id
stringlengths 4
8
| struct_in
stringlengths 301
4.09k
| table_file
stringlengths 19
21
| position
int64 0
7
| answer_coordinates
dict | question_and_history
list | annotator
int64 0
2
| seq_out
stringlengths 1
1.73k
| table_header
list | question
stringlengths 10
291
| table_data
list | answer_text
list | text_in
stringlengths 19
565
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
nt-26
|
col : position | club | played | points | wins | draws | losses | goals for | goals against | goal difference row 1 : 1 | malaga cf | 42 | 79 | 22 | 13 | 7 | 72 | 47 | 25 row 2 : 2 | atletico de madrid b 1 | 42 | 74 | 21 | 11 | 10 | 73 | 51 | 22 row 3 : 3 | cd numancia | 42 | 73 | 21 | 10 | 11 | 68 | 40 | 28 row 4 : 4 | sevilla fc | 42 | 71 | 20 | 11 | 11 | 66 | 50 | 16 row 5 : 5 | rayo vallecano | 42 | 71 | 19 | 14 | 9 | 64 | 49 | 15 row 6 : 6 | ud las palmas | 42 | 68 | 17 | 17 | 8 | 57 | 38 | 19 row 7 : 7 | cd toledo | 42 | 65 | 18 | 11 | 13 | 54 | 49 | 5 row 8 : 8 | sd compostela | 42 | 61 | 16 | 13 | 13 | 60 | 53 | 7 row 9 : 9 | sporting de gijon | 42 | 59 | 16 | 11 | 15 | 47 | 47 | 0 row 10 : 10 | cp merida | 42 | 59 | 15 | 14 | 13 | 48 | 41 | 7 row 11 : 11 | ue lleida | 42 | 59 | 15 | 14 | 13 | 52 | 50 | 2 row 12 : 12 | recreativo de huelva | 42 | 58 | 14 | 16 | 12 | 40 | 35 | 5 row 13 : 13 | ca osasuna | 42 | 57 | 15 | 12 | 15 | 44 | 51 | -7 row 14 : 14 | cd badajoz | 42 | 51 | 12 | 15 | 15 | 35 | 39 | -4 row 15 : 15 | albacete | 42 | 50 | 12 | 14 | 16 | 38 | 43 | -5 row 16 : 16 | cd logrones | 42 | 48 | 12 | 12 | 18 | 48 | 57 | -9 row 17 : 17 | cd leganes | 42 | 47 | 10 | 17 | 15 | 36 | 44 | -8 row 18 : 18 | sd eibar | 42 | 47 | 13 | 8 | 21 | 42 | 56 | -14 row 19 : 19 | mallorca b | 42 | 46 | 12 | 10 | 20 | 52 | 64 | -12 row 20 : 20 | barcelona b | 42 | 44 | 13 | 5 | 24 | 51 | 68 | -17 row 21 : 21 | hercules cf | 42 | 40 | 10 | 10 | 22 | 38 | 66 | -28 row 22 : 22 | cd ourense | 42 | 27 | 7 | 6 | 29 | 35 | 82 | -47
|
table_csv/204_256.csv
| 0 |
{
"column_index": [
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21
]
}
|
[
"what are all the point scores listed for 1998-99 segunda division?"
] | 0 |
79, 74, 73, 71, 71, 68, 65, 61, 59, 59, 59, 58, 57, 51, 50, 48, 47, 47, 46, 44, 40, 27
|
[
"Position",
"Club",
"Played",
"Points",
"Wins",
"Draws",
"Losses",
"Goals for",
"Goals against",
"Goal Difference"
] |
what are all the point scores listed for 1998-99 segunda division?
|
[
[
"1",
"Malaga CF",
"42",
"79",
"22",
"13",
"7",
"72",
"47",
"25"
],
[
"2",
"Atletico de Madrid B 1",
"42",
"74",
"21",
"11",
"10",
"73",
"51",
"22"
],
[
"3",
"CD Numancia",
"42",
"73",
"21",
"10",
"11",
"68",
"40",
"28"
],
[
"4",
"Sevilla FC",
"42",
"71",
"20",
"11",
"11",
"66",
"50",
"16"
],
[
"5",
"Rayo Vallecano",
"42",
"71",
"19",
"14",
"9",
"64",
"49",
"15"
],
[
"6",
"UD Las Palmas",
"42",
"68",
"17",
"17",
"8",
"57",
"38",
"19"
],
[
"7",
"CD Toledo",
"42",
"65",
"18",
"11",
"13",
"54",
"49",
"5"
],
[
"8",
"SD Compostela",
"42",
"61",
"16",
"13",
"13",
"60",
"53",
"7"
],
[
"9",
"Sporting de Gijon",
"42",
"59",
"16",
"11",
"15",
"47",
"47",
"0"
],
[
"10",
"CP Merida",
"42",
"59",
"15",
"14",
"13",
"48",
"41",
"7"
],
[
"11",
"UE Lleida",
"42",
"59",
"15",
"14",
"13",
"52",
"50",
"2"
],
[
"12",
"Recreativo de Huelva",
"42",
"58",
"14",
"16",
"12",
"40",
"35",
"5"
],
[
"13",
"CA Osasuna",
"42",
"57",
"15",
"12",
"15",
"44",
"51",
"-7"
],
[
"14",
"CD Badajoz",
"42",
"51",
"12",
"15",
"15",
"35",
"39",
"-4"
],
[
"15",
"Albacete",
"42",
"50",
"12",
"14",
"16",
"38",
"43",
"-5"
],
[
"16",
"CD Logrones",
"42",
"48",
"12",
"12",
"18",
"48",
"57",
"-9"
],
[
"17",
"CD Leganes",
"42",
"47",
"10",
"17",
"15",
"36",
"44",
"-8"
],
[
"18",
"SD Eibar",
"42",
"47",
"13",
"8",
"21",
"42",
"56",
"-14"
],
[
"19",
"Mallorca B",
"42",
"46",
"12",
"10",
"20",
"52",
"64",
"-12"
],
[
"20",
"Barcelona B",
"42",
"44",
"13",
"5",
"24",
"51",
"68",
"-17"
],
[
"21",
"Hercules CF",
"42",
"40",
"10",
"10",
"22",
"38",
"66",
"-28"
],
[
"22",
"CD Ourense",
"42",
"27",
"7",
"6",
"29",
"35",
"82",
"-47"
]
] |
[
"79",
"74",
"73",
"71",
"71",
"68",
"65",
"61",
"59",
"59",
"59",
"58",
"57",
"51",
"50",
"48",
"47",
"47",
"46",
"44",
"40",
"27"
] |
what are all the point scores listed for 1998-99 segunda division? ||
|
nt-26
|
col : position | club | played | points | wins | draws | losses | goals for | goals against | goal difference row 1 : 1 | malaga cf | 42 | 79 | 22 | 13 | 7 | 72 | 47 | 25 row 2 : 2 | atletico de madrid b 1 | 42 | 74 | 21 | 11 | 10 | 73 | 51 | 22 row 3 : 3 | cd numancia | 42 | 73 | 21 | 10 | 11 | 68 | 40 | 28 row 4 : 4 | sevilla fc | 42 | 71 | 20 | 11 | 11 | 66 | 50 | 16 row 5 : 5 | rayo vallecano | 42 | 71 | 19 | 14 | 9 | 64 | 49 | 15 row 6 : 6 | ud las palmas | 42 | 68 | 17 | 17 | 8 | 57 | 38 | 19 row 7 : 7 | cd toledo | 42 | 65 | 18 | 11 | 13 | 54 | 49 | 5 row 8 : 8 | sd compostela | 42 | 61 | 16 | 13 | 13 | 60 | 53 | 7 row 9 : 9 | sporting de gijon | 42 | 59 | 16 | 11 | 15 | 47 | 47 | 0 row 10 : 10 | cp merida | 42 | 59 | 15 | 14 | 13 | 48 | 41 | 7 row 11 : 11 | ue lleida | 42 | 59 | 15 | 14 | 13 | 52 | 50 | 2 row 12 : 12 | recreativo de huelva | 42 | 58 | 14 | 16 | 12 | 40 | 35 | 5 row 13 : 13 | ca osasuna | 42 | 57 | 15 | 12 | 15 | 44 | 51 | -7 row 14 : 14 | cd badajoz | 42 | 51 | 12 | 15 | 15 | 35 | 39 | -4 row 15 : 15 | albacete | 42 | 50 | 12 | 14 | 16 | 38 | 43 | -5 row 16 : 16 | cd logrones | 42 | 48 | 12 | 12 | 18 | 48 | 57 | -9 row 17 : 17 | cd leganes | 42 | 47 | 10 | 17 | 15 | 36 | 44 | -8 row 18 : 18 | sd eibar | 42 | 47 | 13 | 8 | 21 | 42 | 56 | -14 row 19 : 19 | mallorca b | 42 | 46 | 12 | 10 | 20 | 52 | 64 | -12 row 20 : 20 | barcelona b | 42 | 44 | 13 | 5 | 24 | 51 | 68 | -17 row 21 : 21 | hercules cf | 42 | 40 | 10 | 10 | 22 | 38 | 66 | -28 row 22 : 22 | cd ourense | 42 | 27 | 7 | 6 | 29 | 35 | 82 | -47
|
table_csv/204_256.csv
| 1 |
{
"column_index": [
1
],
"row_index": [
0
]
}
|
[
"what are all the point scores listed for 1998-99 segunda division?",
"what club had a point score of 79?"
] | 0 |
malaga cf
|
[
"Position",
"Club",
"Played",
"Points",
"Wins",
"Draws",
"Losses",
"Goals for",
"Goals against",
"Goal Difference"
] |
what club had a point score of 79?
|
[
[
"1",
"Malaga CF",
"42",
"79",
"22",
"13",
"7",
"72",
"47",
"25"
],
[
"2",
"Atletico de Madrid B 1",
"42",
"74",
"21",
"11",
"10",
"73",
"51",
"22"
],
[
"3",
"CD Numancia",
"42",
"73",
"21",
"10",
"11",
"68",
"40",
"28"
],
[
"4",
"Sevilla FC",
"42",
"71",
"20",
"11",
"11",
"66",
"50",
"16"
],
[
"5",
"Rayo Vallecano",
"42",
"71",
"19",
"14",
"9",
"64",
"49",
"15"
],
[
"6",
"UD Las Palmas",
"42",
"68",
"17",
"17",
"8",
"57",
"38",
"19"
],
[
"7",
"CD Toledo",
"42",
"65",
"18",
"11",
"13",
"54",
"49",
"5"
],
[
"8",
"SD Compostela",
"42",
"61",
"16",
"13",
"13",
"60",
"53",
"7"
],
[
"9",
"Sporting de Gijon",
"42",
"59",
"16",
"11",
"15",
"47",
"47",
"0"
],
[
"10",
"CP Merida",
"42",
"59",
"15",
"14",
"13",
"48",
"41",
"7"
],
[
"11",
"UE Lleida",
"42",
"59",
"15",
"14",
"13",
"52",
"50",
"2"
],
[
"12",
"Recreativo de Huelva",
"42",
"58",
"14",
"16",
"12",
"40",
"35",
"5"
],
[
"13",
"CA Osasuna",
"42",
"57",
"15",
"12",
"15",
"44",
"51",
"-7"
],
[
"14",
"CD Badajoz",
"42",
"51",
"12",
"15",
"15",
"35",
"39",
"-4"
],
[
"15",
"Albacete",
"42",
"50",
"12",
"14",
"16",
"38",
"43",
"-5"
],
[
"16",
"CD Logrones",
"42",
"48",
"12",
"12",
"18",
"48",
"57",
"-9"
],
[
"17",
"CD Leganes",
"42",
"47",
"10",
"17",
"15",
"36",
"44",
"-8"
],
[
"18",
"SD Eibar",
"42",
"47",
"13",
"8",
"21",
"42",
"56",
"-14"
],
[
"19",
"Mallorca B",
"42",
"46",
"12",
"10",
"20",
"52",
"64",
"-12"
],
[
"20",
"Barcelona B",
"42",
"44",
"13",
"5",
"24",
"51",
"68",
"-17"
],
[
"21",
"Hercules CF",
"42",
"40",
"10",
"10",
"22",
"38",
"66",
"-28"
],
[
"22",
"CD Ourense",
"42",
"27",
"7",
"6",
"29",
"35",
"82",
"-47"
]
] |
[
"Malaga CF"
] |
what club had a point score of 79? || what are all the point scores listed for 1998-99 segunda division?
|
nt-26
|
col : position | club | played | points | wins | draws | losses | goals for | goals against | goal difference row 1 : 1 | malaga cf | 42 | 79 | 22 | 13 | 7 | 72 | 47 | 25 row 2 : 2 | atletico de madrid b 1 | 42 | 74 | 21 | 11 | 10 | 73 | 51 | 22 row 3 : 3 | cd numancia | 42 | 73 | 21 | 10 | 11 | 68 | 40 | 28 row 4 : 4 | sevilla fc | 42 | 71 | 20 | 11 | 11 | 66 | 50 | 16 row 5 : 5 | rayo vallecano | 42 | 71 | 19 | 14 | 9 | 64 | 49 | 15 row 6 : 6 | ud las palmas | 42 | 68 | 17 | 17 | 8 | 57 | 38 | 19 row 7 : 7 | cd toledo | 42 | 65 | 18 | 11 | 13 | 54 | 49 | 5 row 8 : 8 | sd compostela | 42 | 61 | 16 | 13 | 13 | 60 | 53 | 7 row 9 : 9 | sporting de gijon | 42 | 59 | 16 | 11 | 15 | 47 | 47 | 0 row 10 : 10 | cp merida | 42 | 59 | 15 | 14 | 13 | 48 | 41 | 7 row 11 : 11 | ue lleida | 42 | 59 | 15 | 14 | 13 | 52 | 50 | 2 row 12 : 12 | recreativo de huelva | 42 | 58 | 14 | 16 | 12 | 40 | 35 | 5 row 13 : 13 | ca osasuna | 42 | 57 | 15 | 12 | 15 | 44 | 51 | -7 row 14 : 14 | cd badajoz | 42 | 51 | 12 | 15 | 15 | 35 | 39 | -4 row 15 : 15 | albacete | 42 | 50 | 12 | 14 | 16 | 38 | 43 | -5 row 16 : 16 | cd logrones | 42 | 48 | 12 | 12 | 18 | 48 | 57 | -9 row 17 : 17 | cd leganes | 42 | 47 | 10 | 17 | 15 | 36 | 44 | -8 row 18 : 18 | sd eibar | 42 | 47 | 13 | 8 | 21 | 42 | 56 | -14 row 19 : 19 | mallorca b | 42 | 46 | 12 | 10 | 20 | 52 | 64 | -12 row 20 : 20 | barcelona b | 42 | 44 | 13 | 5 | 24 | 51 | 68 | -17 row 21 : 21 | hercules cf | 42 | 40 | 10 | 10 | 22 | 38 | 66 | -28 row 22 : 22 | cd ourense | 42 | 27 | 7 | 6 | 29 | 35 | 82 | -47
|
table_csv/204_256.csv
| 0 |
{
"column_index": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
]
}
|
[
"which 10 clubs scored the most points?"
] | 1 |
malaga cf, atletico de madrid b 1, cd numancia, sevilla fc, rayo vallecano, ud las palmas, cd toledo, sd compostela, sporting de gijon, cp merida
|
[
"Position",
"Club",
"Played",
"Points",
"Wins",
"Draws",
"Losses",
"Goals for",
"Goals against",
"Goal Difference"
] |
which 10 clubs scored the most points?
|
[
[
"1",
"Malaga CF",
"42",
"79",
"22",
"13",
"7",
"72",
"47",
"25"
],
[
"2",
"Atletico de Madrid B 1",
"42",
"74",
"21",
"11",
"10",
"73",
"51",
"22"
],
[
"3",
"CD Numancia",
"42",
"73",
"21",
"10",
"11",
"68",
"40",
"28"
],
[
"4",
"Sevilla FC",
"42",
"71",
"20",
"11",
"11",
"66",
"50",
"16"
],
[
"5",
"Rayo Vallecano",
"42",
"71",
"19",
"14",
"9",
"64",
"49",
"15"
],
[
"6",
"UD Las Palmas",
"42",
"68",
"17",
"17",
"8",
"57",
"38",
"19"
],
[
"7",
"CD Toledo",
"42",
"65",
"18",
"11",
"13",
"54",
"49",
"5"
],
[
"8",
"SD Compostela",
"42",
"61",
"16",
"13",
"13",
"60",
"53",
"7"
],
[
"9",
"Sporting de Gijon",
"42",
"59",
"16",
"11",
"15",
"47",
"47",
"0"
],
[
"10",
"CP Merida",
"42",
"59",
"15",
"14",
"13",
"48",
"41",
"7"
],
[
"11",
"UE Lleida",
"42",
"59",
"15",
"14",
"13",
"52",
"50",
"2"
],
[
"12",
"Recreativo de Huelva",
"42",
"58",
"14",
"16",
"12",
"40",
"35",
"5"
],
[
"13",
"CA Osasuna",
"42",
"57",
"15",
"12",
"15",
"44",
"51",
"-7"
],
[
"14",
"CD Badajoz",
"42",
"51",
"12",
"15",
"15",
"35",
"39",
"-4"
],
[
"15",
"Albacete",
"42",
"50",
"12",
"14",
"16",
"38",
"43",
"-5"
],
[
"16",
"CD Logrones",
"42",
"48",
"12",
"12",
"18",
"48",
"57",
"-9"
],
[
"17",
"CD Leganes",
"42",
"47",
"10",
"17",
"15",
"36",
"44",
"-8"
],
[
"18",
"SD Eibar",
"42",
"47",
"13",
"8",
"21",
"42",
"56",
"-14"
],
[
"19",
"Mallorca B",
"42",
"46",
"12",
"10",
"20",
"52",
"64",
"-12"
],
[
"20",
"Barcelona B",
"42",
"44",
"13",
"5",
"24",
"51",
"68",
"-17"
],
[
"21",
"Hercules CF",
"42",
"40",
"10",
"10",
"22",
"38",
"66",
"-28"
],
[
"22",
"CD Ourense",
"42",
"27",
"7",
"6",
"29",
"35",
"82",
"-47"
]
] |
[
"Malaga CF",
"Atletico de Madrid B 1",
"CD Numancia",
"Sevilla FC",
"Rayo Vallecano",
"UD Las Palmas",
"CD Toledo",
"SD Compostela",
"Sporting de Gijon",
"CP Merida"
] |
which 10 clubs scored the most points? ||
|
nt-26
|
col : position | club | played | points | wins | draws | losses | goals for | goals against | goal difference row 1 : 1 | malaga cf | 42 | 79 | 22 | 13 | 7 | 72 | 47 | 25 row 2 : 2 | atletico de madrid b 1 | 42 | 74 | 21 | 11 | 10 | 73 | 51 | 22 row 3 : 3 | cd numancia | 42 | 73 | 21 | 10 | 11 | 68 | 40 | 28 row 4 : 4 | sevilla fc | 42 | 71 | 20 | 11 | 11 | 66 | 50 | 16 row 5 : 5 | rayo vallecano | 42 | 71 | 19 | 14 | 9 | 64 | 49 | 15 row 6 : 6 | ud las palmas | 42 | 68 | 17 | 17 | 8 | 57 | 38 | 19 row 7 : 7 | cd toledo | 42 | 65 | 18 | 11 | 13 | 54 | 49 | 5 row 8 : 8 | sd compostela | 42 | 61 | 16 | 13 | 13 | 60 | 53 | 7 row 9 : 9 | sporting de gijon | 42 | 59 | 16 | 11 | 15 | 47 | 47 | 0 row 10 : 10 | cp merida | 42 | 59 | 15 | 14 | 13 | 48 | 41 | 7 row 11 : 11 | ue lleida | 42 | 59 | 15 | 14 | 13 | 52 | 50 | 2 row 12 : 12 | recreativo de huelva | 42 | 58 | 14 | 16 | 12 | 40 | 35 | 5 row 13 : 13 | ca osasuna | 42 | 57 | 15 | 12 | 15 | 44 | 51 | -7 row 14 : 14 | cd badajoz | 42 | 51 | 12 | 15 | 15 | 35 | 39 | -4 row 15 : 15 | albacete | 42 | 50 | 12 | 14 | 16 | 38 | 43 | -5 row 16 : 16 | cd logrones | 42 | 48 | 12 | 12 | 18 | 48 | 57 | -9 row 17 : 17 | cd leganes | 42 | 47 | 10 | 17 | 15 | 36 | 44 | -8 row 18 : 18 | sd eibar | 42 | 47 | 13 | 8 | 21 | 42 | 56 | -14 row 19 : 19 | mallorca b | 42 | 46 | 12 | 10 | 20 | 52 | 64 | -12 row 20 : 20 | barcelona b | 42 | 44 | 13 | 5 | 24 | 51 | 68 | -17 row 21 : 21 | hercules cf | 42 | 40 | 10 | 10 | 22 | 38 | 66 | -28 row 22 : 22 | cd ourense | 42 | 27 | 7 | 6 | 29 | 35 | 82 | -47
|
table_csv/204_256.csv
| 1 |
{
"column_index": [
1,
1,
1,
1,
1
],
"row_index": [
0,
1,
2,
3,
4
]
}
|
[
"which 10 clubs scored the most points?",
"which of those clubs score at least 70 points?"
] | 1 |
malaga cf, atletico de madrid b 1, cd numancia, sevilla fc, rayo vallecano
|
[
"Position",
"Club",
"Played",
"Points",
"Wins",
"Draws",
"Losses",
"Goals for",
"Goals against",
"Goal Difference"
] |
which of those clubs score at least 70 points?
|
[
[
"1",
"Malaga CF",
"42",
"79",
"22",
"13",
"7",
"72",
"47",
"25"
],
[
"2",
"Atletico de Madrid B 1",
"42",
"74",
"21",
"11",
"10",
"73",
"51",
"22"
],
[
"3",
"CD Numancia",
"42",
"73",
"21",
"10",
"11",
"68",
"40",
"28"
],
[
"4",
"Sevilla FC",
"42",
"71",
"20",
"11",
"11",
"66",
"50",
"16"
],
[
"5",
"Rayo Vallecano",
"42",
"71",
"19",
"14",
"9",
"64",
"49",
"15"
],
[
"6",
"UD Las Palmas",
"42",
"68",
"17",
"17",
"8",
"57",
"38",
"19"
],
[
"7",
"CD Toledo",
"42",
"65",
"18",
"11",
"13",
"54",
"49",
"5"
],
[
"8",
"SD Compostela",
"42",
"61",
"16",
"13",
"13",
"60",
"53",
"7"
],
[
"9",
"Sporting de Gijon",
"42",
"59",
"16",
"11",
"15",
"47",
"47",
"0"
],
[
"10",
"CP Merida",
"42",
"59",
"15",
"14",
"13",
"48",
"41",
"7"
],
[
"11",
"UE Lleida",
"42",
"59",
"15",
"14",
"13",
"52",
"50",
"2"
],
[
"12",
"Recreativo de Huelva",
"42",
"58",
"14",
"16",
"12",
"40",
"35",
"5"
],
[
"13",
"CA Osasuna",
"42",
"57",
"15",
"12",
"15",
"44",
"51",
"-7"
],
[
"14",
"CD Badajoz",
"42",
"51",
"12",
"15",
"15",
"35",
"39",
"-4"
],
[
"15",
"Albacete",
"42",
"50",
"12",
"14",
"16",
"38",
"43",
"-5"
],
[
"16",
"CD Logrones",
"42",
"48",
"12",
"12",
"18",
"48",
"57",
"-9"
],
[
"17",
"CD Leganes",
"42",
"47",
"10",
"17",
"15",
"36",
"44",
"-8"
],
[
"18",
"SD Eibar",
"42",
"47",
"13",
"8",
"21",
"42",
"56",
"-14"
],
[
"19",
"Mallorca B",
"42",
"46",
"12",
"10",
"20",
"52",
"64",
"-12"
],
[
"20",
"Barcelona B",
"42",
"44",
"13",
"5",
"24",
"51",
"68",
"-17"
],
[
"21",
"Hercules CF",
"42",
"40",
"10",
"10",
"22",
"38",
"66",
"-28"
],
[
"22",
"CD Ourense",
"42",
"27",
"7",
"6",
"29",
"35",
"82",
"-47"
]
] |
[
"Malaga CF",
"Atletico de Madrid B 1",
"CD Numancia",
"Sevilla FC",
"Rayo Vallecano"
] |
which of those clubs score at least 70 points? || which 10 clubs scored the most points?
|
nt-26
|
col : position | club | played | points | wins | draws | losses | goals for | goals against | goal difference row 1 : 1 | malaga cf | 42 | 79 | 22 | 13 | 7 | 72 | 47 | 25 row 2 : 2 | atletico de madrid b 1 | 42 | 74 | 21 | 11 | 10 | 73 | 51 | 22 row 3 : 3 | cd numancia | 42 | 73 | 21 | 10 | 11 | 68 | 40 | 28 row 4 : 4 | sevilla fc | 42 | 71 | 20 | 11 | 11 | 66 | 50 | 16 row 5 : 5 | rayo vallecano | 42 | 71 | 19 | 14 | 9 | 64 | 49 | 15 row 6 : 6 | ud las palmas | 42 | 68 | 17 | 17 | 8 | 57 | 38 | 19 row 7 : 7 | cd toledo | 42 | 65 | 18 | 11 | 13 | 54 | 49 | 5 row 8 : 8 | sd compostela | 42 | 61 | 16 | 13 | 13 | 60 | 53 | 7 row 9 : 9 | sporting de gijon | 42 | 59 | 16 | 11 | 15 | 47 | 47 | 0 row 10 : 10 | cp merida | 42 | 59 | 15 | 14 | 13 | 48 | 41 | 7 row 11 : 11 | ue lleida | 42 | 59 | 15 | 14 | 13 | 52 | 50 | 2 row 12 : 12 | recreativo de huelva | 42 | 58 | 14 | 16 | 12 | 40 | 35 | 5 row 13 : 13 | ca osasuna | 42 | 57 | 15 | 12 | 15 | 44 | 51 | -7 row 14 : 14 | cd badajoz | 42 | 51 | 12 | 15 | 15 | 35 | 39 | -4 row 15 : 15 | albacete | 42 | 50 | 12 | 14 | 16 | 38 | 43 | -5 row 16 : 16 | cd logrones | 42 | 48 | 12 | 12 | 18 | 48 | 57 | -9 row 17 : 17 | cd leganes | 42 | 47 | 10 | 17 | 15 | 36 | 44 | -8 row 18 : 18 | sd eibar | 42 | 47 | 13 | 8 | 21 | 42 | 56 | -14 row 19 : 19 | mallorca b | 42 | 46 | 12 | 10 | 20 | 52 | 64 | -12 row 20 : 20 | barcelona b | 42 | 44 | 13 | 5 | 24 | 51 | 68 | -17 row 21 : 21 | hercules cf | 42 | 40 | 10 | 10 | 22 | 38 | 66 | -28 row 22 : 22 | cd ourense | 42 | 27 | 7 | 6 | 29 | 35 | 82 | -47
|
table_csv/204_256.csv
| 2 |
{
"column_index": [
3
],
"row_index": [
0
]
}
|
[
"which 10 clubs scored the most points?",
"which of those clubs score at least 70 points?",
"between those 5 clubs, what was the most points scored?"
] | 1 |
79
|
[
"Position",
"Club",
"Played",
"Points",
"Wins",
"Draws",
"Losses",
"Goals for",
"Goals against",
"Goal Difference"
] |
between those 5 clubs, what was the most points scored?
|
[
[
"1",
"Malaga CF",
"42",
"79",
"22",
"13",
"7",
"72",
"47",
"25"
],
[
"2",
"Atletico de Madrid B 1",
"42",
"74",
"21",
"11",
"10",
"73",
"51",
"22"
],
[
"3",
"CD Numancia",
"42",
"73",
"21",
"10",
"11",
"68",
"40",
"28"
],
[
"4",
"Sevilla FC",
"42",
"71",
"20",
"11",
"11",
"66",
"50",
"16"
],
[
"5",
"Rayo Vallecano",
"42",
"71",
"19",
"14",
"9",
"64",
"49",
"15"
],
[
"6",
"UD Las Palmas",
"42",
"68",
"17",
"17",
"8",
"57",
"38",
"19"
],
[
"7",
"CD Toledo",
"42",
"65",
"18",
"11",
"13",
"54",
"49",
"5"
],
[
"8",
"SD Compostela",
"42",
"61",
"16",
"13",
"13",
"60",
"53",
"7"
],
[
"9",
"Sporting de Gijon",
"42",
"59",
"16",
"11",
"15",
"47",
"47",
"0"
],
[
"10",
"CP Merida",
"42",
"59",
"15",
"14",
"13",
"48",
"41",
"7"
],
[
"11",
"UE Lleida",
"42",
"59",
"15",
"14",
"13",
"52",
"50",
"2"
],
[
"12",
"Recreativo de Huelva",
"42",
"58",
"14",
"16",
"12",
"40",
"35",
"5"
],
[
"13",
"CA Osasuna",
"42",
"57",
"15",
"12",
"15",
"44",
"51",
"-7"
],
[
"14",
"CD Badajoz",
"42",
"51",
"12",
"15",
"15",
"35",
"39",
"-4"
],
[
"15",
"Albacete",
"42",
"50",
"12",
"14",
"16",
"38",
"43",
"-5"
],
[
"16",
"CD Logrones",
"42",
"48",
"12",
"12",
"18",
"48",
"57",
"-9"
],
[
"17",
"CD Leganes",
"42",
"47",
"10",
"17",
"15",
"36",
"44",
"-8"
],
[
"18",
"SD Eibar",
"42",
"47",
"13",
"8",
"21",
"42",
"56",
"-14"
],
[
"19",
"Mallorca B",
"42",
"46",
"12",
"10",
"20",
"52",
"64",
"-12"
],
[
"20",
"Barcelona B",
"42",
"44",
"13",
"5",
"24",
"51",
"68",
"-17"
],
[
"21",
"Hercules CF",
"42",
"40",
"10",
"10",
"22",
"38",
"66",
"-28"
],
[
"22",
"CD Ourense",
"42",
"27",
"7",
"6",
"29",
"35",
"82",
"-47"
]
] |
[
"79"
] |
between those 5 clubs, what was the most points scored? || which of those clubs score at least 70 points? | which 10 clubs scored the most points?
|
nt-26
|
col : position | club | played | points | wins | draws | losses | goals for | goals against | goal difference row 1 : 1 | malaga cf | 42 | 79 | 22 | 13 | 7 | 72 | 47 | 25 row 2 : 2 | atletico de madrid b 1 | 42 | 74 | 21 | 11 | 10 | 73 | 51 | 22 row 3 : 3 | cd numancia | 42 | 73 | 21 | 10 | 11 | 68 | 40 | 28 row 4 : 4 | sevilla fc | 42 | 71 | 20 | 11 | 11 | 66 | 50 | 16 row 5 : 5 | rayo vallecano | 42 | 71 | 19 | 14 | 9 | 64 | 49 | 15 row 6 : 6 | ud las palmas | 42 | 68 | 17 | 17 | 8 | 57 | 38 | 19 row 7 : 7 | cd toledo | 42 | 65 | 18 | 11 | 13 | 54 | 49 | 5 row 8 : 8 | sd compostela | 42 | 61 | 16 | 13 | 13 | 60 | 53 | 7 row 9 : 9 | sporting de gijon | 42 | 59 | 16 | 11 | 15 | 47 | 47 | 0 row 10 : 10 | cp merida | 42 | 59 | 15 | 14 | 13 | 48 | 41 | 7 row 11 : 11 | ue lleida | 42 | 59 | 15 | 14 | 13 | 52 | 50 | 2 row 12 : 12 | recreativo de huelva | 42 | 58 | 14 | 16 | 12 | 40 | 35 | 5 row 13 : 13 | ca osasuna | 42 | 57 | 15 | 12 | 15 | 44 | 51 | -7 row 14 : 14 | cd badajoz | 42 | 51 | 12 | 15 | 15 | 35 | 39 | -4 row 15 : 15 | albacete | 42 | 50 | 12 | 14 | 16 | 38 | 43 | -5 row 16 : 16 | cd logrones | 42 | 48 | 12 | 12 | 18 | 48 | 57 | -9 row 17 : 17 | cd leganes | 42 | 47 | 10 | 17 | 15 | 36 | 44 | -8 row 18 : 18 | sd eibar | 42 | 47 | 13 | 8 | 21 | 42 | 56 | -14 row 19 : 19 | mallorca b | 42 | 46 | 12 | 10 | 20 | 52 | 64 | -12 row 20 : 20 | barcelona b | 42 | 44 | 13 | 5 | 24 | 51 | 68 | -17 row 21 : 21 | hercules cf | 42 | 40 | 10 | 10 | 22 | 38 | 66 | -28 row 22 : 22 | cd ourense | 42 | 27 | 7 | 6 | 29 | 35 | 82 | -47
|
table_csv/204_256.csv
| 3 |
{
"column_index": [
1
],
"row_index": [
0
]
}
|
[
"which 10 clubs scored the most points?",
"which of those clubs score at least 70 points?",
"between those 5 clubs, what was the most points scored?",
"which club scored those 79 points?"
] | 1 |
malaga cf
|
[
"Position",
"Club",
"Played",
"Points",
"Wins",
"Draws",
"Losses",
"Goals for",
"Goals against",
"Goal Difference"
] |
which club scored those 79 points?
|
[
[
"1",
"Malaga CF",
"42",
"79",
"22",
"13",
"7",
"72",
"47",
"25"
],
[
"2",
"Atletico de Madrid B 1",
"42",
"74",
"21",
"11",
"10",
"73",
"51",
"22"
],
[
"3",
"CD Numancia",
"42",
"73",
"21",
"10",
"11",
"68",
"40",
"28"
],
[
"4",
"Sevilla FC",
"42",
"71",
"20",
"11",
"11",
"66",
"50",
"16"
],
[
"5",
"Rayo Vallecano",
"42",
"71",
"19",
"14",
"9",
"64",
"49",
"15"
],
[
"6",
"UD Las Palmas",
"42",
"68",
"17",
"17",
"8",
"57",
"38",
"19"
],
[
"7",
"CD Toledo",
"42",
"65",
"18",
"11",
"13",
"54",
"49",
"5"
],
[
"8",
"SD Compostela",
"42",
"61",
"16",
"13",
"13",
"60",
"53",
"7"
],
[
"9",
"Sporting de Gijon",
"42",
"59",
"16",
"11",
"15",
"47",
"47",
"0"
],
[
"10",
"CP Merida",
"42",
"59",
"15",
"14",
"13",
"48",
"41",
"7"
],
[
"11",
"UE Lleida",
"42",
"59",
"15",
"14",
"13",
"52",
"50",
"2"
],
[
"12",
"Recreativo de Huelva",
"42",
"58",
"14",
"16",
"12",
"40",
"35",
"5"
],
[
"13",
"CA Osasuna",
"42",
"57",
"15",
"12",
"15",
"44",
"51",
"-7"
],
[
"14",
"CD Badajoz",
"42",
"51",
"12",
"15",
"15",
"35",
"39",
"-4"
],
[
"15",
"Albacete",
"42",
"50",
"12",
"14",
"16",
"38",
"43",
"-5"
],
[
"16",
"CD Logrones",
"42",
"48",
"12",
"12",
"18",
"48",
"57",
"-9"
],
[
"17",
"CD Leganes",
"42",
"47",
"10",
"17",
"15",
"36",
"44",
"-8"
],
[
"18",
"SD Eibar",
"42",
"47",
"13",
"8",
"21",
"42",
"56",
"-14"
],
[
"19",
"Mallorca B",
"42",
"46",
"12",
"10",
"20",
"52",
"64",
"-12"
],
[
"20",
"Barcelona B",
"42",
"44",
"13",
"5",
"24",
"51",
"68",
"-17"
],
[
"21",
"Hercules CF",
"42",
"40",
"10",
"10",
"22",
"38",
"66",
"-28"
],
[
"22",
"CD Ourense",
"42",
"27",
"7",
"6",
"29",
"35",
"82",
"-47"
]
] |
[
"Malaga CF"
] |
which club scored those 79 points? || between those 5 clubs, what was the most points scored? | which of those clubs score at least 70 points? | which 10 clubs scored the most points?
|
nt-26
|
col : position | club | played | points | wins | draws | losses | goals for | goals against | goal difference row 1 : 1 | malaga cf | 42 | 79 | 22 | 13 | 7 | 72 | 47 | 25 row 2 : 2 | atletico de madrid b 1 | 42 | 74 | 21 | 11 | 10 | 73 | 51 | 22 row 3 : 3 | cd numancia | 42 | 73 | 21 | 10 | 11 | 68 | 40 | 28 row 4 : 4 | sevilla fc | 42 | 71 | 20 | 11 | 11 | 66 | 50 | 16 row 5 : 5 | rayo vallecano | 42 | 71 | 19 | 14 | 9 | 64 | 49 | 15 row 6 : 6 | ud las palmas | 42 | 68 | 17 | 17 | 8 | 57 | 38 | 19 row 7 : 7 | cd toledo | 42 | 65 | 18 | 11 | 13 | 54 | 49 | 5 row 8 : 8 | sd compostela | 42 | 61 | 16 | 13 | 13 | 60 | 53 | 7 row 9 : 9 | sporting de gijon | 42 | 59 | 16 | 11 | 15 | 47 | 47 | 0 row 10 : 10 | cp merida | 42 | 59 | 15 | 14 | 13 | 48 | 41 | 7 row 11 : 11 | ue lleida | 42 | 59 | 15 | 14 | 13 | 52 | 50 | 2 row 12 : 12 | recreativo de huelva | 42 | 58 | 14 | 16 | 12 | 40 | 35 | 5 row 13 : 13 | ca osasuna | 42 | 57 | 15 | 12 | 15 | 44 | 51 | -7 row 14 : 14 | cd badajoz | 42 | 51 | 12 | 15 | 15 | 35 | 39 | -4 row 15 : 15 | albacete | 42 | 50 | 12 | 14 | 16 | 38 | 43 | -5 row 16 : 16 | cd logrones | 42 | 48 | 12 | 12 | 18 | 48 | 57 | -9 row 17 : 17 | cd leganes | 42 | 47 | 10 | 17 | 15 | 36 | 44 | -8 row 18 : 18 | sd eibar | 42 | 47 | 13 | 8 | 21 | 42 | 56 | -14 row 19 : 19 | mallorca b | 42 | 46 | 12 | 10 | 20 | 52 | 64 | -12 row 20 : 20 | barcelona b | 42 | 44 | 13 | 5 | 24 | 51 | 68 | -17 row 21 : 21 | hercules cf | 42 | 40 | 10 | 10 | 22 | 38 | 66 | -28 row 22 : 22 | cd ourense | 42 | 27 | 7 | 6 | 29 | 35 | 82 | -47
|
table_csv/204_256.csv
| 0 |
{
"column_index": [
0,
1,
1,
1,
1,
1
],
"row_index": [
0,
0,
1,
2,
3,
4
]
}
|
[
"which teams scored 70+ points"
] | 2 |
1, malaga cf, atletico de madrid b 1, cd numancia, sevilla fc, rayo vallecano
|
[
"Position",
"Club",
"Played",
"Points",
"Wins",
"Draws",
"Losses",
"Goals for",
"Goals against",
"Goal Difference"
] |
which teams scored 70+ points
|
[
[
"1",
"Malaga CF",
"42",
"79",
"22",
"13",
"7",
"72",
"47",
"25"
],
[
"2",
"Atletico de Madrid B 1",
"42",
"74",
"21",
"11",
"10",
"73",
"51",
"22"
],
[
"3",
"CD Numancia",
"42",
"73",
"21",
"10",
"11",
"68",
"40",
"28"
],
[
"4",
"Sevilla FC",
"42",
"71",
"20",
"11",
"11",
"66",
"50",
"16"
],
[
"5",
"Rayo Vallecano",
"42",
"71",
"19",
"14",
"9",
"64",
"49",
"15"
],
[
"6",
"UD Las Palmas",
"42",
"68",
"17",
"17",
"8",
"57",
"38",
"19"
],
[
"7",
"CD Toledo",
"42",
"65",
"18",
"11",
"13",
"54",
"49",
"5"
],
[
"8",
"SD Compostela",
"42",
"61",
"16",
"13",
"13",
"60",
"53",
"7"
],
[
"9",
"Sporting de Gijon",
"42",
"59",
"16",
"11",
"15",
"47",
"47",
"0"
],
[
"10",
"CP Merida",
"42",
"59",
"15",
"14",
"13",
"48",
"41",
"7"
],
[
"11",
"UE Lleida",
"42",
"59",
"15",
"14",
"13",
"52",
"50",
"2"
],
[
"12",
"Recreativo de Huelva",
"42",
"58",
"14",
"16",
"12",
"40",
"35",
"5"
],
[
"13",
"CA Osasuna",
"42",
"57",
"15",
"12",
"15",
"44",
"51",
"-7"
],
[
"14",
"CD Badajoz",
"42",
"51",
"12",
"15",
"15",
"35",
"39",
"-4"
],
[
"15",
"Albacete",
"42",
"50",
"12",
"14",
"16",
"38",
"43",
"-5"
],
[
"16",
"CD Logrones",
"42",
"48",
"12",
"12",
"18",
"48",
"57",
"-9"
],
[
"17",
"CD Leganes",
"42",
"47",
"10",
"17",
"15",
"36",
"44",
"-8"
],
[
"18",
"SD Eibar",
"42",
"47",
"13",
"8",
"21",
"42",
"56",
"-14"
],
[
"19",
"Mallorca B",
"42",
"46",
"12",
"10",
"20",
"52",
"64",
"-12"
],
[
"20",
"Barcelona B",
"42",
"44",
"13",
"5",
"24",
"51",
"68",
"-17"
],
[
"21",
"Hercules CF",
"42",
"40",
"10",
"10",
"22",
"38",
"66",
"-28"
],
[
"22",
"CD Ourense",
"42",
"27",
"7",
"6",
"29",
"35",
"82",
"-47"
]
] |
[
"1",
"Malaga CF",
"Atletico de Madrid B 1",
"CD Numancia",
"Sevilla FC",
"Rayo Vallecano"
] |
which teams scored 70+ points ||
|
nt-26
|
col : position | club | played | points | wins | draws | losses | goals for | goals against | goal difference row 1 : 1 | malaga cf | 42 | 79 | 22 | 13 | 7 | 72 | 47 | 25 row 2 : 2 | atletico de madrid b 1 | 42 | 74 | 21 | 11 | 10 | 73 | 51 | 22 row 3 : 3 | cd numancia | 42 | 73 | 21 | 10 | 11 | 68 | 40 | 28 row 4 : 4 | sevilla fc | 42 | 71 | 20 | 11 | 11 | 66 | 50 | 16 row 5 : 5 | rayo vallecano | 42 | 71 | 19 | 14 | 9 | 64 | 49 | 15 row 6 : 6 | ud las palmas | 42 | 68 | 17 | 17 | 8 | 57 | 38 | 19 row 7 : 7 | cd toledo | 42 | 65 | 18 | 11 | 13 | 54 | 49 | 5 row 8 : 8 | sd compostela | 42 | 61 | 16 | 13 | 13 | 60 | 53 | 7 row 9 : 9 | sporting de gijon | 42 | 59 | 16 | 11 | 15 | 47 | 47 | 0 row 10 : 10 | cp merida | 42 | 59 | 15 | 14 | 13 | 48 | 41 | 7 row 11 : 11 | ue lleida | 42 | 59 | 15 | 14 | 13 | 52 | 50 | 2 row 12 : 12 | recreativo de huelva | 42 | 58 | 14 | 16 | 12 | 40 | 35 | 5 row 13 : 13 | ca osasuna | 42 | 57 | 15 | 12 | 15 | 44 | 51 | -7 row 14 : 14 | cd badajoz | 42 | 51 | 12 | 15 | 15 | 35 | 39 | -4 row 15 : 15 | albacete | 42 | 50 | 12 | 14 | 16 | 38 | 43 | -5 row 16 : 16 | cd logrones | 42 | 48 | 12 | 12 | 18 | 48 | 57 | -9 row 17 : 17 | cd leganes | 42 | 47 | 10 | 17 | 15 | 36 | 44 | -8 row 18 : 18 | sd eibar | 42 | 47 | 13 | 8 | 21 | 42 | 56 | -14 row 19 : 19 | mallorca b | 42 | 46 | 12 | 10 | 20 | 52 | 64 | -12 row 20 : 20 | barcelona b | 42 | 44 | 13 | 5 | 24 | 51 | 68 | -17 row 21 : 21 | hercules cf | 42 | 40 | 10 | 10 | 22 | 38 | 66 | -28 row 22 : 22 | cd ourense | 42 | 27 | 7 | 6 | 29 | 35 | 82 | -47
|
table_csv/204_256.csv
| 1 |
{
"column_index": [
1
],
"row_index": [
0
]
}
|
[
"which teams scored 70+ points",
"who scored 79?"
