niryuu/sni-each-converted huggingface dataset:
Command: eq { diff { hop { filter_eq { all_rows ; brand ; gmc } ; year founded } ; hop { filter_eq { all_rows ; brand ; chevrolet } ; year founded } } ; -10 }, interpretation: select the rows whose brand record fuzzily matches to gmc . take the year founded record of this row . select the rows whose brand record fuzzily matches to chevrolet . take the year founded record of this row . the second record is 10 larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; brand ; gmc } ; year founded } ; hop { filter_eq { all_rows ; brand ; chevrolet } ; year founded } } ; -10 }, interpretation: select the rows whose player record fuzzily matches to gary springer . take the pick record of this row . select the rows whose player record fuzzily matches to rich congo . take the pick record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; player ; gary springer } ; pick } ; hop { filter_eq { all_rows ; player ; rich congo } ; pick } }, interpretation: select the rows whose player record fuzzily matches to gary springer . take the pick record of this row . select the rows whose player record fuzzily matches to rich congo . take the pick record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; player ; gary springer } ; pick } ; hop { filter_eq { all_rows ; player ; rich congo } ; pick } }, interpretation: select the rows whose spectators record is equal to 50000 . among these rows , select the rows whose res record fuzzily matches to 3 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; spectators ; 50000 } ; res ; 3 } } ; 2 }, interpretation: select the rows whose spectators record is equal to 50000 . among these rows , select the rows whose res record fuzzily matches to 3 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; spectators ; 50000 } ; res ; 3 } } ; 2 }, interpretation: select the rows whose country record fuzzily matches to france . take the date record of this row . select the rows whose country record fuzzily matches to belgium . take the date record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; country ; france } ; date } ; hop { filter_eq { all_rows ; country ; belgium } ; date } }, interpretation: select the rows whose country record fuzzily matches to france . take the date record of this row . select the rows whose country record fuzzily matches to belgium . take the date record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; country ; france } ; date } ; hop { filter_eq { all_rows ; country ; belgium } ; date } }, interpretation: select the rows whose party record fuzzily matches to democratic . there is only one such row in the table . the incumbent record of this unqiue row is clarence f lea .
Output: no
Command: and { only { filter_eq { all_rows ; party ; democratic } } ; eq { hop { filter_eq { all_rows ; party ; democratic } ; incumbent } ; clarence f lea } }, interpretation: select the rows whose party record fuzzily matches to democratic . there is only one such row in the table . the incumbent record of this unqiue row is clarence f lea .
Output: yes
Command: and { only { filter_eq { all_rows ; party ; democratic } } ; eq { hop { filter_eq { all_rows ; party ; democratic } ; incumbent } ; clarence f lea } }, interpretation: select the rows whose location attendance record fuzzily matches to wachovia center . the sum of the location attendance record of these rows is 89767 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; location attendance ; wachovia center } ; location attendance } ; 89767 }, interpretation: select the rows whose location attendance record fuzzily matches to wachovia center . the sum of the location attendance record of these rows is 89767 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; location attendance ; wachovia center } ; location attendance } ; 89767 }, interpretation: for the party records of all rows , most of them fuzzily match to republican .
Output: no
Command: most_eq { all_rows ; party ; republican }, interpretation: select the rows whose nationality record fuzzily matches to ireland . among these rows , select the rows whose goals record is greater than 60 . the number of such rows is 5 .
Output: no
Command: eq { count { filter_greater { filter_eq { all_rows ; nationality ; ireland } ; goals ; 60 } } ; 5 }, interpretation: select the rows whose nationality record fuzzily matches to ireland . among these rows , select the rows whose goals record is greater than 60 . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_greater { filter_eq { all_rows ; nationality ; ireland } ; goals ; 60 } } ; 5 }, interpretation: select the rows whose country record fuzzily matches to finland . there is only one such row in the table . the company record of this unqiue row is stonesoft .
Output: no
Command: and { only { filter_eq { all_rows ; country ; finland } } ; eq { hop { filter_eq { all_rows ; country ; finland } ; company } ; stonesoft } }, interpretation: select the rows whose country record fuzzily matches to finland . there is only one such row in the table . the company record of this unqiue row is stonesoft .
Output: yes
Command: and { only { filter_eq { all_rows ; country ; finland } } ; eq { hop { filter_eq { all_rows ; country ; finland } ; company } ; stonesoft } }, interpretation: select the rows whose frequency record fuzzily matches to am . there is only one such row in the table . the branding record of this unqiue row is am 1150 .
Output: no
Command: and { only { filter_eq { all_rows ; frequency ; am } } ; eq { hop { filter_eq { all_rows ; frequency ; am } ; branding } ; am 1150 } }, interpretation: select the rows whose frequency record fuzzily matches to am . there is only one such row in the table . the branding record of this unqiue row is am 1150 .
Output: yes
Command: and { only { filter_eq { all_rows ; frequency ; am } } ; eq { hop { filter_eq { all_rows ; frequency ; am } ; branding } ; am 1150 } }, interpretation: for the s host records of all rows , all of them fuzzily match to bob costas and tom hammond .
