rows ; tournament ; african championships } ; year } ; 2006 } } }
select the rows whose time record fuzzily matches to 2:18 . there is only one such row in the table . the nationality record of this unqiue row is germany .
Output: and { only { filter_eq { all_rows ; time ; 2:18 } } ; eq { hop { filter_eq { all_rows ; time ; 2:18 } ; nationality } ; germany } }
select the rows whose manufacturer record fuzzily matches to schenectady . the sum of the quantity made record of these rows is 27 .
Output: round_eq { sum { filter_eq { all_rows ; manufacturer ; schenectady } ; quantity made } ; 27 }
select the row whose attendance record of all rows is maximum . the date record of this row is december 14 , 1958 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; december 14 , 1958 }
select the row whose singapore gross record of all rows is 2nd maximum . the title record of this row is the eye 2 .
Output: eq { hop { nth_argmax { all_rows ; singapore gross ; 2 } ; title } ; the eye 2 }
select the rows whose player record fuzzily matches to ty warren . take the round record of this row . select the rows whose player record fuzzily matches to dan klecko . take the round record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; ty warren } ; round } ; hop { filter_eq { all_rows ; player ; dan klecko } ; round } }
select the row whose crowd record of all rows is maximum . the home team record of this row is south melbourne . the away team record of this row is carlton .
Output: and { eq { hop { argmax { all_rows ; crowd } ; home team } ; south melbourne } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; carlton } }
the average of the mass ( kg ) record of all rows is 56.32 kg .
Output: round_eq { avg { all_rows ; mass ( kg ) } ; 56.32 kg }
the average of the tied record of all rows is 4.16 .
Output: round_eq { avg { all_rows ; tied } ; 4.16 }
select the rows whose directed by record fuzzily matches to alex chapple . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; directed by ; alex chapple } } ; 3 }
select the rows whose tournament record fuzzily matches to turespana masters - open andalucia . take the margin of victory record of this row . select the rows whose tournament record fuzzily matches to bmw international open . take the margin of victory record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; tournament ; turespana masters - open andalucia } ; margin of victory } ; hop { filter_eq { all_rows ; tournament ; bmw international open } ; margin of victory } }
select the row whose against record of all rows is 2nd minimum . the opposing team record of this row is north and midland of scotland .
Output: eq { hop { nth_argmin { all_rows ; against ; 2 } ; opposing team } ; north and midland of scotland }
select the rows whose year record is less than or equal to 1974 . among these rows , select the rows whose place record fuzzily matches to cairo . the number of such rows is 1 .
Output: eq { count { filter_eq { filter_less_eq { all_rows ; year ; 1974 } ; place ; cairo } } ; 1 }
the average of the gold record of all rows is 0.4375 .
Output: round_eq { avg { all_rows ; gold } ; 0.4375 }
select the rows whose nation record fuzzily matches to canada . take the silver record of this row . select the rows whose nation record fuzzily matches to united states . take the silver record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; nation ; canada } ; silver } ; hop { filter_eq { all_rows ; nation ; united states } ; silver } }
select the rows whose date record fuzzily matches to april 25 . take the high points record of this row . select the rows whose date record fuzzily matches to april 27 . take the high points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; april 25 } ; high points } ; hop { filter_eq { all_rows ; date ; april 27 } ; high points } }
select the rows whose rider record fuzzily matches to john hopkins . take the laps record of this row . select the rows whose rider record fuzzily matches to marco melandri . take the laps record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; rider ; john hopkins } ; laps } ; hop { filter_eq { all_rows ; rider ; marco melandri } ; laps } }
select the row whose place record of all rows is minimum . the player record of this row is billy casper .
Output: eq { hop { argmin { all_rows ; place } ; player } ; billy casper }
select the rows whose owner ( s ) record fuzzily matches to private owner . there is only one such row in the table . the number & name record of this unqiue row is no 1163 whitehead .
Output: and { only { filter_eq { all_rows ; owner ( s ) ; private owner } } ; eq { hop { filter_eq { all_rows ; owner ( s ) ; private owner } ; number & name } ; no 1163 whitehead } }
select the rows whose to par record fuzzily matches to +5 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; to par ; +5 } } ; 4 }
for the position records of all rows , most of them fuzzily match to 1st .
Output: most_eq { all_rows ; position ; 1st }
select the rows whose ship record fuzzily matches to lützow . take the total record of this row . select the rows whose ship record fuzzily matches to seydlitz . take the total record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; ship ; lützow } ; total } ; hop { filter_eq { all_rows ; ship ; seydlitz } ; total } }
for the suites records of all rows , most of them are greater than or equal to 125 .