] | 2 |
malaga cf
|
[
"Position",
"Club",
"Played",
"Points",
"Wins",
"Draws",
"Losses",
"Goals for",
"Goals against",
"Goal Difference"
] |
who scored 79?
|
[
[
"1",
"Malaga CF",
"42",
"79",
"22",
"13",
"7",
"72",
"47",
"25"
],
[
"2",
"Atletico de Madrid B 1",
"42",
"74",
"21",
"11",
"10",
"73",
"51",
"22"
],
[
"3",
"CD Numancia",
"42",
"73",
"21",
"10",
"11",
"68",
"40",
"28"
],
[
"4",
"Sevilla FC",
"42",
"71",
"20",
"11",
"11",
"66",
"50",
"16"
],
[
"5",
"Rayo Vallecano",
"42",
"71",
"19",
"14",
"9",
"64",
"49",
"15"
],
[
"6",
"UD Las Palmas",
"42",
"68",
"17",
"17",
"8",
"57",
"38",
"19"
],
[
"7",
"CD Toledo",
"42",
"65",
"18",
"11",
"13",
"54",
"49",
"5"
],
[
"8",
"SD Compostela",
"42",
"61",
"16",
"13",
"13",
"60",
"53",
"7"
],
[
"9",
"Sporting de Gijon",
"42",
"59",
"16",
"11",
"15",
"47",
"47",
"0"
],
[
"10",
"CP Merida",
"42",
"59",
"15",
"14",
"13",
"48",
"41",
"7"
],
[
"11",
"UE Lleida",
"42",
"59",
"15",
"14",
"13",
"52",
"50",
"2"
],
[
"12",
"Recreativo de Huelva",
"42",
"58",
"14",
"16",
"12",
"40",
"35",
"5"
],
[
"13",
"CA Osasuna",
"42",
"57",
"15",
"12",
"15",
"44",
"51",
"-7"
],
[
"14",
"CD Badajoz",
"42",
"51",
"12",
"15",
"15",
"35",
"39",
"-4"
],
[
"15",
"Albacete",
"42",
"50",
"12",
"14",
"16",
"38",
"43",
"-5"
],
[
"16",
"CD Logrones",
"42",
"48",
"12",
"12",
"18",
"48",
"57",
"-9"
],
[
"17",
"CD Leganes",
"42",
"47",
"10",
"17",
"15",
"36",
"44",
"-8"
],
[
"18",
"SD Eibar",
"42",
"47",
"13",
"8",
"21",
"42",
"56",
"-14"
],
[
"19",
"Mallorca B",
"42",
"46",
"12",
"10",
"20",
"52",
"64",
"-12"
],
[
"20",
"Barcelona B",
"42",
"44",
"13",
"5",
"24",
"51",
"68",
"-17"
],
[
"21",
"Hercules CF",
"42",
"40",
"10",
"10",
"22",
"38",
"66",
"-28"
],
[
"22",
"CD Ourense",
"42",
"27",
"7",
"6",
"29",
"35",
"82",
"-47"
]
] |
[
"Malaga CF"
] |
who scored 79? || which teams scored 70+ points
|
nt-1039
|
col : medal | name | sport | event row 1 : silver | nan | badminton | men's team row 2 : silver | aparna popat | badminton | women's singles row 3 : silver | jitender kumar | boxing | middleweight row 4 : silver | jaspal rana | shooting | men's 25 m air pistol row 5 : silver | jaspal rana satendra kumar | shooting | men's air pistol pairs row 6 : silver | dharmaraj wilson | weightlifting | men's 56 kg-combined row 7 : silver | arumugam k. pandian | weightlifting | men's 56 kg-snatch row 8 : silver | arumugam k. pandian | weightlifting | men's 56 kg-clean and jerk row 9 : silver | satheesha rai | weightlifting | men's 77 kg-clean and jerk row 10 : silver | satheesha rai | weightlifting | men's 77 kg-combined
|
table_csv/204_103.csv
| 0 |
{
"column_index": [
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"row_index": [
1,
2,
3,
4,
5,
6,
7,
8,
9
]
}
|
[
"what players received silver medals?"
] | 0 |
aparna popat, jitender kumar, jaspal rana, jaspal rana satendra kumar, dharmaraj wilson, arumugam k. pandian, arumugam k. pandian, satheesha rai, satheesha rai
|
[
"Medal",
"Name",
"Sport",
"Event"
] |
what players received silver medals?
|
[
[
"Silver",
"nan",
"Badminton",
"Men's team"
],
[
"Silver",
"Aparna Popat",
"Badminton",
"Women's singles"
],
[
"Silver",
"Jitender Kumar",
"Boxing",
"Middleweight"
],
[
"Silver",
"Jaspal Rana",
"Shooting",
"Men's 25 m Air Pistol"
],
[
"Silver",
"Jaspal Rana Satendra Kumar",
"Shooting",
"Men's Air Pistol pairs"
],
[
"Silver",
"Dharmaraj Wilson",
"Weightlifting",
"Men's 56 kg-Combined"
],
[
"Silver",
"Arumugam K. Pandian",
"Weightlifting",
"Men's 56 kg-Snatch"
],
[
"Silver",
"Arumugam K. Pandian",
"Weightlifting",
"Men's 56 kg-Clean and jerk"
],
[
"Silver",
"Satheesha Rai",
"Weightlifting",
"Men's 77 kg-Clean and jerk"
],
[
"Silver",
"Satheesha Rai",
"Weightlifting",
"Men's 77 kg-Combined"
]
] |
[
"Aparna Popat",
"Jitender Kumar",
"Jaspal Rana",
"Jaspal Rana Satendra Kumar",
"Dharmaraj Wilson",
"Arumugam K. Pandian",
"Arumugam K. Pandian",
"Satheesha Rai",
"Satheesha Rai"
] |
what players received silver medals? ||
|
nt-1039
|
col : medal | name | sport | event row 1 : silver | nan | badminton | men's team row 2 : silver | aparna popat | badminton | women's singles row 3 : silver | jitender kumar | boxing | middleweight row 4 : silver | jaspal rana | shooting | men's 25 m air pistol row 5 : silver | jaspal rana satendra kumar | shooting | men's air pistol pairs row 6 : silver | dharmaraj wilson | weightlifting | men's 56 kg-combined row 7 : silver | arumugam k. pandian | weightlifting | men's 56 kg-snatch row 8 : silver | arumugam k. pandian | weightlifting | men's 56 kg-clean and jerk row 9 : silver | satheesha rai | weightlifting | men's 77 kg-clean and jerk row 10 : silver | satheesha rai | weightlifting | men's 77 kg-combined
|
table_csv/204_103.csv
| 1 |
{
"column_index": [
1
],
"row_index": [
1
]
}
|
[
"what players received silver medals?",
"of these, which received a silver medal in women's singles?"
] | 0 |
aparna popat
|
[
"Medal",
"Name",
"Sport",
"Event"
] |
of these, which received a silver medal in women's singles?
|
[
[
"Silver",
"nan",
"Badminton",
"Men's team"
],
[
"Silver",
"Aparna Popat",
"Badminton",
"Women's singles"
],
[
"Silver",
"Jitender Kumar",
"Boxing",
"Middleweight"
],
[
"Silver",
"Jaspal Rana",
"Shooting",
"Men's 25 m Air Pistol"
],
[
"Silver",
"Jaspal Rana Satendra Kumar",
"Shooting",
"Men's Air Pistol pairs"
],
[
"Silver",
"Dharmaraj Wilson",
"Weightlifting",
"Men's 56 kg-Combined"
],
[
"Silver",
"Arumugam K. Pandian",
"Weightlifting",
"Men's 56 kg-Snatch"
],
[
"Silver",
"Arumugam K. Pandian",
"Weightlifting",
"Men's 56 kg-Clean and jerk"
],
[
"Silver",
"Satheesha Rai",
"Weightlifting",
"Men's 77 kg-Clean and jerk"
],
[
"Silver",
"Satheesha Rai",
"Weightlifting",
"Men's 77 kg-Combined"
]
] |
[
"Aparna Popat"
] |
of these, which received a silver medal in women's singles? || what players received silver medals?
|
nt-1039
|
col : medal | name | sport | event row 1 : silver | nan | badminton | men's team row 2 : silver | aparna popat | badminton | women's singles row 3 : silver | jitender kumar | boxing | middleweight row 4 : silver | jaspal rana | shooting | men's 25 m air pistol row 5 : silver | jaspal rana satendra kumar | shooting | men's air pistol pairs row 6 : silver | dharmaraj wilson | weightlifting | men's 56 kg-combined row 7 : silver | arumugam k. pandian | weightlifting | men's 56 kg-snatch row 8 : silver | arumugam k. pandian | weightlifting | men's 56 kg-clean and jerk row 9 : silver | satheesha rai | weightlifting | men's 77 kg-clean and jerk row 10 : silver | satheesha rai | weightlifting | men's 77 kg-combined
|
table_csv/204_103.csv
| 0 |
{
"column_index": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
]
}
|
[
"who are all the players?"
] | 1 |
, aparna popat, jitender kumar, jaspal rana, jaspal rana satendra kumar, dharmaraj wilson, arumugam k. pandian, arumugam k. pandian, satheesha rai, satheesha rai
|
[
"Medal",
"Name",
"Sport",
"Event"
] |
who are all the players?
|
[
[
"Silver",
"nan",
"Badminton",
"Men's team"
],
[
"Silver",
"Aparna Popat",
"Badminton",
"Women's singles"
],
[
"Silver",
"Jitender Kumar",
"Boxing",
"Middleweight"
],
[
"Silver",
"Jaspal Rana",
"Shooting",
"Men's 25 m Air Pistol"
],
[
"Silver",
"Jaspal Rana Satendra Kumar",
"Shooting",
"Men's Air Pistol pairs"
],
[
"Silver",
"Dharmaraj Wilson",
"Weightlifting",
"Men's 56 kg-Combined"
],
[
"Silver",
"Arumugam K. Pandian",
"Weightlifting",
"Men's 56 kg-Snatch"
],
[
"Silver",
"Arumugam K. Pandian",
"Weightlifting",
"Men's 56 kg-Clean and jerk"
],
[
"Silver",
"Satheesha Rai",
"Weightlifting",
"Men's 77 kg-Clean and jerk"
],
[
"Silver",
"Satheesha Rai",
"Weightlifting",
"Men's 77 kg-Combined"
]
] |
[
"",
"Aparna Popat",
"Jitender Kumar",
"Jaspal Rana",
"Jaspal Rana Satendra Kumar",
"Dharmaraj Wilson",
"Arumugam K. Pandian",
"Arumugam K. Pandian",
"Satheesha Rai",
"Satheesha Rai"
] |
who are all the players? ||
|
nt-1039
|
col : medal | name | sport | event row 1 : silver | nan | badminton | men's team row 2 : silver | aparna popat | badminton | women's singles row 3 : silver | jitender kumar | boxing | middleweight row 4 : silver | jaspal rana | shooting | men's 25 m air pistol row 5 : silver | jaspal rana satendra kumar | shooting | men's air pistol pairs row 6 : silver | dharmaraj wilson | weightlifting | men's 56 kg-combined row 7 : silver | arumugam k. pandian | weightlifting | men's 56 kg-snatch row 8 : silver | arumugam k. pandian | weightlifting | men's 56 kg-clean and jerk row 9 : silver | satheesha rai | weightlifting | men's 77 kg-clean and jerk row 10 : silver | satheesha rai | weightlifting | men's 77 kg-combined
|
table_csv/204_103.csv
| 1 |
{
"column_index": [
1,
1,
1,
1,
1,
1,
1,
1
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
8
]
}
|
[
"who are all the players?",
"of those, which won silver medals?"
] | 1 |
, aparna popat, jitender kumar, jaspal rana, jaspal rana satendra kumar, dharmaraj wilson, arumugam k. pandian, satheesha rai
|
[
"Medal",
"Name",
"Sport",
"Event"
] |
of those, which won silver medals?
|
[
[
"Silver",
"nan",
"Badminton",
"Men's team"
],
[
"Silver",
"Aparna Popat",
"Badminton",
"Women's singles"
],
[
"Silver",
"Jitender Kumar",
"Boxing",
"Middleweight"
],
[
"Silver",
"Jaspal Rana",
"Shooting",
"Men's 25 m Air Pistol"
],
[
"Silver",
"Jaspal Rana Satendra Kumar",
"Shooting",
"Men's Air Pistol pairs"
],
[
"Silver",
"Dharmaraj Wilson",
"Weightlifting",
"Men's 56 kg-Combined"
],
[
"Silver",
"Arumugam K. Pandian",
"Weightlifting",
"Men's 56 kg-Snatch"
],
[
"Silver",
"Arumugam K. Pandian",
"Weightlifting",
"Men's 56 kg-Clean and jerk"
],
[
"Silver",
"Satheesha Rai",
"Weightlifting",
"Men's 77 kg-Clean and jerk"
],
[
"Silver",
"Satheesha Rai",
"Weightlifting",
"Men's 77 kg-Combined"
]
] |
[
"",
"Aparna Popat",
"Jitender Kumar",
"Jaspal Rana",
"Jaspal Rana Satendra Kumar",
"Dharmaraj Wilson",
"Arumugam K. Pandian",
"Satheesha Rai"
] |
of those, which won silver medals? || who are all the players?
|
nt-1039
|
col : medal | name | sport | event row 1 : silver | nan | badminton | men's team row 2 : silver | aparna popat | badminton | women's singles row 3 : silver | jitender kumar | boxing | middleweight row 4 : silver | jaspal rana | shooting | men's 25 m air pistol row 5 : silver | jaspal rana satendra kumar | shooting | men's air pistol pairs row 6 : silver | dharmaraj wilson | weightlifting | men's 56 kg-combined row 7 : silver | arumugam k. pandian | weightlifting | men's 56 kg-snatch row 8 : silver | arumugam k. pandian | weightlifting | men's 56 kg-clean and jerk row 9 : silver | satheesha rai | weightlifting | men's 77 kg-clean and jerk row 10 : silver | satheesha rai | weightlifting | men's 77 kg-combined
|
table_csv/204_103.csv
| 2 |
{
"column_index": [
1
],
"row_index": [
1
]
}
|
[
"who are all the players?",
"of those, which won silver medals?",
"which competed in women's singles?"
] | 1 |
aparna popat
|
[
"Medal",
"Name",
"Sport",
"Event"
] |
which competed in women's singles?
|
[
[
"Silver",
"nan",
"Badminton",
"Men's team"
],
[
"Silver",
"Aparna Popat",
"Badminton",
"Women's singles"
],
[
"Silver",
"Jitender Kumar",
"Boxing",
"Middleweight"
],
[
"Silver",
"Jaspal Rana",
"Shooting",
"Men's 25 m Air Pistol"
],
[
"Silver",
"Jaspal Rana Satendra Kumar",
"Shooting",
"Men's Air Pistol pairs"
],
[
"Silver",
"Dharmaraj Wilson",
"Weightlifting",
"Men's 56 kg-Combined"
],
[
"Silver",
"Arumugam K. Pandian",
"Weightlifting",
"Men's 56 kg-Snatch"
],
[
"Silver",
"Arumugam K. Pandian",
"Weightlifting",
"Men's 56 kg-Clean and jerk"
],
[
"Silver",
"Satheesha Rai",
"Weightlifting",
"Men's 77 kg-Clean and jerk"
],
[
"Silver",
"Satheesha Rai",
"Weightlifting",
"Men's 77 kg-Combined"
]
] |
[
"Aparna Popat"
] |
which competed in women's singles? || of those, which won silver medals? | who are all the players?
|
nt-1039
|
col : medal | name | sport | event row 1 : silver | nan | badminton | men's team row 2 : silver | aparna popat | badminton | women's singles row 3 : silver | jitender kumar | boxing | middleweight row 4 : silver | jaspal rana | shooting | men's 25 m air pistol row 5 : silver | jaspal rana satendra kumar | shooting | men's air pistol pairs row 6 : silver | dharmaraj wilson | weightlifting | men's 56 kg-combined row 7 : silver | arumugam k. pandian | weightlifting | men's 56 kg-snatch row 8 : silver | arumugam k. pandian | weightlifting | men's 56 kg-clean and jerk row 9 : silver | satheesha rai | weightlifting | men's 77 kg-clean and jerk row 10 : silver | satheesha rai | weightlifting | men's 77 kg-combined
|
table_csv/204_103.csv
| 0 |
{
"column_index": [
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"row_index": [
1,
2,
3,
4,
5,
6,
7,
8,
9
]
}
|
[
"what are the names of all of the women who participated in the 1998 commonwealth games in india?"
] | 2 |
aparna popat, jitender kumar, jaspal rana, jaspal rana satendra kumar, dharmaraj wilson, arumugam k. pandian, arumugam k. pandian, satheesha rai, satheesha rai
|
[
"Medal",
"Name",
"Sport",
"Event"
] |
what are the names of all of the women who participated in the 1998 commonwealth games in india?
|
[
[
"Silver",
"nan",
"Badminton",
"Men's team"
],
[
"Silver",
"Aparna Popat",
"Badminton",
"Women's singles"
],
[
"Silver",
"Jitender Kumar",
"Boxing",
"Middleweight"
],
[
"Silver",
"Jaspal Rana",
"Shooting",
"Men's 25 m Air Pistol"
],
[
"Silver",
"Jaspal Rana Satendra Kumar",
"Shooting",
"Men's Air Pistol pairs"
],
[
"Silver",
"Dharmaraj Wilson",
"Weightlifting",
"Men's 56 kg-Combined"
],
[
"Silver",
"Arumugam K. Pandian",
"Weightlifting",
"Men's 56 kg-Snatch"
],
[
"Silver",
"Arumugam K. Pandian",
"Weightlifting",
"Men's 56 kg-Clean and jerk"
],
[
"Silver",
"Satheesha Rai",
"Weightlifting",
"Men's 77 kg-Clean and jerk"
],
[
"Silver",
"Satheesha Rai",
"Weightlifting",
"Men's 77 kg-Combined"
]
] |
[
"Aparna Popat",
"Jitender Kumar",
"Jaspal Rana",
"Jaspal Rana Satendra Kumar",
"Dharmaraj Wilson",
"Arumugam K. Pandian",
"Arumugam K. Pandian",
"Satheesha Rai",
"Satheesha Rai"
] |
what are the names of all of the women who participated in the 1998 commonwealth games in india? ||
|
nt-1039
|
col : medal | name | sport | event row 1 : silver | nan | badminton | men's team row 2 : silver | aparna popat | badminton | women's singles row 3 : silver | jitender kumar | boxing | middleweight row 4 : silver | jaspal rana | shooting | men's 25 m air pistol row 5 : silver | jaspal rana satendra kumar | shooting | men's air pistol pairs row 6 : silver | dharmaraj wilson | weightlifting | men's 56 kg-combined row 7 : silver | arumugam k. pandian | weightlifting | men's 56 kg-snatch row 8 : silver | arumugam k. pandian | weightlifting | men's 56 kg-clean and jerk row 9 : silver | satheesha rai | weightlifting | men's 77 kg-clean and jerk row 10 : silver | satheesha rai | weightlifting | men's 77 kg-combined
|
table_csv/204_103.csv
| 1 |
{
"column_index": [
1
],
"row_index": [
1
]
}
|
[
"what are the names of all of the women who participated in the 1998 commonwealth games in india?",
"among those women, which won a silver medal in the women's singles event?"
] | 2 |
aparna popat
|
[
"Medal",
"Name",
"Sport",
"Event"
] |
among those women, which won a silver medal in the women's singles event?
|
[
[
"Silver",
"nan",
"Badminton",
"Men's team"
],
[
"Silver",
"Aparna Popat",
"Badminton",
"Women's singles"
],
[
"Silver",
"Jitender Kumar",
"Boxing",
"Middleweight"
],
[
"Silver",
"Jaspal Rana",
"Shooting",
"Men's 25 m Air Pistol"
],
[
"Silver",
"Jaspal Rana Satendra Kumar",
"Shooting",
"Men's Air Pistol pairs"
],
[
"Silver",
"Dharmaraj Wilson",
"Weightlifting",
"Men's 56 kg-Combined"
],
[
"Silver",
"Arumugam K. Pandian",
"Weightlifting",
"Men's 56 kg-Snatch"
],
[
"Silver",
"Arumugam K. Pandian",
"Weightlifting",
"Men's 56 kg-Clean and jerk"
],
[
"Silver",
"Satheesha Rai",
"Weightlifting",
"Men's 77 kg-Clean and jerk"
],
[
"Silver",
"Satheesha Rai",
"Weightlifting",
"Men's 77 kg-Combined"
]
] |
[
"Aparna Popat"
] |
among those women, which won a silver medal in the women's singles event? || what are the names of all of the women who participated in the 1998 commonwealth games in india?
|
nt-6705
|
col : place | player | country | score | to par | money ($) row 1 : 1 | jack nicklaus | united states | 74-71-69-65=279 | -9 | 144,000 row 2 : t2 | tom kite | united states | 70-74-68-68=280 | -8 | 70,400 row 3 : t2 | greg norman | australia | 70-72-68-70=280 | -8 | 70,400 row 4 : 4 | seve ballesteros | spain | 71-68-72-70=281 | -7 | 38,400 row 5 : 5 | nick price | zimbabwe | 79-69-63-71=282 | -6 | 32,000 row 6 : t6 | jay haas | united states | 76-69-71-67=283 | -5 | 27,800 row 7 : t6 | tom watson | united states | 70-74-68-71=283 | -5 | 27,800 row 8 : t8 | tsuneyuki nakajima | japan | 70-71-71-72=284 | -4 | 23,200 row 9 : t8 | payne stewart | united states | 75-71-69-69=284 | -4 | 23,200 row 10 : t8 | bob tway | united states | 70-73-71-70=284 | -4 | 23,200
|
table_csv/203_499.csv
| 0 |
{
"column_index": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
]
}
|
[
"who were all of the players?"
] | 0 |
jack nicklaus, tom kite, greg norman, seve ballesteros, nick price, jay haas, tom watson, tsuneyuki nakajima, payne stewart, bob tway
|
[
"Place",
"Player",
"Country",
"Score",
"To par",
"Money ($)"
] |
who were all of the players?
|
[
[
"1",
"Jack Nicklaus",
"United States",
"74-71-69-65=279",
"-9",
"144,000"
],
[
"T2",
"Tom Kite",
"United States",
"70-74-68-68=280",
"-8",
"70,400"
],
[
"T2",
"Greg Norman",
"Australia",
"70-72-68-70=280",
"-8",
"70,400"
],
[
"4",
"Seve Ballesteros",
"Spain",
"71-68-72-70=281",
"-7",
"38,400"
],
[
"5",
"Nick Price",
"Zimbabwe",
"79-69-63-71=282",
"-6",
"32,000"
],
[
"T6",
"Jay Haas",
"United States",
"76-69-71-67=283",
"-5",
"27,800"
],
[
"T6",
"Tom Watson",
"United States",
"70-74-68-71=283",
"-5",
"27,800"
],
[
"T8",
"Tsuneyuki Nakajima",
"Japan",
"70-71-71-72=284",
"-4",
"23,200"
],
[
"T8",
"Payne Stewart",
"United States",
"75-71-69-69=284",
"-4",
"23,200"
],
[
"T8",
"Bob Tway",
"United States",
"70-73-71-70=284",
"-4",
"23,200"
]
] |
[
"Jack Nicklaus",
"Tom Kite",
"Greg Norman",
"Seve Ballesteros",
"Nick Price",
"Jay Haas",
"Tom Watson",
"Tsuneyuki Nakajima",
"Payne Stewart",
"Bob Tway"
] |
who were all of the players? ||
|
nt-6705
|
col : place | player | country | score | to par | money ($) row 1 : 1 | jack nicklaus | united states | 74-71-69-65=279 | -9 | 144,000 row 2 : t2 | tom kite | united states | 70-74-68-68=280 | -8 | 70,400 row 3 : t2 | greg norman | australia | 70-72-68-70=280 | -8 | 70,400 row 4 : 4 | seve ballesteros | spain | 71-68-72-70=281 | -7 | 38,400 row 5 : 5 | nick price | zimbabwe | 79-69-63-71=282 | -6 | 32,000 row 6 : t6 | jay haas | united states | 76-69-71-67=283 | -5 | 27,800 row 7 : t6 | tom watson | united states | 70-74-68-71=283 | -5 | 27,800 row 8 : t8 | tsuneyuki nakajima | japan | 70-71-71-72=284 | -4 | 23,200 row 9 : t8 | payne stewart | united states | 75-71-69-69=284 | -4 | 23,200 row 10 : t8 | bob tway | united states | 70-73-71-70=284 | -4 | 23,200
|
table_csv/203_499.csv
| 1 |
{
"column_index": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
]
}
|
[
"who were all of the players?",
"what were their places?"
] | 0 |
1, t2, t2, 4, 5, t6, t6, t8, t8, t8
|
[
"Place",
"Player",
"Country",
"Score",
"To par",
"Money ($)"
] |
what were their places?
|
[
[
"1",
"Jack Nicklaus",
"United States",
"74-71-69-65=279",
"-9",
"144,000"
],
[
"T2",
"Tom Kite",
"United States",
"70-74-68-68=280",
"-8",
"70,400"
],
[
"T2",
"Greg Norman",
"Australia",
"70-72-68-70=280",
"-8",
"70,400"
],
[
"4",
"Seve Ballesteros",
"Spain",
"71-68-72-70=281",
"-7",
"38,400"
],
[
"5",
"Nick Price",
"Zimbabwe",
"79-69-63-71=282",
"-6",
"32,000"
],
[
"T6",
"Jay Haas",
"United States",
"76-69-71-67=283",
"-5",
"27,800"
],
[
"T6",
"Tom Watson",
"United States",
"70-74-68-71=283",
"-5",
"27,800"
],
[
"T8",
"Tsuneyuki Nakajima",
"Japan",
"70-71-71-72=284",
"-4",
"23,200"
],
[
"T8",
"Payne Stewart",
"United States",
"75-71-69-69=284",
"-4",
"23,200"
],
[
"T8",
"Bob Tway",
"United States",
"70-73-71-70=284",
"-4",
"23,200"
]
] |
[
"1",
"T2",
"T2",
"4",
"5",
"T6",
"T6",
"T8",
"T8",
"T8"
] |
what were their places? || who were all of the players?
|
nt-6705
|
col : place | player | country | score | to par | money ($) row 1 : 1 | jack nicklaus | united states | 74-71-69-65=279 | -9 | 144,000 row 2 : t2 | tom kite | united states | 70-74-68-68=280 | -8 | 70,400 row 3 : t2 | greg norman | australia | 70-72-68-70=280 | -8 | 70,400 row 4 : 4 | seve ballesteros | spain | 71-68-72-70=281 | -7 | 38,400 row 5 : 5 | nick price | zimbabwe | 79-69-63-71=282 | -6 | 32,000 row 6 : t6 | jay haas | united states | 76-69-71-67=283 | -5 | 27,800 row 7 : t6 | tom watson | united states | 70-74-68-71=283 | -5 | 27,800 row 8 : t8 | tsuneyuki nakajima | japan | 70-71-71-72=284 | -4 | 23,200 row 9 : t8 | payne stewart | united states | 75-71-69-69=284 | -4 | 23,200 row 10 : t8 | bob tway | united states | 70-73-71-70=284 | -4 | 23,200
|
table_csv/203_499.csv
| 2 |
{
"column_index": [
1
],
"row_index": [
3
]
}
|
[
"who were all of the players?",
"what were their places?",
"and who reached 4th place?"
] | 0 |
seve ballesteros
|
[
"Place",
"Player",
"Country",
"Score",
"To par",
"Money ($)"
] |
and who reached 4th place?
|
[
[
"1",
"Jack Nicklaus",
"United States",
"74-71-69-65=279",
"-9",
"144,000"
],
[
"T2",
"Tom Kite",
"United States",
"70-74-68-68=280",
"-8",
"70,400"
],
[
"T2",
"Greg Norman",
"Australia",
"70-72-68-70=280",
"-8",
"70,400"
],
[
"4",
"Seve Ballesteros",
"Spain",
"71-68-72-70=281",
"-7",
"38,400"
],
[
"5",
"Nick Price",
"Zimbabwe",
"79-69-63-71=282",
"-6",
"32,000"
],
[
"T6",
"Jay Haas",
"United States",
"76-69-71-67=283",
"-5",
"27,800"
],
[
"T6",
"Tom Watson",
"United States",
"70-74-68-71=283",
"-5",
"27,800"
],
[
"T8",
"Tsuneyuki Nakajima",
"Japan",
"70-71-71-72=284",
"-4",
"23,200"
],
[
"T8",
"Payne Stewart",
"United States",
"75-71-69-69=284",
"-4",
"23,200"
],
[
"T8",
"Bob Tway",
"United States",
"70-73-71-70=284",
"-4",
"23,200"
]
] |
[
"Seve Ballesteros"
] |
and who reached 4th place? || what were their places? | who were all of the players?
|
nt-6705
|
col : place | player | country | score | to par | money ($) row 1 : 1 | jack nicklaus | united states | 74-71-69-65=279 | -9 | 144,000 row 2 : t2 | tom kite | united states | 70-74-68-68=280 | -8 | 70,400 row 3 : t2 | greg norman | australia | 70-72-68-70=280 | -8 | 70,400 row 4 : 4 | seve ballesteros | spain | 71-68-72-70=281 | -7 | 38,400 row 5 : 5 | nick price | zimbabwe | 79-69-63-71=282 | -6 | 32,000 row 6 : t6 | jay haas | united states | 76-69-71-67=283 | -5 | 27,800 row 7 : t6 | tom watson | united states | 70-74-68-71=283 | -5 | 27,800 row 8 : t8 | tsuneyuki nakajima | japan | 70-71-71-72=284 | -4 | 23,200 row 9 : t8 | payne stewart | united states | 75-71-69-69=284 | -4 | 23,200 row 10 : t8 | bob tway | united states | 70-73-71-70=284 | -4 | 23,200
|
table_csv/203_499.csv
| 0 |
{
"column_index": [
4
],
"row_index": [
3
]
}
|
[
"what was the par of the fourth place golfer?"
] | 1 |
-7
|
[
"Place",
"Player",
"Country",
"Score",
"To par",
"Money ($)"
] |
what was the par of the fourth place golfer?
|
[
[
"1",
"Jack Nicklaus",
"United States",
"74-71-69-65=279",
"-9",
"144,000"
],
[
"T2",
"Tom Kite",
"United States",
"70-74-68-68=280",
"-8",
"70,400"
],
[
"T2",
"Greg Norman",
"Australia",
"70-72-68-70=280",
"-8",
"70,400"
],
[
"4",
"Seve Ballesteros",
"Spain",
"71-68-72-70=281",
"-7",
"38,400"
],
[
"5",
"Nick Price",
"Zimbabwe",
"79-69-63-71=282",
"-6",
"32,000"
],
[
"T6",
"Jay Haas",
"United States",
"76-69-71-67=283",
"-5",
"27,800"
],
[
"T6",
"Tom Watson",
"United States",
"70-74-68-71=283",
"-5",
"27,800"
],
[
"T8",
"Tsuneyuki Nakajima",
"Japan",
"70-71-71-72=284",
"-4",
"23,200"
],
[
"T8",
"Payne Stewart",
"United States",
"75-71-69-69=284",
"-4",
"23,200"
],
[
"T8",
"Bob Tway",
"United States",
"70-73-71-70=284",
"-4",
"23,200"
]
] |
[
"-7"
] |
what was the par of the fourth place golfer? ||
|
nt-6705
|
col : place | player | country | score | to par | money ($) row 1 : 1 | jack nicklaus | united states | 74-71-69-65=279 | -9 | 144,000 row 2 : t2 | tom kite | united states | 70-74-68-68=280 | -8 | 70,400 row 3 : t2 | greg norman | australia | 70-72-68-70=280 | -8 | 70,400 row 4 : 4 | seve ballesteros | spain | 71-68-72-70=281 | -7 | 38,400 row 5 : 5 | nick price | zimbabwe | 79-69-63-71=282 | -6 | 32,000 row 6 : t6 | jay haas | united states | 76-69-71-67=283 | -5 | 27,800 row 7 : t6 | tom watson | united states | 70-74-68-71=283 | -5 | 27,800 row 8 : t8 | tsuneyuki nakajima | japan | 70-71-71-72=284 | -4 | 23,200 row 9 : t8 | payne stewart | united states | 75-71-69-69=284 | -4 | 23,200 row 10 : t8 | bob tway | united states | 70-73-71-70=284 | -4 | 23,200
|
table_csv/203_499.csv
| 1 |
{
"column_index": [
1
],
"row_index": [
3
]
}
|
[
"what was the par of the fourth place golfer?",
"what golfer came in at -7 under par?"
] | 1 |
seve ballesteros
|
[
"Place",
"Player",
"Country",
"Score",
"To par",
"Money ($)"
] |
what golfer came in at -7 under par?
|
[
[
"1",
"Jack Nicklaus",
"United States",
"74-71-69-65=279",
"-9",
"144,000"
],
[
"T2",
"Tom Kite",
"United States",
"70-74-68-68=280",
"-8",
"70,400"
],
[
"T2",
"Greg Norman",
"Australia",
"70-72-68-70=280",
"-8",
"70,400"
],
[
"4",
"Seve Ballesteros",
"Spain",
"71-68-72-70=281",
"-7",
"38,400"
],
[
"5",
"Nick Price",
"Zimbabwe",
"79-69-63-71=282",
"-6",
"32,000"
],
[
"T6",
"Jay Haas",
"United States",
"76-69-71-67=283",
"-5",
"27,800"
],
[
"T6",
"Tom Watson",
"United States",
"70-74-68-71=283",
"-5",
"27,800"
],
[
"T8",
"Tsuneyuki Nakajima",
"Japan",
"70-71-71-72=284",
"-4",
"23,200"
],
[
"T8",
"Payne Stewart",
"United States",
"75-71-69-69=284",
"-4",
"23,200"
],
[
"T8",
"Bob Tway",
"United States",
"70-73-71-70=284",
"-4",
"23,200"
]
] |
[
"Seve Ballesteros"
] |
what golfer came in at -7 under par? || what was the par of the fourth place golfer?
|
nt-6705
|
col : place | player | country | score | to par | money ($) row 1 : 1 | jack nicklaus | united states | 74-71-69-65=279 | -9 | 144,000 row 2 : t2 | tom kite | united states | 70-74-68-68=280 | -8 | 70,400 row 3 : t2 | greg norman | australia | 70-72-68-70=280 | -8 | 70,400 row 4 : 4 | seve ballesteros | spain | 71-68-72-70=281 | -7 | 38,400 row 5 : 5 | nick price | zimbabwe | 79-69-63-71=282 | -6 | 32,000 row 6 : t6 | jay haas | united states | 76-69-71-67=283 | -5 | 27,800 row 7 : t6 | tom watson | united states | 70-74-68-71=283 | -5 | 27,800 row 8 : t8 | tsuneyuki nakajima | japan | 70-71-71-72=284 | -4 | 23,200 row 9 : t8 | payne stewart | united states | 75-71-69-69=284 | -4 | 23,200 row 10 : t8 | bob tway | united states | 70-73-71-70=284 | -4 | 23,200
|
table_csv/203_499.csv
| 0 |
{
"column_index": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9
]
}
|
[
"what are all the players in the 1986 masters tournament?"
] | 2 |
jack nicklaus, tom kite, greg norman, seve ballesteros, nick price, jay haas, tom watson, tsuneyuki nakajima, payne stewart, bob tway
|
[
"Place",
"Player",
"Country",
"Score",
"To par",
"Money ($)"
] |
what are all the players in the 1986 masters tournament?
|
[
[
"1",
"Jack Nicklaus",
"United States",
"74-71-69-65=279",
"-9",
"144,000"
],
[
"T2",
"Tom Kite",
"United States",
"70-74-68-68=280",
"-8",
"70,400"
],
[
"T2",
"Greg Norman",
"Australia",
"70-72-68-70=280",
"-8",
"70,400"
],
[
"4",
"Seve Ballesteros",
"Spain",
"71-68-72-70=281",
"-7",
"38,400"
],
[
"5",
"Nick Price",
"Zimbabwe",
"79-69-63-71=282",
"-6",
"32,000"
],
[
"T6",
"Jay Haas",
"United States",
"76-69-71-67=283",
"-5",
"27,800"
],
[
"T6",
"Tom Watson",
"United States",
"70-74-68-71=283",
"-5",
"27,800"
],
[
"T8",
"Tsuneyuki Nakajima",
"Japan",
"70-71-71-72=284",
"-4",
"23,200"
],
[
"T8",
"Payne Stewart",
"United States",
"75-71-69-69=284",
"-4",
"23,200"
],
[
"T8",
"Bob Tway",
"United States",
"70-73-71-70=284",
"-4",
"23,200"
]
] |
[
"Jack Nicklaus",
"Tom Kite",
"Greg Norman",
"Seve Ballesteros",
"Nick Price",
"Jay Haas",
"Tom Watson",
"Tsuneyuki Nakajima",
"Payne Stewart",
"Bob Tway"
] |
what are all the players in the 1986 masters tournament? ||
|
nt-6705
|
col : place | player | country | score | to par | money ($) row 1 : 1 | jack nicklaus | united states | 74-71-69-65=279 | -9 | 144,000 row 2 : t2 | tom kite | united states | 70-74-68-68=280 | -8 | 70,400 row 3 : t2 | greg norman | australia | 70-72-68-70=280 | -8 | 70,400 row 4 : 4 | seve ballesteros | spain | 71-68-72-70=281 | -7 | 38,400 row 5 : 5 | nick price | zimbabwe | 79-69-63-71=282 | -6 | 32,000 row 6 : t6 | jay haas | united states | 76-69-71-67=283 | -5 | 27,800 row 7 : t6 | tom watson | united states | 70-74-68-71=283 | -5 | 27,800 row 8 : t8 | tsuneyuki nakajima | japan | 70-71-71-72=284 | -4 | 23,200 row 9 : t8 | payne stewart | united states | 75-71-69-69=284 | -4 | 23,200 row 10 : t8 | bob tway | united states | 70-73-71-70=284 | -4 | 23,200
|
table_csv/203_499.csv
| 1 |
{
"column_index": [
1
],
"row_index": [
3
]
}
|
[
"what are all the players in the 1986 masters tournament?",
"of these which placed 4th?"
] | 2 |
seve ballesteros
|
[
"Place",
"Player",
"Country",
"Score",
"To par",
"Money ($)"
] |
of these which placed 4th?
|
[
[
"1",
"Jack Nicklaus",
"United States",
"74-71-69-65=279",
"-9",
"144,000"
],
[
"T2",
"Tom Kite",
"United States",
"70-74-68-68=280",
"-8",
"70,400"
],
[
"T2",
"Greg Norman",
"Australia",
"70-72-68-70=280",
"-8",
"70,400"
],
[
"4",
"Seve Ballesteros",
"Spain",
"71-68-72-70=281",
"-7",
"38,400"
],
[
"5",
"Nick Price",
"Zimbabwe",
"79-69-63-71=282",
"-6",
"32,000"
],
[
"T6",
"Jay Haas",
"United States",
"76-69-71-67=283",
"-5",
"27,800"
],
[
"T6",
"Tom Watson",
"United States",
"70-74-68-71=283",
"-5",
"27,800"
],
[
"T8",
"Tsuneyuki Nakajima",
"Japan",
"70-71-71-72=284",
"-4",
"23,200"
],
[
"T8",
"Payne Stewart",
"United States",
"75-71-69-69=284",
"-4",
"23,200"
],
[
"T8",
"Bob Tway",
"United States",
"70-73-71-70=284",
"-4",
"23,200"
]
] |
[
"Seve Ballesteros"
] |
of these which placed 4th? || what are all the players in the 1986 masters tournament?
|
nt-5722
|
col : rank | nation | gold | silver | bronze | total row 1 : 1 | germany | 17 | 9 | 14 | 40 row 2 : 2 | russia | 12 | 16 | 8 | 36 row 3 : 3 | ukraine | 6 | 11 | 9 | 26 row 4 : 4 | norway | 6 | 2 | 7 | 15 row 5 : 5 | france | 5 | 4 | 3 | 12 row 6 : 6 | switzerland | 3 | 5 | 3 | 11 row 7 : 7 | finland | 3 | 2 | 4 | 9 row 8 : 8 | japan | 2 | 2 | 1 | 5 row 9 : 9 | netherlands | 2 | 0 | 1 | 3 row 10 : 10 | sweden | 1 | 2 | 2 | 5 row 11 : 11 | denmark | 1 | 0 | 1 | 2 row 12 : 12 | unified team | 1 | 0 | 0 | 1 row 13 : 13 | austria | 0 | 4 | 0 | 4 row 14 : 14 | slovakia | 0 | 2 | 1 | 3 row 15 : 15 | italy | 0 | 0 | 1 | 1 row 16 : 15 | poland | 0 | 0 | 1 | 1 row 17 : 15 | canada | 0 | 0 | 1 | 1 row 18 : 15 | belarus | 0 | 0 | 1 | 1 row 19 : 15 | united states | 0 | 0 | 1 | 1 row 20 : total 19 nations | total 19 nations | 59 | 59 | 59 | 177
|
table_csv/204_761.csv
| 0 |
{
"column_index": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18
]
}
|
[
"which teams competed at the winter paralympics?"