Output: no
Command: all_eq { all_rows ; s host ; bob costas and tom hammond }, interpretation: for the s host records of all rows , all of them fuzzily match to bob costas and tom hammond .
Output: yes
Command: all_eq { all_rows ; s host ; bob costas and tom hammond }, interpretation: for the result records of all rows , most of them fuzzily match to w .
Output: no
Command: most_eq { all_rows ; result ; w }, interpretation: select the row whose place record of all rows is minimum . the artist record of this row is krassimir avramov .
Output: no
Command: eq { hop { argmin { all_rows ; place } ; artist } ; krassimir avramov }, interpretation: select the row whose place record of all rows is minimum . the artist record of this row is krassimir avramov .
Output: yes
Command: eq { hop { argmin { all_rows ; place } ; artist } ; krassimir avramov }, interpretation: select the row whose % of votes khuzestan record of all rows is 2nd maximum . the candidates record of this row is akbar hashemi rafsanjani .
Output: no
Command: eq { hop { nth_argmax { all_rows ; % of votes khuzestan ; 2 } ; candidates } ; akbar hashemi rafsanjani }, interpretation: select the row whose % of votes khuzestan record of all rows is 2nd maximum . the candidates record of this row is akbar hashemi rafsanjani .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; % of votes khuzestan ; 2 } ; candidates } ; akbar hashemi rafsanjani }, interpretation: select the rows whose score record fuzzily matches to 0 - 1 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; score ; 0 - 1 } } ; 2 }, interpretation: select the rows whose score record fuzzily matches to 0 - 1 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; score ; 0 - 1 } } ; 2 }, interpretation: the 2nd minimum rank record of all rows is 2nd . the population record of the row with 2nd minimum rank record is 36039 .
Output: no
Command: and { eq { nth_min { all_rows ; rank ; 2 } ; 2nd } ; eq { hop { nth_argmin { all_rows ; rank ; 2 } ; population } ; 36039 } }, interpretation: the 2nd minimum rank record of all rows is 2nd . the population record of the row with 2nd minimum rank record is 36039 .
Output: yes
Command: and { eq { nth_min { all_rows ; rank ; 2 } ; 2nd } ; eq { hop { nth_argmin { all_rows ; rank ; 2 } ; population } ; 36039 } }, interpretation: select the rows whose surface record fuzzily matches to carpet . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; surface ; carpet } } ; 5 }, interpretation: select the rows whose surface record fuzzily matches to carpet . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; surface ; carpet } } ; 5 }, interpretation: 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: no
Command: and { only { filter_eq { all_rows ; rome , italy ; germany } } ; eq { hop { filter_eq { all_rows ; rome , italy ; germany } ; world record } ; world leading } }, interpretation: 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: yes
Command: and { only { filter_eq { all_rows ; rome , italy ; germany } } ; eq { hop { filter_eq { all_rows ; rome , italy ; germany } ; world record } ; world leading } }, interpretation: select the rows whose developer record fuzzily matches to cloud imperium games corporation . there is only one such row in the table . the title record of this unqiue row is star citizen .
Output: no
Command: and { only { filter_eq { all_rows ; developer ; cloud imperium games corporation } } ; eq { hop { filter_eq { all_rows ; developer ; cloud imperium games corporation } ; title } ; star citizen } }, interpretation: select the rows whose developer record fuzzily matches to cloud imperium games corporation . there is only one such row in the table . the title record of this unqiue row is star citizen .
Output: yes
Command: and { only { filter_eq { all_rows ; developer ; cloud imperium games corporation } } ; eq { hop { filter_eq { all_rows ; developer ; cloud imperium games corporation } ; title } ; star citizen } }, interpretation: select the rows whose position record fuzzily matches to defensive end . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; position ; defensive end } } ; 2 }, interpretation: select the rows whose position record fuzzily matches to defensive end . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; position ; defensive end } } ; 2 }, interpretation: for the gained university status records of all rows , most of them are greater than 1920 .
Output: no
Command: most_greater { all_rows ; gained university status ; 1920 }, interpretation: for the gained university status records of all rows , most of them are greater than 1920 .
Output: yes
Command: most_greater { all_rows ; gained university status ; 1920 }, interpretation: select the row whose attendance record of all rows is maximum . the opponent record of this row is new york giants .
Output: no
Command: eq { hop { argmax { all_rows ; attendance } ; opponent } ; new york giants }, interpretation: select the row whose attendance record of all rows is maximum . the opponent record of this row is new york giants .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance } ; opponent } ; new york giants }, interpretation: for the party records of all rows , most of them fuzzily match to democratic - republican .
Output: no
Command: most_eq { all_rows ; party ; democratic - republican }, interpretation: for the party records of all rows , most of them fuzzily match to democratic - republican .
Output: yes
Command: most_eq { all_rows ; party ; democratic - republican }, interpretation: select the row whose location / attendance record of all rows is 1st maximum . the opponent record of this row is new york .
Output: no
Command: eq { hop { nth_argmax { all_rows ; location / attendance ; 1 } ; opponent } ; new york }, interpretation: select the row whose location / attendance record of all rows is 1st maximum . the opponent record of this row is new york .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; location / attendance ; 1 } ; opponent } ; new york }, interpretation: for the venue records of all rows , most of them fuzzily match to hampden park , glasgow .