Output: most_greater_eq { all_rows ; suites ; 125 }
select the row whose original air date record of all rows is maximum . the title record of this row is road to the north pole .
Output: eq { hop { argmax { all_rows ; original air date } ; title } ; road to the north pole }
select the rows whose name record fuzzily matches to niki cross . take the minutes record of this row . select the rows whose name record fuzzily matches to amanda cinalli . take the minutes record of this row . the second record is 60 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; name ; niki cross } ; minutes } ; hop { filter_eq { all_rows ; name ; amanda cinalli } ; minutes } } ; -60 }
select the rows whose country record fuzzily matches to madagascar . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; country ; madagascar } } ; 3 }
for the country records of all rows , all of them fuzzily match to united states .
Output: all_eq { all_rows ; country ; united states }
select the row whose candidates record of all rows is maximum . the incumbent record of this row is joe moakley .
Output: eq { hop { argmax { all_rows ; candidates } ; incumbent } ; joe moakley }
select the row whose appearances record of all rows is maximum . the name record of this row is valdo .
Output: eq { hop { argmax { all_rows ; appearances } ; name } ; valdo }
select the rows whose venue record fuzzily matches to municipal stadium , poznań . the 3rd minimum date record of these rows is 14 june 2012 . the date record of the row with 3rd minimum date record is 14 june 2012 .
Output: and { eq { nth_min { filter_eq { all_rows ; venue ; municipal stadium , poznań } ; date ; 3 } ; 14 june 2012 } ; eq { hop { nth_argmin { filter_eq { all_rows ; venue ; municipal stadium , poznań } ; date ; 3 } ; date } ; 14 june 2012 } }
select the row whose year record of all rows is 2nd maximum . the category record of this row is favorite tv actress - leading role in a drama .
Output: eq { hop { nth_argmax { all_rows ; year ; 2 } ; category } ; favorite tv actress - leading role in a drama }
the sum of the 1 usd = record of all rows is 8429.43027 .
Output: round_eq { sum { all_rows ; 1 usd
select the rows whose platform ( s ) record fuzzily matches to xbox 360 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; platform ( s ) ; xbox 360 } } ; 3 }
the average of the level record of all rows is 8 .
Output: round_eq { avg { all_rows ; level } ; 8 }
select the rows whose film title used in nomination record fuzzily matches to vukovar poste restante . take the year ( ceremony ) record of this row . select the rows whose film title used in nomination record fuzzily matches to underground . take the year ( ceremony ) record of this row . the first record is less than the second record . the year ( ceremony ) record of the first row is 1994 ( 67th ) . the year ( ceremony ) record of the second row is 1995 ( 68th ) .
Output: and { less { hop { filter_eq { all_rows ; film title used in nomination ; vukovar poste restante } ; year ( ceremony ) } ; hop { filter_eq { all_rows ; film title used in nomination ; underground } ; year ( ceremony ) } } ; and { eq { hop { filter_eq { all_rows ; film title used in nomination ; vukovar poste restante } ; year ( ceremony ) } ; 1994 ( 67th ) } ; eq { hop { filter_eq { all_rows ; film title used in nomination ; underground } ; year ( ceremony ) } ; 1995 ( 68th ) } } }
select the rows whose label record fuzzily matches to astralwerks records . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; label ; astralwerks records } } ; 2 }
select the rows whose score record is equal to 71 . the number of such rows is 11 .
Output: eq { count { filter_eq { all_rows ; score ; 71 } } ; 11 }
select the rows whose college record fuzzily matches to saskatchewan . among these rows , select the rows whose position record fuzzily matches to k . there is only one such row in the table . the player record of this unqiue row is matt kellett .
Output: and { only { filter_eq { filter_eq { all_rows ; college ; saskatchewan } ; position ; k } } ; eq { hop { filter_eq { filter_eq { all_rows ; college ; saskatchewan } ; position ; k } ; player } ; matt kellett } }
for the gfl premierships records of all rows , all of them fuzzily match to nil .
Output: all_eq { all_rows ; gfl premierships ; nil }
for the status records of all rows , most of them fuzzily match to loaned .
Output: most_eq { all_rows ; status ; loaned }
the average of the earnings record of all rows is $ 426,356.60 .
Output: round_eq { avg { all_rows ; earnings } ; $ 426,356.60 }
select the rows whose total record is greater than 1 . among these rows , select the rows whose bronze record is equal to 0 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_greater { all_rows ; total ; 1 } ; bronze ; 0 } } ; 2 }
select the rows whose enrollment record is greater than 3000 . the average of the varsity sports record of these rows is 10.4 .