] | 0 |
germany, russia, ukraine, norway, france, switzerland, finland, japan, netherlands, sweden, denmark, unified team, austria, slovakia, italy, poland, canada, belarus, united states
|
[
"Rank",
"Nation",
"Gold",
"Silver",
"Bronze",
"Total"
] |
which teams competed at the winter paralympics?
|
[
[
"1",
"Germany",
"17",
"9",
"14",
"40"
],
[
"2",
"Russia",
"12",
"16",
"8",
"36"
],
[
"3",
"Ukraine",
"6",
"11",
"9",
"26"
],
[
"4",
"Norway",
"6",
"2",
"7",
"15"
],
[
"5",
"France",
"5",
"4",
"3",
"12"
],
[
"6",
"Switzerland",
"3",
"5",
"3",
"11"
],
[
"7",
"Finland",
"3",
"2",
"4",
"9"
],
[
"8",
"Japan",
"2",
"2",
"1",
"5"
],
[
"9",
"Netherlands",
"2",
"0",
"1",
"3"
],
[
"10",
"Sweden",
"1",
"2",
"2",
"5"
],
[
"11",
"Denmark",
"1",
"0",
"1",
"2"
],
[
"12",
"Unified Team",
"1",
"0",
"0",
"1"
],
[
"13",
"Austria",
"0",
"4",
"0",
"4"
],
[
"14",
"Slovakia",
"0",
"2",
"1",
"3"
],
[
"15",
"Italy",
"0",
"0",
"1",
"1"
],
[
"15",
"Poland",
"0",
"0",
"1",
"1"
],
[
"15",
"Canada",
"0",
"0",
"1",
"1"
],
[
"15",
"Belarus",
"0",
"0",
"1",
"1"
],
[
"15",
"United States",
"0",
"0",
"1",
"1"
],
[
"Total 19 nations",
"Total 19 nations",
"59",
"59",
"59",
"177"
]
] |
[
"Germany",
"Russia",
"Ukraine",
"Norway",
"France",
"Switzerland",
"Finland",
"Japan",
"Netherlands",
"Sweden",
"Denmark",
"Unified Team",
"Austria",
"Slovakia",
"Italy",
"Poland",
"Canada",
"Belarus",
"United States"
] |
which teams competed at the winter paralympics? ||
|
nt-5722
|
col : rank | nation | gold | silver | bronze | total row 1 : 1 | germany | 17 | 9 | 14 | 40 row 2 : 2 | russia | 12 | 16 | 8 | 36 row 3 : 3 | ukraine | 6 | 11 | 9 | 26 row 4 : 4 | norway | 6 | 2 | 7 | 15 row 5 : 5 | france | 5 | 4 | 3 | 12 row 6 : 6 | switzerland | 3 | 5 | 3 | 11 row 7 : 7 | finland | 3 | 2 | 4 | 9 row 8 : 8 | japan | 2 | 2 | 1 | 5 row 9 : 9 | netherlands | 2 | 0 | 1 | 3 row 10 : 10 | sweden | 1 | 2 | 2 | 5 row 11 : 11 | denmark | 1 | 0 | 1 | 2 row 12 : 12 | unified team | 1 | 0 | 0 | 1 row 13 : 13 | austria | 0 | 4 | 0 | 4 row 14 : 14 | slovakia | 0 | 2 | 1 | 3 row 15 : 15 | italy | 0 | 0 | 1 | 1 row 16 : 15 | poland | 0 | 0 | 1 | 1 row 17 : 15 | canada | 0 | 0 | 1 | 1 row 18 : 15 | belarus | 0 | 0 | 1 | 1 row 19 : 15 | united states | 0 | 0 | 1 | 1 row 20 : total 19 nations | total 19 nations | 59 | 59 | 59 | 177
|
table_csv/204_761.csv
| 1 |
{
"column_index": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11
]
}
|
[
"which teams competed at the winter paralympics?",
"which of those won a gold medal?"
] | 0 |
germany, russia, ukraine, norway, france, switzerland, finland, japan, netherlands, sweden, denmark, unified team
|
[
"Rank",
"Nation",
"Gold",
"Silver",
"Bronze",
"Total"
] |
which of those won a gold medal?
|
[
[
"1",
"Germany",
"17",
"9",
"14",
"40"
],
[
"2",
"Russia",
"12",
"16",
"8",
"36"
],
[
"3",
"Ukraine",
"6",
"11",
"9",
"26"
],
[
"4",
"Norway",
"6",
"2",
"7",
"15"
],
[
"5",
"France",
"5",
"4",
"3",
"12"
],
[
"6",
"Switzerland",
"3",
"5",
"3",
"11"
],
[
"7",
"Finland",
"3",
"2",
"4",
"9"
],
[
"8",
"Japan",
"2",
"2",
"1",
"5"
],
[
"9",
"Netherlands",
"2",
"0",
"1",
"3"
],
[
"10",
"Sweden",
"1",
"2",
"2",
"5"
],
[
"11",
"Denmark",
"1",
"0",
"1",
"2"
],
[
"12",
"Unified Team",
"1",
"0",
"0",
"1"
],
[
"13",
"Austria",
"0",
"4",
"0",
"4"
],
[
"14",
"Slovakia",
"0",
"2",
"1",
"3"
],
[
"15",
"Italy",
"0",
"0",
"1",
"1"
],
[
"15",
"Poland",
"0",
"0",
"1",
"1"
],
[
"15",
"Canada",
"0",
"0",
"1",
"1"
],
[
"15",
"Belarus",
"0",
"0",
"1",
"1"
],
[
"15",
"United States",
"0",
"0",
"1",
"1"
],
[
"Total 19 nations",
"Total 19 nations",
"59",
"59",
"59",
"177"
]
] |
[
"Germany",
"Russia",
"Ukraine",
"Norway",
"France",
"Switzerland",
"Finland",
"Japan",
"Netherlands",
"Sweden",
"Denmark",
"Unified Team"
] |
which of those won a gold medal? || which teams competed at the winter paralympics?
|
nt-5722
|
col : rank | nation | gold | silver | bronze | total row 1 : 1 | germany | 17 | 9 | 14 | 40 row 2 : 2 | russia | 12 | 16 | 8 | 36 row 3 : 3 | ukraine | 6 | 11 | 9 | 26 row 4 : 4 | norway | 6 | 2 | 7 | 15 row 5 : 5 | france | 5 | 4 | 3 | 12 row 6 : 6 | switzerland | 3 | 5 | 3 | 11 row 7 : 7 | finland | 3 | 2 | 4 | 9 row 8 : 8 | japan | 2 | 2 | 1 | 5 row 9 : 9 | netherlands | 2 | 0 | 1 | 3 row 10 : 10 | sweden | 1 | 2 | 2 | 5 row 11 : 11 | denmark | 1 | 0 | 1 | 2 row 12 : 12 | unified team | 1 | 0 | 0 | 1 row 13 : 13 | austria | 0 | 4 | 0 | 4 row 14 : 14 | slovakia | 0 | 2 | 1 | 3 row 15 : 15 | italy | 0 | 0 | 1 | 1 row 16 : 15 | poland | 0 | 0 | 1 | 1 row 17 : 15 | canada | 0 | 0 | 1 | 1 row 18 : 15 | belarus | 0 | 0 | 1 | 1 row 19 : 15 | united states | 0 | 0 | 1 | 1 row 20 : total 19 nations | total 19 nations | 59 | 59 | 59 | 177
|
table_csv/204_761.csv
| 2 |
{
"column_index": [
1
],
"row_index": [
0
]
}
|
[
"which teams competed at the winter paralympics?",
"which of those won a gold medal?",
"which of those won the most bronze medals?"
] | 0 |
germany
|
[
"Rank",
"Nation",
"Gold",
"Silver",
"Bronze",
"Total"
] |
which of those won the most bronze medals?
|
[
[
"1",
"Germany",
"17",
"9",
"14",
"40"
],
[
"2",
"Russia",
"12",
"16",
"8",
"36"
],
[
"3",
"Ukraine",
"6",
"11",
"9",
"26"
],
[
"4",
"Norway",
"6",
"2",
"7",
"15"
],
[
"5",
"France",
"5",
"4",
"3",
"12"
],
[
"6",
"Switzerland",
"3",
"5",
"3",
"11"
],
[
"7",
"Finland",
"3",
"2",
"4",
"9"
],
[
"8",
"Japan",
"2",
"2",
"1",
"5"
],
[
"9",
"Netherlands",
"2",
"0",
"1",
"3"
],
[
"10",
"Sweden",
"1",
"2",
"2",
"5"
],
[
"11",
"Denmark",
"1",
"0",
"1",
"2"
],
[
"12",
"Unified Team",
"1",
"0",
"0",
"1"
],
[
"13",
"Austria",
"0",
"4",
"0",
"4"
],
[
"14",
"Slovakia",
"0",
"2",
"1",
"3"
],
[
"15",
"Italy",
"0",
"0",
"1",
"1"
],
[
"15",
"Poland",
"0",
"0",
"1",
"1"
],
[
"15",
"Canada",
"0",
"0",
"1",
"1"
],
[
"15",
"Belarus",
"0",
"0",
"1",
"1"
],
[
"15",
"United States",
"0",
"0",
"1",
"1"
],
[
"Total 19 nations",
"Total 19 nations",
"59",
"59",
"59",
"177"
]
] |
[
"Germany"
] |
which of those won the most bronze medals? || which of those won a gold medal? | which teams competed at the winter paralympics?
|
nt-5722
|
col : rank | nation | gold | silver | bronze | total row 1 : 1 | germany | 17 | 9 | 14 | 40 row 2 : 2 | russia | 12 | 16 | 8 | 36 row 3 : 3 | ukraine | 6 | 11 | 9 | 26 row 4 : 4 | norway | 6 | 2 | 7 | 15 row 5 : 5 | france | 5 | 4 | 3 | 12 row 6 : 6 | switzerland | 3 | 5 | 3 | 11 row 7 : 7 | finland | 3 | 2 | 4 | 9 row 8 : 8 | japan | 2 | 2 | 1 | 5 row 9 : 9 | netherlands | 2 | 0 | 1 | 3 row 10 : 10 | sweden | 1 | 2 | 2 | 5 row 11 : 11 | denmark | 1 | 0 | 1 | 2 row 12 : 12 | unified team | 1 | 0 | 0 | 1 row 13 : 13 | austria | 0 | 4 | 0 | 4 row 14 : 14 | slovakia | 0 | 2 | 1 | 3 row 15 : 15 | italy | 0 | 0 | 1 | 1 row 16 : 15 | poland | 0 | 0 | 1 | 1 row 17 : 15 | canada | 0 | 0 | 1 | 1 row 18 : 15 | belarus | 0 | 0 | 1 | 1 row 19 : 15 | united states | 0 | 0 | 1 | 1 row 20 : total 19 nations | total 19 nations | 59 | 59 | 59 | 177
|
table_csv/204_761.csv
| 0 |
{
"column_index": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18
]
}
|
[
"which nations competed during the biathlon at the winter paralympics?"
] | 1 |
germany, russia, ukraine, norway, france, switzerland, finland, japan, netherlands, sweden, denmark, unified team, austria, slovakia, italy, poland, canada, belarus, united states
|
[
"Rank",
"Nation",
"Gold",
"Silver",
"Bronze",
"Total"
] |
which nations competed during the biathlon at the winter paralympics?
|
[
[
"1",
"Germany",
"17",
"9",
"14",
"40"
],
[
"2",
"Russia",
"12",
"16",
"8",
"36"
],
[
"3",
"Ukraine",
"6",
"11",
"9",
"26"
],
[
"4",
"Norway",
"6",
"2",
"7",
"15"
],
[
"5",
"France",
"5",
"4",
"3",
"12"
],
[
"6",
"Switzerland",
"3",
"5",
"3",
"11"
],
[
"7",
"Finland",
"3",
"2",
"4",
"9"
],
[
"8",
"Japan",
"2",
"2",
"1",
"5"
],
[
"9",
"Netherlands",
"2",
"0",
"1",
"3"
],
[
"10",
"Sweden",
"1",
"2",
"2",
"5"
],
[
"11",
"Denmark",
"1",
"0",
"1",
"2"
],
[
"12",
"Unified Team",
"1",
"0",
"0",
"1"
],
[
"13",
"Austria",
"0",
"4",
"0",
"4"
],
[
"14",
"Slovakia",
"0",
"2",
"1",
"3"
],
[
"15",
"Italy",
"0",
"0",
"1",
"1"
],
[
"15",
"Poland",
"0",
"0",
"1",
"1"
],
[
"15",
"Canada",
"0",
"0",
"1",
"1"
],
[
"15",
"Belarus",
"0",
"0",
"1",
"1"
],
[
"15",
"United States",
"0",
"0",
"1",
"1"
],
[
"Total 19 nations",
"Total 19 nations",
"59",
"59",
"59",
"177"
]
] |
[
"Germany",
"Russia",
"Ukraine",
"Norway",
"France",
"Switzerland",
"Finland",
"Japan",
"Netherlands",
"Sweden",
"Denmark",
"Unified Team",
"Austria",
"Slovakia",
"Italy",
"Poland",
"Canada",
"Belarus",
"United States"
] |
which nations competed during the biathlon at the winter paralympics? ||
|
nt-5722
|
col : rank | nation | gold | silver | bronze | total row 1 : 1 | germany | 17 | 9 | 14 | 40 row 2 : 2 | russia | 12 | 16 | 8 | 36 row 3 : 3 | ukraine | 6 | 11 | 9 | 26 row 4 : 4 | norway | 6 | 2 | 7 | 15 row 5 : 5 | france | 5 | 4 | 3 | 12 row 6 : 6 | switzerland | 3 | 5 | 3 | 11 row 7 : 7 | finland | 3 | 2 | 4 | 9 row 8 : 8 | japan | 2 | 2 | 1 | 5 row 9 : 9 | netherlands | 2 | 0 | 1 | 3 row 10 : 10 | sweden | 1 | 2 | 2 | 5 row 11 : 11 | denmark | 1 | 0 | 1 | 2 row 12 : 12 | unified team | 1 | 0 | 0 | 1 row 13 : 13 | austria | 0 | 4 | 0 | 4 row 14 : 14 | slovakia | 0 | 2 | 1 | 3 row 15 : 15 | italy | 0 | 0 | 1 | 1 row 16 : 15 | poland | 0 | 0 | 1 | 1 row 17 : 15 | canada | 0 | 0 | 1 | 1 row 18 : 15 | belarus | 0 | 0 | 1 | 1 row 19 : 15 | united states | 0 | 0 | 1 | 1 row 20 : total 19 nations | total 19 nations | 59 | 59 | 59 | 177
|
table_csv/204_761.csv
| 1 |
{
"column_index": [
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18
]
}
|
[
"which nations competed during the biathlon at the winter paralympics?",
"how many bronze medals did they win?"
] | 1 |
14, 8, 9, 7, 3, 3, 4, 1, 1, 2, 1, 0, 0, 1, 1, 1, 1, 1, 1
|
[
"Rank",
"Nation",
"Gold",
"Silver",
"Bronze",
"Total"
] |
how many bronze medals did they win?
|
[
[
"1",
"Germany",
"17",
"9",
"14",
"40"
],
[
"2",
"Russia",
"12",
"16",
"8",
"36"
],
[
"3",
"Ukraine",
"6",
"11",
"9",
"26"
],
[
"4",
"Norway",
"6",
"2",
"7",
"15"
],
[
"5",
"France",
"5",
"4",
"3",
"12"
],
[
"6",
"Switzerland",
"3",
"5",
"3",
"11"
],
[
"7",
"Finland",
"3",
"2",
"4",
"9"
],
[
"8",
"Japan",
"2",
"2",
"1",
"5"
],
[
"9",
"Netherlands",
"2",
"0",
"1",
"3"
],
[
"10",
"Sweden",
"1",
"2",
"2",
"5"
],
[
"11",
"Denmark",
"1",
"0",
"1",
"2"
],
[
"12",
"Unified Team",
"1",
"0",
"0",
"1"
],
[
"13",
"Austria",
"0",
"4",
"0",
"4"
],
[
"14",
"Slovakia",
"0",
"2",
"1",
"3"
],
[
"15",
"Italy",
"0",
"0",
"1",
"1"
],
[
"15",
"Poland",
"0",
"0",
"1",
"1"
],
[
"15",
"Canada",
"0",
"0",
"1",
"1"
],
[
"15",
"Belarus",
"0",
"0",
"1",
"1"
],
[
"15",
"United States",
"0",
"0",
"1",
"1"
],
[
"Total 19 nations",
"Total 19 nations",
"59",
"59",
"59",
"177"
]
] |
[
"14",
"8",
"9",
"7",
"3",
"3",
"4",
"1",
"1",
"2",
"1",
"0",
"0",
"1",
"1",
"1",
"1",
"1",
"1"
] |
how many bronze medals did they win? || which nations competed during the biathlon at the winter paralympics?
|
nt-5722
|
col : rank | nation | gold | silver | bronze | total row 1 : 1 | germany | 17 | 9 | 14 | 40 row 2 : 2 | russia | 12 | 16 | 8 | 36 row 3 : 3 | ukraine | 6 | 11 | 9 | 26 row 4 : 4 | norway | 6 | 2 | 7 | 15 row 5 : 5 | france | 5 | 4 | 3 | 12 row 6 : 6 | switzerland | 3 | 5 | 3 | 11 row 7 : 7 | finland | 3 | 2 | 4 | 9 row 8 : 8 | japan | 2 | 2 | 1 | 5 row 9 : 9 | netherlands | 2 | 0 | 1 | 3 row 10 : 10 | sweden | 1 | 2 | 2 | 5 row 11 : 11 | denmark | 1 | 0 | 1 | 2 row 12 : 12 | unified team | 1 | 0 | 0 | 1 row 13 : 13 | austria | 0 | 4 | 0 | 4 row 14 : 14 | slovakia | 0 | 2 | 1 | 3 row 15 : 15 | italy | 0 | 0 | 1 | 1 row 16 : 15 | poland | 0 | 0 | 1 | 1 row 17 : 15 | canada | 0 | 0 | 1 | 1 row 18 : 15 | belarus | 0 | 0 | 1 | 1 row 19 : 15 | united states | 0 | 0 | 1 | 1 row 20 : total 19 nations | total 19 nations | 59 | 59 | 59 | 177
|
table_csv/204_761.csv
| 2 |
{
"column_index": [
1
],
"row_index": [
0
]
}
|
[
"which nations competed during the biathlon at the winter paralympics?",
"how many bronze medals did they win?",
"of those nations, which won more than 10 bronze medals?"
] | 1 |
germany
|
[
"Rank",
"Nation",
"Gold",
"Silver",
"Bronze",
"Total"
] |
of those nations, which won more than 10 bronze medals?
|
[
[
"1",
"Germany",
"17",
"9",
"14",
"40"
],
[
"2",
"Russia",
"12",
"16",
"8",
"36"
],
[
"3",
"Ukraine",
"6",
"11",
"9",
"26"
],
[
"4",
"Norway",
"6",
"2",
"7",
"15"
],
[
"5",
"France",
"5",
"4",
"3",
"12"
],
[
"6",
"Switzerland",
"3",
"5",
"3",
"11"
],
[
"7",
"Finland",
"3",
"2",
"4",
"9"
],
[
"8",
"Japan",
"2",
"2",
"1",
"5"
],
[
"9",
"Netherlands",
"2",
"0",
"1",
"3"
],
[
"10",
"Sweden",
"1",
"2",
"2",
"5"
],
[
"11",
"Denmark",
"1",
"0",
"1",
"2"
],
[
"12",
"Unified Team",
"1",
"0",
"0",
"1"
],
[
"13",
"Austria",
"0",
"4",
"0",
"4"
],
[
"14",
"Slovakia",
"0",
"2",
"1",
"3"
],
[
"15",
"Italy",
"0",
"0",
"1",
"1"
],
[
"15",
"Poland",
"0",
"0",
"1",
"1"
],
[
"15",
"Canada",
"0",
"0",
"1",
"1"
],
[
"15",
"Belarus",
"0",
"0",
"1",
"1"
],
[
"15",
"United States",
"0",
"0",
"1",
"1"
],
[
"Total 19 nations",
"Total 19 nations",
"59",
"59",
"59",
"177"
]
] |
[
"Germany"
] |
of those nations, which won more than 10 bronze medals? || how many bronze medals did they win? | which nations competed during the biathlon at the winter paralympics?
|
nt-5722
|
col : rank | nation | gold | silver | bronze | total row 1 : 1 | germany | 17 | 9 | 14 | 40 row 2 : 2 | russia | 12 | 16 | 8 | 36 row 3 : 3 | ukraine | 6 | 11 | 9 | 26 row 4 : 4 | norway | 6 | 2 | 7 | 15 row 5 : 5 | france | 5 | 4 | 3 | 12 row 6 : 6 | switzerland | 3 | 5 | 3 | 11 row 7 : 7 | finland | 3 | 2 | 4 | 9 row 8 : 8 | japan | 2 | 2 | 1 | 5 row 9 : 9 | netherlands | 2 | 0 | 1 | 3 row 10 : 10 | sweden | 1 | 2 | 2 | 5 row 11 : 11 | denmark | 1 | 0 | 1 | 2 row 12 : 12 | unified team | 1 | 0 | 0 | 1 row 13 : 13 | austria | 0 | 4 | 0 | 4 row 14 : 14 | slovakia | 0 | 2 | 1 | 3 row 15 : 15 | italy | 0 | 0 | 1 | 1 row 16 : 15 | poland | 0 | 0 | 1 | 1 row 17 : 15 | canada | 0 | 0 | 1 | 1 row 18 : 15 | belarus | 0 | 0 | 1 | 1 row 19 : 15 | united states | 0 | 0 | 1 | 1 row 20 : total 19 nations | total 19 nations | 59 | 59 | 59 | 177
|
table_csv/204_761.csv
| 0 |
{
"column_index": [
1,
1,
1,
1
],
"row_index": [
0,
1,
2,
3
]
}
|
[
"which nations have won more than five gold medals?"
] | 2 |
germany, russia, ukraine, norway
|
[
"Rank",
"Nation",
"Gold",
"Silver",
"Bronze",
"Total"
] |
which nations have won more than five gold medals?
|
[
[
"1",
"Germany",
"17",
"9",
"14",
"40"
],
[
"2",
"Russia",
"12",
"16",
"8",
"36"
],
[
"3",
"Ukraine",
"6",
"11",
"9",
"26"
],
[
"4",
"Norway",
"6",
"2",
"7",
"15"
],
[
"5",
"France",
"5",
"4",
"3",
"12"
],
[
"6",
"Switzerland",
"3",
"5",
"3",
"11"
],
[
"7",
"Finland",
"3",
"2",
"4",
"9"
],
[
"8",
"Japan",
"2",
"2",
"1",
"5"
],
[
"9",
"Netherlands",
"2",
"0",
"1",
"3"
],
[
"10",
"Sweden",
"1",
"2",
"2",
"5"
],
[
"11",
"Denmark",
"1",
"0",
"1",
"2"
],
[
"12",
"Unified Team",
"1",
"0",
"0",
"1"
],
[
"13",
"Austria",
"0",
"4",
"0",
"4"
],
[
"14",
"Slovakia",
"0",
"2",
"1",
"3"
],
[
"15",
"Italy",
"0",
"0",
"1",
"1"
],
[
"15",
"Poland",
"0",
"0",
"1",
"1"
],
[
"15",
"Canada",
"0",
"0",
"1",
"1"
],
[
"15",
"Belarus",
"0",
"0",
"1",
"1"
],
[
"15",
"United States",
"0",
"0",
"1",
"1"
],
[
"Total 19 nations",
"Total 19 nations",
"59",
"59",
"59",
"177"
]
] |
[
"Germany",
"Russia",
"Ukraine",
"Norway"
] |
which nations have won more than five gold medals? ||
|
nt-5722
|
col : rank | nation | gold | silver | bronze | total row 1 : 1 | germany | 17 | 9 | 14 | 40 row 2 : 2 | russia | 12 | 16 | 8 | 36 row 3 : 3 | ukraine | 6 | 11 | 9 | 26 row 4 : 4 | norway | 6 | 2 | 7 | 15 row 5 : 5 | france | 5 | 4 | 3 | 12 row 6 : 6 | switzerland | 3 | 5 | 3 | 11 row 7 : 7 | finland | 3 | 2 | 4 | 9 row 8 : 8 | japan | 2 | 2 | 1 | 5 row 9 : 9 | netherlands | 2 | 0 | 1 | 3 row 10 : 10 | sweden | 1 | 2 | 2 | 5 row 11 : 11 | denmark | 1 | 0 | 1 | 2 row 12 : 12 | unified team | 1 | 0 | 0 | 1 row 13 : 13 | austria | 0 | 4 | 0 | 4 row 14 : 14 | slovakia | 0 | 2 | 1 | 3 row 15 : 15 | italy | 0 | 0 | 1 | 1 row 16 : 15 | poland | 0 | 0 | 1 | 1 row 17 : 15 | canada | 0 | 0 | 1 | 1 row 18 : 15 | belarus | 0 | 0 | 1 | 1 row 19 : 15 | united states | 0 | 0 | 1 | 1 row 20 : total 19 nations | total 19 nations | 59 | 59 | 59 | 177
|
table_csv/204_761.csv
| 1 |
{
"column_index": [
1,
1,
1
],
"row_index": [
0,
1,
2
]
}
|
[
"which nations have won more than five gold medals?",
"of these nations, which have won more than five silver medals?"
] | 2 |
germany, russia, ukraine
|
[
"Rank",
"Nation",
"Gold",
"Silver",
"Bronze",
"Total"
] |
of these nations, which have won more than five silver medals?
|
[
[
"1",
"Germany",
"17",
"9",
"14",
"40"
],
[
"2",
"Russia",
"12",
"16",
"8",
"36"
],
[
"3",
"Ukraine",
"6",
"11",
"9",
"26"
],
[
"4",
"Norway",
"6",
"2",
"7",
"15"
],
[
"5",
"France",
"5",
"4",
"3",
"12"
],
[
"6",
"Switzerland",
"3",
"5",
"3",
"11"
],
[
"7",
"Finland",
"3",
"2",
"4",
"9"
],
[
"8",
"Japan",
"2",
"2",
"1",
"5"
],
[
"9",
"Netherlands",
"2",
"0",
"1",
"3"
],
[
"10",
"Sweden",
"1",
"2",
"2",
"5"
],
[
"11",
"Denmark",
"1",
"0",
"1",
"2"
],
[
"12",
"Unified Team",
"1",
"0",
"0",
"1"
],
[
"13",
"Austria",
"0",
"4",
"0",
"4"
],
[
"14",
"Slovakia",
"0",
"2",
"1",
"3"
],
[
"15",
"Italy",
"0",
"0",
"1",
"1"
],
[
"15",
"Poland",
"0",
"0",
"1",
"1"
],
[
"15",
"Canada",
"0",
"0",
"1",
"1"
],
[
"15",
"Belarus",
"0",
"0",
"1",
"1"
],
[
"15",
"United States",
"0",
"0",
"1",
"1"
],
[
"Total 19 nations",
"Total 19 nations",
"59",
"59",
"59",
"177"
]
] |
[
"Germany",
"Russia",
"Ukraine"
] |
of these nations, which have won more than five silver medals? || which nations have won more than five gold medals?
|
nt-5722
|
col : rank | nation | gold | silver | bronze | total row 1 : 1 | germany | 17 | 9 | 14 | 40 row 2 : 2 | russia | 12 | 16 | 8 | 36 row 3 : 3 | ukraine | 6 | 11 | 9 | 26 row 4 : 4 | norway | 6 | 2 | 7 | 15 row 5 : 5 | france | 5 | 4 | 3 | 12 row 6 : 6 | switzerland | 3 | 5 | 3 | 11 row 7 : 7 | finland | 3 | 2 | 4 | 9 row 8 : 8 | japan | 2 | 2 | 1 | 5 row 9 : 9 | netherlands | 2 | 0 | 1 | 3 row 10 : 10 | sweden | 1 | 2 | 2 | 5 row 11 : 11 | denmark | 1 | 0 | 1 | 2 row 12 : 12 | unified team | 1 | 0 | 0 | 1 row 13 : 13 | austria | 0 | 4 | 0 | 4 row 14 : 14 | slovakia | 0 | 2 | 1 | 3 row 15 : 15 | italy | 0 | 0 | 1 | 1 row 16 : 15 | poland | 0 | 0 | 1 | 1 row 17 : 15 | canada | 0 | 0 | 1 | 1 row 18 : 15 | belarus | 0 | 0 | 1 | 1 row 19 : 15 | united states | 0 | 0 | 1 | 1 row 20 : total 19 nations | total 19 nations | 59 | 59 | 59 | 177
|
table_csv/204_761.csv
| 2 |
{
"column_index": [
1
],
"row_index": [
0
]
}
|
[
"which nations have won more than five gold medals?",
"of these nations, which have won more than five silver medals?",
"of the remaining nations, which has won more than ten bronze medals?"
] | 2 |
germany
|
[
"Rank",
"Nation",
"Gold",
"Silver",
"Bronze",
"Total"
] |
of the remaining nations, which has won more than ten bronze medals?
|
[
[
"1",
"Germany",
"17",
"9",
"14",
"40"
],
[
"2",
"Russia",
"12",
"16",
"8",
"36"
],
[
"3",
"Ukraine",
"6",
"11",
"9",
"26"
],
[
"4",
"Norway",
"6",
"2",
"7",
"15"
],
[
"5",
"France",
"5",
"4",
"3",
"12"
],
[
"6",
"Switzerland",
"3",
"5",
"3",
"11"
],
[
"7",
"Finland",
"3",
"2",
"4",
"9"
],
[
"8",
"Japan",
"2",
"2",
"1",
"5"
],
[
"9",
"Netherlands",
"2",
"0",
"1",
"3"
],
[
"10",
"Sweden",
"1",
"2",
"2",
"5"
],
[
"11",
"Denmark",
"1",
"0",
"1",
"2"
],
[
"12",
"Unified Team",
"1",
"0",
"0",
"1"
],
[
"13",
"Austria",
"0",
"4",
"0",
"4"
],
[
"14",
"Slovakia",
"0",
"2",
"1",
"3"
],
[
"15",
"Italy",
"0",
"0",
"1",
"1"
],
[
"15",
"Poland",
"0",
"0",
"1",
"1"
],
[
"15",
"Canada",
"0",
"0",
"1",
"1"
],
[
"15",
"Belarus",
"0",
"0",
"1",
"1"
],
[
"15",
"United States",
"0",
"0",
"1",
"1"
],
[
"Total 19 nations",
"Total 19 nations",
"59",
"59",
"59",
"177"
]
] |
[
"Germany"
] |
of the remaining nations, which has won more than ten bronze medals? || of these nations, which have won more than five silver medals? | which nations have won more than five gold medals?
|
ns-3453
|
col : issue | volume | title | main feature story | first/early appearance story/stories | release date row 1 : 1 | 24 | the avengers | ultron unlimited (avengers vol 3 #0 and #19 | the coming of the avengers (avengers vol 1 #1) | 27 dec 2013 row 2 : 2 | 12 | spider-man | happy birthday (amazing spider-man vol 2 #57-58 | spider-man (amazing fantasy #15) the sinister six ( | 15 jan 2014 row 3 : 3 | 55 | wolverine | get mystique (wolverine vol. 3 #62 | and now... the wolverine (incredible hulk | 29 jan 2014 row 4 : 4 | 29 | hawkeye | hawkeye (hawkeye vol.1 #1-4). | hawkeye, the marskman (tales of suspense | 12 feb 2014 row 5 : 5 | 10 | the hulk | dogs of war (the incredible hulk volume 2 #12-20 | none | 26 feb 2014 row 6 : 6 | 22 | jean grey | here comes yesterday (all-new x-men #1- | x-men origins: jean grey | 12 mar 2014 row 7 : 7 | 49 | power man | power man and iron fist #50-53 | luke cage #1-3 | 26 mar 2014 row 8 : 8 | tbc | captain america | captain america #247-255 | ? | 9 apr 2014 row 9 : 9 | tbc | iron man | the five nightmares (iron man (vol. | tales of suspense #39 | 23 apr 2014 row 10 : 10 | tbc | the x-men | x-men: children of the atom #1-6 | x-men (vol. 1) #1 | 7 may 2014 row 11 : 11 | tbc | black widow | tbc | tbc | 21 may 2014 row 12 : 12 | tbc | the human torch (jim hammond) | tbc | tbc | 4 june 2014 row 13 : 13 | tbc | warriors three | tbc | tbc | 18 june 2014 row 14 : 14 | tbc | cyclops | tbc | tbc | 2 july 2014 row 15 : 15 | tbc | captain marvel | tbc | tbc | 16 july 2014
|
table_csv/204_696.csv
| 0 |
{
"column_index": [
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14
]
}
|
[
"what are all of the novel titles?"
] | 0 |
the avengers, spider-man, wolverine, hawkeye, the hulk, jean grey, power man, captain america, iron man, the x-men, black widow, the human torch (jim hammond), warriors three, cyclops, captain marvel
|
[
"Issue",
"Volume",
"Title",
"Main Feature Story",
"First/Early Appearance Story/Stories",
"Release Date"
] |
what are all of the novel titles?
|
[
[
"1",
"24",
"The Avengers",
"Ultron Unlimited (Avengers Vol 3 #0 and #19",
"The Coming Of The Avengers (Avengers Vol 1 #1)",
"27 Dec 2013"
],
[
"2",
"12",
"Spider-Man",
"Happy Birthday (Amazing Spider-Man Vol 2 #57-58",
"Spider-Man (Amazing Fantasy #15) The Sinister Six (",
"15 Jan 2014"
],
[
"3",
"55",
"Wolverine",
"Get Mystique (Wolverine Vol. 3 #62",
"And Now... The Wolverine (Incredible Hulk",
"29 Jan 2014"
],
[
"4",
"29",
"Hawkeye",
"Hawkeye (Hawkeye Vol.1 #1-4).",
"Hawkeye, The Marskman (Tales of Suspense",
"12 Feb 2014"
],
[
"5",
"10",
"The Hulk",
"Dogs Of War (The Incredible Hulk volume 2 #12-20",
"None",
"26 Feb 2014"
],
[
"6",
"22",
"Jean Grey",
"Here Comes Yesterday (All-New X-Men #1-",
"X-Men Origins: Jean Grey",
"12 Mar 2014"
],
[
"7",
"49",
"Power Man",
"Power Man and Iron Fist #50-53",
"Luke Cage #1-3",
"26 Mar 2014"
],
[
"8",
"TBC",
"Captain America",
"Captain America #247-255",
"?",
"9 Apr 2014"
],
[
"9",
"TBC",
"Iron Man",
"The Five Nightmares (Iron Man (Vol.",
"Tales of Suspense #39",
"23 Apr 2014"
],
[
"10",
"TBC",
"The X-Men",
"X-Men: Children of the Atom #1-6",
"X-Men (Vol. 1) #1",
"7 May 2014"
],
[
"11",
"TBC",
"Black Widow",
"TBC",
"TBC",
"21 May 2014"
],
[
"12",
"TBC",
"The Human Torch (Jim Hammond)",
"TBC",
"TBC",
"4 June 2014"
],
[
"13",
"TBC",
"Warriors Three",
"TBC",
"TBC",
"18 June 2014"
],
[
"14",
"TBC",
"Cyclops",
"TBC",
"TBC",
"2 July 2014"
],
[
"15",
"TBC",
"Captain Marvel",
"TBC",
"TBC",
"16 July 2014"
]
] |
[
"The Avengers",
"Spider-Man",
"Wolverine",
"Hawkeye",
"The Hulk",
"Jean Grey",
"Power Man",
"Captain America",
"Iron Man",
"The X-Men",
"Black Widow",
"The Human Torch (Jim Hammond)",
"Warriors Three",
"Cyclops",
"Captain Marvel"
] |
what are all of the novel titles? ||
|
ns-3453
|
col : issue | volume | title | main feature story | first/early appearance story/stories | release date row 1 : 1 | 24 | the avengers | ultron unlimited (avengers vol 3 #0 and #19 | the coming of the avengers (avengers vol 1 #1) | 27 dec 2013 row 2 : 2 | 12 | spider-man | happy birthday (amazing spider-man vol 2 #57-58 | spider-man (amazing fantasy #15) the sinister six ( | 15 jan 2014 row 3 : 3 | 55 | wolverine | get mystique (wolverine vol. 3 #62 | and now... the wolverine (incredible hulk | 29 jan 2014 row 4 : 4 | 29 | hawkeye | hawkeye (hawkeye vol.1 #1-4). | hawkeye, the marskman (tales of suspense | 12 feb 2014 row 5 : 5 | 10 | the hulk | dogs of war (the incredible hulk volume 2 #12-20 | none | 26 feb 2014 row 6 : 6 | 22 | jean grey | here comes yesterday (all-new x-men #1- | x-men origins: jean grey | 12 mar 2014 row 7 : 7 | 49 | power man | power man and iron fist #50-53 | luke cage #1-3 | 26 mar 2014 row 8 : 8 | tbc | captain america | captain america #247-255 | ? | 9 apr 2014 row 9 : 9 | tbc | iron man | the five nightmares (iron man (vol. | tales of suspense #39 | 23 apr 2014 row 10 : 10 | tbc | the x-men | x-men: children of the atom #1-6 | x-men (vol. 1) #1 | 7 may 2014 row 11 : 11 | tbc | black widow | tbc | tbc | 21 may 2014 row 12 : 12 | tbc | the human torch (jim hammond) | tbc | tbc | 4 june 2014 row 13 : 13 | tbc | warriors three | tbc | tbc | 18 june 2014 row 14 : 14 | tbc | cyclops | tbc | tbc | 2 july 2014 row 15 : 15 | tbc | captain marvel | tbc | tbc | 16 july 2014
|
table_csv/204_696.csv
| 1 |
{
"column_index": [
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14
]
}
|
[
"what are all of the novel titles?",
"when were they released?"
] | 0 |
27 dec 2013, 15 jan 2014, 29 jan 2014, 12 feb 2014, 26 feb 2014, 12 mar 2014, 26 mar 2014, 9 apr 2014, 23 apr 2014, 7 may 2014, 21 may 2014, 4 june 2014, 18 june 2014, 2 july 2014, 16 july 2014
|
[
"Issue",
"Volume",
"Title",
"Main Feature Story",
"First/Early Appearance Story/Stories",
"Release Date"
] |
when were they released?
|
[
[
"1",
"24",
"The Avengers",
"Ultron Unlimited (Avengers Vol 3 #0 and #19",
"The Coming Of The Avengers (Avengers Vol 1 #1)",
"27 Dec 2013"
],
[
"2",
"12",
"Spider-Man",
"Happy Birthday (Amazing Spider-Man Vol 2 #57-58",
"Spider-Man (Amazing Fantasy #15) The Sinister Six (",
"15 Jan 2014"
],
[
"3",
"55",
"Wolverine",
"Get Mystique (Wolverine Vol. 3 #62",
"And Now... The Wolverine (Incredible Hulk",
"29 Jan 2014"
],
[
"4",
"29",
"Hawkeye",
"Hawkeye (Hawkeye Vol.1 #1-4).",
"Hawkeye, The Marskman (Tales of Suspense",
"12 Feb 2014"
],
[
"5",
"10",
"The Hulk",
"Dogs Of War (The Incredible Hulk volume 2 #12-20",
"None",
"26 Feb 2014"
],
[
"6",
"22",
"Jean Grey",
"Here Comes Yesterday (All-New X-Men #1-",
"X-Men Origins: Jean Grey",
"12 Mar 2014"
],
[
"7",
"49",
"Power Man",
"Power Man and Iron Fist #50-53",
"Luke Cage #1-3",
"26 Mar 2014"
],
[
"8",
"TBC",
"Captain America",
"Captain America #247-255",
"?",
"9 Apr 2014"
],
[
"9",
"TBC",
"Iron Man",
"The Five Nightmares (Iron Man (Vol.",
"Tales of Suspense #39",
"23 Apr 2014"
],
[
"10",
"TBC",
"The X-Men",
"X-Men: Children of the Atom #1-6",
"X-Men (Vol. 1) #1",
"7 May 2014"
],
[
"11",
"TBC",
"Black Widow",
"TBC",
"TBC",
"21 May 2014"
],
[
"12",
"TBC",
"The Human Torch (Jim Hammond)",
"TBC",
"TBC",
"4 June 2014"
],
[
"13",
"TBC",
"Warriors Three",
"TBC",
"TBC",
"18 June 2014"
],
[
"14",
"TBC",
"Cyclops",
"TBC",
"TBC",
"2 July 2014"
],
[
"15",
"TBC",
"Captain Marvel",
"TBC",
"TBC",
"16 July 2014"
]
] |
[
"27 Dec 2013",
"15 Jan 2014",
"29 Jan 2014",
"12 Feb 2014",
"26 Feb 2014",
"12 Mar 2014",
"26 Mar 2014",
"9 Apr 2014",
"23 Apr 2014",
"7 May 2014",
"21 May 2014",
"4 June 2014",
"18 June 2014",
"2 July 2014",
"16 July 2014"
] |
when were they released? || what are all of the novel titles?
|
ns-3453
|
col : issue | volume | title | main feature story | first/early appearance story/stories | release date row 1 : 1 | 24 | the avengers | ultron unlimited (avengers vol 3 #0 and #19 | the coming of the avengers (avengers vol 1 #1) | 27 dec 2013 row 2 : 2 | 12 | spider-man | happy birthday (amazing spider-man vol 2 #57-58 | spider-man (amazing fantasy #15) the sinister six ( | 15 jan 2014 row 3 : 3 | 55 | wolverine | get mystique (wolverine vol. 3 #62 | and now... the wolverine (incredible hulk | 29 jan 2014 row 4 : 4 | 29 | hawkeye | hawkeye (hawkeye vol.1 #1-4). | hawkeye, the marskman (tales of suspense | 12 feb 2014 row 5 : 5 | 10 | the hulk | dogs of war (the incredible hulk volume 2 #12-20 | none | 26 feb 2014 row 6 : 6 | 22 | jean grey | here comes yesterday (all-new x-men #1- | x-men origins: jean grey | 12 mar 2014 row 7 : 7 | 49 | power man | power man and iron fist #50-53 | luke cage #1-3 | 26 mar 2014 row 8 : 8 | tbc | captain america | captain america #247-255 | ? | 9 apr 2014 row 9 : 9 | tbc | iron man | the five nightmares (iron man (vol. | tales of suspense #39 | 23 apr 2014 row 10 : 10 | tbc | the x-men | x-men: children of the atom #1-6 | x-men (vol. 1) #1 | 7 may 2014 row 11 : 11 | tbc | black widow | tbc | tbc | 21 may 2014 row 12 : 12 | tbc | the human torch (jim hammond) | tbc | tbc | 4 june 2014 row 13 : 13 | tbc | warriors three | tbc | tbc | 18 june 2014 row 14 : 14 | tbc | cyclops | tbc | tbc | 2 july 2014 row 15 : 15 | tbc | captain marvel | tbc | tbc | 16 july 2014
|
table_csv/204_696.csv
| 2 |
{
"column_index": [
5,
5
],
"row_index": [
7,
9
]
}
|
[
"what are all of the novel titles?",
"when were they released?",
"what about just x-men and captain america?"