Output: no
Command: most_eq { all_rows ; venue ; hampden park , glasgow }, interpretation: for the venue records of all rows , most of them fuzzily match to hampden park , glasgow .
Output: yes
Command: most_eq { all_rows ; venue ; hampden park , glasgow }, interpretation: select the rows whose college record fuzzily matches to oklahoma . there is only one such row in the table . the name record of this unqiue row is dominique franks .
Output: no
Command: and { only { filter_eq { all_rows ; college ; oklahoma } } ; eq { hop { filter_eq { all_rows ; college ; oklahoma } ; name } ; dominique franks } }, interpretation: select the rows whose college record fuzzily matches to oklahoma . there is only one such row in the table . the name record of this unqiue row is dominique franks .
Output: yes
Command: and { only { filter_eq { all_rows ; college ; oklahoma } } ; eq { hop { filter_eq { all_rows ; college ; oklahoma } ; name } ; dominique franks } }, interpretation: select the rows whose host city record fuzzily matches to buffalo , new york . take the year record of this row . select the rows whose host city record fuzzily matches to orlando , florida . take the year record of this row . the second record is 1 larger than the first record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; host city ; buffalo , new york } ; year } ; hop { filter_eq { all_rows ; host city ; orlando , florida } ; year } } ; -1 }, interpretation: select the rows whose host city record fuzzily matches to buffalo , new york . take the year record of this row . select the rows whose host city record fuzzily matches to orlando , florida . take the year record of this row . the second record is 1 larger than the first record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; host city ; buffalo , new york } ; year } ; hop { filter_eq { all_rows ; host city ; orlando , florida } ; year } } ; -1 }, interpretation: select the row whose games record of all rows is maximum . the player record of this row is kevin anderson .
Output: no
Command: eq { hop { argmax { all_rows ; games } ; player } ; kevin anderson }, interpretation: select the row whose games record of all rows is maximum . the player record of this row is kevin anderson .
Output: yes
Command: eq { hop { argmax { all_rows ; games } ; player } ; kevin anderson }, interpretation: select the row whose time record of all rows is 1st minimum . the name record of this row is leisel jones .
Output: no
Command: eq { hop { nth_argmin { all_rows ; time ; 1 } ; name } ; leisel jones }, interpretation: select the row whose time record of all rows is 1st minimum . the name record of this row is leisel jones .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; time ; 1 } ; name } ; leisel jones }, interpretation: the average of the matches record of all rows is 4.1 .
Output: no
Command: round_eq { avg { all_rows ; matches } ; 4.1 }, interpretation: the average of the matches record of all rows is 4.1 .
Output: yes
Command: round_eq { avg { all_rows ; matches } ; 4.1 }, interpretation: select the rows whose issue price record is equal to 79.95 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; issue price ; 79.95 } } ; 2 }, interpretation: select the rows whose issue price record is equal to 79.95 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; issue price ; 79.95 } } ; 2 }, interpretation: select the rows whose bronze record is equal to 1 . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; bronze ; 1 } } ; 4 }, interpretation: select the rows whose bronze record is equal to 1 . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; bronze ; 1 } } ; 4 }, interpretation: select the row whose qual 2 record of all rows is 2nd minimum . the name record of this row is paul tracy .
Output: no
Command: eq { hop { nth_argmin { all_rows ; qual 2 ; 2 } ; name } ; paul tracy }, interpretation: select the row whose qual 2 record of all rows is 2nd minimum . the name record of this row is paul tracy .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; qual 2 ; 2 } ; name } ; paul tracy }, interpretation: select the row whose points record of all rows is maximum . the name record of this row is natalia linichuk / gennadi karponosov .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; name } ; natalia linichuk / gennadi karponosov }, interpretation: select the row whose points record of all rows is maximum . the name record of this row is natalia linichuk / gennadi karponosov .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; name } ; natalia linichuk / gennadi karponosov }, interpretation: select the row whose first elected record of all rows is 2nd maximum . the incumbent record of this row is louis capozzoli .
Output: no
Command: eq { hop { nth_argmax { all_rows ; first elected ; 2 } ; incumbent } ; louis capozzoli }, interpretation: select the row whose first elected record of all rows is 2nd maximum . the incumbent record of this row is louis capozzoli .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; first elected ; 2 } ; incumbent } ; louis capozzoli }, interpretation: for the league / division records of all rows , most of them fuzzily match to first division .
Output: no
Command: most_eq { all_rows ; league / division ; first division }, interpretation: for the league / division records of all rows , most of them fuzzily match to first division .
Output: yes
Command: most_eq { all_rows ; league / division ; first division }, interpretation: select the rows whose rank record fuzzily matches to 1 . take the silver record of this row . select the rows whose rank record fuzzily matches to 2 . take the silver record of this row . the first record is 1 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; rank ; 1 } ; silver } ; hop { filter_eq { all_rows ; rank ; 2 } ; silver } } ; 1 }, interpretation: select the rows whose rank record fuzzily matches to 1 . take the silver record of this row . select the rows whose rank record fuzzily matches to 2 . take the silver record of this row . the first record is 1 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; rank ; 1 } ; silver } ; hop { filter_eq { all_rows ; rank ; 2 } ; silver } } ; 1 }, interpretation: select the row whose total poll ( % ) record of all rows is maximum . the constituency record of this row is oslo .