Output: round_eq { avg { filter_greater { all_rows ; enrollment ; 3000 } ; varsity sports } ; 10.4 }
select the rows whose opposing teams record fuzzily matches to wales . take the against record of this row . select the rows whose opposing teams record fuzzily matches to scotland . take the against record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opposing teams ; wales } ; against } ; hop { filter_eq { all_rows ; opposing teams ; scotland } ; against } }
select the row whose year record of all rows is 2nd maximum . the role record of this row is ahana sharma .
Output: eq { hop { nth_argmax { all_rows ; year ; 2 } ; role } ; ahana sharma }
for the ihsaa class records of all rows , all of them fuzzily match to a .
Output: all_eq { all_rows ; ihsaa class ; a }
select the rows whose class record fuzzily matches to freshman . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; class ; freshman } } ; 3 }
select the rows whose laps record is less than 7 . there is only one such row in the table . the year record of this unqiue row is 1980 .
Output: and { only { filter_less { all_rows ; laps ; 7 } } ; eq { hop { filter_less { all_rows ; laps ; 7 } ; year } ; 1980 } }
select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose to par record is equal to -1 . there is only one such row in the table . the player record of this unqiue row is jack nicklaus .
Output: and { only { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } } ; eq { hop { filter_eq { filter_eq { all_rows ; country ; united states } ; to par ; -1 } ; player } ; jack nicklaus } }
the average of the total record of all rows is 3.75 .
Output: round_eq { avg { all_rows ; total } ; 3.75 }
select the rows whose status record fuzzily matches to six nations . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; status ; six nations } } ; 5 }
select the rows whose location record fuzzily matches to beijing . take the year record of this row . select the rows whose location record fuzzily matches to hiroshima . take the year record of this row . the second record is 4 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; location ; beijing } ; year } ; hop { filter_eq { all_rows ; location ; hiroshima } ; year } } ; -4 }
select the rows whose incumbent record fuzzily matches to curt weldon . take the opponent record of this row . select the rows whose incumbent record fuzzily matches to john murtha . take the opponent record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; incumbent ; curt weldon } ; opponent } ; hop { filter_eq { all_rows ; incumbent ; john murtha } ; opponent } }
select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table . the tournament record of this unqiue row is kyoto .
Output: and { only { filter_eq { all_rows ; surface ; carpet } } ; eq { hop { filter_eq { all_rows ; surface ; carpet } ; tournament } ; kyoto } }
for the played records of all rows , all of them are equal to 38 .
Output: all_eq { all_rows ; played ; 38 }
select the rows whose location attendance record fuzzily matches to ford center . the sum of the location attendance record of these rows is over 36000 .
Output: round_eq { sum { filter_eq { all_rows ; location attendance ; ford center } ; location attendance } ; over 36000 }
select the rows whose game site record fuzzily matches to mile high stadium . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; game site ; mile high stadium } } ; 7 }
for the original air date records of all rows , most of them fuzzily match to february .
Output: most_eq { all_rows ; original air date ; february }
select the rows whose branding record fuzzily matches to dzec radyo agila 1062 . take the power ( kw ) record of this row . select the rows whose branding record fuzzily matches to dwin radyo agila dagupan . take the power ( kw ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; branding ; dzec radyo agila 1062 } ; power ( kw ) } ; hop { filter_eq { all_rows ; branding ; dwin radyo agila dagupan } ; power ( kw ) } }
select the rows whose method record fuzzily matches to ko ( punch ) . there is only one such row in the table .
Output: only { filter_eq { all_rows ; method ; ko ( punch ) } }
select the rows whose state record fuzzily matches to jammu & kashmir . for the completion schedule records of these rows , most of them are equal to 2011 .
Output: most_eq { filter_eq { all_rows ; state ; jammu & kashmir } ; completion schedule ; 2011 }
select the rows whose record record fuzzily matches to 3 - 3 . there is only one such row in the table . the high points record of this unqiue row is lebron james ( 25 ) .
Output: and { only { filter_eq { all_rows ; record ; 3 - 3 } } ; eq { hop { filter_eq { all_rows ; record ; 3 - 3 } ; high points } ; lebron james ( 25 ) } }
for the original channel records of all rows , most of them fuzzily match to itv .
Output: most_eq { all_rows ; original channel ; itv }
select the row whose 1st leg record of all rows is maximum . the team 2 record of this row is al - sekka al - hadid .