] | 0 |
9 apr 2014, 7 may 2014
|
[
"Issue",
"Volume",
"Title",
"Main Feature Story",
"First/Early Appearance Story/Stories",
"Release Date"
] |
what about just x-men and captain america?
|
[
[
"1",
"24",
"The Avengers",
"Ultron Unlimited (Avengers Vol 3 #0 and #19",
"The Coming Of The Avengers (Avengers Vol 1 #1)",
"27 Dec 2013"
],
[
"2",
"12",
"Spider-Man",
"Happy Birthday (Amazing Spider-Man Vol 2 #57-58",
"Spider-Man (Amazing Fantasy #15) The Sinister Six (",
"15 Jan 2014"
],
[
"3",
"55",
"Wolverine",
"Get Mystique (Wolverine Vol. 3 #62",
"And Now... The Wolverine (Incredible Hulk",
"29 Jan 2014"
],
[
"4",
"29",
"Hawkeye",
"Hawkeye (Hawkeye Vol.1 #1-4).",
"Hawkeye, The Marskman (Tales of Suspense",
"12 Feb 2014"
],
[
"5",
"10",
"The Hulk",
"Dogs Of War (The Incredible Hulk volume 2 #12-20",
"None",
"26 Feb 2014"
],
[
"6",
"22",
"Jean Grey",
"Here Comes Yesterday (All-New X-Men #1-",
"X-Men Origins: Jean Grey",
"12 Mar 2014"
],
[
"7",
"49",
"Power Man",
"Power Man and Iron Fist #50-53",
"Luke Cage #1-3",
"26 Mar 2014"
],
[
"8",
"TBC",
"Captain America",
"Captain America #247-255",
"?",
"9 Apr 2014"
],
[
"9",
"TBC",
"Iron Man",
"The Five Nightmares (Iron Man (Vol.",
"Tales of Suspense #39",
"23 Apr 2014"
],
[
"10",
"TBC",
"The X-Men",
"X-Men: Children of the Atom #1-6",
"X-Men (Vol. 1) #1",
"7 May 2014"
],
[
"11",
"TBC",
"Black Widow",
"TBC",
"TBC",
"21 May 2014"
],
[
"12",
"TBC",
"The Human Torch (Jim Hammond)",
"TBC",
"TBC",
"4 June 2014"
],
[
"13",
"TBC",
"Warriors Three",
"TBC",
"TBC",
"18 June 2014"
],
[
"14",
"TBC",
"Cyclops",
"TBC",
"TBC",
"2 July 2014"
],
[
"15",
"TBC",
"Captain Marvel",
"TBC",
"TBC",
"16 July 2014"
]
] |
[
"9 Apr 2014",
"7 May 2014"
] |
what about just x-men and captain america? || when were they released? | what are all of the novel titles?
|
ns-3453
|
col : issue | volume | title | main feature story | first/early appearance story/stories | release date row 1 : 1 | 24 | the avengers | ultron unlimited (avengers vol 3 #0 and #19 | the coming of the avengers (avengers vol 1 #1) | 27 dec 2013 row 2 : 2 | 12 | spider-man | happy birthday (amazing spider-man vol 2 #57-58 | spider-man (amazing fantasy #15) the sinister six ( | 15 jan 2014 row 3 : 3 | 55 | wolverine | get mystique (wolverine vol. 3 #62 | and now... the wolverine (incredible hulk | 29 jan 2014 row 4 : 4 | 29 | hawkeye | hawkeye (hawkeye vol.1 #1-4). | hawkeye, the marskman (tales of suspense | 12 feb 2014 row 5 : 5 | 10 | the hulk | dogs of war (the incredible hulk volume 2 #12-20 | none | 26 feb 2014 row 6 : 6 | 22 | jean grey | here comes yesterday (all-new x-men #1- | x-men origins: jean grey | 12 mar 2014 row 7 : 7 | 49 | power man | power man and iron fist #50-53 | luke cage #1-3 | 26 mar 2014 row 8 : 8 | tbc | captain america | captain america #247-255 | ? | 9 apr 2014 row 9 : 9 | tbc | iron man | the five nightmares (iron man (vol. | tales of suspense #39 | 23 apr 2014 row 10 : 10 | tbc | the x-men | x-men: children of the atom #1-6 | x-men (vol. 1) #1 | 7 may 2014 row 11 : 11 | tbc | black widow | tbc | tbc | 21 may 2014 row 12 : 12 | tbc | the human torch (jim hammond) | tbc | tbc | 4 june 2014 row 13 : 13 | tbc | warriors three | tbc | tbc | 18 june 2014 row 14 : 14 | tbc | cyclops | tbc | tbc | 2 july 2014 row 15 : 15 | tbc | captain marvel | tbc | tbc | 16 july 2014
|
table_csv/204_696.csv
| 3 |
{
"column_index": [
2
],
"row_index": [
7
]
}
|
[
"what are all of the novel titles?",
"when were they released?",
"what about just x-men and captain america?",
"which was released first?"
] | 0 |
captain america
|
[
"Issue",
"Volume",
"Title",
"Main Feature Story",
"First/Early Appearance Story/Stories",
"Release Date"
] |
which was released first?
|
[
[
"1",
"24",
"The Avengers",
"Ultron Unlimited (Avengers Vol 3 #0 and #19",
"The Coming Of The Avengers (Avengers Vol 1 #1)",
"27 Dec 2013"
],
[
"2",
"12",
"Spider-Man",
"Happy Birthday (Amazing Spider-Man Vol 2 #57-58",
"Spider-Man (Amazing Fantasy #15) The Sinister Six (",
"15 Jan 2014"
],
[
"3",
"55",
"Wolverine",
"Get Mystique (Wolverine Vol. 3 #62",
"And Now... The Wolverine (Incredible Hulk",
"29 Jan 2014"
],
[
"4",
"29",
"Hawkeye",
"Hawkeye (Hawkeye Vol.1 #1-4).",
"Hawkeye, The Marskman (Tales of Suspense",
"12 Feb 2014"
],
[
"5",
"10",
"The Hulk",
"Dogs Of War (The Incredible Hulk volume 2 #12-20",
"None",
"26 Feb 2014"
],
[
"6",
"22",
"Jean Grey",
"Here Comes Yesterday (All-New X-Men #1-",
"X-Men Origins: Jean Grey",
"12 Mar 2014"
],
[
"7",
"49",
"Power Man",
"Power Man and Iron Fist #50-53",
"Luke Cage #1-3",
"26 Mar 2014"
],
[
"8",
"TBC",
"Captain America",
"Captain America #247-255",
"?",
"9 Apr 2014"
],
[
"9",
"TBC",
"Iron Man",
"The Five Nightmares (Iron Man (Vol.",
"Tales of Suspense #39",
"23 Apr 2014"
],
[
"10",
"TBC",
"The X-Men",
"X-Men: Children of the Atom #1-6",
"X-Men (Vol. 1) #1",
"7 May 2014"
],
[
"11",
"TBC",
"Black Widow",
"TBC",
"TBC",
"21 May 2014"
],
[
"12",
"TBC",
"The Human Torch (Jim Hammond)",
"TBC",
"TBC",
"4 June 2014"
],
[
"13",
"TBC",
"Warriors Three",
"TBC",
"TBC",
"18 June 2014"
],
[
"14",
"TBC",
"Cyclops",
"TBC",
"TBC",
"2 July 2014"
],
[
"15",
"TBC",
"Captain Marvel",
"TBC",
"TBC",
"16 July 2014"
]
] |
[
"Captain America"
] |
which was released first? || what about just x-men and captain america? | when were they released? | what are all of the novel titles?
|
ns-3453
|
col : issue | volume | title | main feature story | first/early appearance story/stories | release date row 1 : 1 | 24 | the avengers | ultron unlimited (avengers vol 3 #0 and #19 | the coming of the avengers (avengers vol 1 #1) | 27 dec 2013 row 2 : 2 | 12 | spider-man | happy birthday (amazing spider-man vol 2 #57-58 | spider-man (amazing fantasy #15) the sinister six ( | 15 jan 2014 row 3 : 3 | 55 | wolverine | get mystique (wolverine vol. 3 #62 | and now... the wolverine (incredible hulk | 29 jan 2014 row 4 : 4 | 29 | hawkeye | hawkeye (hawkeye vol.1 #1-4). | hawkeye, the marskman (tales of suspense | 12 feb 2014 row 5 : 5 | 10 | the hulk | dogs of war (the incredible hulk volume 2 #12-20 | none | 26 feb 2014 row 6 : 6 | 22 | jean grey | here comes yesterday (all-new x-men #1- | x-men origins: jean grey | 12 mar 2014 row 7 : 7 | 49 | power man | power man and iron fist #50-53 | luke cage #1-3 | 26 mar 2014 row 8 : 8 | tbc | captain america | captain america #247-255 | ? | 9 apr 2014 row 9 : 9 | tbc | iron man | the five nightmares (iron man (vol. | tales of suspense #39 | 23 apr 2014 row 10 : 10 | tbc | the x-men | x-men: children of the atom #1-6 | x-men (vol. 1) #1 | 7 may 2014 row 11 : 11 | tbc | black widow | tbc | tbc | 21 may 2014 row 12 : 12 | tbc | the human torch (jim hammond) | tbc | tbc | 4 june 2014 row 13 : 13 | tbc | warriors three | tbc | tbc | 18 june 2014 row 14 : 14 | tbc | cyclops | tbc | tbc | 2 july 2014 row 15 : 15 | tbc | captain marvel | tbc | tbc | 16 july 2014
|
table_csv/204_696.csv
| 0 |
{
"column_index": [
5
],
"row_index": [
9
]
}
|
[
"when was x-men released?"
] | 1 |
7 may 2014
|
[
"Issue",
"Volume",
"Title",
"Main Feature Story",
"First/Early Appearance Story/Stories",
"Release Date"
] |
when was x-men released?
|
[
[
"1",
"24",
"The Avengers",
"Ultron Unlimited (Avengers Vol 3 #0 and #19",
"The Coming Of The Avengers (Avengers Vol 1 #1)",
"27 Dec 2013"
],
[
"2",
"12",
"Spider-Man",
"Happy Birthday (Amazing Spider-Man Vol 2 #57-58",
"Spider-Man (Amazing Fantasy #15) The Sinister Six (",
"15 Jan 2014"
],
[
"3",
"55",
"Wolverine",
"Get Mystique (Wolverine Vol. 3 #62",
"And Now... The Wolverine (Incredible Hulk",
"29 Jan 2014"
],
[
"4",
"29",
"Hawkeye",
"Hawkeye (Hawkeye Vol.1 #1-4).",
"Hawkeye, The Marskman (Tales of Suspense",
"12 Feb 2014"
],
[
"5",
"10",
"The Hulk",
"Dogs Of War (The Incredible Hulk volume 2 #12-20",
"None",
"26 Feb 2014"
],
[
"6",
"22",
"Jean Grey",
"Here Comes Yesterday (All-New X-Men #1-",
"X-Men Origins: Jean Grey",
"12 Mar 2014"
],
[
"7",
"49",
"Power Man",
"Power Man and Iron Fist #50-53",
"Luke Cage #1-3",
"26 Mar 2014"
],
[
"8",
"TBC",
"Captain America",
"Captain America #247-255",
"?",
"9 Apr 2014"
],
[
"9",
"TBC",
"Iron Man",
"The Five Nightmares (Iron Man (Vol.",
"Tales of Suspense #39",
"23 Apr 2014"
],
[
"10",
"TBC",
"The X-Men",
"X-Men: Children of the Atom #1-6",
"X-Men (Vol. 1) #1",
"7 May 2014"
],
[
"11",
"TBC",
"Black Widow",
"TBC",
"TBC",
"21 May 2014"
],
[
"12",
"TBC",
"The Human Torch (Jim Hammond)",
"TBC",
"TBC",
"4 June 2014"
],
[
"13",
"TBC",
"Warriors Three",
"TBC",
"TBC",
"18 June 2014"
],
[
"14",
"TBC",
"Cyclops",
"TBC",
"TBC",
"2 July 2014"
],
[
"15",
"TBC",
"Captain Marvel",
"TBC",
"TBC",
"16 July 2014"
]
] |
[
"7 May 2014"
] |
when was x-men released? ||
|
ns-3453
|
col : issue | volume | title | main feature story | first/early appearance story/stories | release date row 1 : 1 | 24 | the avengers | ultron unlimited (avengers vol 3 #0 and #19 | the coming of the avengers (avengers vol 1 #1) | 27 dec 2013 row 2 : 2 | 12 | spider-man | happy birthday (amazing spider-man vol 2 #57-58 | spider-man (amazing fantasy #15) the sinister six ( | 15 jan 2014 row 3 : 3 | 55 | wolverine | get mystique (wolverine vol. 3 #62 | and now... the wolverine (incredible hulk | 29 jan 2014 row 4 : 4 | 29 | hawkeye | hawkeye (hawkeye vol.1 #1-4). | hawkeye, the marskman (tales of suspense | 12 feb 2014 row 5 : 5 | 10 | the hulk | dogs of war (the incredible hulk volume 2 #12-20 | none | 26 feb 2014 row 6 : 6 | 22 | jean grey | here comes yesterday (all-new x-men #1- | x-men origins: jean grey | 12 mar 2014 row 7 : 7 | 49 | power man | power man and iron fist #50-53 | luke cage #1-3 | 26 mar 2014 row 8 : 8 | tbc | captain america | captain america #247-255 | ? | 9 apr 2014 row 9 : 9 | tbc | iron man | the five nightmares (iron man (vol. | tales of suspense #39 | 23 apr 2014 row 10 : 10 | tbc | the x-men | x-men: children of the atom #1-6 | x-men (vol. 1) #1 | 7 may 2014 row 11 : 11 | tbc | black widow | tbc | tbc | 21 may 2014 row 12 : 12 | tbc | the human torch (jim hammond) | tbc | tbc | 4 june 2014 row 13 : 13 | tbc | warriors three | tbc | tbc | 18 june 2014 row 14 : 14 | tbc | cyclops | tbc | tbc | 2 july 2014 row 15 : 15 | tbc | captain marvel | tbc | tbc | 16 july 2014
|
table_csv/204_696.csv
| 1 |
{
"column_index": [
5
],
"row_index": [
7
]
}
|
[
"when was x-men released?",
"when was captain america released?"
] | 1 |
9 apr 2014
|
[
"Issue",
"Volume",
"Title",
"Main Feature Story",
"First/Early Appearance Story/Stories",
"Release Date"
] |
when was captain america released?
|
[
[
"1",
"24",
"The Avengers",
"Ultron Unlimited (Avengers Vol 3 #0 and #19",
"The Coming Of The Avengers (Avengers Vol 1 #1)",
"27 Dec 2013"
],
[
"2",
"12",
"Spider-Man",
"Happy Birthday (Amazing Spider-Man Vol 2 #57-58",
"Spider-Man (Amazing Fantasy #15) The Sinister Six (",
"15 Jan 2014"
],
[
"3",
"55",
"Wolverine",
"Get Mystique (Wolverine Vol. 3 #62",
"And Now... The Wolverine (Incredible Hulk",
"29 Jan 2014"
],
[
"4",
"29",
"Hawkeye",
"Hawkeye (Hawkeye Vol.1 #1-4).",
"Hawkeye, The Marskman (Tales of Suspense",
"12 Feb 2014"
],
[
"5",
"10",
"The Hulk",
"Dogs Of War (The Incredible Hulk volume 2 #12-20",
"None",
"26 Feb 2014"
],
[
"6",
"22",
"Jean Grey",
"Here Comes Yesterday (All-New X-Men #1-",
"X-Men Origins: Jean Grey",
"12 Mar 2014"
],
[
"7",
"49",
"Power Man",
"Power Man and Iron Fist #50-53",
"Luke Cage #1-3",
"26 Mar 2014"
],
[
"8",
"TBC",
"Captain America",
"Captain America #247-255",
"?",
"9 Apr 2014"
],
[
"9",
"TBC",
"Iron Man",
"The Five Nightmares (Iron Man (Vol.",
"Tales of Suspense #39",
"23 Apr 2014"
],
[
"10",
"TBC",
"The X-Men",
"X-Men: Children of the Atom #1-6",
"X-Men (Vol. 1) #1",
"7 May 2014"
],
[
"11",
"TBC",
"Black Widow",
"TBC",
"TBC",
"21 May 2014"
],
[
"12",
"TBC",
"The Human Torch (Jim Hammond)",
"TBC",
"TBC",
"4 June 2014"
],
[
"13",
"TBC",
"Warriors Three",
"TBC",
"TBC",
"18 June 2014"
],
[
"14",
"TBC",
"Cyclops",
"TBC",
"TBC",
"2 July 2014"
],
[
"15",
"TBC",
"Captain Marvel",
"TBC",
"TBC",
"16 July 2014"
]
] |
[
"9 Apr 2014"
] |
when was captain america released? || when was x-men released?
|
ns-3453
|
col : issue | volume | title | main feature story | first/early appearance story/stories | release date row 1 : 1 | 24 | the avengers | ultron unlimited (avengers vol 3 #0 and #19 | the coming of the avengers (avengers vol 1 #1) | 27 dec 2013 row 2 : 2 | 12 | spider-man | happy birthday (amazing spider-man vol 2 #57-58 | spider-man (amazing fantasy #15) the sinister six ( | 15 jan 2014 row 3 : 3 | 55 | wolverine | get mystique (wolverine vol. 3 #62 | and now... the wolverine (incredible hulk | 29 jan 2014 row 4 : 4 | 29 | hawkeye | hawkeye (hawkeye vol.1 #1-4). | hawkeye, the marskman (tales of suspense | 12 feb 2014 row 5 : 5 | 10 | the hulk | dogs of war (the incredible hulk volume 2 #12-20 | none | 26 feb 2014 row 6 : 6 | 22 | jean grey | here comes yesterday (all-new x-men #1- | x-men origins: jean grey | 12 mar 2014 row 7 : 7 | 49 | power man | power man and iron fist #50-53 | luke cage #1-3 | 26 mar 2014 row 8 : 8 | tbc | captain america | captain america #247-255 | ? | 9 apr 2014 row 9 : 9 | tbc | iron man | the five nightmares (iron man (vol. | tales of suspense #39 | 23 apr 2014 row 10 : 10 | tbc | the x-men | x-men: children of the atom #1-6 | x-men (vol. 1) #1 | 7 may 2014 row 11 : 11 | tbc | black widow | tbc | tbc | 21 may 2014 row 12 : 12 | tbc | the human torch (jim hammond) | tbc | tbc | 4 june 2014 row 13 : 13 | tbc | warriors three | tbc | tbc | 18 june 2014 row 14 : 14 | tbc | cyclops | tbc | tbc | 2 july 2014 row 15 : 15 | tbc | captain marvel | tbc | tbc | 16 july 2014
|
table_csv/204_696.csv
| 2 |
{
"column_index": [
2
],
"row_index": [
7
]
}
|
[
"when was x-men released?",
"when was captain america released?",
"which of these was released earlier?"
] | 1 |
captain america
|
[
"Issue",
"Volume",
"Title",
"Main Feature Story",
"First/Early Appearance Story/Stories",
"Release Date"
] |
which of these was released earlier?
|
[
[
"1",
"24",
"The Avengers",
"Ultron Unlimited (Avengers Vol 3 #0 and #19",
"The Coming Of The Avengers (Avengers Vol 1 #1)",
"27 Dec 2013"
],
[
"2",
"12",
"Spider-Man",
"Happy Birthday (Amazing Spider-Man Vol 2 #57-58",
"Spider-Man (Amazing Fantasy #15) The Sinister Six (",
"15 Jan 2014"
],
[
"3",
"55",
"Wolverine",
"Get Mystique (Wolverine Vol. 3 #62",
"And Now... The Wolverine (Incredible Hulk",
"29 Jan 2014"
],
[
"4",
"29",
"Hawkeye",
"Hawkeye (Hawkeye Vol.1 #1-4).",
"Hawkeye, The Marskman (Tales of Suspense",
"12 Feb 2014"
],
[
"5",
"10",
"The Hulk",
"Dogs Of War (The Incredible Hulk volume 2 #12-20",
"None",
"26 Feb 2014"
],
[
"6",
"22",
"Jean Grey",
"Here Comes Yesterday (All-New X-Men #1-",
"X-Men Origins: Jean Grey",
"12 Mar 2014"
],
[
"7",
"49",
"Power Man",
"Power Man and Iron Fist #50-53",
"Luke Cage #1-3",
"26 Mar 2014"
],
[
"8",
"TBC",
"Captain America",
"Captain America #247-255",
"?",
"9 Apr 2014"
],
[
"9",
"TBC",
"Iron Man",
"The Five Nightmares (Iron Man (Vol.",
"Tales of Suspense #39",
"23 Apr 2014"
],
[
"10",
"TBC",
"The X-Men",
"X-Men: Children of the Atom #1-6",
"X-Men (Vol. 1) #1",
"7 May 2014"
],
[
"11",
"TBC",
"Black Widow",
"TBC",
"TBC",
"21 May 2014"
],
[
"12",
"TBC",
"The Human Torch (Jim Hammond)",
"TBC",
"TBC",
"4 June 2014"
],
[
"13",
"TBC",
"Warriors Three",
"TBC",
"TBC",
"18 June 2014"
],
[
"14",
"TBC",
"Cyclops",
"TBC",
"TBC",
"2 July 2014"
],
[
"15",
"TBC",
"Captain Marvel",
"TBC",
"TBC",
"16 July 2014"
]
] |
[
"Captain America"
] |
which of these was released earlier? || when was captain america released? | when was x-men released?
|
ns-3453
|
col : issue | volume | title | main feature story | first/early appearance story/stories | release date row 1 : 1 | 24 | the avengers | ultron unlimited (avengers vol 3 #0 and #19 | the coming of the avengers (avengers vol 1 #1) | 27 dec 2013 row 2 : 2 | 12 | spider-man | happy birthday (amazing spider-man vol 2 #57-58 | spider-man (amazing fantasy #15) the sinister six ( | 15 jan 2014 row 3 : 3 | 55 | wolverine | get mystique (wolverine vol. 3 #62 | and now... the wolverine (incredible hulk | 29 jan 2014 row 4 : 4 | 29 | hawkeye | hawkeye (hawkeye vol.1 #1-4). | hawkeye, the marskman (tales of suspense | 12 feb 2014 row 5 : 5 | 10 | the hulk | dogs of war (the incredible hulk volume 2 #12-20 | none | 26 feb 2014 row 6 : 6 | 22 | jean grey | here comes yesterday (all-new x-men #1- | x-men origins: jean grey | 12 mar 2014 row 7 : 7 | 49 | power man | power man and iron fist #50-53 | luke cage #1-3 | 26 mar 2014 row 8 : 8 | tbc | captain america | captain america #247-255 | ? | 9 apr 2014 row 9 : 9 | tbc | iron man | the five nightmares (iron man (vol. | tales of suspense #39 | 23 apr 2014 row 10 : 10 | tbc | the x-men | x-men: children of the atom #1-6 | x-men (vol. 1) #1 | 7 may 2014 row 11 : 11 | tbc | black widow | tbc | tbc | 21 may 2014 row 12 : 12 | tbc | the human torch (jim hammond) | tbc | tbc | 4 june 2014 row 13 : 13 | tbc | warriors three | tbc | tbc | 18 june 2014 row 14 : 14 | tbc | cyclops | tbc | tbc | 2 july 2014 row 15 : 15 | tbc | captain marvel | tbc | tbc | 16 july 2014
|
table_csv/204_696.csv
| 0 |
{
"column_index": [
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2,
2
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14
]
}
|
[
"what are all of the x-men and captain america movies?"
] | 2 |
the avengers, spider-man, wolverine, hawkeye, the hulk, jean grey, power man, captain america, iron man, the x-men, black widow, the human torch (jim hammond), warriors three, cyclops, captain marvel
|
[
"Issue",
"Volume",
"Title",
"Main Feature Story",
"First/Early Appearance Story/Stories",
"Release Date"
] |
what are all of the x-men and captain america movies?
|
[
[
"1",
"24",
"The Avengers",
"Ultron Unlimited (Avengers Vol 3 #0 and #19",
"The Coming Of The Avengers (Avengers Vol 1 #1)",
"27 Dec 2013"
],
[
"2",
"12",
"Spider-Man",
"Happy Birthday (Amazing Spider-Man Vol 2 #57-58",
"Spider-Man (Amazing Fantasy #15) The Sinister Six (",
"15 Jan 2014"
],
[
"3",
"55",
"Wolverine",
"Get Mystique (Wolverine Vol. 3 #62",
"And Now... The Wolverine (Incredible Hulk",
"29 Jan 2014"
],
[
"4",
"29",
"Hawkeye",
"Hawkeye (Hawkeye Vol.1 #1-4).",
"Hawkeye, The Marskman (Tales of Suspense",
"12 Feb 2014"
],
[
"5",
"10",
"The Hulk",
"Dogs Of War (The Incredible Hulk volume 2 #12-20",
"None",
"26 Feb 2014"
],
[
"6",
"22",
"Jean Grey",
"Here Comes Yesterday (All-New X-Men #1-",
"X-Men Origins: Jean Grey",
"12 Mar 2014"
],
[
"7",
"49",
"Power Man",
"Power Man and Iron Fist #50-53",
"Luke Cage #1-3",
"26 Mar 2014"
],
[
"8",
"TBC",
"Captain America",
"Captain America #247-255",
"?",
"9 Apr 2014"
],
[
"9",
"TBC",
"Iron Man",
"The Five Nightmares (Iron Man (Vol.",
"Tales of Suspense #39",
"23 Apr 2014"
],
[
"10",
"TBC",
"The X-Men",
"X-Men: Children of the Atom #1-6",
"X-Men (Vol. 1) #1",
"7 May 2014"
],
[
"11",
"TBC",
"Black Widow",
"TBC",
"TBC",
"21 May 2014"
],
[
"12",
"TBC",
"The Human Torch (Jim Hammond)",
"TBC",
"TBC",
"4 June 2014"
],
[
"13",
"TBC",
"Warriors Three",
"TBC",
"TBC",
"18 June 2014"
],
[
"14",
"TBC",
"Cyclops",
"TBC",
"TBC",
"2 July 2014"
],
[
"15",
"TBC",
"Captain Marvel",
"TBC",
"TBC",
"16 July 2014"
]
] |
[
"The Avengers",
"Spider-Man",
"Wolverine",
"Hawkeye",
"The Hulk",
"Jean Grey",
"Power Man",
"Captain America",
"Iron Man",
"The X-Men",
"Black Widow",
"The Human Torch (Jim Hammond)",
"Warriors Three",
"Cyclops",
"Captain Marvel"
] |
what are all of the x-men and captain america movies? ||
|
ns-3453
|
col : issue | volume | title | main feature story | first/early appearance story/stories | release date row 1 : 1 | 24 | the avengers | ultron unlimited (avengers vol 3 #0 and #19 | the coming of the avengers (avengers vol 1 #1) | 27 dec 2013 row 2 : 2 | 12 | spider-man | happy birthday (amazing spider-man vol 2 #57-58 | spider-man (amazing fantasy #15) the sinister six ( | 15 jan 2014 row 3 : 3 | 55 | wolverine | get mystique (wolverine vol. 3 #62 | and now... the wolverine (incredible hulk | 29 jan 2014 row 4 : 4 | 29 | hawkeye | hawkeye (hawkeye vol.1 #1-4). | hawkeye, the marskman (tales of suspense | 12 feb 2014 row 5 : 5 | 10 | the hulk | dogs of war (the incredible hulk volume 2 #12-20 | none | 26 feb 2014 row 6 : 6 | 22 | jean grey | here comes yesterday (all-new x-men #1- | x-men origins: jean grey | 12 mar 2014 row 7 : 7 | 49 | power man | power man and iron fist #50-53 | luke cage #1-3 | 26 mar 2014 row 8 : 8 | tbc | captain america | captain america #247-255 | ? | 9 apr 2014 row 9 : 9 | tbc | iron man | the five nightmares (iron man (vol. | tales of suspense #39 | 23 apr 2014 row 10 : 10 | tbc | the x-men | x-men: children of the atom #1-6 | x-men (vol. 1) #1 | 7 may 2014 row 11 : 11 | tbc | black widow | tbc | tbc | 21 may 2014 row 12 : 12 | tbc | the human torch (jim hammond) | tbc | tbc | 4 june 2014 row 13 : 13 | tbc | warriors three | tbc | tbc | 18 june 2014 row 14 : 14 | tbc | cyclops | tbc | tbc | 2 july 2014 row 15 : 15 | tbc | captain marvel | tbc | tbc | 16 july 2014
|
table_csv/204_696.csv
| 1 |
{
"column_index": [
2
],
"row_index": [
7
]
}
|
[
"what are all of the x-men and captain america movies?",
"which of those were released first?"
] | 2 |
captain america
|
[
"Issue",
"Volume",
"Title",
"Main Feature Story",
"First/Early Appearance Story/Stories",
"Release Date"
] |
which of those were released first?
|
[
[
"1",
"24",
"The Avengers",
"Ultron Unlimited (Avengers Vol 3 #0 and #19",
"The Coming Of The Avengers (Avengers Vol 1 #1)",
"27 Dec 2013"
],
[
"2",
"12",
"Spider-Man",
"Happy Birthday (Amazing Spider-Man Vol 2 #57-58",
"Spider-Man (Amazing Fantasy #15) The Sinister Six (",
"15 Jan 2014"
],
[
"3",
"55",
"Wolverine",
"Get Mystique (Wolverine Vol. 3 #62",
"And Now... The Wolverine (Incredible Hulk",
"29 Jan 2014"
],
[
"4",
"29",
"Hawkeye",
"Hawkeye (Hawkeye Vol.1 #1-4).",
"Hawkeye, The Marskman (Tales of Suspense",
"12 Feb 2014"
],
[
"5",
"10",
"The Hulk",
"Dogs Of War (The Incredible Hulk volume 2 #12-20",
"None",
"26 Feb 2014"
],
[
"6",
"22",
"Jean Grey",
"Here Comes Yesterday (All-New X-Men #1-",
"X-Men Origins: Jean Grey",
"12 Mar 2014"
],
[
"7",
"49",
"Power Man",
"Power Man and Iron Fist #50-53",
"Luke Cage #1-3",
"26 Mar 2014"
],
[
"8",
"TBC",
"Captain America",
"Captain America #247-255",
"?",
"9 Apr 2014"
],
[
"9",
"TBC",
"Iron Man",
"The Five Nightmares (Iron Man (Vol.",
"Tales of Suspense #39",
"23 Apr 2014"
],
[
"10",
"TBC",
"The X-Men",
"X-Men: Children of the Atom #1-6",
"X-Men (Vol. 1) #1",
"7 May 2014"
],
[
"11",
"TBC",
"Black Widow",
"TBC",
"TBC",
"21 May 2014"
],
[
"12",
"TBC",
"The Human Torch (Jim Hammond)",
"TBC",
"TBC",
"4 June 2014"
],
[
"13",
"TBC",
"Warriors Three",
"TBC",
"TBC",
"18 June 2014"
],
[
"14",
"TBC",
"Cyclops",
"TBC",
"TBC",
"2 July 2014"
],
[
"15",
"TBC",
"Captain Marvel",
"TBC",
"TBC",
"16 July 2014"
]
] |
[
"Captain America"
] |
which of those were released first? || what are all of the x-men and captain america movies?
|
nt-13301
|
col : symbol | gsi | meaning | unicode | description row 1 : ^ | * | base triangulation surveying point | u+25ec | dot in upward-pointing triangle row 2 : nan | * | electronic triangulation point | nan | dot in upward-pointing triangle with flag row 3 : [?] | * | benchmark | u+22a1 | dot in square row 4 : [?] | * | factory | u+26ed | gear without hub row 5 : [?] | * | lighthouse | u+26ef | map symbol for lighthouse row 6 : [?] | * | power station | u+26ee | gear with handles row 7 : wen | * | elementary or junior high school | u+6587 | kanji bun row 8 : [?] | * | high school | nan | kanji bun in a circle row 9 : nan | * | university | nan | kanji bun with a smaller kanji da (for daiga row 10 : nan | * | technical college | nan | kanji bun with a smaller kanji zhuan ( row 11 : @ | * | post office | u+3036 | down tack (t-shape) with overbar in row 12 : @ | x | sub post office (not distribution centre) | u+3012 | down tack (t-shape) with overbar row 13 : nan | * | police station | u+2b59 | heavy circled saltire row 14 : nan | * | koban (police box) | u+2613 | diagonal cross (saltire) row 15 : [?] | * | public health centre | u+2295 | greek cross in circle row 16 : [?] | * | hospital | nan | greek cross in shield row 17 : nan | * | prefectural office | u+26fb | oval bullseye row 18 : nan | * | city hall | u+2b57 | heavy circle with circle inside row 19 : * | * | ward office | u+25c9 | fisheye row 20 : nan | * | town hall | u+2b58 | heavy circle row 21 : [?] | * | shinto shrine | u+26e9 | shinto shrine row 22 : wan | * | buddhist temple | u+534d | manji (swastika) row 23 : [?] | * | castle | u+26eb | castle row 24 : [?] | * | cemetery | u+26fc | headstone graveyard symbol row 25 : nan | * | onsen (hot springs) | u+2668 | oval with three vertical wavy lines row 26 : [?] | * | historical landmark | u+26ec | historic site row 27 : [?] | * | summit | u+26f0 | mountain
|
table_csv/203_540.csv
| 0 |
{
"column_index": [
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3
],
"row_index": [
0,
2,
3,
4,
5,
6,
10,
11,
12,
13,
14,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26
]
}
|
[
"what are the list of unicode for the japanase map symbols?"
] | 0 |
u+25ec, u+22a1, u+26ed, u+26ef, u+26ee, u+6587, u+3036, u+3012, u+2b59, u+2613, u+2295, u+26fb, u+2b57, u+25c9, u+2b58, u+26e9, u+534d, u+26eb, u+26fc, u+2668, u+26ec, u+26f0
|
[
"Symbol",
"GSI",
"Meaning",
"Unicode",
"Description"
] |
what are the list of unicode for the japanase map symbols?
|
[
[
"^",
"*",
"Base triangulation surveying point",
"U+25EC",
"Dot in upward-pointing triangle"
],
[
"nan",
"*",
"Electronic triangulation point",
"nan",
"Dot in upward-pointing triangle with flag"
],
[
"[?]",
"*",
"Benchmark",
"U+22A1",
"Dot in square"
],
[
"[?]",
"*",
"Factory",
"U+26ED",
"Gear without hub"
],
[
"[?]",
"*",
"Lighthouse",
"U+26EF",
"Map symbol for lighthouse"
],
[
"[?]",
"*",
"Power station",
"U+26EE",
"Gear with handles"
],
[
"Wen",
"*",
"Elementary or junior high school",
"U+6587",
"Kanji bun"
],
[
"[?]",
"*",
"High school",
"nan",
"Kanji bun in a circle"
],
[
"nan",
"*",
"University",
"nan",
"Kanji bun with a smaller kanji Da (for daiga"
],
[
"nan",
"*",
"Technical college",
"nan",
"Kanji bun with a smaller kanji Zhuan ("
],
[
"@",
"*",
"Post office",
"U+3036",
"Down tack (T-shape) with overbar in"
],
[
"@",
"x",
"Sub post office (not distribution centre)",
"U+3012",
"Down tack (T-shape) with overbar"
],
[
"nan",
"*",
"Police station",
"U+2B59",
"Heavy circled saltire"
],
[
"nan",
"*",
"Koban (police box)",
"U+2613",
"Diagonal cross (saltire)"
],
[
"[?]",
"*",
"Public health centre",
"U+2295",
"Greek cross in circle"
],
[
"[?]",
"*",
"Hospital",
"nan",
"Greek cross in shield"
],
[
"nan",
"*",
"Prefectural Office",
"U+26FB",
"Oval bullseye"
],
[
"nan",
"*",
"City hall",
"U+2B57",
"Heavy circle with circle inside"
],
[
"*",
"*",
"Ward office",
"U+25C9",
"Fisheye"
],
[
"nan",
"*",
"Town hall",
"U+2B58",
"Heavy circle"
],
[
"[?]",
"*",
"Shinto shrine",
"U+26E9",
"Shinto shrine"
],
[
"Wan",
"*",
"Buddhist temple",
"U+534D",
"Manji (Swastika)"
],
[
"[?]",
"*",
"Castle",
"U+26EB",
"Castle"
],
[
"[?]",
"*",
"Cemetery",
"U+26FC",
"Headstone graveyard symbol"
],
[
"nan",
"*",
"Onsen (hot springs)",
"U+2668",
"Oval with three vertical wavy lines"
],
[
"[?]",
"*",
"Historical landmark",
"U+26EC",
"Historic site"
],
[
"[?]",
"*",
"Summit",
"U+26F0",
"Mountain"
]
] |
[
"U+25EC",
"U+22A1",
"U+26ED",
"U+26EF",
"U+26EE",
"U+6587",
"U+3036",
"U+3012",
"U+2B59",
"U+2613",
"U+2295",
"U+26FB",
"U+2B57",
"U+25C9",
"U+2B58",
"U+26E9",
"U+534D",
"U+26EB",
"U+26FC",
"U+2668",
"U+26EC",
"U+26F0"
] |
what are the list of unicode for the japanase map symbols? ||
|
nt-13301
|
col : symbol | gsi | meaning | unicode | description row 1 : ^ | * | base triangulation surveying point | u+25ec | dot in upward-pointing triangle row 2 : nan | * | electronic triangulation point | nan | dot in upward-pointing triangle with flag row 3 : [?] | * | benchmark | u+22a1 | dot in square row 4 : [?] | * | factory | u+26ed | gear without hub row 5 : [?] | * | lighthouse | u+26ef | map symbol for lighthouse row 6 : [?] | * | power station | u+26ee | gear with handles row 7 : wen | * | elementary or junior high school | u+6587 | kanji bun row 8 : [?] | * | high school | nan | kanji bun in a circle row 9 : nan | * | university | nan | kanji bun with a smaller kanji da (for daiga row 10 : nan | * | technical college | nan | kanji bun with a smaller kanji zhuan ( row 11 : @ | * | post office | u+3036 | down tack (t-shape) with overbar in row 12 : @ | x | sub post office (not distribution centre) | u+3012 | down tack (t-shape) with overbar row 13 : nan | * | police station | u+2b59 | heavy circled saltire row 14 : nan | * | koban (police box) | u+2613 | diagonal cross (saltire) row 15 : [?] | * | public health centre | u+2295 | greek cross in circle row 16 : [?] | * | hospital | nan | greek cross in shield row 17 : nan | * | prefectural office | u+26fb | oval bullseye row 18 : nan | * | city hall | u+2b57 | heavy circle with circle inside row 19 : * | * | ward office | u+25c9 | fisheye row 20 : nan | * | town hall | u+2b58 | heavy circle row 21 : [?] | * | shinto shrine | u+26e9 | shinto shrine row 22 : wan | * | buddhist temple | u+534d | manji (swastika) row 23 : [?] | * | castle | u+26eb | castle row 24 : [?] | * | cemetery | u+26fc | headstone graveyard symbol row 25 : nan | * | onsen (hot springs) | u+2668 | oval with three vertical wavy lines row 26 : [?] | * | historical landmark | u+26ec | historic site row 27 : [?] | * | summit | u+26f0 | mountain
|
table_csv/203_540.csv
| 1 |
{
"column_index": [
3,
3,
3,
3,
3,
3,
3,
3
],
"row_index": [
0,
2,
3,
4,
5,
20,
22,
25
]
}
|
[
"what are the list of unicode for the japanase map symbols?",
"which contain vowels after the u?"
] | 0 |
u+25ec, u+22a1, u+26ed, u+26ef, u+26ee, u+26e9, u+26eb, u+26ec
|
[
"Symbol",
"GSI",
"Meaning",
"Unicode",
"Description"
] |
which contain vowels after the u?
|
[
[
"^",
"*",
"Base triangulation surveying point",
"U+25EC",
"Dot in upward-pointing triangle"
],
[
"nan",
"*",
"Electronic triangulation point",
"nan",
"Dot in upward-pointing triangle with flag"
],
[
"[?]",
"*",
"Benchmark",
"U+22A1",
"Dot in square"
],
[
"[?]",
"*",
"Factory",
"U+26ED",
"Gear without hub"
],
[
"[?]",
"*",
"Lighthouse",
"U+26EF",
"Map symbol for lighthouse"
],
[
"[?]",
"*",
"Power station",
"U+26EE",
"Gear with handles"
],
[
"Wen",
"*",
"Elementary or junior high school",
"U+6587",
"Kanji bun"
],
[
"[?]",
"*",
"High school",
"nan",
"Kanji bun in a circle"
],
[
"nan",
"*",
"University",
"nan",
"Kanji bun with a smaller kanji Da (for daiga"
],
[
"nan",
"*",
"Technical college",
"nan",
"Kanji bun with a smaller kanji Zhuan ("
],
[
"@",
"*",
"Post office",
"U+3036",
"Down tack (T-shape) with overbar in"
],
[
"@",
"x",
"Sub post office (not distribution centre)",
"U+3012",
"Down tack (T-shape) with overbar"
],
[
"nan",
"*",
"Police station",
"U+2B59",
"Heavy circled saltire"
],
[
"nan",
"*",
"Koban (police box)",
"U+2613",
"Diagonal cross (saltire)"
],
[
"[?]",
"*",
"Public health centre",
"U+2295",
"Greek cross in circle"
],
[
"[?]",
"*",
"Hospital",
"nan",
"Greek cross in shield"
],
[
"nan",
"*",
"Prefectural Office",
"U+26FB",
"Oval bullseye"
],
[
"nan",
"*",
"City hall",
"U+2B57",
"Heavy circle with circle inside"
],
[
"*",
"*",
"Ward office",
"U+25C9",
"Fisheye"
],
[
"nan",
"*",
"Town hall",
"U+2B58",
"Heavy circle"
],
[
"[?]",
"*",
"Shinto shrine",
"U+26E9",
"Shinto shrine"
],
[
"Wan",
"*",
"Buddhist temple",
"U+534D",
"Manji (Swastika)"
],
[
"[?]",
"*",
"Castle",
"U+26EB",
"Castle"
],
[
"[?]",
"*",
"Cemetery",
"U+26FC",
"Headstone graveyard symbol"
],
[
"nan",
"*",
"Onsen (hot springs)",
"U+2668",
"Oval with three vertical wavy lines"
],
[
"[?]",
"*",
"Historical landmark",
"U+26EC",
"Historic site"
],
[
"[?]",
"*",
"Summit",
"U+26F0",
"Mountain"
]
] |
[
"U+25EC",
"U+22A1",
"U+26ED",
"U+26EF",
"U+26EE",
"U+26E9",
"U+26EB",
"U+26EC"
] |
which contain vowels after the u? || what are the list of unicode for the japanase map symbols?
|
nt-13301
|
col : symbol | gsi | meaning | unicode | description row 1 : ^ | * | base triangulation surveying point | u+25ec | dot in upward-pointing triangle row 2 : nan | * | electronic triangulation point | nan | dot in upward-pointing triangle with flag row 3 : [?] | * | benchmark | u+22a1 | dot in square row 4 : [?] | * | factory | u+26ed | gear without hub row 5 : [?] | * | lighthouse | u+26ef | map symbol for lighthouse row 6 : [?] | * | power station | u+26ee | gear with handles row 7 : wen | * | elementary or junior high school | u+6587 | kanji bun row 8 : [?] | * | high school | nan | kanji bun in a circle row 9 : nan | * | university | nan | kanji bun with a smaller kanji da (for daiga row 10 : nan | * | technical college | nan | kanji bun with a smaller kanji zhuan ( row 11 : @ | * | post office | u+3036 | down tack (t-shape) with overbar in row 12 : @ | x | sub post office (not distribution centre) | u+3012 | down tack (t-shape) with overbar row 13 : nan | * | police station | u+2b59 | heavy circled saltire row 14 : nan | * | koban (police box) | u+2613 | diagonal cross (saltire) row 15 : [?] | * | public health centre | u+2295 | greek cross in circle row 16 : [?] | * | hospital | nan | greek cross in shield row 17 : nan | * | prefectural office | u+26fb | oval bullseye row 18 : nan | * | city hall | u+2b57 | heavy circle with circle inside row 19 : * | * | ward office | u+25c9 | fisheye row 20 : nan | * | town hall | u+2b58 | heavy circle row 21 : [?] | * | shinto shrine | u+26e9 | shinto shrine row 22 : wan | * | buddhist temple | u+534d | manji (swastika) row 23 : [?] | * | castle | u+26eb | castle row 24 : [?] | * | cemetery | u+26fc | headstone graveyard symbol row 25 : nan | * | onsen (hot springs) | u+2668 | oval with three vertical wavy lines row 26 : [?] | * | historical landmark | u+26ec | historic site row 27 : [?] | * | summit | u+26f0 | mountain
|
table_csv/203_540.csv
| 2 |
{
"column_index": [
3
],
"row_index": [
2
]
}
|
[
"what are the list of unicode for the japanase map symbols?",
"which contain vowels after the u?",
"of the above which ones have only an a?"