Output: no
Command: eq { hop { argmax { all_rows ; total poll ( % ) } ; constituency } ; oslo }, interpretation: select the row whose total poll ( % ) record of all rows is maximum . the constituency record of this row is oslo .
Output: yes
Command: eq { hop { argmax { all_rows ; total poll ( % ) } ; constituency } ; oslo }, interpretation: select the rows whose high points record fuzzily matches to richard hamilton . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; high points ; richard hamilton } } ; 4 }, interpretation: select the rows whose high points record fuzzily matches to richard hamilton . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; high points ; richard hamilton } } ; 4 }, interpretation: the sum of the uk viewers ( million ) record of all rows is 96.41 .
Output: no
Command: round_eq { sum { all_rows ; uk viewers ( million ) } ; 96.41 }, interpretation: the sum of the uk viewers ( million ) record of all rows is 96.41 .
Output: yes
Command: round_eq { sum { all_rows ; uk viewers ( million ) } ; 96.41 }, interpretation: for the high assists records of all rows , most of them fuzzily match to g payton .
Output: no
Command: most_eq { all_rows ; high assists ; g payton }, interpretation: for the high assists records of all rows , most of them fuzzily match to g payton .
Output: yes
Command: most_eq { all_rows ; high assists ; g payton }, interpretation: select the rows whose score record fuzzily matches to 4:1 . there is only one such row in the table . the team 1 record of this unqiue row is muangthong united . the team 2 record of this unqiue row is persiwa wamena .
Output: no
Command: and { only { filter_eq { all_rows ; score ; 4:1 } } ; and { eq { hop { filter_eq { all_rows ; score ; 4:1 } ; team 1 } ; muangthong united } ; eq { hop { filter_eq { all_rows ; score ; 4:1 } ; team 2 } ; persiwa wamena } } }, interpretation: select the rows whose score record fuzzily matches to 4:1 . there is only one such row in the table . the team 1 record of this unqiue row is muangthong united . the team 2 record of this unqiue row is persiwa wamena .
Output: yes
Command: and { only { filter_eq { all_rows ; score ; 4:1 } } ; and { eq { hop { filter_eq { all_rows ; score ; 4:1 } ; team 1 } ; muangthong united } ; eq { hop { filter_eq { all_rows ; score ; 4:1 } ; team 2 } ; persiwa wamena } } }, interpretation: select the rows whose agent record fuzzily matches to arsine . take the effectiveness as blood agent record of this row . select the rows whose agent record fuzzily matches to vinyl arsine . take the effectiveness as blood agent record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; agent ; arsine } ; effectiveness as blood agent } ; hop { filter_eq { all_rows ; agent ; vinyl arsine } ; effectiveness as blood agent } }, interpretation: select the rows whose agent record fuzzily matches to arsine . take the effectiveness as blood agent record of this row . select the rows whose agent record fuzzily matches to vinyl arsine . take the effectiveness as blood agent record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; agent ; arsine } ; effectiveness as blood agent } ; hop { filter_eq { all_rows ; agent ; vinyl arsine } ; effectiveness as blood agent } }, interpretation: select the row whose score record of all rows is 2nd maximum . the player record of this row is mark boucher .
Output: no
Command: eq { hop { nth_argmax { all_rows ; score ; 2 } ; player } ; mark boucher }, interpretation: select the row whose score record of all rows is 2nd maximum . the player record of this row is mark boucher .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; score ; 2 } ; player } ; mark boucher }, interpretation: for the country of origin records of all rows , most of them fuzzily match to united states .
Output: no
Command: most_eq { all_rows ; country of origin ; united states }, interpretation: for the country of origin records of all rows , most of them fuzzily match to united states .
Output: yes
Command: most_eq { all_rows ; country of origin ; united states }, interpretation: select the rows whose venue record fuzzily matches to princes park . take the crowd record of this row . select the rows whose venue record fuzzily matches to brunswick street oval . take the crowd record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; venue ; princes park } ; crowd } ; hop { filter_eq { all_rows ; venue ; brunswick street oval } ; crowd } }, interpretation: select the rows whose venue record fuzzily matches to princes park . take the crowd record of this row . select the rows whose venue record fuzzily matches to brunswick street oval . take the crowd record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; venue ; princes park } ; crowd } ; hop { filter_eq { all_rows ; venue ; brunswick street oval } ; crowd } }, interpretation: select the rows whose game site record fuzzily matches to qualcomm stadium . among these rows , select the rows whose time record fuzzily matches to 5:15 pm . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; game site ; qualcomm stadium } ; time ; 5:15 pm } } ; 3 }, interpretation: select the rows whose game site record fuzzily matches to qualcomm stadium . among these rows , select the rows whose time record fuzzily matches to 5:15 pm . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; game site ; qualcomm stadium } ; time ; 5:15 pm } } ; 3 }, interpretation: select the rows whose score record fuzzily matches to 5 . there is only one such row in the table . the home team record of this unqiue row is sheffield wednesday . the away team record of this unqiue row is oldham athletic .