Output: eq { hop { argmax { all_rows ; 1st leg } ; team 2 } ; al - sekka al - hadid }
select the rows whose rome , italy record fuzzily matches to germany . there is only one such row in the table . the world record record of this unqiue row is world leading .
Output: and { only { filter_eq { all_rows ; rome , italy ; germany } } ; eq { hop { filter_eq { all_rows ; rome , italy ; germany } ; world record } ; world leading } }
select the rows whose frequency mhz record is greater than 100 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; frequency mhz ; 100 } } ; 3 }
select the rows whose house name record fuzzily matches to gongola . take the founded record of this row . select the rows whose house name record fuzzily matches to benue . take the founded record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; house name ; gongola } ; founded } ; hop { filter_eq { all_rows ; house name ; benue } ; founded } }
select the rows whose opened record is equal to 2004 . there is only one such row in the table . the stadium record of this unqiue row is indiana invaders soccer complex .
Output: and { only { filter_eq { all_rows ; opened ; 2004 } } ; eq { hop { filter_eq { all_rows ; opened ; 2004 } ; stadium } ; indiana invaders soccer complex } }
select the rows whose original airdate record fuzzily matches to may . there is only one such row in the table . the episode title record of this unqiue row is secret soviet bases .
Output: and { only { filter_eq { all_rows ; original airdate ; may } } ; eq { hop { filter_eq { all_rows ; original airdate ; may } ; episode title } ; secret soviet bases } }
the average of the wins record of all rows is 1.43 .
Output: round_eq { avg { all_rows ; wins } ; 1.43 }
select the row whose year record of all rows is 2nd minimum . the venue record of this row is warsaw , poland .
Output: eq { hop { nth_argmin { all_rows ; year ; 2 } ; venue } ; warsaw , poland }
select the rows whose population record is less than 10,000,000 . select the row whose gdp ( nominal ) record of these rows is 2nd maximum . the country record of this row is tajikistan .
Output: eq { hop { nth_argmax { filter_less { all_rows ; population ; 10,000,000 } ; gdp ( nominal ) ; 2 } ; country } ; tajikistan }
select the rows whose opponent record fuzzily matches to new york jets . select the row whose date record of these rows is 2nd minimum . the bills points record of this row is 34 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; opponent ; new york jets } ; date ; 2 } ; bills points } ; 34 }
select the row whose no in series record of all rows is 1st maximum . the family / families record of this row is the williams family .
Output: eq { hop { nth_argmax { all_rows ; no in series ; 1 } ; family / families } ; the williams family }
select the rows whose written by record fuzzily matches to sang kyu kim . there is only one such row in the table . the title record of this unqiue row is road narrows .
Output: and { only { filter_eq { all_rows ; written by ; sang kyu kim } } ; eq { hop { filter_eq { all_rows ; written by ; sang kyu kim } ; title } ; road narrows } }
the average of the age record of all rows is 30 .
Output: round_eq { avg { all_rows ; age } ; 30 }
select the rows whose directed by record fuzzily matches to steve mann . among these rows , select the rows whose written by record fuzzily matches to john ridley . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; directed by ; steve mann } ; written by ; john ridley } } ; 3 }
select the row whose td 's record of all rows is 2nd maximum . the player record of this row is lenzie jackson .
Output: eq { hop { nth_argmax { all_rows ; td 's ; 2 } ; player } ; lenzie jackson }
the average of the money record of all rows is 1584 .
Output: round_eq { avg { all_rows ; money } ; 1584 }
select the row whose design flow ( lpm ) record of all rows is maximum . the location record of this row is marcala , hon .
Output: eq { hop { argmax { all_rows ; design flow ( lpm ) } ; location } ; marcala , hon }
select the rows whose written by record fuzzily matches to amy engelberg & wendy engelberg . there is only one such row in the table . the title record of this unqiue row is dead model walking .
Output: and { only { filter_eq { all_rows ; written by ; amy engelberg & wendy engelberg } } ; eq { hop { filter_eq { all_rows ; written by ; amy engelberg & wendy engelberg } ; title } ; dead model walking } }
the 1st minimum 2004 - 2005 season record of all rows is 1st in the liga . the club record of the row with 1st minimum 2004 - 2005 season record is benfica .
Output: and { eq { nth_min { all_rows ; 2004 - 2005 season ; 1 } ; 1st in the liga } ; eq { hop { nth_argmin { all_rows ; 2004 - 2005 season ; 1 } ; club } ; benfica } }