] | 0 |
u+22a1
|
[
"Symbol",
"GSI",
"Meaning",
"Unicode",
"Description"
] |
of the above which ones have only an a?
|
[
[
"^",
"*",
"Base triangulation surveying point",
"U+25EC",
"Dot in upward-pointing triangle"
],
[
"nan",
"*",
"Electronic triangulation point",
"nan",
"Dot in upward-pointing triangle with flag"
],
[
"[?]",
"*",
"Benchmark",
"U+22A1",
"Dot in square"
],
[
"[?]",
"*",
"Factory",
"U+26ED",
"Gear without hub"
],
[
"[?]",
"*",
"Lighthouse",
"U+26EF",
"Map symbol for lighthouse"
],
[
"[?]",
"*",
"Power station",
"U+26EE",
"Gear with handles"
],
[
"Wen",
"*",
"Elementary or junior high school",
"U+6587",
"Kanji bun"
],
[
"[?]",
"*",
"High school",
"nan",
"Kanji bun in a circle"
],
[
"nan",
"*",
"University",
"nan",
"Kanji bun with a smaller kanji Da (for daiga"
],
[
"nan",
"*",
"Technical college",
"nan",
"Kanji bun with a smaller kanji Zhuan ("
],
[
"@",
"*",
"Post office",
"U+3036",
"Down tack (T-shape) with overbar in"
],
[
"@",
"x",
"Sub post office (not distribution centre)",
"U+3012",
"Down tack (T-shape) with overbar"
],
[
"nan",
"*",
"Police station",
"U+2B59",
"Heavy circled saltire"
],
[
"nan",
"*",
"Koban (police box)",
"U+2613",
"Diagonal cross (saltire)"
],
[
"[?]",
"*",
"Public health centre",
"U+2295",
"Greek cross in circle"
],
[
"[?]",
"*",
"Hospital",
"nan",
"Greek cross in shield"
],
[
"nan",
"*",
"Prefectural Office",
"U+26FB",
"Oval bullseye"
],
[
"nan",
"*",
"City hall",
"U+2B57",
"Heavy circle with circle inside"
],
[
"*",
"*",
"Ward office",
"U+25C9",
"Fisheye"
],
[
"nan",
"*",
"Town hall",
"U+2B58",
"Heavy circle"
],
[
"[?]",
"*",
"Shinto shrine",
"U+26E9",
"Shinto shrine"
],
[
"Wan",
"*",
"Buddhist temple",
"U+534D",
"Manji (Swastika)"
],
[
"[?]",
"*",
"Castle",
"U+26EB",
"Castle"
],
[
"[?]",
"*",
"Cemetery",
"U+26FC",
"Headstone graveyard symbol"
],
[
"nan",
"*",
"Onsen (hot springs)",
"U+2668",
"Oval with three vertical wavy lines"
],
[
"[?]",
"*",
"Historical landmark",
"U+26EC",
"Historic site"
],
[
"[?]",
"*",
"Summit",
"U+26F0",
"Mountain"
]
] |
[
"U+22A1"
] |
of the above which ones have only an a? || which contain vowels after the u? | what are the list of unicode for the japanase map symbols?
|
nt-13301
|
col : symbol | gsi | meaning | unicode | description row 1 : ^ | * | base triangulation surveying point | u+25ec | dot in upward-pointing triangle row 2 : nan | * | electronic triangulation point | nan | dot in upward-pointing triangle with flag row 3 : [?] | * | benchmark | u+22a1 | dot in square row 4 : [?] | * | factory | u+26ed | gear without hub row 5 : [?] | * | lighthouse | u+26ef | map symbol for lighthouse row 6 : [?] | * | power station | u+26ee | gear with handles row 7 : wen | * | elementary or junior high school | u+6587 | kanji bun row 8 : [?] | * | high school | nan | kanji bun in a circle row 9 : nan | * | university | nan | kanji bun with a smaller kanji da (for daiga row 10 : nan | * | technical college | nan | kanji bun with a smaller kanji zhuan ( row 11 : @ | * | post office | u+3036 | down tack (t-shape) with overbar in row 12 : @ | x | sub post office (not distribution centre) | u+3012 | down tack (t-shape) with overbar row 13 : nan | * | police station | u+2b59 | heavy circled saltire row 14 : nan | * | koban (police box) | u+2613 | diagonal cross (saltire) row 15 : [?] | * | public health centre | u+2295 | greek cross in circle row 16 : [?] | * | hospital | nan | greek cross in shield row 17 : nan | * | prefectural office | u+26fb | oval bullseye row 18 : nan | * | city hall | u+2b57 | heavy circle with circle inside row 19 : * | * | ward office | u+25c9 | fisheye row 20 : nan | * | town hall | u+2b58 | heavy circle row 21 : [?] | * | shinto shrine | u+26e9 | shinto shrine row 22 : wan | * | buddhist temple | u+534d | manji (swastika) row 23 : [?] | * | castle | u+26eb | castle row 24 : [?] | * | cemetery | u+26fc | headstone graveyard symbol row 25 : nan | * | onsen (hot springs) | u+2668 | oval with three vertical wavy lines row 26 : [?] | * | historical landmark | u+26ec | historic site row 27 : [?] | * | summit | u+26f0 | mountain
|
table_csv/203_540.csv
| 0 |
{
"column_index": [
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3
],
"row_index": [
0,
2,
3,
4,
5,
6,
10,
11,
12,
13,
14,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26
]
}
|
[
"what unicodes are listed?"
] | 1 |
u+25ec, u+22a1, u+26ed, u+26ef, u+26ee, u+6587, u+3036, u+3012, u+2b59, u+2613, u+2295, u+26fb, u+2b57, u+25c9, u+2b58, u+26e9, u+534d, u+26eb, u+26fc, u+2668, u+26ec, u+26f0
|
[
"Symbol",
"GSI",
"Meaning",
"Unicode",
"Description"
] |
what unicodes are listed?
|
[
[
"^",
"*",
"Base triangulation surveying point",
"U+25EC",
"Dot in upward-pointing triangle"
],
[
"nan",
"*",
"Electronic triangulation point",
"nan",
"Dot in upward-pointing triangle with flag"
],
[
"[?]",
"*",
"Benchmark",
"U+22A1",
"Dot in square"
],
[
"[?]",
"*",
"Factory",
"U+26ED",
"Gear without hub"
],
[
"[?]",
"*",
"Lighthouse",
"U+26EF",
"Map symbol for lighthouse"
],
[
"[?]",
"*",
"Power station",
"U+26EE",
"Gear with handles"
],
[
"Wen",
"*",
"Elementary or junior high school",
"U+6587",
"Kanji bun"
],
[
"[?]",
"*",
"High school",
"nan",
"Kanji bun in a circle"
],
[
"nan",
"*",
"University",
"nan",
"Kanji bun with a smaller kanji Da (for daiga"
],
[
"nan",
"*",
"Technical college",
"nan",
"Kanji bun with a smaller kanji Zhuan ("
],
[
"@",
"*",
"Post office",
"U+3036",
"Down tack (T-shape) with overbar in"
],
[
"@",
"x",
"Sub post office (not distribution centre)",
"U+3012",
"Down tack (T-shape) with overbar"
],
[
"nan",
"*",
"Police station",
"U+2B59",
"Heavy circled saltire"
],
[
"nan",
"*",
"Koban (police box)",
"U+2613",
"Diagonal cross (saltire)"
],
[
"[?]",
"*",
"Public health centre",
"U+2295",
"Greek cross in circle"
],
[
"[?]",
"*",
"Hospital",
"nan",
"Greek cross in shield"
],
[
"nan",
"*",
"Prefectural Office",
"U+26FB",
"Oval bullseye"
],
[
"nan",
"*",
"City hall",
"U+2B57",
"Heavy circle with circle inside"
],
[
"*",
"*",
"Ward office",
"U+25C9",
"Fisheye"
],
[
"nan",
"*",
"Town hall",
"U+2B58",
"Heavy circle"
],
[
"[?]",
"*",
"Shinto shrine",
"U+26E9",
"Shinto shrine"
],
[
"Wan",
"*",
"Buddhist temple",
"U+534D",
"Manji (Swastika)"
],
[
"[?]",
"*",
"Castle",
"U+26EB",
"Castle"
],
[
"[?]",
"*",
"Cemetery",
"U+26FC",
"Headstone graveyard symbol"
],
[
"nan",
"*",
"Onsen (hot springs)",
"U+2668",
"Oval with three vertical wavy lines"
],
[
"[?]",
"*",
"Historical landmark",
"U+26EC",
"Historic site"
],
[
"[?]",
"*",
"Summit",
"U+26F0",
"Mountain"
]
] |
[
"U+25EC",
"U+22A1",
"U+26ED",
"U+26EF",
"U+26EE",
"U+6587",
"U+3036",
"U+3012",
"U+2B59",
"U+2613",
"U+2295",
"U+26FB",
"U+2B57",
"U+25C9",
"U+2B58",
"U+26E9",
"U+534D",
"U+26EB",
"U+26FC",
"U+2668",
"U+26EC",
"U+26F0"
] |
what unicodes are listed? ||
|
nt-13301
|
col : symbol | gsi | meaning | unicode | description row 1 : ^ | * | base triangulation surveying point | u+25ec | dot in upward-pointing triangle row 2 : nan | * | electronic triangulation point | nan | dot in upward-pointing triangle with flag row 3 : [?] | * | benchmark | u+22a1 | dot in square row 4 : [?] | * | factory | u+26ed | gear without hub row 5 : [?] | * | lighthouse | u+26ef | map symbol for lighthouse row 6 : [?] | * | power station | u+26ee | gear with handles row 7 : wen | * | elementary or junior high school | u+6587 | kanji bun row 8 : [?] | * | high school | nan | kanji bun in a circle row 9 : nan | * | university | nan | kanji bun with a smaller kanji da (for daiga row 10 : nan | * | technical college | nan | kanji bun with a smaller kanji zhuan ( row 11 : @ | * | post office | u+3036 | down tack (t-shape) with overbar in row 12 : @ | x | sub post office (not distribution centre) | u+3012 | down tack (t-shape) with overbar row 13 : nan | * | police station | u+2b59 | heavy circled saltire row 14 : nan | * | koban (police box) | u+2613 | diagonal cross (saltire) row 15 : [?] | * | public health centre | u+2295 | greek cross in circle row 16 : [?] | * | hospital | nan | greek cross in shield row 17 : nan | * | prefectural office | u+26fb | oval bullseye row 18 : nan | * | city hall | u+2b57 | heavy circle with circle inside row 19 : * | * | ward office | u+25c9 | fisheye row 20 : nan | * | town hall | u+2b58 | heavy circle row 21 : [?] | * | shinto shrine | u+26e9 | shinto shrine row 22 : wan | * | buddhist temple | u+534d | manji (swastika) row 23 : [?] | * | castle | u+26eb | castle row 24 : [?] | * | cemetery | u+26fc | headstone graveyard symbol row 25 : nan | * | onsen (hot springs) | u+2668 | oval with three vertical wavy lines row 26 : [?] | * | historical landmark | u+26ec | historic site row 27 : [?] | * | summit | u+26f0 | mountain
|
table_csv/203_540.csv
| 1 |
{
"column_index": [
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3
],
"row_index": [
0,
2,
3,
4,
5,
12,
16,
17,
18,
19,
20,
21,
22,
23,
25,
26
]
}
|
[
"what unicodes are listed?",
"of those listed unicodes, which contain letters (besides u)?"
] | 1 |
u+25ec, u+22a1, u+26ed, u+26ef, u+26ee, u+2b59, u+26fb, u+2b57, u+25c9, u+2b58, u+26e9, u+534d, u+26eb, u+26fc, u+26ec, u+26f0
|
[
"Symbol",
"GSI",
"Meaning",
"Unicode",
"Description"
] |
of those listed unicodes, which contain letters (besides u)?
|
[
[
"^",
"*",
"Base triangulation surveying point",
"U+25EC",
"Dot in upward-pointing triangle"
],
[
"nan",
"*",
"Electronic triangulation point",
"nan",
"Dot in upward-pointing triangle with flag"
],
[
"[?]",
"*",
"Benchmark",
"U+22A1",
"Dot in square"
],
[
"[?]",
"*",
"Factory",
"U+26ED",
"Gear without hub"
],
[
"[?]",
"*",
"Lighthouse",
"U+26EF",
"Map symbol for lighthouse"
],
[
"[?]",
"*",
"Power station",
"U+26EE",
"Gear with handles"
],
[
"Wen",
"*",
"Elementary or junior high school",
"U+6587",
"Kanji bun"
],
[
"[?]",
"*",
"High school",
"nan",
"Kanji bun in a circle"
],
[
"nan",
"*",
"University",
"nan",
"Kanji bun with a smaller kanji Da (for daiga"
],
[
"nan",
"*",
"Technical college",
"nan",
"Kanji bun with a smaller kanji Zhuan ("
],
[
"@",
"*",
"Post office",
"U+3036",
"Down tack (T-shape) with overbar in"
],
[
"@",
"x",
"Sub post office (not distribution centre)",
"U+3012",
"Down tack (T-shape) with overbar"
],
[
"nan",
"*",
"Police station",
"U+2B59",
"Heavy circled saltire"
],
[
"nan",
"*",
"Koban (police box)",
"U+2613",
"Diagonal cross (saltire)"
],
[
"[?]",
"*",
"Public health centre",
"U+2295",
"Greek cross in circle"
],
[
"[?]",
"*",
"Hospital",
"nan",
"Greek cross in shield"
],
[
"nan",
"*",
"Prefectural Office",
"U+26FB",
"Oval bullseye"
],
[
"nan",
"*",
"City hall",
"U+2B57",
"Heavy circle with circle inside"
],
[
"*",
"*",
"Ward office",
"U+25C9",
"Fisheye"
],
[
"nan",
"*",
"Town hall",
"U+2B58",
"Heavy circle"
],
[
"[?]",
"*",
"Shinto shrine",
"U+26E9",
"Shinto shrine"
],
[
"Wan",
"*",
"Buddhist temple",
"U+534D",
"Manji (Swastika)"
],
[
"[?]",
"*",
"Castle",
"U+26EB",
"Castle"
],
[
"[?]",
"*",
"Cemetery",
"U+26FC",
"Headstone graveyard symbol"
],
[
"nan",
"*",
"Onsen (hot springs)",
"U+2668",
"Oval with three vertical wavy lines"
],
[
"[?]",
"*",
"Historical landmark",
"U+26EC",
"Historic site"
],
[
"[?]",
"*",
"Summit",
"U+26F0",
"Mountain"
]
] |
[
"U+25EC",
"U+22A1",
"U+26ED",
"U+26EF",
"U+26EE",
"U+2B59",
"U+26FB",
"U+2B57",
"U+25C9",
"U+2B58",
"U+26E9",
"U+534D",
"U+26EB",
"U+26FC",
"U+26EC",
"U+26F0"
] |
of those listed unicodes, which contain letters (besides u)? || what unicodes are listed?
|
nt-13301
|
col : symbol | gsi | meaning | unicode | description row 1 : ^ | * | base triangulation surveying point | u+25ec | dot in upward-pointing triangle row 2 : nan | * | electronic triangulation point | nan | dot in upward-pointing triangle with flag row 3 : [?] | * | benchmark | u+22a1 | dot in square row 4 : [?] | * | factory | u+26ed | gear without hub row 5 : [?] | * | lighthouse | u+26ef | map symbol for lighthouse row 6 : [?] | * | power station | u+26ee | gear with handles row 7 : wen | * | elementary or junior high school | u+6587 | kanji bun row 8 : [?] | * | high school | nan | kanji bun in a circle row 9 : nan | * | university | nan | kanji bun with a smaller kanji da (for daiga row 10 : nan | * | technical college | nan | kanji bun with a smaller kanji zhuan ( row 11 : @ | * | post office | u+3036 | down tack (t-shape) with overbar in row 12 : @ | x | sub post office (not distribution centre) | u+3012 | down tack (t-shape) with overbar row 13 : nan | * | police station | u+2b59 | heavy circled saltire row 14 : nan | * | koban (police box) | u+2613 | diagonal cross (saltire) row 15 : [?] | * | public health centre | u+2295 | greek cross in circle row 16 : [?] | * | hospital | nan | greek cross in shield row 17 : nan | * | prefectural office | u+26fb | oval bullseye row 18 : nan | * | city hall | u+2b57 | heavy circle with circle inside row 19 : * | * | ward office | u+25c9 | fisheye row 20 : nan | * | town hall | u+2b58 | heavy circle row 21 : [?] | * | shinto shrine | u+26e9 | shinto shrine row 22 : wan | * | buddhist temple | u+534d | manji (swastika) row 23 : [?] | * | castle | u+26eb | castle row 24 : [?] | * | cemetery | u+26fc | headstone graveyard symbol row 25 : nan | * | onsen (hot springs) | u+2668 | oval with three vertical wavy lines row 26 : [?] | * | historical landmark | u+26ec | historic site row 27 : [?] | * | summit | u+26f0 | mountain
|
table_csv/203_540.csv
| 2 |
{
"column_index": [
3
],
"row_index": [
2
]
}
|
[
"what unicodes are listed?",
"of those listed unicodes, which contain letters (besides u)?",
"of those listed unicodes, which contain the letter a?"
] | 1 |
u+22a1
|
[
"Symbol",
"GSI",
"Meaning",
"Unicode",
"Description"
] |
of those listed unicodes, which contain the letter a?
|
[
[
"^",
"*",
"Base triangulation surveying point",
"U+25EC",
"Dot in upward-pointing triangle"
],
[
"nan",
"*",
"Electronic triangulation point",
"nan",
"Dot in upward-pointing triangle with flag"
],
[
"[?]",
"*",
"Benchmark",
"U+22A1",
"Dot in square"
],
[
"[?]",
"*",
"Factory",
"U+26ED",
"Gear without hub"
],
[
"[?]",
"*",
"Lighthouse",
"U+26EF",
"Map symbol for lighthouse"
],
[
"[?]",
"*",
"Power station",
"U+26EE",
"Gear with handles"
],
[
"Wen",
"*",
"Elementary or junior high school",
"U+6587",
"Kanji bun"
],
[
"[?]",
"*",
"High school",
"nan",
"Kanji bun in a circle"
],
[
"nan",
"*",
"University",
"nan",
"Kanji bun with a smaller kanji Da (for daiga"
],
[
"nan",
"*",
"Technical college",
"nan",
"Kanji bun with a smaller kanji Zhuan ("
],
[
"@",
"*",
"Post office",
"U+3036",
"Down tack (T-shape) with overbar in"
],
[
"@",
"x",
"Sub post office (not distribution centre)",
"U+3012",
"Down tack (T-shape) with overbar"
],
[
"nan",
"*",
"Police station",
"U+2B59",
"Heavy circled saltire"
],
[
"nan",
"*",
"Koban (police box)",
"U+2613",
"Diagonal cross (saltire)"
],
[
"[?]",
"*",
"Public health centre",
"U+2295",
"Greek cross in circle"
],
[
"[?]",
"*",
"Hospital",
"nan",
"Greek cross in shield"
],
[
"nan",
"*",
"Prefectural Office",
"U+26FB",
"Oval bullseye"
],
[
"nan",
"*",
"City hall",
"U+2B57",
"Heavy circle with circle inside"
],
[
"*",
"*",
"Ward office",
"U+25C9",
"Fisheye"
],
[
"nan",
"*",
"Town hall",
"U+2B58",
"Heavy circle"
],
[
"[?]",
"*",
"Shinto shrine",
"U+26E9",
"Shinto shrine"
],
[
"Wan",
"*",
"Buddhist temple",
"U+534D",
"Manji (Swastika)"
],
[
"[?]",
"*",
"Castle",
"U+26EB",
"Castle"
],
[
"[?]",
"*",
"Cemetery",
"U+26FC",
"Headstone graveyard symbol"
],
[
"nan",
"*",
"Onsen (hot springs)",
"U+2668",
"Oval with three vertical wavy lines"
],
[
"[?]",
"*",
"Historical landmark",
"U+26EC",
"Historic site"
],
[
"[?]",
"*",
"Summit",
"U+26F0",
"Mountain"
]
] |
[
"U+22A1"
] |
of those listed unicodes, which contain the letter a? || of those listed unicodes, which contain letters (besides u)? | what unicodes are listed?
|
nt-13301
|
col : symbol | gsi | meaning | unicode | description row 1 : ^ | * | base triangulation surveying point | u+25ec | dot in upward-pointing triangle row 2 : nan | * | electronic triangulation point | nan | dot in upward-pointing triangle with flag row 3 : [?] | * | benchmark | u+22a1 | dot in square row 4 : [?] | * | factory | u+26ed | gear without hub row 5 : [?] | * | lighthouse | u+26ef | map symbol for lighthouse row 6 : [?] | * | power station | u+26ee | gear with handles row 7 : wen | * | elementary or junior high school | u+6587 | kanji bun row 8 : [?] | * | high school | nan | kanji bun in a circle row 9 : nan | * | university | nan | kanji bun with a smaller kanji da (for daiga row 10 : nan | * | technical college | nan | kanji bun with a smaller kanji zhuan ( row 11 : @ | * | post office | u+3036 | down tack (t-shape) with overbar in row 12 : @ | x | sub post office (not distribution centre) | u+3012 | down tack (t-shape) with overbar row 13 : nan | * | police station | u+2b59 | heavy circled saltire row 14 : nan | * | koban (police box) | u+2613 | diagonal cross (saltire) row 15 : [?] | * | public health centre | u+2295 | greek cross in circle row 16 : [?] | * | hospital | nan | greek cross in shield row 17 : nan | * | prefectural office | u+26fb | oval bullseye row 18 : nan | * | city hall | u+2b57 | heavy circle with circle inside row 19 : * | * | ward office | u+25c9 | fisheye row 20 : nan | * | town hall | u+2b58 | heavy circle row 21 : [?] | * | shinto shrine | u+26e9 | shinto shrine row 22 : wan | * | buddhist temple | u+534d | manji (swastika) row 23 : [?] | * | castle | u+26eb | castle row 24 : [?] | * | cemetery | u+26fc | headstone graveyard symbol row 25 : nan | * | onsen (hot springs) | u+2668 | oval with three vertical wavy lines row 26 : [?] | * | historical landmark | u+26ec | historic site row 27 : [?] | * | summit | u+26f0 | mountain
|
table_csv/203_540.csv
| 0 |
{
"column_index": [
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3
],
"row_index": [
0,
2,
3,
4,
5,
6,
10,
11,
12,
13,
14,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25,
26
]
}
|
[
"what are all the unicodes?"
] | 2 |
u+25ec, u+22a1, u+26ed, u+26ef, u+26ee, u+6587, u+3036, u+3012, u+2b59, u+2613, u+2295, u+26fb, u+2b57, u+25c9, u+2b58, u+26e9, u+534d, u+26eb, u+26fc, u+2668, u+26ec, u+26f0
|
[
"Symbol",
"GSI",
"Meaning",
"Unicode",
"Description"
] |
what are all the unicodes?
|
[
[
"^",
"*",
"Base triangulation surveying point",
"U+25EC",
"Dot in upward-pointing triangle"
],
[
"nan",
"*",
"Electronic triangulation point",
"nan",
"Dot in upward-pointing triangle with flag"
],
[
"[?]",
"*",
"Benchmark",
"U+22A1",
"Dot in square"
],
[
"[?]",
"*",
"Factory",
"U+26ED",
"Gear without hub"
],
[
"[?]",
"*",
"Lighthouse",
"U+26EF",
"Map symbol for lighthouse"
],
[
"[?]",
"*",
"Power station",
"U+26EE",
"Gear with handles"
],
[
"Wen",
"*",
"Elementary or junior high school",
"U+6587",
"Kanji bun"
],
[
"[?]",
"*",
"High school",
"nan",
"Kanji bun in a circle"
],
[
"nan",
"*",
"University",
"nan",
"Kanji bun with a smaller kanji Da (for daiga"
],
[
"nan",
"*",
"Technical college",
"nan",
"Kanji bun with a smaller kanji Zhuan ("
],
[
"@",
"*",
"Post office",
"U+3036",
"Down tack (T-shape) with overbar in"
],
[
"@",
"x",
"Sub post office (not distribution centre)",
"U+3012",
"Down tack (T-shape) with overbar"
],
[
"nan",
"*",
"Police station",
"U+2B59",
"Heavy circled saltire"
],
[
"nan",
"*",
"Koban (police box)",
"U+2613",
"Diagonal cross (saltire)"
],
[
"[?]",
"*",
"Public health centre",
"U+2295",
"Greek cross in circle"
],
[
"[?]",
"*",
"Hospital",
"nan",
"Greek cross in shield"
],
[
"nan",
"*",
"Prefectural Office",
"U+26FB",
"Oval bullseye"
],
[
"nan",
"*",
"City hall",
"U+2B57",
"Heavy circle with circle inside"
],
[
"*",
"*",
"Ward office",
"U+25C9",
"Fisheye"
],
[
"nan",
"*",
"Town hall",
"U+2B58",
"Heavy circle"
],
[
"[?]",
"*",
"Shinto shrine",
"U+26E9",
"Shinto shrine"
],
[
"Wan",
"*",
"Buddhist temple",
"U+534D",
"Manji (Swastika)"
],
[
"[?]",
"*",
"Castle",
"U+26EB",
"Castle"
],
[
"[?]",
"*",
"Cemetery",
"U+26FC",
"Headstone graveyard symbol"
],
[
"nan",
"*",
"Onsen (hot springs)",
"U+2668",
"Oval with three vertical wavy lines"
],
[
"[?]",
"*",
"Historical landmark",
"U+26EC",
"Historic site"
],
[
"[?]",
"*",
"Summit",
"U+26F0",
"Mountain"
]
] |
[
"U+25EC",
"U+22A1",
"U+26ED",
"U+26EF",
"U+26EE",
"U+6587",
"U+3036",
"U+3012",
"U+2B59",
"U+2613",
"U+2295",
"U+26FB",
"U+2B57",
"U+25C9",
"U+2B58",
"U+26E9",
"U+534D",
"U+26EB",
"U+26FC",
"U+2668",
"U+26EC",
"U+26F0"
] |
what are all the unicodes? ||
|
nt-13301
|
col : symbol | gsi | meaning | unicode | description row 1 : ^ | * | base triangulation surveying point | u+25ec | dot in upward-pointing triangle row 2 : nan | * | electronic triangulation point | nan | dot in upward-pointing triangle with flag row 3 : [?] | * | benchmark | u+22a1 | dot in square row 4 : [?] | * | factory | u+26ed | gear without hub row 5 : [?] | * | lighthouse | u+26ef | map symbol for lighthouse row 6 : [?] | * | power station | u+26ee | gear with handles row 7 : wen | * | elementary or junior high school | u+6587 | kanji bun row 8 : [?] | * | high school | nan | kanji bun in a circle row 9 : nan | * | university | nan | kanji bun with a smaller kanji da (for daiga row 10 : nan | * | technical college | nan | kanji bun with a smaller kanji zhuan ( row 11 : @ | * | post office | u+3036 | down tack (t-shape) with overbar in row 12 : @ | x | sub post office (not distribution centre) | u+3012 | down tack (t-shape) with overbar row 13 : nan | * | police station | u+2b59 | heavy circled saltire row 14 : nan | * | koban (police box) | u+2613 | diagonal cross (saltire) row 15 : [?] | * | public health centre | u+2295 | greek cross in circle row 16 : [?] | * | hospital | nan | greek cross in shield row 17 : nan | * | prefectural office | u+26fb | oval bullseye row 18 : nan | * | city hall | u+2b57 | heavy circle with circle inside row 19 : * | * | ward office | u+25c9 | fisheye row 20 : nan | * | town hall | u+2b58 | heavy circle row 21 : [?] | * | shinto shrine | u+26e9 | shinto shrine row 22 : wan | * | buddhist temple | u+534d | manji (swastika) row 23 : [?] | * | castle | u+26eb | castle row 24 : [?] | * | cemetery | u+26fc | headstone graveyard symbol row 25 : nan | * | onsen (hot springs) | u+2668 | oval with three vertical wavy lines row 26 : [?] | * | historical landmark | u+26ec | historic site row 27 : [?] | * | summit | u+26f0 | mountain
|
table_csv/203_540.csv
| 1 |
{
"column_index": [
3
],
"row_index": [
2
]
}
|
[
"what are all the unicodes?",
"which of these have the letter a in them?"
] | 2 |
u+22a1
|
[
"Symbol",
"GSI",
"Meaning",
"Unicode",
"Description"
] |
which of these have the letter a in them?
|
[
[
"^",
"*",
"Base triangulation surveying point",
"U+25EC",
"Dot in upward-pointing triangle"
],
[
"nan",
"*",
"Electronic triangulation point",
"nan",
"Dot in upward-pointing triangle with flag"
],
[
"[?]",
"*",
"Benchmark",
"U+22A1",
"Dot in square"
],
[
"[?]",
"*",
"Factory",
"U+26ED",
"Gear without hub"
],
[
"[?]",
"*",
"Lighthouse",
"U+26EF",
"Map symbol for lighthouse"
],
[
"[?]",
"*",
"Power station",
"U+26EE",
"Gear with handles"
],
[
"Wen",
"*",
"Elementary or junior high school",
"U+6587",
"Kanji bun"
],
[
"[?]",
"*",
"High school",
"nan",
"Kanji bun in a circle"
],
[
"nan",
"*",
"University",
"nan",
"Kanji bun with a smaller kanji Da (for daiga"
],
[
"nan",
"*",
"Technical college",
"nan",
"Kanji bun with a smaller kanji Zhuan ("
],
[
"@",
"*",
"Post office",
"U+3036",
"Down tack (T-shape) with overbar in"
],
[
"@",
"x",
"Sub post office (not distribution centre)",
"U+3012",
"Down tack (T-shape) with overbar"
],
[
"nan",
"*",
"Police station",
"U+2B59",
"Heavy circled saltire"
],
[
"nan",
"*",
"Koban (police box)",
"U+2613",
"Diagonal cross (saltire)"
],
[
"[?]",
"*",
"Public health centre",
"U+2295",
"Greek cross in circle"
],
[
"[?]",
"*",
"Hospital",
"nan",
"Greek cross in shield"
],
[
"nan",
"*",
"Prefectural Office",
"U+26FB",
"Oval bullseye"
],
[
"nan",
"*",
"City hall",
"U+2B57",
"Heavy circle with circle inside"
],
[
"*",
"*",
"Ward office",
"U+25C9",
"Fisheye"
],
[
"nan",
"*",
"Town hall",
"U+2B58",
"Heavy circle"
],
[
"[?]",
"*",
"Shinto shrine",
"U+26E9",
"Shinto shrine"
],
[
"Wan",
"*",
"Buddhist temple",
"U+534D",
"Manji (Swastika)"
],
[
"[?]",
"*",
"Castle",
"U+26EB",
"Castle"
],
[
"[?]",
"*",
"Cemetery",
"U+26FC",
"Headstone graveyard symbol"
],
[
"nan",
"*",
"Onsen (hot springs)",
"U+2668",
"Oval with three vertical wavy lines"
],
[
"[?]",
"*",
"Historical landmark",
"U+26EC",
"Historic site"
],
[
"[?]",
"*",
"Summit",
"U+26F0",
"Mountain"
]
] |
[
"U+22A1"
] |
which of these have the letter a in them? || what are all the unicodes?
|
nt-8713
|
col : rank | diver | preliminary points | preliminary rank | final points | final rank | final total row 1 : nan | ingrid kramer (eua) | 56.3 | 1 | 34.98 | 2.0 | 91.28 row 2 : nan | paula jean myers-pope (usa) | 54.7 | 2 | 35.24 | 1.0 | 89.94 row 3 : nan | ninel krutova (urs) | 53.38 | 3 | 33.61 | 3.0 | 86.99 row 4 : 4.0 | juno stover-irwin (usa) | 51.9 | 6 | 31.69 | 4.0 | 83.59 row 5 : 5.0 | raisa gorokhovskaya (urs) | 51.53 | 8 | 31.5 | 5.0 | 83.03 row 6 : 6.0 | norma thomas (gbr) | 51.77 | 7 | 30.44 | 7.0 | 82.21 row 7 : 7.0 | nicolle darrigrand-pellissard (fra | 49.68 | 12 | 31.5 | 5.0 | 81.18 row 8 : 8.0 | phyllis long (gbr) | 52.12 | 5 | 28.86 | 9.0 | 80.98 row 9 : 9.0 | irene macdonald (can) | 51.31 | 9 | 29.18 | 8.0 | 80.49 row 10 : 10.0 | kumiko watanabe (jpn) | 51.04 | 10 | 28.56 | 10.0 | 79.6 row 11 : 11.0 | kanoko tsutani-mabuchi (jpn | 49.76 | 11 | 28.14 | 11.0 | 77.9 row 12 : 12.0 | birte christoffersen-hanson (swe) | 53.03 | 4 | 24.4 | 12.0 | 77.43 row 13 : 13.0 | maria teresa adames (mex) | 49.54 | 13 | nan | nan | nan row 14 : 14.0 | hanna laursen (den) | 48.89 | 14 | nan | nan | nan row 15 : 15.0 | gabriele schope (eua) | 48.81 | 15 | nan | nan | nan row 16 : 16.0 | bende velin (den) | 48.35 | 16 | nan | nan | nan row 17 : 17.0 | laura conter (ita) | 45.55 | 17 | nan | nan | nan row 18 : 18.0 | susan knight (aus) | 43.03 | 18 | nan | nan | nan
|
table_csv/203_780.csv
| 0 |
{
"column_index": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17
]
}
|
[
"who are all of the drivers?"
] | 0 |
ingrid kramer (eua), paula jean myers-pope (usa), ninel krutova (urs), juno stover-irwin (usa), raisa gorokhovskaya (urs), norma thomas (gbr), nicolle darrigrand-pellissard (fra, phyllis long (gbr), irene macdonald (can), kumiko watanabe (jpn), kanoko tsutani-mabuchi (jpn, birte christoffersen-hanson (swe), maria teresa adames (mex), hanna laursen (den), gabriele schope (eua), bende velin (den), laura conter (ita), susan knight (aus)
|
[
"Rank",
"Diver",
"Preliminary Points",
"Preliminary Rank",
"Final Points",
"Final Rank",
"Final Total"
] |
who are all of the drivers?
|
[
[
"nan",
"Ingrid Kramer (EUA)",
"56.3",
"1",
"34.98",
"2.0",
"91.28"
],
[
"nan",
"Paula Jean Myers-Pope (USA)",
"54.7",
"2",
"35.24",
"1.0",
"89.94"
],
[
"nan",
"Ninel Krutova (URS)",
"53.38",
"3",
"33.61",
"3.0",
"86.99"
],
[
"4.0",
"Juno Stover-Irwin (USA)",
"51.9",
"6",
"31.69",
"4.0",
"83.59"
],
[
"5.0",
"Raisa Gorokhovskaya (URS)",
"51.53",
"8",
"31.5",
"5.0",
"83.03"
],
[
"6.0",
"Norma Thomas (GBR)",
"51.77",
"7",
"30.44",
"7.0",
"82.21"
],
[
"7.0",
"Nicolle Darrigrand-Pellissard (FRA",
"49.68",
"12",
"31.5",
"5.0",
"81.18"
],
[
"8.0",
"Phyllis Long (GBR)",
"52.12",
"5",
"28.86",
"9.0",
"80.98"
],
[
"9.0",
"Irene MacDonald (CAN)",
"51.31",
"9",
"29.18",
"8.0",
"80.49"
],
[
"10.0",
"Kumiko Watanabe (JPN)",
"51.04",
"10",
"28.56",
"10.0",
"79.6"
],
[
"11.0",
"Kanoko Tsutani-Mabuchi (JPN",
"49.76",
"11",
"28.14",
"11.0",
"77.9"
],
[
"12.0",
"Birte Christoffersen-Hanson (SWE)",
"53.03",
"4",
"24.4",
"12.0",
"77.43"
],
[
"13.0",
"Maria Teresa Adames (MEX)",
"49.54",
"13",
"nan",
"nan",
"nan"
],
[
"14.0",
"Hanna Laursen (DEN)",
"48.89",
"14",
"nan",
"nan",
"nan"
],
[
"15.0",
"Gabriele Schope (EUA)",
"48.81",
"15",
"nan",
"nan",
"nan"
],
[
"16.0",
"Bende Velin (DEN)",
"48.35",
"16",
"nan",
"nan",
"nan"
],
[
"17.0",
"Laura Conter (ITA)",
"45.55",
"17",
"nan",
"nan",
"nan"
],
[
"18.0",
"Susan Knight (AUS)",
"43.03",
"18",
"nan",
"nan",
"nan"
]
] |
[
"Ingrid Kramer (EUA)",
"Paula Jean Myers-Pope (USA)",
"Ninel Krutova (URS)",
"Juno Stover-Irwin (USA)",
"Raisa Gorokhovskaya (URS)",
"Norma Thomas (GBR)",
"Nicolle Darrigrand-Pellissard (FRA",
"Phyllis Long (GBR)",
"Irene MacDonald (CAN)",
"Kumiko Watanabe (JPN)",
"Kanoko Tsutani-Mabuchi (JPN",
"Birte Christoffersen-Hanson (SWE)",
"Maria Teresa Adames (MEX)",
"Hanna Laursen (DEN)",
"Gabriele Schope (EUA)",
"Bende Velin (DEN)",
"Laura Conter (ITA)",
"Susan Knight (AUS)"
] |
who are all of the drivers? ||
|
nt-8713
|
col : rank | diver | preliminary points | preliminary rank | final points | final rank | final total row 1 : nan | ingrid kramer (eua) | 56.3 | 1 | 34.98 | 2.0 | 91.28 row 2 : nan | paula jean myers-pope (usa) | 54.7 | 2 | 35.24 | 1.0 | 89.94 row 3 : nan | ninel krutova (urs) | 53.38 | 3 | 33.61 | 3.0 | 86.99 row 4 : 4.0 | juno stover-irwin (usa) | 51.9 | 6 | 31.69 | 4.0 | 83.59 row 5 : 5.0 | raisa gorokhovskaya (urs) | 51.53 | 8 | 31.5 | 5.0 | 83.03 row 6 : 6.0 | norma thomas (gbr) | 51.77 | 7 | 30.44 | 7.0 | 82.21 row 7 : 7.0 | nicolle darrigrand-pellissard (fra | 49.68 | 12 | 31.5 | 5.0 | 81.18 row 8 : 8.0 | phyllis long (gbr) | 52.12 | 5 | 28.86 | 9.0 | 80.98 row 9 : 9.0 | irene macdonald (can) | 51.31 | 9 | 29.18 | 8.0 | 80.49 row 10 : 10.0 | kumiko watanabe (jpn) | 51.04 | 10 | 28.56 | 10.0 | 79.6 row 11 : 11.0 | kanoko tsutani-mabuchi (jpn | 49.76 | 11 | 28.14 | 11.0 | 77.9 row 12 : 12.0 | birte christoffersen-hanson (swe) | 53.03 | 4 | 24.4 | 12.0 | 77.43 row 13 : 13.0 | maria teresa adames (mex) | 49.54 | 13 | nan | nan | nan row 14 : 14.0 | hanna laursen (den) | 48.89 | 14 | nan | nan | nan row 15 : 15.0 | gabriele schope (eua) | 48.81 | 15 | nan | nan | nan row 16 : 16.0 | bende velin (den) | 48.35 | 16 | nan | nan | nan row 17 : 17.0 | laura conter (ita) | 45.55 | 17 | nan | nan | nan row 18 : 18.0 | susan knight (aus) | 43.03 | 18 | nan | nan | nan
|
table_csv/203_780.csv
| 1 |
{
"column_index": [
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17
]
}
|
[
"who are all of the drivers?",
"what were their final points?"
] | 0 |
34.98, 35.24, 33.61, 31.69, 31.50, 30.44, 31.50, 28.86, 29.18, 28.56, 28.14, 24.40, , , , , ,
|
[
"Rank",
"Diver",
"Preliminary Points",
"Preliminary Rank",
"Final Points",
"Final Rank",
"Final Total"
] |
what were their final points?
|
[
[
"nan",
"Ingrid Kramer (EUA)",
"56.3",
"1",
"34.98",
"2.0",
"91.28"
],
[
"nan",
"Paula Jean Myers-Pope (USA)",
"54.7",
"2",
"35.24",
"1.0",
"89.94"
],
[
"nan",
"Ninel Krutova (URS)",
"53.38",
"3",
"33.61",
"3.0",
"86.99"
],
[
"4.0",
"Juno Stover-Irwin (USA)",
"51.9",
"6",
"31.69",
"4.0",
"83.59"
],
[
"5.0",
"Raisa Gorokhovskaya (URS)",
"51.53",
"8",
"31.5",
"5.0",
"83.03"
],
[
"6.0",
"Norma Thomas (GBR)",
"51.77",
"7",
"30.44",
"7.0",
"82.21"
],
[
"7.0",
"Nicolle Darrigrand-Pellissard (FRA",
"49.68",
"12",
"31.5",
"5.0",
"81.18"
],
[
"8.0",
"Phyllis Long (GBR)",
"52.12",
"5",
"28.86",
"9.0",
"80.98"
],
[
"9.0",
"Irene MacDonald (CAN)",
"51.31",
"9",
"29.18",
"8.0",
"80.49"
],
[
"10.0",
"Kumiko Watanabe (JPN)",
"51.04",
"10",
"28.56",
"10.0",
"79.6"
],
[
"11.0",
"Kanoko Tsutani-Mabuchi (JPN",
"49.76",
"11",
"28.14",
"11.0",
"77.9"
],
[
"12.0",
"Birte Christoffersen-Hanson (SWE)",
"53.03",
"4",
"24.4",
"12.0",
"77.43"
],
[
"13.0",
"Maria Teresa Adames (MEX)",
"49.54",
"13",
"nan",
"nan",
"nan"
],
[
"14.0",
"Hanna Laursen (DEN)",
"48.89",
"14",
"nan",
"nan",
"nan"
],
[
"15.0",
"Gabriele Schope (EUA)",
"48.81",
"15",
"nan",
"nan",
"nan"
],
[
"16.0",
"Bende Velin (DEN)",
"48.35",
"16",
"nan",
"nan",
"nan"
],
[
"17.0",
"Laura Conter (ITA)",
"45.55",
"17",
"nan",
"nan",
"nan"
],
[
"18.0",
"Susan Knight (AUS)",
"43.03",
"18",
"nan",
"nan",
"nan"
]
] |
[
"34.98",
"35.24",
"33.61",
"31.69",
"31.50",
"30.44",
"31.50",
"28.86",
"29.18",
"28.56",
"28.14",
"24.40",
"",
"",
"",
"",
"",
""
] |
what were their final points? || who are all of the drivers?
|
nt-8713
|
col : rank | diver | preliminary points | preliminary rank | final points | final rank | final total row 1 : nan | ingrid kramer (eua) | 56.3 | 1 | 34.98 | 2.0 | 91.28 row 2 : nan | paula jean myers-pope (usa) | 54.7 | 2 | 35.24 | 1.0 | 89.94 row 3 : nan | ninel krutova (urs) | 53.38 | 3 | 33.61 | 3.0 | 86.99 row 4 : 4.0 | juno stover-irwin (usa) | 51.9 | 6 | 31.69 | 4.0 | 83.59 row 5 : 5.0 | raisa gorokhovskaya (urs) | 51.53 | 8 | 31.5 | 5.0 | 83.03 row 6 : 6.0 | norma thomas (gbr) | 51.77 | 7 | 30.44 | 7.0 | 82.21 row 7 : 7.0 | nicolle darrigrand-pellissard (fra | 49.68 | 12 | 31.5 | 5.0 | 81.18 row 8 : 8.0 | phyllis long (gbr) | 52.12 | 5 | 28.86 | 9.0 | 80.98 row 9 : 9.0 | irene macdonald (can) | 51.31 | 9 | 29.18 | 8.0 | 80.49 row 10 : 10.0 | kumiko watanabe (jpn) | 51.04 | 10 | 28.56 | 10.0 | 79.6 row 11 : 11.0 | kanoko tsutani-mabuchi (jpn | 49.76 | 11 | 28.14 | 11.0 | 77.9 row 12 : 12.0 | birte christoffersen-hanson (swe) | 53.03 | 4 | 24.4 | 12.0 | 77.43 row 13 : 13.0 | maria teresa adames (mex) | 49.54 | 13 | nan | nan | nan row 14 : 14.0 | hanna laursen (den) | 48.89 | 14 | nan | nan | nan row 15 : 15.0 | gabriele schope (eua) | 48.81 | 15 | nan | nan | nan row 16 : 16.0 | bende velin (den) | 48.35 | 16 | nan | nan | nan row 17 : 17.0 | laura conter (ita) | 45.55 | 17 | nan | nan | nan row 18 : 18.0 | susan knight (aus) | 43.03 | 18 | nan | nan | nan
|
table_csv/203_780.csv
| 2 |
{
"column_index": [
4
],
"row_index": [
0
]
}
|
[
"who are all of the drivers?",
"what were their final points?",
"and which point is associated with ingrid kramer (eua)?"