Output: no
Command: and { only { filter_eq { all_rows ; score ; 5 } } ; and { eq { hop { filter_eq { all_rows ; score ; 5 } ; home team } ; sheffield wednesday } ; eq { hop { filter_eq { all_rows ; score ; 5 } ; away team } ; oldham athletic } } }, interpretation: select the rows whose score record fuzzily matches to 5 . there is only one such row in the table . the home team record of this unqiue row is sheffield wednesday . the away team record of this unqiue row is oldham athletic .
Output: yes
Command: and { only { filter_eq { all_rows ; score ; 5 } } ; and { eq { hop { filter_eq { all_rows ; score ; 5 } ; home team } ; sheffield wednesday } ; eq { hop { filter_eq { all_rows ; score ; 5 } ; away team } ; oldham athletic } } }, interpretation: select the rows whose original air date record fuzzily matches to november . there is only one such row in the table . the title record of this unqiue row is class action satisfaction .
Output: no
Command: and { only { filter_eq { all_rows ; original air date ; november } } ; eq { hop { filter_eq { all_rows ; original air date ; november } ; title } ; class action satisfaction } }, interpretation: select the rows whose original air date record fuzzily matches to november . there is only one such row in the table . the title record of this unqiue row is class action satisfaction .
Output: yes
Command: and { only { filter_eq { all_rows ; original air date ; november } } ; eq { hop { filter_eq { all_rows ; original air date ; november } ; title } ; class action satisfaction } }, interpretation: select the row whose prominence record of all rows is 15th maximum . the mountain peak record of this row is mount fernow .
Output: no
Command: eq { hop { nth_argmax { all_rows ; prominence ; 15 } ; mountain peak } ; mount fernow }, interpretation: select the row whose prominence record of all rows is 15th maximum . the mountain peak record of this row is mount fernow .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; prominence ; 15 } ; mountain peak } ; mount fernow }, interpretation: select the rows whose vocalist record fuzzily matches to grin . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; vocalist ; grin } } ; 3 }, interpretation: select the rows whose vocalist record fuzzily matches to grin . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; vocalist ; grin } } ; 3 }, interpretation: select the row whose winning score record of all rows is 2nd maximum . the tournament record of this row is corning classic .
Output: no
Command: eq { hop { nth_argmax { all_rows ; winning score ; 2 } ; tournament } ; corning classic }, interpretation: select the row whose winning score record of all rows is 2nd maximum . the tournament record of this row is corning classic .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; winning score ; 2 } ; tournament } ; corning classic }, interpretation: select the rows whose actual version record fuzzily matches to n / a . there is only one such row in the table . the name record of this unqiue row is estyjs .
Output: no
Command: and { only { filter_eq { all_rows ; actual version ; n / a } } ; eq { hop { filter_eq { all_rows ; actual version ; n / a } ; name } ; estyjs } }, interpretation: select the rows whose actual version record fuzzily matches to n / a . there is only one such row in the table . the name record of this unqiue row is estyjs .
Output: yes
Command: and { only { filter_eq { all_rows ; actual version ; n / a } } ; eq { hop { filter_eq { all_rows ; actual version ; n / a } ; name } ; estyjs } }, interpretation: the average of the result record of all rows is 10.15 th .
Output: no
Command: round_eq { avg { all_rows ; result } ; 10.15 th }, interpretation: the average of the result record of all rows is 10.15 th .
Output: yes
Command: round_eq { avg { all_rows ; result } ; 10.15 th }, interpretation: the sum of the against record of all rows is 53 .
Output: no
Command: round_eq { sum { all_rows ; against } ; 53 }, interpretation: the sum of the against record of all rows is 53 .
Output: yes
Command: round_eq { sum { all_rows ; against } ; 53 }, interpretation: select the rows whose set 1 record fuzzily matches to 10:21 . there is only one such row in the table . the score record of this unqiue row is 0 - 2 .
Output: no
Command: and { only { filter_eq { all_rows ; set 1 ; 10:21 } } ; eq { hop { filter_eq { all_rows ; set 1 ; 10:21 } ; score } ; 0 - 2 } }, interpretation: select the rows whose set 1 record fuzzily matches to 10:21 . there is only one such row in the table . the score record of this unqiue row is 0 - 2 .
Output: yes
Command: and { only { filter_eq { all_rows ; set 1 ; 10:21 } } ; eq { hop { filter_eq { all_rows ; set 1 ; 10:21 } ; score } ; 0 - 2 } }, interpretation: select the row whose original air date record of all rows is 1st maximum . the family / families record of this row is the haynes family and the potter family .
Output: no
Command: eq { hop { nth_argmax { all_rows ; original air date ; 1 } ; family / families } ; the haynes family and the potter family }, interpretation: select the row whose original air date record of all rows is 1st maximum . the family / families record of this row is the haynes family and the potter family .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; original air date ; 1 } ; family / families } ; the haynes family and the potter family }, interpretation: for the surface records of all rows , most of them fuzzily match to clay .
Output: no
Command: most_eq { all_rows ; surface ; clay }, interpretation: for the date records of all rows , all of them are greater than 2000 .