] | 0 |
34.98
|
[
"Rank",
"Diver",
"Preliminary Points",
"Preliminary Rank",
"Final Points",
"Final Rank",
"Final Total"
] |
and which point is associated with ingrid kramer (eua)?
|
[
[
"nan",
"Ingrid Kramer (EUA)",
"56.3",
"1",
"34.98",
"2.0",
"91.28"
],
[
"nan",
"Paula Jean Myers-Pope (USA)",
"54.7",
"2",
"35.24",
"1.0",
"89.94"
],
[
"nan",
"Ninel Krutova (URS)",
"53.38",
"3",
"33.61",
"3.0",
"86.99"
],
[
"4.0",
"Juno Stover-Irwin (USA)",
"51.9",
"6",
"31.69",
"4.0",
"83.59"
],
[
"5.0",
"Raisa Gorokhovskaya (URS)",
"51.53",
"8",
"31.5",
"5.0",
"83.03"
],
[
"6.0",
"Norma Thomas (GBR)",
"51.77",
"7",
"30.44",
"7.0",
"82.21"
],
[
"7.0",
"Nicolle Darrigrand-Pellissard (FRA",
"49.68",
"12",
"31.5",
"5.0",
"81.18"
],
[
"8.0",
"Phyllis Long (GBR)",
"52.12",
"5",
"28.86",
"9.0",
"80.98"
],
[
"9.0",
"Irene MacDonald (CAN)",
"51.31",
"9",
"29.18",
"8.0",
"80.49"
],
[
"10.0",
"Kumiko Watanabe (JPN)",
"51.04",
"10",
"28.56",
"10.0",
"79.6"
],
[
"11.0",
"Kanoko Tsutani-Mabuchi (JPN",
"49.76",
"11",
"28.14",
"11.0",
"77.9"
],
[
"12.0",
"Birte Christoffersen-Hanson (SWE)",
"53.03",
"4",
"24.4",
"12.0",
"77.43"
],
[
"13.0",
"Maria Teresa Adames (MEX)",
"49.54",
"13",
"nan",
"nan",
"nan"
],
[
"14.0",
"Hanna Laursen (DEN)",
"48.89",
"14",
"nan",
"nan",
"nan"
],
[
"15.0",
"Gabriele Schope (EUA)",
"48.81",
"15",
"nan",
"nan",
"nan"
],
[
"16.0",
"Bende Velin (DEN)",
"48.35",
"16",
"nan",
"nan",
"nan"
],
[
"17.0",
"Laura Conter (ITA)",
"45.55",
"17",
"nan",
"nan",
"nan"
],
[
"18.0",
"Susan Knight (AUS)",
"43.03",
"18",
"nan",
"nan",
"nan"
]
] |
[
"34.98"
] |
and which point is associated with ingrid kramer (eua)? || what were their final points? | who are all of the drivers?
|
nt-8713
|
col : rank | diver | preliminary points | preliminary rank | final points | final rank | final total row 1 : nan | ingrid kramer (eua) | 56.3 | 1 | 34.98 | 2.0 | 91.28 row 2 : nan | paula jean myers-pope (usa) | 54.7 | 2 | 35.24 | 1.0 | 89.94 row 3 : nan | ninel krutova (urs) | 53.38 | 3 | 33.61 | 3.0 | 86.99 row 4 : 4.0 | juno stover-irwin (usa) | 51.9 | 6 | 31.69 | 4.0 | 83.59 row 5 : 5.0 | raisa gorokhovskaya (urs) | 51.53 | 8 | 31.5 | 5.0 | 83.03 row 6 : 6.0 | norma thomas (gbr) | 51.77 | 7 | 30.44 | 7.0 | 82.21 row 7 : 7.0 | nicolle darrigrand-pellissard (fra | 49.68 | 12 | 31.5 | 5.0 | 81.18 row 8 : 8.0 | phyllis long (gbr) | 52.12 | 5 | 28.86 | 9.0 | 80.98 row 9 : 9.0 | irene macdonald (can) | 51.31 | 9 | 29.18 | 8.0 | 80.49 row 10 : 10.0 | kumiko watanabe (jpn) | 51.04 | 10 | 28.56 | 10.0 | 79.6 row 11 : 11.0 | kanoko tsutani-mabuchi (jpn | 49.76 | 11 | 28.14 | 11.0 | 77.9 row 12 : 12.0 | birte christoffersen-hanson (swe) | 53.03 | 4 | 24.4 | 12.0 | 77.43 row 13 : 13.0 | maria teresa adames (mex) | 49.54 | 13 | nan | nan | nan row 14 : 14.0 | hanna laursen (den) | 48.89 | 14 | nan | nan | nan row 15 : 15.0 | gabriele schope (eua) | 48.81 | 15 | nan | nan | nan row 16 : 16.0 | bende velin (den) | 48.35 | 16 | nan | nan | nan row 17 : 17.0 | laura conter (ita) | 45.55 | 17 | nan | nan | nan row 18 : 18.0 | susan knight (aus) | 43.03 | 18 | nan | nan | nan
|
table_csv/203_780.csv
| 0 |
{
"column_index": [
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11
]
}
|
[
"how many final points did each diver score (of those with a final point total listed)?"
] | 1 |
34.98, 35.24, 33.61, 31.69, 31.50, 30.44, 31.50, 28.86, 29.18, 28.56, 28.14, 24.40
|
[
"Rank",
"Diver",
"Preliminary Points",
"Preliminary Rank",
"Final Points",
"Final Rank",
"Final Total"
] |
how many final points did each diver score (of those with a final point total listed)?
|
[
[
"nan",
"Ingrid Kramer (EUA)",
"56.3",
"1",
"34.98",
"2.0",
"91.28"
],
[
"nan",
"Paula Jean Myers-Pope (USA)",
"54.7",
"2",
"35.24",
"1.0",
"89.94"
],
[
"nan",
"Ninel Krutova (URS)",
"53.38",
"3",
"33.61",
"3.0",
"86.99"
],
[
"4.0",
"Juno Stover-Irwin (USA)",
"51.9",
"6",
"31.69",
"4.0",
"83.59"
],
[
"5.0",
"Raisa Gorokhovskaya (URS)",
"51.53",
"8",
"31.5",
"5.0",
"83.03"
],
[
"6.0",
"Norma Thomas (GBR)",
"51.77",
"7",
"30.44",
"7.0",
"82.21"
],
[
"7.0",
"Nicolle Darrigrand-Pellissard (FRA",
"49.68",
"12",
"31.5",
"5.0",
"81.18"
],
[
"8.0",
"Phyllis Long (GBR)",
"52.12",
"5",
"28.86",
"9.0",
"80.98"
],
[
"9.0",
"Irene MacDonald (CAN)",
"51.31",
"9",
"29.18",
"8.0",
"80.49"
],
[
"10.0",
"Kumiko Watanabe (JPN)",
"51.04",
"10",
"28.56",
"10.0",
"79.6"
],
[
"11.0",
"Kanoko Tsutani-Mabuchi (JPN",
"49.76",
"11",
"28.14",
"11.0",
"77.9"
],
[
"12.0",
"Birte Christoffersen-Hanson (SWE)",
"53.03",
"4",
"24.4",
"12.0",
"77.43"
],
[
"13.0",
"Maria Teresa Adames (MEX)",
"49.54",
"13",
"nan",
"nan",
"nan"
],
[
"14.0",
"Hanna Laursen (DEN)",
"48.89",
"14",
"nan",
"nan",
"nan"
],
[
"15.0",
"Gabriele Schope (EUA)",
"48.81",
"15",
"nan",
"nan",
"nan"
],
[
"16.0",
"Bende Velin (DEN)",
"48.35",
"16",
"nan",
"nan",
"nan"
],
[
"17.0",
"Laura Conter (ITA)",
"45.55",
"17",
"nan",
"nan",
"nan"
],
[
"18.0",
"Susan Knight (AUS)",
"43.03",
"18",
"nan",
"nan",
"nan"
]
] |
[
"34.98",
"35.24",
"33.61",
"31.69",
"31.50",
"30.44",
"31.50",
"28.86",
"29.18",
"28.56",
"28.14",
"24.40"
] |
how many final points did each diver score (of those with a final point total listed)? ||
|
nt-8713
|
col : rank | diver | preliminary points | preliminary rank | final points | final rank | final total row 1 : nan | ingrid kramer (eua) | 56.3 | 1 | 34.98 | 2.0 | 91.28 row 2 : nan | paula jean myers-pope (usa) | 54.7 | 2 | 35.24 | 1.0 | 89.94 row 3 : nan | ninel krutova (urs) | 53.38 | 3 | 33.61 | 3.0 | 86.99 row 4 : 4.0 | juno stover-irwin (usa) | 51.9 | 6 | 31.69 | 4.0 | 83.59 row 5 : 5.0 | raisa gorokhovskaya (urs) | 51.53 | 8 | 31.5 | 5.0 | 83.03 row 6 : 6.0 | norma thomas (gbr) | 51.77 | 7 | 30.44 | 7.0 | 82.21 row 7 : 7.0 | nicolle darrigrand-pellissard (fra | 49.68 | 12 | 31.5 | 5.0 | 81.18 row 8 : 8.0 | phyllis long (gbr) | 52.12 | 5 | 28.86 | 9.0 | 80.98 row 9 : 9.0 | irene macdonald (can) | 51.31 | 9 | 29.18 | 8.0 | 80.49 row 10 : 10.0 | kumiko watanabe (jpn) | 51.04 | 10 | 28.56 | 10.0 | 79.6 row 11 : 11.0 | kanoko tsutani-mabuchi (jpn | 49.76 | 11 | 28.14 | 11.0 | 77.9 row 12 : 12.0 | birte christoffersen-hanson (swe) | 53.03 | 4 | 24.4 | 12.0 | 77.43 row 13 : 13.0 | maria teresa adames (mex) | 49.54 | 13 | nan | nan | nan row 14 : 14.0 | hanna laursen (den) | 48.89 | 14 | nan | nan | nan row 15 : 15.0 | gabriele schope (eua) | 48.81 | 15 | nan | nan | nan row 16 : 16.0 | bende velin (den) | 48.35 | 16 | nan | nan | nan row 17 : 17.0 | laura conter (ita) | 45.55 | 17 | nan | nan | nan row 18 : 18.0 | susan knight (aus) | 43.03 | 18 | nan | nan | nan
|
table_csv/203_780.csv
| 1 |
{
"column_index": [
4
],
"row_index": [
0
]
}
|
[
"how many final points did each diver score (of those with a final point total listed)?",
"how many did ingrid kramer (eua) score?"
] | 1 |
34.98
|
[
"Rank",
"Diver",
"Preliminary Points",
"Preliminary Rank",
"Final Points",
"Final Rank",
"Final Total"
] |
how many did ingrid kramer (eua) score?
|
[
[
"nan",
"Ingrid Kramer (EUA)",
"56.3",
"1",
"34.98",
"2.0",
"91.28"
],
[
"nan",
"Paula Jean Myers-Pope (USA)",
"54.7",
"2",
"35.24",
"1.0",
"89.94"
],
[
"nan",
"Ninel Krutova (URS)",
"53.38",
"3",
"33.61",
"3.0",
"86.99"
],
[
"4.0",
"Juno Stover-Irwin (USA)",
"51.9",
"6",
"31.69",
"4.0",
"83.59"
],
[
"5.0",
"Raisa Gorokhovskaya (URS)",
"51.53",
"8",
"31.5",
"5.0",
"83.03"
],
[
"6.0",
"Norma Thomas (GBR)",
"51.77",
"7",
"30.44",
"7.0",
"82.21"
],
[
"7.0",
"Nicolle Darrigrand-Pellissard (FRA",
"49.68",
"12",
"31.5",
"5.0",
"81.18"
],
[
"8.0",
"Phyllis Long (GBR)",
"52.12",
"5",
"28.86",
"9.0",
"80.98"
],
[
"9.0",
"Irene MacDonald (CAN)",
"51.31",
"9",
"29.18",
"8.0",
"80.49"
],
[
"10.0",
"Kumiko Watanabe (JPN)",
"51.04",
"10",
"28.56",
"10.0",
"79.6"
],
[
"11.0",
"Kanoko Tsutani-Mabuchi (JPN",
"49.76",
"11",
"28.14",
"11.0",
"77.9"
],
[
"12.0",
"Birte Christoffersen-Hanson (SWE)",
"53.03",
"4",
"24.4",
"12.0",
"77.43"
],
[
"13.0",
"Maria Teresa Adames (MEX)",
"49.54",
"13",
"nan",
"nan",
"nan"
],
[
"14.0",
"Hanna Laursen (DEN)",
"48.89",
"14",
"nan",
"nan",
"nan"
],
[
"15.0",
"Gabriele Schope (EUA)",
"48.81",
"15",
"nan",
"nan",
"nan"
],
[
"16.0",
"Bende Velin (DEN)",
"48.35",
"16",
"nan",
"nan",
"nan"
],
[
"17.0",
"Laura Conter (ITA)",
"45.55",
"17",
"nan",
"nan",
"nan"
],
[
"18.0",
"Susan Knight (AUS)",
"43.03",
"18",
"nan",
"nan",
"nan"
]
] |
[
"34.98"
] |
how many did ingrid kramer (eua) score? || how many final points did each diver score (of those with a final point total listed)?
|
nt-8713
|
col : rank | diver | preliminary points | preliminary rank | final points | final rank | final total row 1 : nan | ingrid kramer (eua) | 56.3 | 1 | 34.98 | 2.0 | 91.28 row 2 : nan | paula jean myers-pope (usa) | 54.7 | 2 | 35.24 | 1.0 | 89.94 row 3 : nan | ninel krutova (urs) | 53.38 | 3 | 33.61 | 3.0 | 86.99 row 4 : 4.0 | juno stover-irwin (usa) | 51.9 | 6 | 31.69 | 4.0 | 83.59 row 5 : 5.0 | raisa gorokhovskaya (urs) | 51.53 | 8 | 31.5 | 5.0 | 83.03 row 6 : 6.0 | norma thomas (gbr) | 51.77 | 7 | 30.44 | 7.0 | 82.21 row 7 : 7.0 | nicolle darrigrand-pellissard (fra | 49.68 | 12 | 31.5 | 5.0 | 81.18 row 8 : 8.0 | phyllis long (gbr) | 52.12 | 5 | 28.86 | 9.0 | 80.98 row 9 : 9.0 | irene macdonald (can) | 51.31 | 9 | 29.18 | 8.0 | 80.49 row 10 : 10.0 | kumiko watanabe (jpn) | 51.04 | 10 | 28.56 | 10.0 | 79.6 row 11 : 11.0 | kanoko tsutani-mabuchi (jpn | 49.76 | 11 | 28.14 | 11.0 | 77.9 row 12 : 12.0 | birte christoffersen-hanson (swe) | 53.03 | 4 | 24.4 | 12.0 | 77.43 row 13 : 13.0 | maria teresa adames (mex) | 49.54 | 13 | nan | nan | nan row 14 : 14.0 | hanna laursen (den) | 48.89 | 14 | nan | nan | nan row 15 : 15.0 | gabriele schope (eua) | 48.81 | 15 | nan | nan | nan row 16 : 16.0 | bende velin (den) | 48.35 | 16 | nan | nan | nan row 17 : 17.0 | laura conter (ita) | 45.55 | 17 | nan | nan | nan row 18 : 18.0 | susan knight (aus) | 43.03 | 18 | nan | nan | nan
|
table_csv/203_780.csv
| 0 |
{
"column_index": [
2,
4
],
"row_index": [
0,
0
]
}
|
[
"how many points did ingrid kramer score?"
] | 2 |
56.30, 34.98
|
[
"Rank",
"Diver",
"Preliminary Points",
"Preliminary Rank",
"Final Points",
"Final Rank",
"Final Total"
] |
how many points did ingrid kramer score?
|
[
[
"nan",
"Ingrid Kramer (EUA)",
"56.3",
"1",
"34.98",
"2.0",
"91.28"
],
[
"nan",
"Paula Jean Myers-Pope (USA)",
"54.7",
"2",
"35.24",
"1.0",
"89.94"
],
[
"nan",
"Ninel Krutova (URS)",
"53.38",
"3",
"33.61",
"3.0",
"86.99"
],
[
"4.0",
"Juno Stover-Irwin (USA)",
"51.9",
"6",
"31.69",
"4.0",
"83.59"
],
[
"5.0",
"Raisa Gorokhovskaya (URS)",
"51.53",
"8",
"31.5",
"5.0",
"83.03"
],
[
"6.0",
"Norma Thomas (GBR)",
"51.77",
"7",
"30.44",
"7.0",
"82.21"
],
[
"7.0",
"Nicolle Darrigrand-Pellissard (FRA",
"49.68",
"12",
"31.5",
"5.0",
"81.18"
],
[
"8.0",
"Phyllis Long (GBR)",
"52.12",
"5",
"28.86",
"9.0",
"80.98"
],
[
"9.0",
"Irene MacDonald (CAN)",
"51.31",
"9",
"29.18",
"8.0",
"80.49"
],
[
"10.0",
"Kumiko Watanabe (JPN)",
"51.04",
"10",
"28.56",
"10.0",
"79.6"
],
[
"11.0",
"Kanoko Tsutani-Mabuchi (JPN",
"49.76",
"11",
"28.14",
"11.0",
"77.9"
],
[
"12.0",
"Birte Christoffersen-Hanson (SWE)",
"53.03",
"4",
"24.4",
"12.0",
"77.43"
],
[
"13.0",
"Maria Teresa Adames (MEX)",
"49.54",
"13",
"nan",
"nan",
"nan"
],
[
"14.0",
"Hanna Laursen (DEN)",
"48.89",
"14",
"nan",
"nan",
"nan"
],
[
"15.0",
"Gabriele Schope (EUA)",
"48.81",
"15",
"nan",
"nan",
"nan"
],
[
"16.0",
"Bende Velin (DEN)",
"48.35",
"16",
"nan",
"nan",
"nan"
],
[
"17.0",
"Laura Conter (ITA)",
"45.55",
"17",
"nan",
"nan",
"nan"
],
[
"18.0",
"Susan Knight (AUS)",
"43.03",
"18",
"nan",
"nan",
"nan"
]
] |
[
"56.30",
"34.98"
] |
how many points did ingrid kramer score? ||
|
nt-8713
|
col : rank | diver | preliminary points | preliminary rank | final points | final rank | final total row 1 : nan | ingrid kramer (eua) | 56.3 | 1 | 34.98 | 2.0 | 91.28 row 2 : nan | paula jean myers-pope (usa) | 54.7 | 2 | 35.24 | 1.0 | 89.94 row 3 : nan | ninel krutova (urs) | 53.38 | 3 | 33.61 | 3.0 | 86.99 row 4 : 4.0 | juno stover-irwin (usa) | 51.9 | 6 | 31.69 | 4.0 | 83.59 row 5 : 5.0 | raisa gorokhovskaya (urs) | 51.53 | 8 | 31.5 | 5.0 | 83.03 row 6 : 6.0 | norma thomas (gbr) | 51.77 | 7 | 30.44 | 7.0 | 82.21 row 7 : 7.0 | nicolle darrigrand-pellissard (fra | 49.68 | 12 | 31.5 | 5.0 | 81.18 row 8 : 8.0 | phyllis long (gbr) | 52.12 | 5 | 28.86 | 9.0 | 80.98 row 9 : 9.0 | irene macdonald (can) | 51.31 | 9 | 29.18 | 8.0 | 80.49 row 10 : 10.0 | kumiko watanabe (jpn) | 51.04 | 10 | 28.56 | 10.0 | 79.6 row 11 : 11.0 | kanoko tsutani-mabuchi (jpn | 49.76 | 11 | 28.14 | 11.0 | 77.9 row 12 : 12.0 | birte christoffersen-hanson (swe) | 53.03 | 4 | 24.4 | 12.0 | 77.43 row 13 : 13.0 | maria teresa adames (mex) | 49.54 | 13 | nan | nan | nan row 14 : 14.0 | hanna laursen (den) | 48.89 | 14 | nan | nan | nan row 15 : 15.0 | gabriele schope (eua) | 48.81 | 15 | nan | nan | nan row 16 : 16.0 | bende velin (den) | 48.35 | 16 | nan | nan | nan row 17 : 17.0 | laura conter (ita) | 45.55 | 17 | nan | nan | nan row 18 : 18.0 | susan knight (aus) | 43.03 | 18 | nan | nan | nan
|
table_csv/203_780.csv
| 1 |
{
"column_index": [
4
],
"row_index": [
0
]
}
|
[
"how many points did ingrid kramer score?",
"how many did she score in the final?"
] | 2 |
34.98
|
[
"Rank",
"Diver",
"Preliminary Points",
"Preliminary Rank",
"Final Points",
"Final Rank",
"Final Total"
] |
how many did she score in the final?
|
[
[
"nan",
"Ingrid Kramer (EUA)",
"56.3",
"1",
"34.98",
"2.0",
"91.28"
],
[
"nan",
"Paula Jean Myers-Pope (USA)",
"54.7",
"2",
"35.24",
"1.0",
"89.94"
],
[
"nan",
"Ninel Krutova (URS)",
"53.38",
"3",
"33.61",
"3.0",
"86.99"
],
[
"4.0",
"Juno Stover-Irwin (USA)",
"51.9",
"6",
"31.69",
"4.0",
"83.59"
],
[
"5.0",
"Raisa Gorokhovskaya (URS)",
"51.53",
"8",
"31.5",
"5.0",
"83.03"
],
[
"6.0",
"Norma Thomas (GBR)",
"51.77",
"7",
"30.44",
"7.0",
"82.21"
],
[
"7.0",
"Nicolle Darrigrand-Pellissard (FRA",
"49.68",
"12",
"31.5",
"5.0",
"81.18"
],
[
"8.0",
"Phyllis Long (GBR)",
"52.12",
"5",
"28.86",
"9.0",
"80.98"
],
[
"9.0",
"Irene MacDonald (CAN)",
"51.31",
"9",
"29.18",
"8.0",
"80.49"
],
[
"10.0",
"Kumiko Watanabe (JPN)",
"51.04",
"10",
"28.56",
"10.0",
"79.6"
],
[
"11.0",
"Kanoko Tsutani-Mabuchi (JPN",
"49.76",
"11",
"28.14",
"11.0",
"77.9"
],
[
"12.0",
"Birte Christoffersen-Hanson (SWE)",
"53.03",
"4",
"24.4",
"12.0",
"77.43"
],
[
"13.0",
"Maria Teresa Adames (MEX)",
"49.54",
"13",
"nan",
"nan",
"nan"
],
[
"14.0",
"Hanna Laursen (DEN)",
"48.89",
"14",
"nan",
"nan",
"nan"
],
[
"15.0",
"Gabriele Schope (EUA)",
"48.81",
"15",
"nan",
"nan",
"nan"
],
[
"16.0",
"Bende Velin (DEN)",
"48.35",
"16",
"nan",
"nan",
"nan"
],
[
"17.0",
"Laura Conter (ITA)",
"45.55",
"17",
"nan",
"nan",
"nan"
],
[
"18.0",
"Susan Knight (AUS)",
"43.03",
"18",
"nan",
"nan",
"nan"
]
] |
[
"34.98"
] |
how many did she score in the final? || how many points did ingrid kramer score?
|
nt-8384
|
col : outcome | no. | date | tournament | surface | opponent | score row 1 : runner-up | 1.0 | 1 august 2011 | sao paulo, brazil | clay | maria fernanda alves | 6-4, 5-7, 3-6 row 2 : winner | 1.0 | 24 october 2011 | goiania, brazil | clay | barbara luz | 6-2, 6-0 row 3 : winner | 2.0 | 2 april 2012 | ribeirao preto, brazil | hard | natasha fourouclas | 6-0, 6-1 row 4 : winner | 3.0 | 25 march 2013 | ribeirao preto, brazil | clay | andrea benitez | 7-6(7-2), 6-2 row 5 : winner | 4.0 | 15 april 2013 | antalya 15, turkey | hard | tereza martincova | 6-4, 6-3 row 6 : runner-up | 2.0 | 20 may 2013 | caserta, italy | clay | renata voracova | 4-6, 1-6 row 7 : runner-up | 3.0 | 17 june 2013 | lenzerheide, switzerland | clay | laura siegemund | 2-6, 3-6
|
table_csv/204_868.csv
| 0 |
{
"column_index": [
5,
5,
5,
5,
5,
5,
5
],
"row_index": [
0,
1,
2,
3,
4,
5,
6
]
}
|
[
"who were all the opponents?"
] | 0 |
maria fernanda alves, barbara luz, natasha fourouclas, andrea benitez, tereza martincova, renata voracova, laura siegemund
|
[
"Outcome",
"No.",
"Date",
"Tournament",
"Surface",
"Opponent",
"Score"
] |
who were all the opponents?
|
[
[
"Runner-up",
"1.0",
"1 August 2011",
"Sao Paulo, Brazil",
"Clay",
"Maria Fernanda Alves",
"6-4, 5-7, 3-6"
],
[
"Winner",
"1.0",
"24 October 2011",
"Goiania, Brazil",
"Clay",
"Barbara Luz",
"6-2, 6-0"
],
[
"Winner",
"2.0",
"2 April 2012",
"Ribeirao Preto, Brazil",
"Hard",
"Natasha Fourouclas",
"6-0, 6-1"
],
[
"Winner",
"3.0",
"25 March 2013",
"Ribeirao Preto, Brazil",
"Clay",
"Andrea Benitez",
"7-6(7-2), 6-2"
],
[
"Winner",
"4.0",
"15 April 2013",
"Antalya 15, Turkey",
"Hard",
"Tereza Martincova",
"6-4, 6-3"
],
[
"Runner-up",
"2.0",
"20 May 2013",
"Caserta, Italy",
"Clay",
"Renata Voracova",
"4-6, 1-6"
],
[
"Runner-up",
"3.0",
"17 June 2013",
"Lenzerheide, Switzerland",
"Clay",
"Laura Siegemund",
"2-6, 3-6"
]
] |
[
"Maria Fernanda Alves",
"Barbara Luz",
"Natasha Fourouclas",
"Andrea Benitez",
"Tereza Martincova",
"Renata Voracova",
"Laura Siegemund"
] |
who were all the opponents? ||
|
nt-8384
|
col : outcome | no. | date | tournament | surface | opponent | score row 1 : runner-up | 1.0 | 1 august 2011 | sao paulo, brazil | clay | maria fernanda alves | 6-4, 5-7, 3-6 row 2 : winner | 1.0 | 24 october 2011 | goiania, brazil | clay | barbara luz | 6-2, 6-0 row 3 : winner | 2.0 | 2 april 2012 | ribeirao preto, brazil | hard | natasha fourouclas | 6-0, 6-1 row 4 : winner | 3.0 | 25 march 2013 | ribeirao preto, brazil | clay | andrea benitez | 7-6(7-2), 6-2 row 5 : winner | 4.0 | 15 april 2013 | antalya 15, turkey | hard | tereza martincova | 6-4, 6-3 row 6 : runner-up | 2.0 | 20 may 2013 | caserta, italy | clay | renata voracova | 4-6, 1-6 row 7 : runner-up | 3.0 | 17 june 2013 | lenzerheide, switzerland | clay | laura siegemund | 2-6, 3-6
|
table_csv/204_868.csv
| 1 |
{
"column_index": [
5
],
"row_index": [
6
]
}
|
[
"who were all the opponents?",
"which of these did they play in switzerland?"
] | 0 |
laura siegemund
|
[
"Outcome",
"No.",
"Date",
"Tournament",
"Surface",
"Opponent",
"Score"
] |
which of these did they play in switzerland?
|
[
[
"Runner-up",
"1.0",
"1 August 2011",
"Sao Paulo, Brazil",
"Clay",
"Maria Fernanda Alves",
"6-4, 5-7, 3-6"
],
[
"Winner",
"1.0",
"24 October 2011",
"Goiania, Brazil",
"Clay",
"Barbara Luz",
"6-2, 6-0"
],
[
"Winner",
"2.0",
"2 April 2012",
"Ribeirao Preto, Brazil",
"Hard",
"Natasha Fourouclas",
"6-0, 6-1"
],
[
"Winner",
"3.0",
"25 March 2013",
"Ribeirao Preto, Brazil",
"Clay",
"Andrea Benitez",
"7-6(7-2), 6-2"
],
[
"Winner",
"4.0",
"15 April 2013",
"Antalya 15, Turkey",
"Hard",
"Tereza Martincova",
"6-4, 6-3"
],
[
"Runner-up",
"2.0",
"20 May 2013",
"Caserta, Italy",
"Clay",
"Renata Voracova",
"4-6, 1-6"
],
[
"Runner-up",
"3.0",
"17 June 2013",
"Lenzerheide, Switzerland",
"Clay",
"Laura Siegemund",
"2-6, 3-6"
]
] |
[
"Laura Siegemund"
] |
which of these did they play in switzerland? || who were all the opponents?
|
nt-8384
|
col : outcome | no. | date | tournament | surface | opponent | score row 1 : runner-up | 1.0 | 1 august 2011 | sao paulo, brazil | clay | maria fernanda alves | 6-4, 5-7, 3-6 row 2 : winner | 1.0 | 24 october 2011 | goiania, brazil | clay | barbara luz | 6-2, 6-0 row 3 : winner | 2.0 | 2 april 2012 | ribeirao preto, brazil | hard | natasha fourouclas | 6-0, 6-1 row 4 : winner | 3.0 | 25 march 2013 | ribeirao preto, brazil | clay | andrea benitez | 7-6(7-2), 6-2 row 5 : winner | 4.0 | 15 april 2013 | antalya 15, turkey | hard | tereza martincova | 6-4, 6-3 row 6 : runner-up | 2.0 | 20 may 2013 | caserta, italy | clay | renata voracova | 4-6, 1-6 row 7 : runner-up | 3.0 | 17 june 2013 | lenzerheide, switzerland | clay | laura siegemund | 2-6, 3-6
|
table_csv/204_868.csv
| 0 |
{
"column_index": [
3,
3,
3,
3,
3,
3,
3
],
"row_index": [
0,
1,
2,
3,
4,
5,
6
]
}
|
[
"what are the tournaments that beatriz haddad maia has been a part of?"
] | 1 |
sao paulo, brazil, goiania, brazil, ribeirao preto, brazil, ribeirao preto, brazil, antalya 15, turkey, caserta, italy, lenzerheide, switzerland
|
[
"Outcome",
"No.",
"Date",
"Tournament",
"Surface",
"Opponent",
"Score"
] |
what are the tournaments that beatriz haddad maia has been a part of?
|
[
[
"Runner-up",
"1.0",
"1 August 2011",
"Sao Paulo, Brazil",
"Clay",
"Maria Fernanda Alves",
"6-4, 5-7, 3-6"
],
[
"Winner",
"1.0",
"24 October 2011",
"Goiania, Brazil",
"Clay",
"Barbara Luz",
"6-2, 6-0"
],
[
"Winner",
"2.0",
"2 April 2012",
"Ribeirao Preto, Brazil",
"Hard",
"Natasha Fourouclas",
"6-0, 6-1"
],
[
"Winner",
"3.0",
"25 March 2013",
"Ribeirao Preto, Brazil",
"Clay",
"Andrea Benitez",
"7-6(7-2), 6-2"
],
[
"Winner",
"4.0",
"15 April 2013",
"Antalya 15, Turkey",
"Hard",
"Tereza Martincova",
"6-4, 6-3"
],
[
"Runner-up",
"2.0",
"20 May 2013",
"Caserta, Italy",
"Clay",
"Renata Voracova",
"4-6, 1-6"
],
[
"Runner-up",
"3.0",
"17 June 2013",
"Lenzerheide, Switzerland",
"Clay",
"Laura Siegemund",
"2-6, 3-6"
]
] |
[
"Sao Paulo, Brazil",
"Goiania, Brazil",
"Ribeirao Preto, Brazil",
"Ribeirao Preto, Brazil",
"Antalya 15, Turkey",
"Caserta, Italy",
"Lenzerheide, Switzerland"
] |
what are the tournaments that beatriz haddad maia has been a part of? ||
|
nt-8384
|
col : outcome | no. | date | tournament | surface | opponent | score row 1 : runner-up | 1.0 | 1 august 2011 | sao paulo, brazil | clay | maria fernanda alves | 6-4, 5-7, 3-6 row 2 : winner | 1.0 | 24 october 2011 | goiania, brazil | clay | barbara luz | 6-2, 6-0 row 3 : winner | 2.0 | 2 april 2012 | ribeirao preto, brazil | hard | natasha fourouclas | 6-0, 6-1 row 4 : winner | 3.0 | 25 march 2013 | ribeirao preto, brazil | clay | andrea benitez | 7-6(7-2), 6-2 row 5 : winner | 4.0 | 15 april 2013 | antalya 15, turkey | hard | tereza martincova | 6-4, 6-3 row 6 : runner-up | 2.0 | 20 may 2013 | caserta, italy | clay | renata voracova | 4-6, 1-6 row 7 : runner-up | 3.0 | 17 june 2013 | lenzerheide, switzerland | clay | laura siegemund | 2-6, 3-6
|
table_csv/204_868.csv
| 1 |
{
"column_index": [
3
],
"row_index": [
6
]
}
|
[
"what are the tournaments that beatriz haddad maia has been a part of?",
"which of these tournaments are in switzerland?"
] | 1 |
lenzerheide, switzerland
|
[
"Outcome",
"No.",
"Date",
"Tournament",
"Surface",
"Opponent",
"Score"
] |
which of these tournaments are in switzerland?
|
[
[
"Runner-up",
"1.0",
"1 August 2011",
"Sao Paulo, Brazil",
"Clay",
"Maria Fernanda Alves",
"6-4, 5-7, 3-6"
],
[
"Winner",
"1.0",
"24 October 2011",
"Goiania, Brazil",
"Clay",
"Barbara Luz",
"6-2, 6-0"
],
[
"Winner",
"2.0",
"2 April 2012",
"Ribeirao Preto, Brazil",
"Hard",
"Natasha Fourouclas",
"6-0, 6-1"
],
[
"Winner",
"3.0",
"25 March 2013",
"Ribeirao Preto, Brazil",
"Clay",
"Andrea Benitez",
"7-6(7-2), 6-2"
],
[
"Winner",
"4.0",
"15 April 2013",
"Antalya 15, Turkey",
"Hard",
"Tereza Martincova",
"6-4, 6-3"
],
[
"Runner-up",
"2.0",
"20 May 2013",
"Caserta, Italy",
"Clay",
"Renata Voracova",
"4-6, 1-6"
],
[
"Runner-up",
"3.0",
"17 June 2013",
"Lenzerheide, Switzerland",
"Clay",
"Laura Siegemund",
"2-6, 3-6"
]
] |
[
"Lenzerheide, Switzerland"
] |
which of these tournaments are in switzerland? || what are the tournaments that beatriz haddad maia has been a part of?
|
nt-8384
|
col : outcome | no. | date | tournament | surface | opponent | score row 1 : runner-up | 1.0 | 1 august 2011 | sao paulo, brazil | clay | maria fernanda alves | 6-4, 5-7, 3-6 row 2 : winner | 1.0 | 24 october 2011 | goiania, brazil | clay | barbara luz | 6-2, 6-0 row 3 : winner | 2.0 | 2 april 2012 | ribeirao preto, brazil | hard | natasha fourouclas | 6-0, 6-1 row 4 : winner | 3.0 | 25 march 2013 | ribeirao preto, brazil | clay | andrea benitez | 7-6(7-2), 6-2 row 5 : winner | 4.0 | 15 april 2013 | antalya 15, turkey | hard | tereza martincova | 6-4, 6-3 row 6 : runner-up | 2.0 | 20 may 2013 | caserta, italy | clay | renata voracova | 4-6, 1-6 row 7 : runner-up | 3.0 | 17 june 2013 | lenzerheide, switzerland | clay | laura siegemund | 2-6, 3-6
|
table_csv/204_868.csv
| 2 |
{
"column_index": [
5
],
"row_index": [
6
]
}
|
[
"what are the tournaments that beatriz haddad maia has been a part of?",
"which of these tournaments are in switzerland?",
"who was the opponent for this tournament?"
] | 1 |
laura siegemund
|
[
"Outcome",
"No.",
"Date",
"Tournament",
"Surface",
"Opponent",
"Score"
] |
who was the opponent for this tournament?
|
[
[
"Runner-up",
"1.0",
"1 August 2011",
"Sao Paulo, Brazil",
"Clay",
"Maria Fernanda Alves",
"6-4, 5-7, 3-6"
],
[
"Winner",
"1.0",
"24 October 2011",
"Goiania, Brazil",
"Clay",
"Barbara Luz",
"6-2, 6-0"
],
[
"Winner",
"2.0",
"2 April 2012",
"Ribeirao Preto, Brazil",
"Hard",
"Natasha Fourouclas",
"6-0, 6-1"
],
[
"Winner",
"3.0",
"25 March 2013",
"Ribeirao Preto, Brazil",
"Clay",
"Andrea Benitez",
"7-6(7-2), 6-2"
],
[
"Winner",
"4.0",
"15 April 2013",
"Antalya 15, Turkey",
"Hard",
"Tereza Martincova",
"6-4, 6-3"
],
[
"Runner-up",
"2.0",
"20 May 2013",
"Caserta, Italy",
"Clay",
"Renata Voracova",
"4-6, 1-6"
],
[
"Runner-up",
"3.0",
"17 June 2013",
"Lenzerheide, Switzerland",
"Clay",
"Laura Siegemund",
"2-6, 3-6"
]
] |
[
"Laura Siegemund"
] |
who was the opponent for this tournament? || which of these tournaments are in switzerland? | what are the tournaments that beatriz haddad maia has been a part of?
|
nt-8384
|
col : outcome | no. | date | tournament | surface | opponent | score row 1 : runner-up | 1.0 | 1 august 2011 | sao paulo, brazil | clay | maria fernanda alves | 6-4, 5-7, 3-6 row 2 : winner | 1.0 | 24 october 2011 | goiania, brazil | clay | barbara luz | 6-2, 6-0 row 3 : winner | 2.0 | 2 april 2012 | ribeirao preto, brazil | hard | natasha fourouclas | 6-0, 6-1 row 4 : winner | 3.0 | 25 march 2013 | ribeirao preto, brazil | clay | andrea benitez | 7-6(7-2), 6-2 row 5 : winner | 4.0 | 15 april 2013 | antalya 15, turkey | hard | tereza martincova | 6-4, 6-3 row 6 : runner-up | 2.0 | 20 may 2013 | caserta, italy | clay | renata voracova | 4-6, 1-6 row 7 : runner-up | 3.0 | 17 june 2013 | lenzerheide, switzerland | clay | laura siegemund | 2-6, 3-6
|
table_csv/204_868.csv
| 0 |
{
"column_index": [
5,
5,
5,
5,
5,
5,
5
],
"row_index": [
0,
1,
2,
3,
4,
5,
6
]
}
|
[
"who did this player face?"
] | 2 |
maria fernanda alves, barbara luz, natasha fourouclas, andrea benitez, tereza martincova, renata voracova, laura siegemund
|
[
"Outcome",
"No.",
"Date",
"Tournament",
"Surface",
"Opponent",
"Score"
] |
who did this player face?
|
[
[
"Runner-up",
"1.0",
"1 August 2011",
"Sao Paulo, Brazil",
"Clay",
"Maria Fernanda Alves",
"6-4, 5-7, 3-6"
],
[
"Winner",
"1.0",
"24 October 2011",
"Goiania, Brazil",
"Clay",
"Barbara Luz",
"6-2, 6-0"
],
[
"Winner",
"2.0",
"2 April 2012",
"Ribeirao Preto, Brazil",
"Hard",
"Natasha Fourouclas",
"6-0, 6-1"
],
[
"Winner",
"3.0",
"25 March 2013",
"Ribeirao Preto, Brazil",
"Clay",
"Andrea Benitez",
"7-6(7-2), 6-2"
],
[
"Winner",
"4.0",
"15 April 2013",
"Antalya 15, Turkey",
"Hard",
"Tereza Martincova",
"6-4, 6-3"
],
[
"Runner-up",
"2.0",
"20 May 2013",
"Caserta, Italy",
"Clay",
"Renata Voracova",
"4-6, 1-6"
],
[
"Runner-up",
"3.0",
"17 June 2013",
"Lenzerheide, Switzerland",
"Clay",
"Laura Siegemund",
"2-6, 3-6"
]
] |
[
"Maria Fernanda Alves",
"Barbara Luz",
"Natasha Fourouclas",
"Andrea Benitez",
"Tereza Martincova",
"Renata Voracova",
"Laura Siegemund"
] |
who did this player face? ||
|
nt-8384
|
col : outcome | no. | date | tournament | surface | opponent | score row 1 : runner-up | 1.0 | 1 august 2011 | sao paulo, brazil | clay | maria fernanda alves | 6-4, 5-7, 3-6 row 2 : winner | 1.0 | 24 october 2011 | goiania, brazil | clay | barbara luz | 6-2, 6-0 row 3 : winner | 2.0 | 2 april 2012 | ribeirao preto, brazil | hard | natasha fourouclas | 6-0, 6-1 row 4 : winner | 3.0 | 25 march 2013 | ribeirao preto, brazil | clay | andrea benitez | 7-6(7-2), 6-2 row 5 : winner | 4.0 | 15 april 2013 | antalya 15, turkey | hard | tereza martincova | 6-4, 6-3 row 6 : runner-up | 2.0 | 20 may 2013 | caserta, italy | clay | renata voracova | 4-6, 1-6 row 7 : runner-up | 3.0 | 17 june 2013 | lenzerheide, switzerland | clay | laura siegemund | 2-6, 3-6
|
table_csv/204_868.csv
| 1 |
{
"column_index": [
5
],
"row_index": [
6
]
}
|
[
"who did this player face?",
"which opponent did she face in switzerland?"