Output: no
Command: all_greater { all_rows ; date ; 2000 }, interpretation: for the date records of all rows , all of them are greater than 2000 .
Output: yes
Command: all_greater { all_rows ; date ; 2000 }, interpretation: select the rows whose status record fuzzily matches to test match . there is only one such row in the table . the opposing teams record of this unqiue row is south africa .
Output: no
Command: and { only { filter_eq { all_rows ; status ; test match } } ; eq { hop { filter_eq { all_rows ; status ; test match } ; opposing teams } ; south africa } }, interpretation: select the rows whose status record fuzzily matches to test match . there is only one such row in the table . the opposing teams record of this unqiue row is south africa .
Output: yes
Command: and { only { filter_eq { all_rows ; status ; test match } } ; eq { hop { filter_eq { all_rows ; status ; test match } ; opposing teams } ; south africa } }, interpretation: for the political party records of all rows , most of them fuzzily match to non - party .
Output: no
Command: most_eq { all_rows ; political party ; non - party }, interpretation: for the political party records of all rows , most of them fuzzily match to non - party .
Output: yes
Command: most_eq { all_rows ; political party ; non - party }, interpretation: the 5th minimum track record of all rows is 5 . the song title record of the row with 5th minimum track record is crazy arms .
Output: no
Command: and { eq { nth_min { all_rows ; track ; 5 } ; 5 } ; eq { hop { nth_argmin { all_rows ; track ; 5 } ; song title } ; crazy arms } }, interpretation: the 5th minimum track record of all rows is 5 . the song title record of the row with 5th minimum track record is crazy arms .
Output: yes
Command: and { eq { nth_min { all_rows ; track ; 5 } ; 5 } ; eq { hop { nth_argmin { all_rows ; track ; 5 } ; song title } ; crazy arms } }, interpretation: for the party records of all rows , most of them fuzzily match to republican .
Output: no
Command: most_eq { all_rows ; party ; republican }, interpretation: the 1st minimum date record of all rows is march 22 .
Output: no
Command: eq { nth_min { all_rows ; date ; 1 } ; march 22 }, interpretation: the 1st minimum date record of all rows is march 22 .
Output: yes
Command: eq { nth_min { all_rows ; date ; 1 } ; march 22 }, interpretation: select the rows whose opponent record fuzzily matches to new york giants . there is only one such row in the table . the week record of this unqiue row is 1 .
Output: no
Command: and { only { filter_eq { all_rows ; opponent ; new york giants } } ; eq { hop { filter_eq { all_rows ; opponent ; new york giants } ; week } ; 1 } }, interpretation: select the rows whose opponent record fuzzily matches to new york giants . there is only one such row in the table . the week record of this unqiue row is 1 .
Output: yes
Command: and { only { filter_eq { all_rows ; opponent ; new york giants } } ; eq { hop { filter_eq { all_rows ; opponent ; new york giants } ; week } ; 1 } }, interpretation: select the rows whose surface record fuzzily matches to hard . the number of such rows is 7 .
Output: no
Command: eq { count { filter_eq { all_rows ; surface ; hard } } ; 7 }, interpretation: select the rows whose surface record fuzzily matches to hard . the number of such rows is 7 .
Output: yes
Command: eq { count { filter_eq { all_rows ; surface ; hard } } ; 7 }, interpretation: the average of the first elected record of all rows is 1953.5 .
Output: no
Command: round_eq { avg { all_rows ; first elected } ; 1953.5 }, interpretation: the average of the first elected record of all rows is 1953.5 .
Output: yes
Command: round_eq { avg { all_rows ; first elected } ; 1953.5 }, interpretation: select the rows whose won record is greater than or equal to 10 . select the row whose lost record of these rows is 3rd maximum . the club record of this row is pontyclun rfc .
Output: no
Command: eq { hop { nth_argmax { filter_greater_eq { all_rows ; won ; 10 } ; lost ; 3 } ; club } ; pontyclun rfc }, interpretation: select the rows whose won record is greater than or equal to 10 . select the row whose lost record of these rows is 3rd maximum . the club record of this row is pontyclun rfc .
Output: yes
Command: eq { hop { nth_argmax { filter_greater_eq { all_rows ; won ; 10 } ; lost ; 3 } ; club } ; pontyclun rfc }, interpretation: select the rows whose directed by record fuzzily matches to michael zinberg . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; directed by ; michael zinberg } } ; 3 }, interpretation: select the rows whose directed by record fuzzily matches to michael zinberg . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; directed by ; michael zinberg } } ; 3 }, interpretation: select the row whose omim record of all rows is maximum . the enzyme record of this row is uroporphyrinogen iii synthase .
Output: no
Command: eq { hop { argmax { all_rows ; omim } ; enzyme } ; uroporphyrinogen iii synthase }, interpretation: select the row whose omim record of all rows is maximum . the enzyme record of this row is uroporphyrinogen iii synthase .
Output: yes
Command: eq { hop { argmax { all_rows ; omim } ; enzyme } ; uroporphyrinogen iii synthase }, interpretation: for the license records of all rows , most of them fuzzily match to proprietary .