] | 2 |
laura siegemund
|
[
"Outcome",
"No.",
"Date",
"Tournament",
"Surface",
"Opponent",
"Score"
] |
which opponent did she face in switzerland?
|
[
[
"Runner-up",
"1.0",
"1 August 2011",
"Sao Paulo, Brazil",
"Clay",
"Maria Fernanda Alves",
"6-4, 5-7, 3-6"
],
[
"Winner",
"1.0",
"24 October 2011",
"Goiania, Brazil",
"Clay",
"Barbara Luz",
"6-2, 6-0"
],
[
"Winner",
"2.0",
"2 April 2012",
"Ribeirao Preto, Brazil",
"Hard",
"Natasha Fourouclas",
"6-0, 6-1"
],
[
"Winner",
"3.0",
"25 March 2013",
"Ribeirao Preto, Brazil",
"Clay",
"Andrea Benitez",
"7-6(7-2), 6-2"
],
[
"Winner",
"4.0",
"15 April 2013",
"Antalya 15, Turkey",
"Hard",
"Tereza Martincova",
"6-4, 6-3"
],
[
"Runner-up",
"2.0",
"20 May 2013",
"Caserta, Italy",
"Clay",
"Renata Voracova",
"4-6, 1-6"
],
[
"Runner-up",
"3.0",
"17 June 2013",
"Lenzerheide, Switzerland",
"Clay",
"Laura Siegemund",
"2-6, 3-6"
]
] |
[
"Laura Siegemund"
] |
which opponent did she face in switzerland? || who did this player face?
|
ns-2250
|
col : team | wins | losses | win % | gb row 1 : detroit tigers | 104 | 58 | 0.642 | 0.0 row 2 : toronto blue jays | 89 | 73 | 0.549 | 15.0 row 3 : new york yankees | 87 | 75 | 0.537 | 17.0 row 4 : boston red sox | 86 | 76 | 0.531 | 18.0 row 5 : baltimore orioles | 85 | 77 | 0.525 | 19.0 row 6 : cleveland indians | 75 | 87 | 0.463 | 29.0 row 7 : milwaukee brewers | 67 | 94 | 0.416 | 36.5
|
table_csv/204_905.csv
| 0 |
{
"column_index": [
3
],
"row_index": [
3
]
}
|
[
"what win ratio did the red sox have?"
] | 0 |
.531
|
[
"Team",
"Wins",
"Losses",
"Win %",
"GB"
] |
what win ratio did the red sox have?
|
[
[
"Detroit Tigers",
"104",
"58",
"0.642",
"0.0"
],
[
"Toronto Blue Jays",
"89",
"73",
"0.549",
"15.0"
],
[
"New York Yankees",
"87",
"75",
"0.537",
"17.0"
],
[
"Boston Red Sox",
"86",
"76",
"0.531",
"18.0"
],
[
"Baltimore Orioles",
"85",
"77",
"0.525",
"19.0"
],
[
"Cleveland Indians",
"75",
"87",
"0.463",
"29.0"
],
[
"Milwaukee Brewers",
"67",
"94",
"0.416",
"36.5"
]
] |
[
".531"
] |
what win ratio did the red sox have? ||
|
ns-2250
|
col : team | wins | losses | win % | gb row 1 : detroit tigers | 104 | 58 | 0.642 | 0.0 row 2 : toronto blue jays | 89 | 73 | 0.549 | 15.0 row 3 : new york yankees | 87 | 75 | 0.537 | 17.0 row 4 : boston red sox | 86 | 76 | 0.531 | 18.0 row 5 : baltimore orioles | 85 | 77 | 0.525 | 19.0 row 6 : cleveland indians | 75 | 87 | 0.463 | 29.0 row 7 : milwaukee brewers | 67 | 94 | 0.416 | 36.5
|
table_csv/204_905.csv
| 1 |
{
"column_index": [
3
],
"row_index": [
4
]
}
|
[
"what win ratio did the red sox have?",
"what win ratio did the orieles have?"
] | 0 |
.525
|
[
"Team",
"Wins",
"Losses",
"Win %",
"GB"
] |
what win ratio did the orieles have?
|
[
[
"Detroit Tigers",
"104",
"58",
"0.642",
"0.0"
],
[
"Toronto Blue Jays",
"89",
"73",
"0.549",
"15.0"
],
[
"New York Yankees",
"87",
"75",
"0.537",
"17.0"
],
[
"Boston Red Sox",
"86",
"76",
"0.531",
"18.0"
],
[
"Baltimore Orioles",
"85",
"77",
"0.525",
"19.0"
],
[
"Cleveland Indians",
"75",
"87",
"0.463",
"29.0"
],
[
"Milwaukee Brewers",
"67",
"94",
"0.416",
"36.5"
]
] |
[
".525"
] |
what win ratio did the orieles have? || what win ratio did the red sox have?
|
ns-2250
|
col : team | wins | losses | win % | gb row 1 : detroit tigers | 104 | 58 | 0.642 | 0.0 row 2 : toronto blue jays | 89 | 73 | 0.549 | 15.0 row 3 : new york yankees | 87 | 75 | 0.537 | 17.0 row 4 : boston red sox | 86 | 76 | 0.531 | 18.0 row 5 : baltimore orioles | 85 | 77 | 0.525 | 19.0 row 6 : cleveland indians | 75 | 87 | 0.463 | 29.0 row 7 : milwaukee brewers | 67 | 94 | 0.416 | 36.5
|
table_csv/204_905.csv
| 2 |
{
"column_index": [
0
],
"row_index": [
4
]
}
|
[
"what win ratio did the red sox have?",
"what win ratio did the orieles have?",
"which was a win ratio of 0.525?"
] | 0 |
baltimore orioles
|
[
"Team",
"Wins",
"Losses",
"Win %",
"GB"
] |
which was a win ratio of 0.525?
|
[
[
"Detroit Tigers",
"104",
"58",
"0.642",
"0.0"
],
[
"Toronto Blue Jays",
"89",
"73",
"0.549",
"15.0"
],
[
"New York Yankees",
"87",
"75",
"0.537",
"17.0"
],
[
"Boston Red Sox",
"86",
"76",
"0.531",
"18.0"
],
[
"Baltimore Orioles",
"85",
"77",
"0.525",
"19.0"
],
[
"Cleveland Indians",
"75",
"87",
"0.463",
"29.0"
],
[
"Milwaukee Brewers",
"67",
"94",
"0.416",
"36.5"
]
] |
[
"Baltimore Orioles"
] |
which was a win ratio of 0.525? || what win ratio did the orieles have? | what win ratio did the red sox have?
|
ns-2250
|
col : team | wins | losses | win % | gb row 1 : detroit tigers | 104 | 58 | 0.642 | 0.0 row 2 : toronto blue jays | 89 | 73 | 0.549 | 15.0 row 3 : new york yankees | 87 | 75 | 0.537 | 17.0 row 4 : boston red sox | 86 | 76 | 0.531 | 18.0 row 5 : baltimore orioles | 85 | 77 | 0.525 | 19.0 row 6 : cleveland indians | 75 | 87 | 0.463 | 29.0 row 7 : milwaukee brewers | 67 | 94 | 0.416 | 36.5
|
table_csv/204_905.csv
| 0 |
{
"column_index": [
0,
0,
0,
0,
0,
0,
0
],
"row_index": [
0,
1,
2,
3,
4,
5,
6
]
}
|
[
"who were the teams that played the red sox in the 1984 boston red sox season?"
] | 1 |
detroit tigers, toronto blue jays, new york yankees, boston red sox, baltimore orioles, cleveland indians, milwaukee brewers
|
[
"Team",
"Wins",
"Losses",
"Win %",
"GB"
] |
who were the teams that played the red sox in the 1984 boston red sox season?
|
[
[
"Detroit Tigers",
"104",
"58",
"0.642",
"0.0"
],
[
"Toronto Blue Jays",
"89",
"73",
"0.549",
"15.0"
],
[
"New York Yankees",
"87",
"75",
"0.537",
"17.0"
],
[
"Boston Red Sox",
"86",
"76",
"0.531",
"18.0"
],
[
"Baltimore Orioles",
"85",
"77",
"0.525",
"19.0"
],
[
"Cleveland Indians",
"75",
"87",
"0.463",
"29.0"
],
[
"Milwaukee Brewers",
"67",
"94",
"0.416",
"36.5"
]
] |
[
"Detroit Tigers",
"Toronto Blue Jays",
"New York Yankees",
"Boston Red Sox",
"Baltimore Orioles",
"Cleveland Indians",
"Milwaukee Brewers"
] |
who were the teams that played the red sox in the 1984 boston red sox season? ||
|
ns-2250
|
col : team | wins | losses | win % | gb row 1 : detroit tigers | 104 | 58 | 0.642 | 0.0 row 2 : toronto blue jays | 89 | 73 | 0.549 | 15.0 row 3 : new york yankees | 87 | 75 | 0.537 | 17.0 row 4 : boston red sox | 86 | 76 | 0.531 | 18.0 row 5 : baltimore orioles | 85 | 77 | 0.525 | 19.0 row 6 : cleveland indians | 75 | 87 | 0.463 | 29.0 row 7 : milwaukee brewers | 67 | 94 | 0.416 | 36.5
|
table_csv/204_905.csv
| 1 |
{
"column_index": [
0
],
"row_index": [
4
]
}
|
[
"who were the teams that played the red sox in the 1984 boston red sox season?",
"of these which won .525 percent?"
] | 1 |
baltimore orioles
|
[
"Team",
"Wins",
"Losses",
"Win %",
"GB"
] |
of these which won .525 percent?
|
[
[
"Detroit Tigers",
"104",
"58",
"0.642",
"0.0"
],
[
"Toronto Blue Jays",
"89",
"73",
"0.549",
"15.0"
],
[
"New York Yankees",
"87",
"75",
"0.537",
"17.0"
],
[
"Boston Red Sox",
"86",
"76",
"0.531",
"18.0"
],
[
"Baltimore Orioles",
"85",
"77",
"0.525",
"19.0"
],
[
"Cleveland Indians",
"75",
"87",
"0.463",
"29.0"
],
[
"Milwaukee Brewers",
"67",
"94",
"0.416",
"36.5"
]
] |
[
"Baltimore Orioles"
] |
of these which won .525 percent? || who were the teams that played the red sox in the 1984 boston red sox season?
|
ns-2250
|
col : team | wins | losses | win % | gb row 1 : detroit tigers | 104 | 58 | 0.642 | 0.0 row 2 : toronto blue jays | 89 | 73 | 0.549 | 15.0 row 3 : new york yankees | 87 | 75 | 0.537 | 17.0 row 4 : boston red sox | 86 | 76 | 0.531 | 18.0 row 5 : baltimore orioles | 85 | 77 | 0.525 | 19.0 row 6 : cleveland indians | 75 | 87 | 0.463 | 29.0 row 7 : milwaukee brewers | 67 | 94 | 0.416 | 36.5
|
table_csv/204_905.csv
| 0 |
{
"column_index": [
0,
0,
0,
0,
0,
0,
0
],
"row_index": [
0,
1,
2,
3,
4,
5,
6
]
}
|
[
"who were the teams played i teh 1984 boston red sox season?"
] | 2 |
detroit tigers, toronto blue jays, new york yankees, boston red sox, baltimore orioles, cleveland indians, milwaukee brewers
|
[
"Team",
"Wins",
"Losses",
"Win %",
"GB"
] |
who were the teams played i teh 1984 boston red sox season?
|
[
[
"Detroit Tigers",
"104",
"58",
"0.642",
"0.0"
],
[
"Toronto Blue Jays",
"89",
"73",
"0.549",
"15.0"
],
[
"New York Yankees",
"87",
"75",
"0.537",
"17.0"
],
[
"Boston Red Sox",
"86",
"76",
"0.531",
"18.0"
],
[
"Baltimore Orioles",
"85",
"77",
"0.525",
"19.0"
],
[
"Cleveland Indians",
"75",
"87",
"0.463",
"29.0"
],
[
"Milwaukee Brewers",
"67",
"94",
"0.416",
"36.5"
]
] |
[
"Detroit Tigers",
"Toronto Blue Jays",
"New York Yankees",
"Boston Red Sox",
"Baltimore Orioles",
"Cleveland Indians",
"Milwaukee Brewers"
] |
who were the teams played i teh 1984 boston red sox season? ||
|
ns-2250
|
col : team | wins | losses | win % | gb row 1 : detroit tigers | 104 | 58 | 0.642 | 0.0 row 2 : toronto blue jays | 89 | 73 | 0.549 | 15.0 row 3 : new york yankees | 87 | 75 | 0.537 | 17.0 row 4 : boston red sox | 86 | 76 | 0.531 | 18.0 row 5 : baltimore orioles | 85 | 77 | 0.525 | 19.0 row 6 : cleveland indians | 75 | 87 | 0.463 | 29.0 row 7 : milwaukee brewers | 67 | 94 | 0.416 | 36.5
|
table_csv/204_905.csv
| 1 |
{
"column_index": [
0
],
"row_index": [
4
]
}
|
[
"who were the teams played i teh 1984 boston red sox season?",
"of those which had a .5255 ratio?"
] | 2 |
baltimore orioles
|
[
"Team",
"Wins",
"Losses",
"Win %",
"GB"
] |
of those which had a .5255 ratio?
|
[
[
"Detroit Tigers",
"104",
"58",
"0.642",
"0.0"
],
[
"Toronto Blue Jays",
"89",
"73",
"0.549",
"15.0"
],
[
"New York Yankees",
"87",
"75",
"0.537",
"17.0"
],
[
"Boston Red Sox",
"86",
"76",
"0.531",
"18.0"
],
[
"Baltimore Orioles",
"85",
"77",
"0.525",
"19.0"
],
[
"Cleveland Indians",
"75",
"87",
"0.463",
"29.0"
],
[
"Milwaukee Brewers",
"67",
"94",
"0.416",
"36.5"
]
] |
[
"Baltimore Orioles"
] |
of those which had a .5255 ratio? || who were the teams played i teh 1984 boston red sox season?
|
nt-6252
|
col : institution | location | athletic nickname | undergraduate enrollment | graduate enrollment | total enrollment | 2013 endowment (and us rank) | academic staff | motto row 1 : brown university | providence, rhode island | bears | 6,316 | 2,333 | 8,649 | $2.7 billion (30th) | 736 | in deo speramus (in god we hope) row 2 : columbia university | new york, new york | lions | 7,160 | 15,760 | 22,920 | $8.2 billion (9th) | 3,763 | in lumine tuo videbimus lumen (in th row 3 : cornell university | ithaca, new york | big red | 13,931 | 6,702 | 20,633 | $5.3 billion (18th) | 2,908 | i would found an institution where any person can find instruction in any study. row 4 : dartmouth college | hanover, new hampshire | big green | 4,248 | 1,893 | 6,141 | $3.7 billion (22nd) | 571 | vox clamantis in deserto (the voice of one crying row 5 : harvard university | cambridge, massachusetts | crimson | 7,181 | 14,044 | 21,225 | $32.3 billion (1st) | 4,671 | veritas (truth) row 6 : princeton university | princeton, new jersey | tigers | 5,113 | 2,479 | 7,592 | $18.2 billion (5th) | 1,172 | dei sub numine viget (under god's power she row 7 : university of pennsylvania | philadelphia, pennsylvania | quakers | 10,337 | 10,306 | 20,643 | $7.7 billion (11th) | 4,464 | leges sine moribus vanae (laws without moral row 8 : yale university | new haven, connecticut | bulldogs | 5,275 | 6,391 | 11,666 | $20.8 billion (2nd) | 4,140 | vrym vtvmym lux et ver
|
table_csv/203_592.csv
| 0 |
{
"column_index": [
0,
0,
0,
0,
0,
0,
0,
0
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7
]
}
|
[
"which universities are listed as ivy league?"
] | 0 |
brown university, columbia university, cornell university, dartmouth college, harvard university, princeton university, university of pennsylvania, yale university
|
[
"Institution",
"Location",
"Athletic nickname",
"Undergraduate enrollment",
"Graduate enrollment",
"Total enrollment",
"2013 Endowment (and US rank)",
"Academic staff",
"Motto"
] |
which universities are listed as ivy league?
|
[
[
"Brown University",
"Providence, Rhode Island",
"Bears",
"6,316",
"2,333",
"8,649",
"$2.7 billion (30th)",
"736",
"In Deo Speramus (In God We Hope)"
],
[
"Columbia University",
"New York, New York",
"Lions",
"7,160",
"15,760",
"22,920",
"$8.2 billion (9th)",
"3,763",
"In lumine Tuo videbimus lumen (In Th"
],
[
"Cornell University",
"Ithaca, New York",
"Big Red",
"13,931",
"6,702",
"20,633",
"$5.3 billion (18th)",
"2,908",
"I would found an institution where any person can find instruction in any study."
],
[
"Dartmouth College",
"Hanover, New Hampshire",
"Big Green",
"4,248",
"1,893",
"6,141",
"$3.7 billion (22nd)",
"571",
"Vox clamantis in deserto (The voice of one crying"
],
[
"Harvard University",
"Cambridge, Massachusetts",
"Crimson",
"7,181",
"14,044",
"21,225",
"$32.3 billion (1st)",
"4,671",
"Veritas (Truth)"
],
[
"Princeton University",
"Princeton, New Jersey",
"Tigers",
"5,113",
"2,479",
"7,592",
"$18.2 billion (5th)",
"1,172",
"Dei sub numine viget (Under God's power she"
],
[
"University of Pennsylvania",
"Philadelphia, Pennsylvania",
"Quakers",
"10,337",
"10,306",
"20,643",
"$7.7 billion (11th)",
"4,464",
"Leges sine moribus vanae (Laws without moral"
],
[
"Yale University",
"New Haven, Connecticut",
"Bulldogs",
"5,275",
"6,391",
"11,666",
"$20.8 billion (2nd)",
"4,140",
"vrym vtvmym Lux et ver"
]
] |
[
"Brown University",
"Columbia University",
"Cornell University",
"Dartmouth College",
"Harvard University",
"Princeton University",
"University of Pennsylvania",
"Yale University"
] |
which universities are listed as ivy league? ||
|
nt-6252
|
col : institution | location | athletic nickname | undergraduate enrollment | graduate enrollment | total enrollment | 2013 endowment (and us rank) | academic staff | motto row 1 : brown university | providence, rhode island | bears | 6,316 | 2,333 | 8,649 | $2.7 billion (30th) | 736 | in deo speramus (in god we hope) row 2 : columbia university | new york, new york | lions | 7,160 | 15,760 | 22,920 | $8.2 billion (9th) | 3,763 | in lumine tuo videbimus lumen (in th row 3 : cornell university | ithaca, new york | big red | 13,931 | 6,702 | 20,633 | $5.3 billion (18th) | 2,908 | i would found an institution where any person can find instruction in any study. row 4 : dartmouth college | hanover, new hampshire | big green | 4,248 | 1,893 | 6,141 | $3.7 billion (22nd) | 571 | vox clamantis in deserto (the voice of one crying row 5 : harvard university | cambridge, massachusetts | crimson | 7,181 | 14,044 | 21,225 | $32.3 billion (1st) | 4,671 | veritas (truth) row 6 : princeton university | princeton, new jersey | tigers | 5,113 | 2,479 | 7,592 | $18.2 billion (5th) | 1,172 | dei sub numine viget (under god's power she row 7 : university of pennsylvania | philadelphia, pennsylvania | quakers | 10,337 | 10,306 | 20,643 | $7.7 billion (11th) | 4,464 | leges sine moribus vanae (laws without moral row 8 : yale university | new haven, connecticut | bulldogs | 5,275 | 6,391 | 11,666 | $20.8 billion (2nd) | 4,140 | vrym vtvmym lux et ver
|
table_csv/203_592.csv
| 1 |
{
"column_index": [
0,
0,
0,
0,
0
],
"row_index": [
1,
2,
4,
6,
7
]
}
|
[
"which universities are listed as ivy league?",
"of these, which have over 10,000 in total enrollment?"
] | 0 |
columbia university, cornell university, harvard university, university of pennsylvania, yale university
|
[
"Institution",
"Location",
"Athletic nickname",
"Undergraduate enrollment",
"Graduate enrollment",
"Total enrollment",
"2013 Endowment (and US rank)",
"Academic staff",
"Motto"
] |
of these, which have over 10,000 in total enrollment?
|
[
[
"Brown University",
"Providence, Rhode Island",
"Bears",
"6,316",
"2,333",
"8,649",
"$2.7 billion (30th)",
"736",
"In Deo Speramus (In God We Hope)"
],
[
"Columbia University",
"New York, New York",
"Lions",
"7,160",
"15,760",
"22,920",
"$8.2 billion (9th)",
"3,763",
"In lumine Tuo videbimus lumen (In Th"
],
[
"Cornell University",
"Ithaca, New York",
"Big Red",
"13,931",
"6,702",
"20,633",
"$5.3 billion (18th)",
"2,908",
"I would found an institution where any person can find instruction in any study."
],
[
"Dartmouth College",
"Hanover, New Hampshire",
"Big Green",
"4,248",
"1,893",
"6,141",
"$3.7 billion (22nd)",
"571",
"Vox clamantis in deserto (The voice of one crying"
],
[
"Harvard University",
"Cambridge, Massachusetts",
"Crimson",
"7,181",
"14,044",
"21,225",
"$32.3 billion (1st)",
"4,671",
"Veritas (Truth)"
],
[
"Princeton University",
"Princeton, New Jersey",
"Tigers",
"5,113",
"2,479",
"7,592",
"$18.2 billion (5th)",
"1,172",
"Dei sub numine viget (Under God's power she"
],
[
"University of Pennsylvania",
"Philadelphia, Pennsylvania",
"Quakers",
"10,337",
"10,306",
"20,643",
"$7.7 billion (11th)",
"4,464",
"Leges sine moribus vanae (Laws without moral"
],
[
"Yale University",
"New Haven, Connecticut",
"Bulldogs",
"5,275",
"6,391",
"11,666",
"$20.8 billion (2nd)",
"4,140",
"vrym vtvmym Lux et ver"
]
] |
[
"Columbia University",
"Cornell University",
"Harvard University",
"University of Pennsylvania",
"Yale University"
] |
of these, which have over 10,000 in total enrollment? || which universities are listed as ivy league?
|
nt-6252
|
col : institution | location | athletic nickname | undergraduate enrollment | graduate enrollment | total enrollment | 2013 endowment (and us rank) | academic staff | motto row 1 : brown university | providence, rhode island | bears | 6,316 | 2,333 | 8,649 | $2.7 billion (30th) | 736 | in deo speramus (in god we hope) row 2 : columbia university | new york, new york | lions | 7,160 | 15,760 | 22,920 | $8.2 billion (9th) | 3,763 | in lumine tuo videbimus lumen (in th row 3 : cornell university | ithaca, new york | big red | 13,931 | 6,702 | 20,633 | $5.3 billion (18th) | 2,908 | i would found an institution where any person can find instruction in any study. row 4 : dartmouth college | hanover, new hampshire | big green | 4,248 | 1,893 | 6,141 | $3.7 billion (22nd) | 571 | vox clamantis in deserto (the voice of one crying row 5 : harvard university | cambridge, massachusetts | crimson | 7,181 | 14,044 | 21,225 | $32.3 billion (1st) | 4,671 | veritas (truth) row 6 : princeton university | princeton, new jersey | tigers | 5,113 | 2,479 | 7,592 | $18.2 billion (5th) | 1,172 | dei sub numine viget (under god's power she row 7 : university of pennsylvania | philadelphia, pennsylvania | quakers | 10,337 | 10,306 | 20,643 | $7.7 billion (11th) | 4,464 | leges sine moribus vanae (laws without moral row 8 : yale university | new haven, connecticut | bulldogs | 5,275 | 6,391 | 11,666 | $20.8 billion (2nd) | 4,140 | vrym vtvmym lux et ver
|
table_csv/203_592.csv
| 2 |
{
"column_index": [
0
],
"row_index": [
7
]
}
|
[
"which universities are listed as ivy league?",
"of these, which have over 10,000 in total enrollment?",
"of these, whose mascot is the bulldog?"
] | 0 |
yale university
|
[
"Institution",
"Location",
"Athletic nickname",
"Undergraduate enrollment",
"Graduate enrollment",
"Total enrollment",
"2013 Endowment (and US rank)",
"Academic staff",
"Motto"
] |
of these, whose mascot is the bulldog?
|
[
[
"Brown University",
"Providence, Rhode Island",
"Bears",
"6,316",
"2,333",
"8,649",
"$2.7 billion (30th)",
"736",
"In Deo Speramus (In God We Hope)"
],
[
"Columbia University",
"New York, New York",
"Lions",
"7,160",
"15,760",
"22,920",
"$8.2 billion (9th)",
"3,763",
"In lumine Tuo videbimus lumen (In Th"
],
[
"Cornell University",
"Ithaca, New York",
"Big Red",
"13,931",
"6,702",
"20,633",
"$5.3 billion (18th)",
"2,908",
"I would found an institution where any person can find instruction in any study."
],
[
"Dartmouth College",
"Hanover, New Hampshire",
"Big Green",
"4,248",
"1,893",
"6,141",
"$3.7 billion (22nd)",
"571",
"Vox clamantis in deserto (The voice of one crying"
],
[
"Harvard University",
"Cambridge, Massachusetts",
"Crimson",
"7,181",
"14,044",
"21,225",
"$32.3 billion (1st)",
"4,671",
"Veritas (Truth)"
],
[
"Princeton University",
"Princeton, New Jersey",
"Tigers",
"5,113",
"2,479",
"7,592",
"$18.2 billion (5th)",
"1,172",
"Dei sub numine viget (Under God's power she"
],
[
"University of Pennsylvania",
"Philadelphia, Pennsylvania",
"Quakers",
"10,337",
"10,306",
"20,643",
"$7.7 billion (11th)",
"4,464",
"Leges sine moribus vanae (Laws without moral"
],
[
"Yale University",
"New Haven, Connecticut",
"Bulldogs",
"5,275",
"6,391",
"11,666",
"$20.8 billion (2nd)",
"4,140",
"vrym vtvmym Lux et ver"
]
] |
[
"Yale University"
] |
of these, whose mascot is the bulldog? || of these, which have over 10,000 in total enrollment? | which universities are listed as ivy league?
|
nt-6252
|
col : institution | location | athletic nickname | undergraduate enrollment | graduate enrollment | total enrollment | 2013 endowment (and us rank) | academic staff | motto row 1 : brown university | providence, rhode island | bears | 6,316 | 2,333 | 8,649 | $2.7 billion (30th) | 736 | in deo speramus (in god we hope) row 2 : columbia university | new york, new york | lions | 7,160 | 15,760 | 22,920 | $8.2 billion (9th) | 3,763 | in lumine tuo videbimus lumen (in th row 3 : cornell university | ithaca, new york | big red | 13,931 | 6,702 | 20,633 | $5.3 billion (18th) | 2,908 | i would found an institution where any person can find instruction in any study. row 4 : dartmouth college | hanover, new hampshire | big green | 4,248 | 1,893 | 6,141 | $3.7 billion (22nd) | 571 | vox clamantis in deserto (the voice of one crying row 5 : harvard university | cambridge, massachusetts | crimson | 7,181 | 14,044 | 21,225 | $32.3 billion (1st) | 4,671 | veritas (truth) row 6 : princeton university | princeton, new jersey | tigers | 5,113 | 2,479 | 7,592 | $18.2 billion (5th) | 1,172 | dei sub numine viget (under god's power she row 7 : university of pennsylvania | philadelphia, pennsylvania | quakers | 10,337 | 10,306 | 20,643 | $7.7 billion (11th) | 4,464 | leges sine moribus vanae (laws without moral row 8 : yale university | new haven, connecticut | bulldogs | 5,275 | 6,391 | 11,666 | $20.8 billion (2nd) | 4,140 | vrym vtvmym lux et ver
|
table_csv/203_592.csv
| 0 |
{
"column_index": [
0,
0,
0,
0,
0,
0,
0,
0
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7
]
}
|
[
"what are all of the universities?"
] | 1 |
brown university, columbia university, cornell university, dartmouth college, harvard university, princeton university, university of pennsylvania, yale university
|
[
"Institution",
"Location",
"Athletic nickname",
"Undergraduate enrollment",
"Graduate enrollment",
"Total enrollment",
"2013 Endowment (and US rank)",
"Academic staff",
"Motto"
] |
what are all of the universities?
|
[
[
"Brown University",
"Providence, Rhode Island",
"Bears",
"6,316",
"2,333",
"8,649",
"$2.7 billion (30th)",
"736",
"In Deo Speramus (In God We Hope)"
],
[
"Columbia University",
"New York, New York",
"Lions",
"7,160",
"15,760",
"22,920",
"$8.2 billion (9th)",
"3,763",
"In lumine Tuo videbimus lumen (In Th"
],
[
"Cornell University",
"Ithaca, New York",
"Big Red",
"13,931",
"6,702",
"20,633",
"$5.3 billion (18th)",
"2,908",
"I would found an institution where any person can find instruction in any study."
],
[
"Dartmouth College",
"Hanover, New Hampshire",
"Big Green",
"4,248",
"1,893",
"6,141",
"$3.7 billion (22nd)",
"571",
"Vox clamantis in deserto (The voice of one crying"
],
[
"Harvard University",
"Cambridge, Massachusetts",
"Crimson",
"7,181",
"14,044",
"21,225",
"$32.3 billion (1st)",
"4,671",
"Veritas (Truth)"
],
[
"Princeton University",
"Princeton, New Jersey",
"Tigers",
"5,113",
"2,479",
"7,592",
"$18.2 billion (5th)",
"1,172",
"Dei sub numine viget (Under God's power she"
],
[
"University of Pennsylvania",
"Philadelphia, Pennsylvania",
"Quakers",
"10,337",
"10,306",
"20,643",
"$7.7 billion (11th)",
"4,464",
"Leges sine moribus vanae (Laws without moral"
],
[
"Yale University",
"New Haven, Connecticut",
"Bulldogs",
"5,275",
"6,391",
"11,666",
"$20.8 billion (2nd)",
"4,140",
"vrym vtvmym Lux et ver"
]
] |
[
"Brown University",
"Columbia University",
"Cornell University",
"Dartmouth College",
"Harvard University",
"Princeton University",
"University of Pennsylvania",
"Yale University"
] |
what are all of the universities? ||
|
nt-6252
|
col : institution | location | athletic nickname | undergraduate enrollment | graduate enrollment | total enrollment | 2013 endowment (and us rank) | academic staff | motto row 1 : brown university | providence, rhode island | bears | 6,316 | 2,333 | 8,649 | $2.7 billion (30th) | 736 | in deo speramus (in god we hope) row 2 : columbia university | new york, new york | lions | 7,160 | 15,760 | 22,920 | $8.2 billion (9th) | 3,763 | in lumine tuo videbimus lumen (in th row 3 : cornell university | ithaca, new york | big red | 13,931 | 6,702 | 20,633 | $5.3 billion (18th) | 2,908 | i would found an institution where any person can find instruction in any study. row 4 : dartmouth college | hanover, new hampshire | big green | 4,248 | 1,893 | 6,141 | $3.7 billion (22nd) | 571 | vox clamantis in deserto (the voice of one crying row 5 : harvard university | cambridge, massachusetts | crimson | 7,181 | 14,044 | 21,225 | $32.3 billion (1st) | 4,671 | veritas (truth) row 6 : princeton university | princeton, new jersey | tigers | 5,113 | 2,479 | 7,592 | $18.2 billion (5th) | 1,172 | dei sub numine viget (under god's power she row 7 : university of pennsylvania | philadelphia, pennsylvania | quakers | 10,337 | 10,306 | 20,643 | $7.7 billion (11th) | 4,464 | leges sine moribus vanae (laws without moral row 8 : yale university | new haven, connecticut | bulldogs | 5,275 | 6,391 | 11,666 | $20.8 billion (2nd) | 4,140 | vrym vtvmym lux et ver
|
table_csv/203_592.csv
| 1 |
{
"column_index": [
5,
5,
5,
5,
5,
5,
5,
5
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7
]
}
|
[
"what are all of the universities?",
"what about their enrollment?"
] | 1 |
8,649, 22,920, 20,633, 6,141, 21,225, 7,592, 20,643, 11,666
|
[
"Institution",
"Location",
"Athletic nickname",
"Undergraduate enrollment",
"Graduate enrollment",
"Total enrollment",
"2013 Endowment (and US rank)",
"Academic staff",
"Motto"
] |
what about their enrollment?
|
[
[
"Brown University",
"Providence, Rhode Island",
"Bears",
"6,316",
"2,333",
"8,649",
"$2.7 billion (30th)",
"736",
"In Deo Speramus (In God We Hope)"
],
[
"Columbia University",
"New York, New York",
"Lions",
"7,160",
"15,760",
"22,920",
"$8.2 billion (9th)",
"3,763",
"In lumine Tuo videbimus lumen (In Th"
],
[
"Cornell University",
"Ithaca, New York",
"Big Red",
"13,931",
"6,702",
"20,633",
"$5.3 billion (18th)",
"2,908",
"I would found an institution where any person can find instruction in any study."
],
[
"Dartmouth College",
"Hanover, New Hampshire",
"Big Green",
"4,248",
"1,893",
"6,141",
"$3.7 billion (22nd)",
"571",
"Vox clamantis in deserto (The voice of one crying"
],
[
"Harvard University",
"Cambridge, Massachusetts",
"Crimson",
"7,181",
"14,044",
"21,225",
"$32.3 billion (1st)",
"4,671",
"Veritas (Truth)"
],
[
"Princeton University",
"Princeton, New Jersey",
"Tigers",
"5,113",
"2,479",
"7,592",
"$18.2 billion (5th)",
"1,172",
"Dei sub numine viget (Under God's power she"
],
[
"University of Pennsylvania",
"Philadelphia, Pennsylvania",
"Quakers",
"10,337",
"10,306",
"20,643",
"$7.7 billion (11th)",
"4,464",
"Leges sine moribus vanae (Laws without moral"
],
[
"Yale University",
"New Haven, Connecticut",
"Bulldogs",
"5,275",
"6,391",
"11,666",
"$20.8 billion (2nd)",
"4,140",
"vrym vtvmym Lux et ver"
]
] |
[
"8,649",
"22,920",
"20,633",
"6,141",
"21,225",
"7,592",
"20,643",
"11,666"
] |
what about their enrollment? || what are all of the universities?
|
nt-6252
|
col : institution | location | athletic nickname | undergraduate enrollment | graduate enrollment | total enrollment | 2013 endowment (and us rank) | academic staff | motto row 1 : brown university | providence, rhode island | bears | 6,316 | 2,333 | 8,649 | $2.7 billion (30th) | 736 | in deo speramus (in god we hope) row 2 : columbia university | new york, new york | lions | 7,160 | 15,760 | 22,920 | $8.2 billion (9th) | 3,763 | in lumine tuo videbimus lumen (in th row 3 : cornell university | ithaca, new york | big red | 13,931 | 6,702 | 20,633 | $5.3 billion (18th) | 2,908 | i would found an institution where any person can find instruction in any study. row 4 : dartmouth college | hanover, new hampshire | big green | 4,248 | 1,893 | 6,141 | $3.7 billion (22nd) | 571 | vox clamantis in deserto (the voice of one crying row 5 : harvard university | cambridge, massachusetts | crimson | 7,181 | 14,044 | 21,225 | $32.3 billion (1st) | 4,671 | veritas (truth) row 6 : princeton university | princeton, new jersey | tigers | 5,113 | 2,479 | 7,592 | $18.2 billion (5th) | 1,172 | dei sub numine viget (under god's power she row 7 : university of pennsylvania | philadelphia, pennsylvania | quakers | 10,337 | 10,306 | 20,643 | $7.7 billion (11th) | 4,464 | leges sine moribus vanae (laws without moral row 8 : yale university | new haven, connecticut | bulldogs | 5,275 | 6,391 | 11,666 | $20.8 billion (2nd) | 4,140 | vrym vtvmym lux et ver
|
table_csv/203_592.csv
| 2 |
{
"column_index": [
2,
2,
2,
2,
2,
2,
2,
2
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7
]
}
|
[
"what are all of the universities?",
"what about their enrollment?",
"and their athletic names?"
] | 1 |
bears, lions, big red, big green, crimson, tigers, quakers, bulldogs
|
[
"Institution",
"Location",
"Athletic nickname",
"Undergraduate enrollment",
"Graduate enrollment",
"Total enrollment",
"2013 Endowment (and US rank)",
"Academic staff",
"Motto"
] |
and their athletic names?
|
[
[
"Brown University",
"Providence, Rhode Island",
"Bears",
"6,316",
"2,333",
"8,649",
"$2.7 billion (30th)",
"736",
"In Deo Speramus (In God We Hope)"
],
[
"Columbia University",
"New York, New York",
"Lions",
"7,160",
"15,760",
"22,920",
"$8.2 billion (9th)",
"3,763",
"In lumine Tuo videbimus lumen (In Th"
],
[
"Cornell University",
"Ithaca, New York",
"Big Red",
"13,931",
"6,702",
"20,633",
"$5.3 billion (18th)",
"2,908",
"I would found an institution where any person can find instruction in any study."
],
[
"Dartmouth College",
"Hanover, New Hampshire",
"Big Green",
"4,248",
"1,893",
"6,141",
"$3.7 billion (22nd)",
"571",
"Vox clamantis in deserto (The voice of one crying"
],
[
"Harvard University",
"Cambridge, Massachusetts",
"Crimson",
"7,181",
"14,044",
"21,225",
"$32.3 billion (1st)",
"4,671",
"Veritas (Truth)"
],
[
"Princeton University",
"Princeton, New Jersey",
"Tigers",
"5,113",
"2,479",
"7,592",
"$18.2 billion (5th)",
"1,172",
"Dei sub numine viget (Under God's power she"
],
[
"University of Pennsylvania",
"Philadelphia, Pennsylvania",
"Quakers",
"10,337",
"10,306",
"20,643",
"$7.7 billion (11th)",
"4,464",
"Leges sine moribus vanae (Laws without moral"
],
[
"Yale University",
"New Haven, Connecticut",
"Bulldogs",
"5,275",
"6,391",
"11,666",
"$20.8 billion (2nd)",
"4,140",
"vrym vtvmym Lux et ver"
]
] |
[
"Bears",
"Lions",
"Big Red",
"Big Green",
"Crimson",
"Tigers",
"Quakers",
"Bulldogs"
] |
and their athletic names? || what about their enrollment? | what are all of the universities?
|
nt-6252
|
col : institution | location | athletic nickname | undergraduate enrollment | graduate enrollment | total enrollment | 2013 endowment (and us rank) | academic staff | motto row 1 : brown university | providence, rhode island | bears | 6,316 | 2,333 | 8,649 | $2.7 billion (30th) | 736 | in deo speramus (in god we hope) row 2 : columbia university | new york, new york | lions | 7,160 | 15,760 | 22,920 | $8.2 billion (9th) | 3,763 | in lumine tuo videbimus lumen (in th row 3 : cornell university | ithaca, new york | big red | 13,931 | 6,702 | 20,633 | $5.3 billion (18th) | 2,908 | i would found an institution where any person can find instruction in any study. row 4 : dartmouth college | hanover, new hampshire | big green | 4,248 | 1,893 | 6,141 | $3.7 billion (22nd) | 571 | vox clamantis in deserto (the voice of one crying row 5 : harvard university | cambridge, massachusetts | crimson | 7,181 | 14,044 | 21,225 | $32.3 billion (1st) | 4,671 | veritas (truth) row 6 : princeton university | princeton, new jersey | tigers | 5,113 | 2,479 | 7,592 | $18.2 billion (5th) | 1,172 | dei sub numine viget (under god's power she row 7 : university of pennsylvania | philadelphia, pennsylvania | quakers | 10,337 | 10,306 | 20,643 | $7.7 billion (11th) | 4,464 | leges sine moribus vanae (laws without moral row 8 : yale university | new haven, connecticut | bulldogs | 5,275 | 6,391 | 11,666 | $20.8 billion (2nd) | 4,140 | vrym vtvmym lux et ver
|
table_csv/203_592.csv
| 3 |
{
"column_index": [
0
],
"row_index": [
7
]
}
|
[
"what are all of the universities?",
"what about their enrollment?",
"and their athletic names?",
"and among those, which university do the bulldogs belong to?"
] | 1 |
yale university
|
[
"Institution",
"Location",
"Athletic nickname",
"Undergraduate enrollment",
"Graduate enrollment",
"Total enrollment",
"2013 Endowment (and US rank)",
"Academic staff",
"Motto"
] |
and among those, which university do the bulldogs belong to?
|
[
[
"Brown University",
"Providence, Rhode Island",
"Bears",
"6,316",
"2,333",
"8,649",
"$2.7 billion (30th)",
"736",
"In Deo Speramus (In God We Hope)"
],
[
"Columbia University",
"New York, New York",
"Lions",
"7,160",
"15,760",
"22,920",
"$8.2 billion (9th)",
"3,763",
"In lumine Tuo videbimus lumen (In Th"
],
[
"Cornell University",
"Ithaca, New York",
"Big Red",
"13,931",
"6,702",
"20,633",
"$5.3 billion (18th)",
"2,908",
"I would found an institution where any person can find instruction in any study."
],
[
"Dartmouth College",
"Hanover, New Hampshire",
"Big Green",
"4,248",
"1,893",
"6,141",
"$3.7 billion (22nd)",
"571",
"Vox clamantis in deserto (The voice of one crying"
],
[
"Harvard University",
"Cambridge, Massachusetts",
"Crimson",
"7,181",
"14,044",
"21,225",
"$32.3 billion (1st)",
"4,671",
"Veritas (Truth)"
],
[
"Princeton University",
"Princeton, New Jersey",
"Tigers",
"5,113",
"2,479",
"7,592",
"$18.2 billion (5th)",
"1,172",
"Dei sub numine viget (Under God's power she"
],
[
"University of Pennsylvania",
"Philadelphia, Pennsylvania",
"Quakers",
"10,337",
"10,306",
"20,643",
"$7.7 billion (11th)",
"4,464",
"Leges sine moribus vanae (Laws without moral"
],
[
"Yale University",
"New Haven, Connecticut",
"Bulldogs",
"5,275",
"6,391",
"11,666",
"$20.8 billion (2nd)",
"4,140",
"vrym vtvmym Lux et ver"
]
] |
[
"Yale University"
] |
and among those, which university do the bulldogs belong to? || and their athletic names? | what about their enrollment? | what are all of the universities?
|
nt-6252
|
col : institution | location | athletic nickname | undergraduate enrollment | graduate enrollment | total enrollment | 2013 endowment (and us rank) | academic staff | motto row 1 : brown university | providence, rhode island | bears | 6,316 | 2,333 | 8,649 | $2.7 billion (30th) | 736 | in deo speramus (in god we hope) row 2 : columbia university | new york, new york | lions | 7,160 | 15,760 | 22,920 | $8.2 billion (9th) | 3,763 | in lumine tuo videbimus lumen (in th row 3 : cornell university | ithaca, new york | big red | 13,931 | 6,702 | 20,633 | $5.3 billion (18th) | 2,908 | i would found an institution where any person can find instruction in any study. row 4 : dartmouth college | hanover, new hampshire | big green | 4,248 | 1,893 | 6,141 | $3.7 billion (22nd) | 571 | vox clamantis in deserto (the voice of one crying row 5 : harvard university | cambridge, massachusetts | crimson | 7,181 | 14,044 | 21,225 | $32.3 billion (1st) | 4,671 | veritas (truth) row 6 : princeton university | princeton, new jersey | tigers | 5,113 | 2,479 | 7,592 | $18.2 billion (5th) | 1,172 | dei sub numine viget (under god's power she row 7 : university of pennsylvania | philadelphia, pennsylvania | quakers | 10,337 | 10,306 | 20,643 | $7.7 billion (11th) | 4,464 | leges sine moribus vanae (laws without moral row 8 : yale university | new haven, connecticut | bulldogs | 5,275 | 6,391 | 11,666 | $20.8 billion (2nd) | 4,140 | vrym vtvmym lux et ver
|
table_csv/203_592.csv
| 0 |
{
"column_index": [
0,
0,
0,
0,
0
],
"row_index": [
1,
2,
4,
6,
7
]
}
|
[
"which of the universities have a total enrollment of greater than 10000?"