Output: no
Command: most_eq { all_rows ; license ; proprietary }, interpretation: for the license records of all rows , most of them fuzzily match to proprietary .
Output: yes
Command: most_eq { all_rows ; license ; proprietary }, interpretation: select the row whose year record of all rows is maximum . the chassis record of this row is ags jh25b ags jh27 .
Output: no
Command: eq { hop { argmax { all_rows ; year } ; chassis } ; ags jh25b ags jh27 }, interpretation: select the row whose year record of all rows is maximum . the chassis record of this row is ags jh25b ags jh27 .
Output: yes
Command: eq { hop { argmax { all_rows ; year } ; chassis } ; ags jh25b ags jh27 }, interpretation: select the rows whose date record fuzzily matches to 2004 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; date ; 2004 } } ; 3 }, interpretation: select the rows whose date record fuzzily matches to 2004 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; date ; 2004 } } ; 3 }, interpretation: select the rows whose music record fuzzily matches to george gershwin . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; music ; george gershwin } } ; 4 }, interpretation: select the rows whose music record fuzzily matches to george gershwin . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; music ; george gershwin } } ; 4 }, interpretation: select the rows whose player record fuzzily matches to mike weir . take the place record of this row . select the rows whose player record fuzzily matches to scott verplank . take the place record of this row . the first record is 2 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; player ; mike weir } ; place } ; hop { filter_eq { all_rows ; player ; scott verplank } ; place } } ; 2 }, interpretation: select the rows whose player record fuzzily matches to mike weir . take the place record of this row . select the rows whose player record fuzzily matches to scott verplank . take the place record of this row . the first record is 2 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; player ; mike weir } ; place } ; hop { filter_eq { all_rows ; player ; scott verplank } ; place } } ; 2 }, interpretation: select the rows whose seasons record is equal to 4 . there is only one such row in the table . the country record of this unqiue row is south korea .
Output: no
Command: and { only { filter_eq { all_rows ; seasons ; 4 } } ; eq { hop { filter_eq { all_rows ; seasons ; 4 } ; country } ; south korea } }, interpretation: select the rows whose seasons record is equal to 4 . there is only one such row in the table . the country record of this unqiue row is south korea .
Output: yes
Command: and { only { filter_eq { all_rows ; seasons ; 4 } } ; eq { hop { filter_eq { all_rows ; seasons ; 4 } ; country } ; south korea } }, interpretation: select the rows whose visual arts record fuzzily matches to sharon hayes . there is only one such row in the table . the year record of this unqiue row is 2013 .
Output: no
Command: and { only { filter_eq { all_rows ; visual arts ; sharon hayes } } ; eq { hop { filter_eq { all_rows ; visual arts ; sharon hayes } ; year } ; 2013 } }, interpretation: select the rows whose visual arts record fuzzily matches to sharon hayes . there is only one such row in the table . the year record of this unqiue row is 2013 .
Output: yes
Command: and { only { filter_eq { all_rows ; visual arts ; sharon hayes } } ; eq { hop { filter_eq { all_rows ; visual arts ; sharon hayes } ; year } ; 2013 } }, interpretation: for the played records of all rows , most of them are equal to 14 .
Output: no
Command: most_eq { all_rows ; played ; 14 }, interpretation: for the played records of all rows , most of them are equal to 14 .
Output: yes
Command: most_eq { all_rows ; played ; 14 }, interpretation: for the high points records of all rows , most of them fuzzily match to r pierce .
Output: no
Command: most_eq { all_rows ; high points ; r pierce }, interpretation: for the high points records of all rows , most of them fuzzily match to r pierce .
Output: yes
Command: most_eq { all_rows ; high points ; r pierce }, interpretation: select the rows whose title record fuzzily matches to face / off . take the year record of this row . select the rows whose title record fuzzily matches to antz . take the year record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; title ; face / off } ; year } ; hop { filter_eq { all_rows ; title ; antz } ; year } }, interpretation: select the rows whose title record fuzzily matches to face / off . take the year record of this row . select the rows whose title record fuzzily matches to antz . take the year record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; title ; face / off } ; year } ; hop { filter_eq { all_rows ; title ; antz } ; year } }, interpretation: the average of the starts record of all rows is 7.67 .
Output: no
Command: round_eq { avg { all_rows ; starts } ; 7.67 }, interpretation: the average of the starts record of all rows is 7.67 .
Output: yes
Command: round_eq { avg { all_rows ; starts } ; 7.67 }, interpretation: select the row whose points record of all rows is maximum . the club record of this row is hércules cf .
Output: no
Command: eq { hop { argmax { all_rows ; points } ; club } ; hércules cf }, interpretation: select the row whose points record of all rows is maximum . the club record of this row is hércules cf .
Output: yes
Command: eq { hop { argmax { all_rows ; points } ; club } ; hércules cf }, interpretation: select the row whose after record of all rows is minimum . the player record of this row is matt kuchar .
Output: no
Command: eq { hop { argmin { all_rows ; after } ; player } ; matt kuchar }, interpretation: select the row whose after record of all rows is minimum . the player record of this row is matt kuchar .
Output: yes
Command: eq { hop { argmin { all_rows ; after } ; player } ; matt kuchar }, interpretation: the 3rd minimum time record of all rows is 6:41.39 . the rank record of the row with 3rd minimum time record is 3 . the country record of the row with 3rd minimum time record is australia .
Output: no
Command: and { eq { nth_min { all_rows ; time ; 3 } ; 6:41.39 } ; and { eq { hop { nth_argmin { all_rows ; time ; 3 } ; rank } ; 3 } ; eq { hop { nth_argmin { all_rows ; time ; 3 } ; country } ; australia } } }, interpretation: the 3rd minimum time record of all rows is 6:41.39 . the rank record of the row with 3rd minimum time record is 3 . the country record of the row with 3rd minimum time record is australia .
Output: yes
Command: and { eq { nth_min { all_rows ; time ; 3 } ; 6:41.39 } ; and { eq { hop { nth_argmin { all_rows ; time ; 3 } ; rank } ; 3 } ; eq { hop { nth_argmin { all_rows ; time ; 3 } ; country } ; australia } } }, interpretation: select the row whose pick record of all rows is 6th minimum . the player record of this row is mike wacker .
Output: no
Command: eq { hop { nth_argmin { all_rows ; pick ; 6 } ; player } ; mike wacker }, interpretation: select the row whose pick record of all rows is 6th minimum . the player record of this row is mike wacker .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; pick ; 6 } ; player } ; mike wacker }, interpretation: select the rows whose goal difference record is less than 0 . the number of such rows is 5 .
Output: no
Command: eq { count { filter_less { all_rows ; goal difference ; 0 } } ; 5 }, interpretation: select the rows whose goal difference record is less than 0 . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_less { all_rows ; goal difference ; 0 } } ; 5 }, interpretation: select the rows whose country record fuzzily matches to nigeria . take the penalty record of this row . select the rows whose country record fuzzily matches to uzbekistan . take the penalty record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; country ; nigeria } ; penalty } ; hop { filter_eq { all_rows ; country ; uzbekistan } ; penalty } }, interpretation: select the rows whose country record fuzzily matches to nigeria . take the penalty record of this row . select the rows whose country record fuzzily matches to uzbekistan . take the penalty record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; country ; nigeria } ; penalty } ; hop { filter_eq { all_rows ; country ; uzbekistan } ; penalty } }, interpretation: select the rows whose date of polls record fuzzily matches to december . there is only one such row in the table . the state record of this unqiue row is mizoram .
Output: no
Command: and { only { filter_eq { all_rows ; date of polls ; december } } ; eq { hop { filter_eq { all_rows ; date of polls ; december } ; state } ; mizoram } }, interpretation: select the rows whose date of polls record fuzzily matches to december . there is only one such row in the table . the state record of this unqiue row is mizoram .
Output: yes
Command: and { only { filter_eq { all_rows ; date of polls ; december } } ; eq { hop { filter_eq { all_rows ; date of polls ; december } ; state } ; mizoram } }, interpretation: select the rows whose season record is greater than 2005 . among these rows , select the rows whose teams record is equal to 16 . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { filter_greater { all_rows ; season ; 2005 } ; teams ; 16 } } ; 4 }, interpretation: select the rows whose season record is greater than 2005 . among these rows , select the rows whose teams record is equal to 16 . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { filter_greater { all_rows ; season ; 2005 } ; teams ; 16 } } ; 4 }, interpretation: select the rows whose remittances 2011 record is greater than 10 . among these rows , select the rows whose remittances 2010 record is greater than 50 . there is only one such row in the table . the country record of this unqiue row is india .
Output: no
Command: and { only { filter_greater { filter_greater { all_rows ; remittances 2011 ; 10 } ; remittances 2010 ; 50 } } ; eq { hop { filter_greater { filter_greater { all_rows ; remittances 2011 ; 10 } ; remittances 2010 ; 50 } ; country } ; india } }, interpretation: select the rows whose remittances 2011 record is greater than 10 . among these rows , select the rows whose remittances 2010 record is greater than 50 . there is only one such row in the table . the country record of this unqiue row is india .
Output: yes
Command: and { only { filter_greater { filter_greater { all_rows ; remittances 2011 ; 10 } ; remittances 2010 ; 50 } } ; eq { hop { filter_greater { filter_greater { all_rows ; remittances 2011 ; 10 } ; remittances 2010 ; 50 } ; country } ; india } }, interpretation: for the result records of all rows , most of them fuzzily match to w .
Output: no
Command: most_eq { all_rows ; result ; w }, interpretation: the average of the against record of all rows is 14.6 .
Output: no
Command: round_eq { avg { all_rows ; against } ; 14.6 }, interpretation: the average of the against record of all rows is 14.6 .
Output: yes
Command: round_eq { avg { all_rows ; against } ; 14.6 }, interpretation: select the rows whose tv time record fuzzily matches to fox . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; tv time ; fox } } ; 2 }, interpretation: select the rows whose tv time record fuzzily matches to fox . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; tv time ; fox } } ; 2 }, interpretation: select the row whose torque ( nm ) / rpm record of all rows is maximum . the model / engine record of this row is 2.0 duratec he .
Output: no