] | 2 |
columbia university, cornell university, harvard university, university of pennsylvania, yale university
|
[
"Institution",
"Location",
"Athletic nickname",
"Undergraduate enrollment",
"Graduate enrollment",
"Total enrollment",
"2013 Endowment (and US rank)",
"Academic staff",
"Motto"
] |
which of the universities have a total enrollment of greater than 10000?
|
[
[
"Brown University",
"Providence, Rhode Island",
"Bears",
"6,316",
"2,333",
"8,649",
"$2.7 billion (30th)",
"736",
"In Deo Speramus (In God We Hope)"
],
[
"Columbia University",
"New York, New York",
"Lions",
"7,160",
"15,760",
"22,920",
"$8.2 billion (9th)",
"3,763",
"In lumine Tuo videbimus lumen (In Th"
],
[
"Cornell University",
"Ithaca, New York",
"Big Red",
"13,931",
"6,702",
"20,633",
"$5.3 billion (18th)",
"2,908",
"I would found an institution where any person can find instruction in any study."
],
[
"Dartmouth College",
"Hanover, New Hampshire",
"Big Green",
"4,248",
"1,893",
"6,141",
"$3.7 billion (22nd)",
"571",
"Vox clamantis in deserto (The voice of one crying"
],
[
"Harvard University",
"Cambridge, Massachusetts",
"Crimson",
"7,181",
"14,044",
"21,225",
"$32.3 billion (1st)",
"4,671",
"Veritas (Truth)"
],
[
"Princeton University",
"Princeton, New Jersey",
"Tigers",
"5,113",
"2,479",
"7,592",
"$18.2 billion (5th)",
"1,172",
"Dei sub numine viget (Under God's power she"
],
[
"University of Pennsylvania",
"Philadelphia, Pennsylvania",
"Quakers",
"10,337",
"10,306",
"20,643",
"$7.7 billion (11th)",
"4,464",
"Leges sine moribus vanae (Laws without moral"
],
[
"Yale University",
"New Haven, Connecticut",
"Bulldogs",
"5,275",
"6,391",
"11,666",
"$20.8 billion (2nd)",
"4,140",
"vrym vtvmym Lux et ver"
]
] |
[
"Columbia University",
"Cornell University",
"Harvard University",
"University of Pennsylvania",
"Yale University"
] |
which of the universities have a total enrollment of greater than 10000? ||
|
nt-6252
|
col : institution | location | athletic nickname | undergraduate enrollment | graduate enrollment | total enrollment | 2013 endowment (and us rank) | academic staff | motto row 1 : brown university | providence, rhode island | bears | 6,316 | 2,333 | 8,649 | $2.7 billion (30th) | 736 | in deo speramus (in god we hope) row 2 : columbia university | new york, new york | lions | 7,160 | 15,760 | 22,920 | $8.2 billion (9th) | 3,763 | in lumine tuo videbimus lumen (in th row 3 : cornell university | ithaca, new york | big red | 13,931 | 6,702 | 20,633 | $5.3 billion (18th) | 2,908 | i would found an institution where any person can find instruction in any study. row 4 : dartmouth college | hanover, new hampshire | big green | 4,248 | 1,893 | 6,141 | $3.7 billion (22nd) | 571 | vox clamantis in deserto (the voice of one crying row 5 : harvard university | cambridge, massachusetts | crimson | 7,181 | 14,044 | 21,225 | $32.3 billion (1st) | 4,671 | veritas (truth) row 6 : princeton university | princeton, new jersey | tigers | 5,113 | 2,479 | 7,592 | $18.2 billion (5th) | 1,172 | dei sub numine viget (under god's power she row 7 : university of pennsylvania | philadelphia, pennsylvania | quakers | 10,337 | 10,306 | 20,643 | $7.7 billion (11th) | 4,464 | leges sine moribus vanae (laws without moral row 8 : yale university | new haven, connecticut | bulldogs | 5,275 | 6,391 | 11,666 | $20.8 billion (2nd) | 4,140 | vrym vtvmym lux et ver
|
table_csv/203_592.csv
| 1 |
{
"column_index": [
0,
0,
0
],
"row_index": [
4,
6,
7
]
}
|
[
"which of the universities have a total enrollment of greater than 10000?",
"of these, which ones has an academic staff of greater than 4000?"
] | 2 |
harvard university, university of pennsylvania, yale university
|
[
"Institution",
"Location",
"Athletic nickname",
"Undergraduate enrollment",
"Graduate enrollment",
"Total enrollment",
"2013 Endowment (and US rank)",
"Academic staff",
"Motto"
] |
of these, which ones has an academic staff of greater than 4000?
|
[
[
"Brown University",
"Providence, Rhode Island",
"Bears",
"6,316",
"2,333",
"8,649",
"$2.7 billion (30th)",
"736",
"In Deo Speramus (In God We Hope)"
],
[
"Columbia University",
"New York, New York",
"Lions",
"7,160",
"15,760",
"22,920",
"$8.2 billion (9th)",
"3,763",
"In lumine Tuo videbimus lumen (In Th"
],
[
"Cornell University",
"Ithaca, New York",
"Big Red",
"13,931",
"6,702",
"20,633",
"$5.3 billion (18th)",
"2,908",
"I would found an institution where any person can find instruction in any study."
],
[
"Dartmouth College",
"Hanover, New Hampshire",
"Big Green",
"4,248",
"1,893",
"6,141",
"$3.7 billion (22nd)",
"571",
"Vox clamantis in deserto (The voice of one crying"
],
[
"Harvard University",
"Cambridge, Massachusetts",
"Crimson",
"7,181",
"14,044",
"21,225",
"$32.3 billion (1st)",
"4,671",
"Veritas (Truth)"
],
[
"Princeton University",
"Princeton, New Jersey",
"Tigers",
"5,113",
"2,479",
"7,592",
"$18.2 billion (5th)",
"1,172",
"Dei sub numine viget (Under God's power she"
],
[
"University of Pennsylvania",
"Philadelphia, Pennsylvania",
"Quakers",
"10,337",
"10,306",
"20,643",
"$7.7 billion (11th)",
"4,464",
"Leges sine moribus vanae (Laws without moral"
],
[
"Yale University",
"New Haven, Connecticut",
"Bulldogs",
"5,275",
"6,391",
"11,666",
"$20.8 billion (2nd)",
"4,140",
"vrym vtvmym Lux et ver"
]
] |
[
"Harvard University",
"University of Pennsylvania",
"Yale University"
] |
of these, which ones has an academic staff of greater than 4000? || which of the universities have a total enrollment of greater than 10000?
|
nt-6252
|
col : institution | location | athletic nickname | undergraduate enrollment | graduate enrollment | total enrollment | 2013 endowment (and us rank) | academic staff | motto row 1 : brown university | providence, rhode island | bears | 6,316 | 2,333 | 8,649 | $2.7 billion (30th) | 736 | in deo speramus (in god we hope) row 2 : columbia university | new york, new york | lions | 7,160 | 15,760 | 22,920 | $8.2 billion (9th) | 3,763 | in lumine tuo videbimus lumen (in th row 3 : cornell university | ithaca, new york | big red | 13,931 | 6,702 | 20,633 | $5.3 billion (18th) | 2,908 | i would found an institution where any person can find instruction in any study. row 4 : dartmouth college | hanover, new hampshire | big green | 4,248 | 1,893 | 6,141 | $3.7 billion (22nd) | 571 | vox clamantis in deserto (the voice of one crying row 5 : harvard university | cambridge, massachusetts | crimson | 7,181 | 14,044 | 21,225 | $32.3 billion (1st) | 4,671 | veritas (truth) row 6 : princeton university | princeton, new jersey | tigers | 5,113 | 2,479 | 7,592 | $18.2 billion (5th) | 1,172 | dei sub numine viget (under god's power she row 7 : university of pennsylvania | philadelphia, pennsylvania | quakers | 10,337 | 10,306 | 20,643 | $7.7 billion (11th) | 4,464 | leges sine moribus vanae (laws without moral row 8 : yale university | new haven, connecticut | bulldogs | 5,275 | 6,391 | 11,666 | $20.8 billion (2nd) | 4,140 | vrym vtvmym lux et ver
|
table_csv/203_592.csv
| 2 |
{
"column_index": [
0
],
"row_index": [
7
]
}
|
[
"which of the universities have a total enrollment of greater than 10000?",
"of these, which ones has an academic staff of greater than 4000?",
"of the remaining universities, which one has the athletic nickname the bulldogs?"
] | 2 |
yale university
|
[
"Institution",
"Location",
"Athletic nickname",
"Undergraduate enrollment",
"Graduate enrollment",
"Total enrollment",
"2013 Endowment (and US rank)",
"Academic staff",
"Motto"
] |
of the remaining universities, which one has the athletic nickname the bulldogs?
|
[
[
"Brown University",
"Providence, Rhode Island",
"Bears",
"6,316",
"2,333",
"8,649",
"$2.7 billion (30th)",
"736",
"In Deo Speramus (In God We Hope)"
],
[
"Columbia University",
"New York, New York",
"Lions",
"7,160",
"15,760",
"22,920",
"$8.2 billion (9th)",
"3,763",
"In lumine Tuo videbimus lumen (In Th"
],
[
"Cornell University",
"Ithaca, New York",
"Big Red",
"13,931",
"6,702",
"20,633",
"$5.3 billion (18th)",
"2,908",
"I would found an institution where any person can find instruction in any study."
],
[
"Dartmouth College",
"Hanover, New Hampshire",
"Big Green",
"4,248",
"1,893",
"6,141",
"$3.7 billion (22nd)",
"571",
"Vox clamantis in deserto (The voice of one crying"
],
[
"Harvard University",
"Cambridge, Massachusetts",
"Crimson",
"7,181",
"14,044",
"21,225",
"$32.3 billion (1st)",
"4,671",
"Veritas (Truth)"
],
[
"Princeton University",
"Princeton, New Jersey",
"Tigers",
"5,113",
"2,479",
"7,592",
"$18.2 billion (5th)",
"1,172",
"Dei sub numine viget (Under God's power she"
],
[
"University of Pennsylvania",
"Philadelphia, Pennsylvania",
"Quakers",
"10,337",
"10,306",
"20,643",
"$7.7 billion (11th)",
"4,464",
"Leges sine moribus vanae (Laws without moral"
],
[
"Yale University",
"New Haven, Connecticut",
"Bulldogs",
"5,275",
"6,391",
"11,666",
"$20.8 billion (2nd)",
"4,140",
"vrym vtvmym Lux et ver"
]
] |
[
"Yale University"
] |
of the remaining universities, which one has the athletic nickname the bulldogs? || of these, which ones has an academic staff of greater than 4000? | which of the universities have a total enrollment of greater than 10000?
|
nt-6250
|
col : # | district | land area (km2) | population (2010 census) | density (persons/km2) row 1 : 1.0 | anse la raye | 30.9 | 6,247 | 210 row 2 : 2.0 | praslin | 16.0 | 2,044 | 119 row 3 : 3.0 | castries | 79.5 | 65,656 | 776 row 4 : 4.0 | choiseul | 31.3 | 6,098 | 206 row 5 : 5.0 | dennery | 69.7 | 12,599 | 182 row 6 : 6.0 | forest reserve | 78.0 | 0 | 0 row 7 : 7.0 | gros islet | 101.5 | 25,210 | 196 row 8 : 8.0 | laborie | 37.8 | 6,701 | 210 row 9 : 9.0 | micoud | 77.7 | 16,284 | 220 row 10 : 10.0 | soufriere | 50.5 | 8,472 | 144 row 11 : 11.0 | vieux fort | 43.8 | 16,284 | 371 row 12 : nan | saint lucia | 608.7 | 165,595 | 256
|
table_csv/203_212.csv
| 0 |
{
"column_index": [
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10
]
}
|
[
"what are the populations of the quarters?"
] | 0 |
6,247, 2,044, 65,656, 6,098, 12,599, 0, 25,210, 6,701, 16,284, 8,472, 16,284
|
[
"#",
"District",
"Land area (km2)",
"Population (2010 census)",
"Density (persons/km2)"
] |
what are the populations of the quarters?
|
[
[
"1.0",
"Anse la Raye",
"30.9",
"6,247",
"210"
],
[
"2.0",
"Praslin",
"16.0",
"2,044",
"119"
],
[
"3.0",
"Castries",
"79.5",
"65,656",
"776"
],
[
"4.0",
"Choiseul",
"31.3",
"6,098",
"206"
],
[
"5.0",
"Dennery",
"69.7",
"12,599",
"182"
],
[
"6.0",
"Forest Reserve",
"78.0",
"0",
"0"
],
[
"7.0",
"Gros Islet",
"101.5",
"25,210",
"196"
],
[
"8.0",
"Laborie",
"37.8",
"6,701",
"210"
],
[
"9.0",
"Micoud",
"77.7",
"16,284",
"220"
],
[
"10.0",
"Soufriere",
"50.5",
"8,472",
"144"
],
[
"11.0",
"Vieux Fort",
"43.8",
"16,284",
"371"
],
[
"nan",
"Saint Lucia",
"608.7",
"165,595",
"256"
]
] |
[
"6,247",
"2,044",
"65,656",
"6,098",
"12,599",
"0",
"25,210",
"6,701",
"16,284",
"8,472",
"16,284"
] |
what are the populations of the quarters? ||
|
nt-6250
|
col : # | district | land area (km2) | population (2010 census) | density (persons/km2) row 1 : 1.0 | anse la raye | 30.9 | 6,247 | 210 row 2 : 2.0 | praslin | 16.0 | 2,044 | 119 row 3 : 3.0 | castries | 79.5 | 65,656 | 776 row 4 : 4.0 | choiseul | 31.3 | 6,098 | 206 row 5 : 5.0 | dennery | 69.7 | 12,599 | 182 row 6 : 6.0 | forest reserve | 78.0 | 0 | 0 row 7 : 7.0 | gros islet | 101.5 | 25,210 | 196 row 8 : 8.0 | laborie | 37.8 | 6,701 | 210 row 9 : 9.0 | micoud | 77.7 | 16,284 | 220 row 10 : 10.0 | soufriere | 50.5 | 8,472 | 144 row 11 : 11.0 | vieux fort | 43.8 | 16,284 | 371 row 12 : nan | saint lucia | 608.7 | 165,595 | 256
|
table_csv/203_212.csv
| 1 |
{
"column_index": [
1,
1
],
"row_index": [
2,
6
]
}
|
[
"what are the populations of the quarters?",
"which of the quarters have populations greater than 20,000?"
] | 0 |
castries, gros islet
|
[
"#",
"District",
"Land area (km2)",
"Population (2010 census)",
"Density (persons/km2)"
] |
which of the quarters have populations greater than 20,000?
|
[
[
"1.0",
"Anse la Raye",
"30.9",
"6,247",
"210"
],
[
"2.0",
"Praslin",
"16.0",
"2,044",
"119"
],
[
"3.0",
"Castries",
"79.5",
"65,656",
"776"
],
[
"4.0",
"Choiseul",
"31.3",
"6,098",
"206"
],
[
"5.0",
"Dennery",
"69.7",
"12,599",
"182"
],
[
"6.0",
"Forest Reserve",
"78.0",
"0",
"0"
],
[
"7.0",
"Gros Islet",
"101.5",
"25,210",
"196"
],
[
"8.0",
"Laborie",
"37.8",
"6,701",
"210"
],
[
"9.0",
"Micoud",
"77.7",
"16,284",
"220"
],
[
"10.0",
"Soufriere",
"50.5",
"8,472",
"144"
],
[
"11.0",
"Vieux Fort",
"43.8",
"16,284",
"371"
],
[
"nan",
"Saint Lucia",
"608.7",
"165,595",
"256"
]
] |
[
"Castries",
"Gros Islet"
] |
which of the quarters have populations greater than 20,000? || what are the populations of the quarters?
|
nt-6250
|
col : # | district | land area (km2) | population (2010 census) | density (persons/km2) row 1 : 1.0 | anse la raye | 30.9 | 6,247 | 210 row 2 : 2.0 | praslin | 16.0 | 2,044 | 119 row 3 : 3.0 | castries | 79.5 | 65,656 | 776 row 4 : 4.0 | choiseul | 31.3 | 6,098 | 206 row 5 : 5.0 | dennery | 69.7 | 12,599 | 182 row 6 : 6.0 | forest reserve | 78.0 | 0 | 0 row 7 : 7.0 | gros islet | 101.5 | 25,210 | 196 row 8 : 8.0 | laborie | 37.8 | 6,701 | 210 row 9 : 9.0 | micoud | 77.7 | 16,284 | 220 row 10 : 10.0 | soufriere | 50.5 | 8,472 | 144 row 11 : 11.0 | vieux fort | 43.8 | 16,284 | 371 row 12 : nan | saint lucia | 608.7 | 165,595 | 256
|
table_csv/203_212.csv
| 2 |
{
"column_index": [
1
],
"row_index": [
6
]
}
|
[
"what are the populations of the quarters?",
"which of the quarters have populations greater than 20,000?",
"which of these places is not castries?"
] | 0 |
gros islet
|
[
"#",
"District",
"Land area (km2)",
"Population (2010 census)",
"Density (persons/km2)"
] |
which of these places is not castries?
|
[
[
"1.0",
"Anse la Raye",
"30.9",
"6,247",
"210"
],
[
"2.0",
"Praslin",
"16.0",
"2,044",
"119"
],
[
"3.0",
"Castries",
"79.5",
"65,656",
"776"
],
[
"4.0",
"Choiseul",
"31.3",
"6,098",
"206"
],
[
"5.0",
"Dennery",
"69.7",
"12,599",
"182"
],
[
"6.0",
"Forest Reserve",
"78.0",
"0",
"0"
],
[
"7.0",
"Gros Islet",
"101.5",
"25,210",
"196"
],
[
"8.0",
"Laborie",
"37.8",
"6,701",
"210"
],
[
"9.0",
"Micoud",
"77.7",
"16,284",
"220"
],
[
"10.0",
"Soufriere",
"50.5",
"8,472",
"144"
],
[
"11.0",
"Vieux Fort",
"43.8",
"16,284",
"371"
],
[
"nan",
"Saint Lucia",
"608.7",
"165,595",
"256"
]
] |
[
"Gros Islet"
] |
which of these places is not castries? || which of the quarters have populations greater than 20,000? | what are the populations of the quarters?
|
nt-6250
|
col : # | district | land area (km2) | population (2010 census) | density (persons/km2) row 1 : 1.0 | anse la raye | 30.9 | 6,247 | 210 row 2 : 2.0 | praslin | 16.0 | 2,044 | 119 row 3 : 3.0 | castries | 79.5 | 65,656 | 776 row 4 : 4.0 | choiseul | 31.3 | 6,098 | 206 row 5 : 5.0 | dennery | 69.7 | 12,599 | 182 row 6 : 6.0 | forest reserve | 78.0 | 0 | 0 row 7 : 7.0 | gros islet | 101.5 | 25,210 | 196 row 8 : 8.0 | laborie | 37.8 | 6,701 | 210 row 9 : 9.0 | micoud | 77.7 | 16,284 | 220 row 10 : 10.0 | soufriere | 50.5 | 8,472 | 144 row 11 : 11.0 | vieux fort | 43.8 | 16,284 | 371 row 12 : nan | saint lucia | 608.7 | 165,595 | 256
|
table_csv/203_212.csv
| 0 |
{
"column_index": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10
]
}
|
[
"what are all of the districts?"
] | 1 |
anse la raye, praslin, castries, choiseul, dennery, forest reserve, gros islet, laborie, micoud, soufriere, vieux fort
|
[
"#",
"District",
"Land area (km2)",
"Population (2010 census)",
"Density (persons/km2)"
] |
what are all of the districts?
|
[
[
"1.0",
"Anse la Raye",
"30.9",
"6,247",
"210"
],
[
"2.0",
"Praslin",
"16.0",
"2,044",
"119"
],
[
"3.0",
"Castries",
"79.5",
"65,656",
"776"
],
[
"4.0",
"Choiseul",
"31.3",
"6,098",
"206"
],
[
"5.0",
"Dennery",
"69.7",
"12,599",
"182"
],
[
"6.0",
"Forest Reserve",
"78.0",
"0",
"0"
],
[
"7.0",
"Gros Islet",
"101.5",
"25,210",
"196"
],
[
"8.0",
"Laborie",
"37.8",
"6,701",
"210"
],
[
"9.0",
"Micoud",
"77.7",
"16,284",
"220"
],
[
"10.0",
"Soufriere",
"50.5",
"8,472",
"144"
],
[
"11.0",
"Vieux Fort",
"43.8",
"16,284",
"371"
],
[
"nan",
"Saint Lucia",
"608.7",
"165,595",
"256"
]
] |
[
"Anse la Raye",
"Praslin",
"Castries",
"Choiseul",
"Dennery",
"Forest Reserve",
"Gros Islet",
"Laborie",
"Micoud",
"Soufriere",
"Vieux Fort"
] |
what are all of the districts? ||
|
nt-6250
|
col : # | district | land area (km2) | population (2010 census) | density (persons/km2) row 1 : 1.0 | anse la raye | 30.9 | 6,247 | 210 row 2 : 2.0 | praslin | 16.0 | 2,044 | 119 row 3 : 3.0 | castries | 79.5 | 65,656 | 776 row 4 : 4.0 | choiseul | 31.3 | 6,098 | 206 row 5 : 5.0 | dennery | 69.7 | 12,599 | 182 row 6 : 6.0 | forest reserve | 78.0 | 0 | 0 row 7 : 7.0 | gros islet | 101.5 | 25,210 | 196 row 8 : 8.0 | laborie | 37.8 | 6,701 | 210 row 9 : 9.0 | micoud | 77.7 | 16,284 | 220 row 10 : 10.0 | soufriere | 50.5 | 8,472 | 144 row 11 : 11.0 | vieux fort | 43.8 | 16,284 | 371 row 12 : nan | saint lucia | 608.7 | 165,595 | 256
|
table_csv/203_212.csv
| 1 |
{
"column_index": [
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10
]
}
|
[
"what are all of the districts?",
"what about their populations?"
] | 1 |
6,247, 2,044, 65,656, 6,098, 12,599, 0, 25,210, 6,701, 16,284, 8,472, 16,284
|
[
"#",
"District",
"Land area (km2)",
"Population (2010 census)",
"Density (persons/km2)"
] |
what about their populations?
|
[
[
"1.0",
"Anse la Raye",
"30.9",
"6,247",
"210"
],
[
"2.0",
"Praslin",
"16.0",
"2,044",
"119"
],
[
"3.0",
"Castries",
"79.5",
"65,656",
"776"
],
[
"4.0",
"Choiseul",
"31.3",
"6,098",
"206"
],
[
"5.0",
"Dennery",
"69.7",
"12,599",
"182"
],
[
"6.0",
"Forest Reserve",
"78.0",
"0",
"0"
],
[
"7.0",
"Gros Islet",
"101.5",
"25,210",
"196"
],
[
"8.0",
"Laborie",
"37.8",
"6,701",
"210"
],
[
"9.0",
"Micoud",
"77.7",
"16,284",
"220"
],
[
"10.0",
"Soufriere",
"50.5",
"8,472",
"144"
],
[
"11.0",
"Vieux Fort",
"43.8",
"16,284",
"371"
],
[
"nan",
"Saint Lucia",
"608.7",
"165,595",
"256"
]
] |
[
"6,247",
"2,044",
"65,656",
"6,098",
"12,599",
"0",
"25,210",
"6,701",
"16,284",
"8,472",
"16,284"
] |
what about their populations? || what are all of the districts?
|
nt-6250
|
col : # | district | land area (km2) | population (2010 census) | density (persons/km2) row 1 : 1.0 | anse la raye | 30.9 | 6,247 | 210 row 2 : 2.0 | praslin | 16.0 | 2,044 | 119 row 3 : 3.0 | castries | 79.5 | 65,656 | 776 row 4 : 4.0 | choiseul | 31.3 | 6,098 | 206 row 5 : 5.0 | dennery | 69.7 | 12,599 | 182 row 6 : 6.0 | forest reserve | 78.0 | 0 | 0 row 7 : 7.0 | gros islet | 101.5 | 25,210 | 196 row 8 : 8.0 | laborie | 37.8 | 6,701 | 210 row 9 : 9.0 | micoud | 77.7 | 16,284 | 220 row 10 : 10.0 | soufriere | 50.5 | 8,472 | 144 row 11 : 11.0 | vieux fort | 43.8 | 16,284 | 371 row 12 : nan | saint lucia | 608.7 | 165,595 | 256
|
table_csv/203_212.csv
| 2 |
{
"column_index": [
1
],
"row_index": [
6
]
}
|
[
"what are all of the districts?",
"what about their populations?",
"and which district is the second-most populated?"
] | 1 |
gros islet
|
[
"#",
"District",
"Land area (km2)",
"Population (2010 census)",
"Density (persons/km2)"
] |
and which district is the second-most populated?
|
[
[
"1.0",
"Anse la Raye",
"30.9",
"6,247",
"210"
],
[
"2.0",
"Praslin",
"16.0",
"2,044",
"119"
],
[
"3.0",
"Castries",
"79.5",
"65,656",
"776"
],
[
"4.0",
"Choiseul",
"31.3",
"6,098",
"206"
],
[
"5.0",
"Dennery",
"69.7",
"12,599",
"182"
],
[
"6.0",
"Forest Reserve",
"78.0",
"0",
"0"
],
[
"7.0",
"Gros Islet",
"101.5",
"25,210",
"196"
],
[
"8.0",
"Laborie",
"37.8",
"6,701",
"210"
],
[
"9.0",
"Micoud",
"77.7",
"16,284",
"220"
],
[
"10.0",
"Soufriere",
"50.5",
"8,472",
"144"
],
[
"11.0",
"Vieux Fort",
"43.8",
"16,284",
"371"
],
[
"nan",
"Saint Lucia",
"608.7",
"165,595",
"256"
]
] |
[
"Gros Islet"
] |
and which district is the second-most populated? || what about their populations? | what are all of the districts?
|
nt-6250
|
col : # | district | land area (km2) | population (2010 census) | density (persons/km2) row 1 : 1.0 | anse la raye | 30.9 | 6,247 | 210 row 2 : 2.0 | praslin | 16.0 | 2,044 | 119 row 3 : 3.0 | castries | 79.5 | 65,656 | 776 row 4 : 4.0 | choiseul | 31.3 | 6,098 | 206 row 5 : 5.0 | dennery | 69.7 | 12,599 | 182 row 6 : 6.0 | forest reserve | 78.0 | 0 | 0 row 7 : 7.0 | gros islet | 101.5 | 25,210 | 196 row 8 : 8.0 | laborie | 37.8 | 6,701 | 210 row 9 : 9.0 | micoud | 77.7 | 16,284 | 220 row 10 : 10.0 | soufriere | 50.5 | 8,472 | 144 row 11 : 11.0 | vieux fort | 43.8 | 16,284 | 371 row 12 : nan | saint lucia | 608.7 | 165,595 | 256
|
table_csv/203_212.csv
| 0 |
{
"column_index": [
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10
]
}
|
[
"what are the districts of saint lucia?"
] | 2 |
anse la raye, praslin, castries, choiseul, dennery, forest reserve, gros islet, laborie, micoud, soufriere, vieux fort
|
[
"#",
"District",
"Land area (km2)",
"Population (2010 census)",
"Density (persons/km2)"
] |
what are the districts of saint lucia?
|
[
[
"1.0",
"Anse la Raye",
"30.9",
"6,247",
"210"
],
[
"2.0",
"Praslin",
"16.0",
"2,044",
"119"
],
[
"3.0",
"Castries",
"79.5",
"65,656",
"776"
],
[
"4.0",
"Choiseul",
"31.3",
"6,098",
"206"
],
[
"5.0",
"Dennery",
"69.7",
"12,599",
"182"
],
[
"6.0",
"Forest Reserve",
"78.0",
"0",
"0"
],
[
"7.0",
"Gros Islet",
"101.5",
"25,210",
"196"
],
[
"8.0",
"Laborie",
"37.8",
"6,701",
"210"
],
[
"9.0",
"Micoud",
"77.7",
"16,284",
"220"
],
[
"10.0",
"Soufriere",
"50.5",
"8,472",
"144"
],
[
"11.0",
"Vieux Fort",
"43.8",
"16,284",
"371"
],
[
"nan",
"Saint Lucia",
"608.7",
"165,595",
"256"
]
] |
[
"Anse la Raye",
"Praslin",
"Castries",
"Choiseul",
"Dennery",
"Forest Reserve",
"Gros Islet",
"Laborie",
"Micoud",
"Soufriere",
"Vieux Fort"
] |
what are the districts of saint lucia? ||
|
nt-6250
|
col : # | district | land area (km2) | population (2010 census) | density (persons/km2) row 1 : 1.0 | anse la raye | 30.9 | 6,247 | 210 row 2 : 2.0 | praslin | 16.0 | 2,044 | 119 row 3 : 3.0 | castries | 79.5 | 65,656 | 776 row 4 : 4.0 | choiseul | 31.3 | 6,098 | 206 row 5 : 5.0 | dennery | 69.7 | 12,599 | 182 row 6 : 6.0 | forest reserve | 78.0 | 0 | 0 row 7 : 7.0 | gros islet | 101.5 | 25,210 | 196 row 8 : 8.0 | laborie | 37.8 | 6,701 | 210 row 9 : 9.0 | micoud | 77.7 | 16,284 | 220 row 10 : 10.0 | soufriere | 50.5 | 8,472 | 144 row 11 : 11.0 | vieux fort | 43.8 | 16,284 | 371 row 12 : nan | saint lucia | 608.7 | 165,595 | 256
|
table_csv/203_212.csv
| 1 |
{
"column_index": [
3,
3,
3,
3,
3,
3,
3,
3,
3,
3,
3
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10
]
}
|
[
"what are the districts of saint lucia?",
"what are the populations of those districts?"
] | 2 |
6,247, 2,044, 65,656, 6,098, 12,599, 0, 25,210, 6,701, 16,284, 8,472, 16,284
|
[
"#",
"District",
"Land area (km2)",
"Population (2010 census)",
"Density (persons/km2)"
] |
what are the populations of those districts?
|
[
[
"1.0",
"Anse la Raye",
"30.9",
"6,247",
"210"
],
[
"2.0",
"Praslin",
"16.0",
"2,044",
"119"
],
[
"3.0",
"Castries",
"79.5",
"65,656",
"776"
],
[
"4.0",
"Choiseul",
"31.3",
"6,098",
"206"
],
[
"5.0",
"Dennery",
"69.7",
"12,599",
"182"
],
[
"6.0",
"Forest Reserve",
"78.0",
"0",
"0"
],
[
"7.0",
"Gros Islet",
"101.5",
"25,210",
"196"
],
[
"8.0",
"Laborie",
"37.8",
"6,701",
"210"
],
[
"9.0",
"Micoud",
"77.7",
"16,284",
"220"
],
[
"10.0",
"Soufriere",
"50.5",
"8,472",
"144"
],
[
"11.0",
"Vieux Fort",
"43.8",
"16,284",
"371"
],
[
"nan",
"Saint Lucia",
"608.7",
"165,595",
"256"
]
] |
[
"6,247",
"2,044",
"65,656",
"6,098",
"12,599",
"0",
"25,210",
"6,701",
"16,284",
"8,472",
"16,284"
] |
what are the populations of those districts? || what are the districts of saint lucia?
|
nt-6250
|
col : # | district | land area (km2) | population (2010 census) | density (persons/km2) row 1 : 1.0 | anse la raye | 30.9 | 6,247 | 210 row 2 : 2.0 | praslin | 16.0 | 2,044 | 119 row 3 : 3.0 | castries | 79.5 | 65,656 | 776 row 4 : 4.0 | choiseul | 31.3 | 6,098 | 206 row 5 : 5.0 | dennery | 69.7 | 12,599 | 182 row 6 : 6.0 | forest reserve | 78.0 | 0 | 0 row 7 : 7.0 | gros islet | 101.5 | 25,210 | 196 row 8 : 8.0 | laborie | 37.8 | 6,701 | 210 row 9 : 9.0 | micoud | 77.7 | 16,284 | 220 row 10 : 10.0 | soufriere | 50.5 | 8,472 | 144 row 11 : 11.0 | vieux fort | 43.8 | 16,284 | 371 row 12 : nan | saint lucia | 608.7 | 165,595 | 256
|
table_csv/203_212.csv
| 2 |
{
"column_index": [
3,
3
],
"row_index": [
2,
6
]
}
|
[
"what are the districts of saint lucia?",
"what are the populations of those districts?",
"which of these populations are greater than 20 000?"
] | 2 |
65,656, 25,210
|
[
"#",
"District",
"Land area (km2)",
"Population (2010 census)",
"Density (persons/km2)"
] |
which of these populations are greater than 20 000?
|
[
[
"1.0",
"Anse la Raye",
"30.9",
"6,247",
"210"
],
[
"2.0",
"Praslin",
"16.0",
"2,044",
"119"
],
[
"3.0",
"Castries",
"79.5",
"65,656",
"776"
],
[
"4.0",
"Choiseul",
"31.3",
"6,098",
"206"
],
[
"5.0",
"Dennery",
"69.7",
"12,599",
"182"
],
[
"6.0",
"Forest Reserve",
"78.0",
"0",
"0"
],
[
"7.0",
"Gros Islet",
"101.5",
"25,210",
"196"
],
[
"8.0",
"Laborie",
"37.8",
"6,701",
"210"
],
[
"9.0",
"Micoud",
"77.7",
"16,284",
"220"
],
[
"10.0",
"Soufriere",
"50.5",
"8,472",
"144"
],
[
"11.0",
"Vieux Fort",
"43.8",
"16,284",
"371"
],
[
"nan",
"Saint Lucia",
"608.7",
"165,595",
"256"
]
] |
[
"65,656",
"25,210"
] |
which of these populations are greater than 20 000? || what are the populations of those districts? | what are the districts of saint lucia?
|
nt-6250
|
col : # | district | land area (km2) | population (2010 census) | density (persons/km2) row 1 : 1.0 | anse la raye | 30.9 | 6,247 | 210 row 2 : 2.0 | praslin | 16.0 | 2,044 | 119 row 3 : 3.0 | castries | 79.5 | 65,656 | 776 row 4 : 4.0 | choiseul | 31.3 | 6,098 | 206 row 5 : 5.0 | dennery | 69.7 | 12,599 | 182 row 6 : 6.0 | forest reserve | 78.0 | 0 | 0 row 7 : 7.0 | gros islet | 101.5 | 25,210 | 196 row 8 : 8.0 | laborie | 37.8 | 6,701 | 210 row 9 : 9.0 | micoud | 77.7 | 16,284 | 220 row 10 : 10.0 | soufriere | 50.5 | 8,472 | 144 row 11 : 11.0 | vieux fort | 43.8 | 16,284 | 371 row 12 : nan | saint lucia | 608.7 | 165,595 | 256
|
table_csv/203_212.csv
| 3 |
{
"column_index": [
1,
1
],
"row_index": [
2,
6
]
}
|
[
"what are the districts of saint lucia?",
"what are the populations of those districts?",
"which of these populations are greater than 20 000?",
"which districts have these populations?"
] | 2 |
castries, gros islet
|
[
"#",
"District",
"Land area (km2)",
"Population (2010 census)",
"Density (persons/km2)"
] |
which districts have these populations?
|
[
[
"1.0",
"Anse la Raye",
"30.9",
"6,247",
"210"
],
[
"2.0",
"Praslin",
"16.0",
"2,044",
"119"
],
[
"3.0",
"Castries",
"79.5",
"65,656",
"776"
],
[
"4.0",
"Choiseul",
"31.3",
"6,098",
"206"
],
[
"5.0",
"Dennery",
"69.7",
"12,599",
"182"
],
[
"6.0",
"Forest Reserve",
"78.0",
"0",
"0"
],
[
"7.0",
"Gros Islet",
"101.5",
"25,210",
"196"
],
[
"8.0",
"Laborie",
"37.8",
"6,701",
"210"
],
[
"9.0",
"Micoud",
"77.7",
"16,284",
"220"
],
[
"10.0",
"Soufriere",
"50.5",
"8,472",
"144"
],
[
"11.0",
"Vieux Fort",
"43.8",
"16,284",
"371"
],
[
"nan",
"Saint Lucia",
"608.7",
"165,595",
"256"
]
] |
[
"Castries",
"Gros Islet"
] |
which districts have these populations? || which of these populations are greater than 20 000? | what are the populations of those districts? | what are the districts of saint lucia?
|
nt-6250
|
col : # | district | land area (km2) | population (2010 census) | density (persons/km2) row 1 : 1.0 | anse la raye | 30.9 | 6,247 | 210 row 2 : 2.0 | praslin | 16.0 | 2,044 | 119 row 3 : 3.0 | castries | 79.5 | 65,656 | 776 row 4 : 4.0 | choiseul | 31.3 | 6,098 | 206 row 5 : 5.0 | dennery | 69.7 | 12,599 | 182 row 6 : 6.0 | forest reserve | 78.0 | 0 | 0 row 7 : 7.0 | gros islet | 101.5 | 25,210 | 196 row 8 : 8.0 | laborie | 37.8 | 6,701 | 210 row 9 : 9.0 | micoud | 77.7 | 16,284 | 220 row 10 : 10.0 | soufriere | 50.5 | 8,472 | 144 row 11 : 11.0 | vieux fort | 43.8 | 16,284 | 371 row 12 : nan | saint lucia | 608.7 | 165,595 | 256
|
table_csv/203_212.csv
| 4 |
{
"column_index": [
1
],
"row_index": [
6
]
}
|
[
"what are the districts of saint lucia?",
"what are the populations of those districts?",
"which of these populations are greater than 20 000?",
"which districts have these populations?",
"which districts are not castries?"
] | 2 |
gros islet
|
[
"#",
"District",
"Land area (km2)",
"Population (2010 census)",
"Density (persons/km2)"
] |
which districts are not castries?
|
[
[
"1.0",
"Anse la Raye",
"30.9",
"6,247",
"210"
],
[
"2.0",
"Praslin",
"16.0",
"2,044",
"119"
],
[
"3.0",
"Castries",
"79.5",
"65,656",
"776"
],
[
"4.0",
"Choiseul",
"31.3",
"6,098",
"206"
],
[
"5.0",
"Dennery",
"69.7",
"12,599",
"182"
],
[
"6.0",
"Forest Reserve",
"78.0",
"0",
"0"
],
[
"7.0",
"Gros Islet",
"101.5",
"25,210",
"196"
],
[
"8.0",
"Laborie",
"37.8",
"6,701",
"210"
],
[
"9.0",
"Micoud",
"77.7",
"16,284",
"220"
],
[
"10.0",
"Soufriere",
"50.5",
"8,472",
"144"
],
[
"11.0",
"Vieux Fort",
"43.8",
"16,284",
"371"
],
[
"nan",
"Saint Lucia",
"608.7",
"165,595",
"256"
]
] |
[
"Gros Islet"
] |
which districts are not castries? || which districts have these populations? | which of these populations are greater than 20 000? | what are the populations of those districts? | what are the districts of saint lucia?
|
nt-13946
|
col : place | nation | 5 hoops | 3 balls, 2 ribbons | total row 1 : 1 | bulgaria | 19.800 (1) | 19.800 (1) | 39.6 row 2 : 2 | spain | 19.700 (3) | 19.700 (2) | 39.4 row 3 : 3 | belarus | 19.733 (2) | 19.600 (3) | 39.333 row 4 : 4 | russia | 19.600 (4) | 19.566 (4) | 39.166 row 5 : 5 | ukraine | 19.366 (5) | 15.500 (5) | 38.866 row 6 : 6 | japan | 19.166 (6) | 19.333 (7) | 38.499 row 7 : 7 | italy | 19.066 (7) | 19.400 (6) | 38.466 row 8 : 8 | hungary | 18.766 (8) | 19.166 (8) | 37.932
|
table_csv/204_979.csv
| 0 |
{
"column_index": [
1,
1,
1
],
"row_index": [
0,
1,
2
]
}
|
[
"what countries beat russia overall?"
] | 0 |
bulgaria, spain, belarus
|
[
"Place",
"Nation",
"5 Hoops",
"3 Balls, 2 Ribbons",
"Total"
] |
what countries beat russia overall?
|
[
[
"1",
"Bulgaria",
"19.800 (1)",
"19.800 (1)",
"39.6"
],
[
"2",
"Spain",
"19.700 (3)",
"19.700 (2)",
"39.4"
],
[
"3",
"Belarus",
"19.733 (2)",
"19.600 (3)",
"39.333"
],
[
"4",
"Russia",
"19.600 (4)",
"19.566 (4)",
"39.166"
],
[
"5",
"Ukraine",
"19.366 (5)",
"15.500 (5)",
"38.866"
],
[
"6",
"Japan",
"19.166 (6)",
"19.333 (7)",
"38.499"
],
[
"7",
"Italy",
"19.066 (7)",
"19.400 (6)",
"38.466"
],
[
"8",
"Hungary",
"18.766 (8)",
"19.166 (8)",
"37.932"
]
] |
[
"Bulgaria",
"Spain",
"Belarus"
] |
what countries beat russia overall? ||
|
nt-13946
|
col : place | nation | 5 hoops | 3 balls, 2 ribbons | total row 1 : 1 | bulgaria | 19.800 (1) | 19.800 (1) | 39.6 row 2 : 2 | spain | 19.700 (3) | 19.700 (2) | 39.4 row 3 : 3 | belarus | 19.733 (2) | 19.600 (3) | 39.333 row 4 : 4 | russia | 19.600 (4) | 19.566 (4) | 39.166 row 5 : 5 | ukraine | 19.366 (5) | 15.500 (5) | 38.866 row 6 : 6 | japan | 19.166 (6) | 19.333 (7) | 38.499 row 7 : 7 | italy | 19.066 (7) | 19.400 (6) | 38.466 row 8 : 8 | hungary | 18.766 (8) | 19.166 (8) | 37.932
|
table_csv/204_979.csv
| 1 |
{
"column_index": [
1
],
"row_index": [
2
]
}
|
[
"what countries beat russia overall?",
"which of these is not spain or bulgaria?"
] | 0 |
belarus
|
[
"Place",
"Nation",
"5 Hoops",
"3 Balls, 2 Ribbons",
"Total"
] |
which of these is not spain or bulgaria?
|
[
[
"1",
"Bulgaria",
"19.800 (1)",
"19.800 (1)",
"39.6"
],
[
"2",
"Spain",
"19.700 (3)",
"19.700 (2)",
"39.4"
],
[
"3",
"Belarus",
"19.733 (2)",
"19.600 (3)",
"39.333"
],
[
"4",
"Russia",
"19.600 (4)",
"19.566 (4)",
"39.166"
],
[
"5",
"Ukraine",
"19.366 (5)",
"15.500 (5)",
"38.866"
],
[
"6",
"Japan",
"19.166 (6)",
"19.333 (7)",
"38.499"
],
[
"7",
"Italy",
"19.066 (7)",
"19.400 (6)",
"38.466"
],
[
"8",
"Hungary",
"18.766 (8)",
"19.166 (8)",
"37.932"
]
] |
[
"Belarus"
] |
which of these is not spain or bulgaria? || what countries beat russia overall?
|
nt-13946
|
col : place | nation | 5 hoops | 3 balls, 2 ribbons | total row 1 : 1 | bulgaria | 19.800 (1) | 19.800 (1) | 39.6 row 2 : 2 | spain | 19.700 (3) | 19.700 (2) | 39.4 row 3 : 3 | belarus | 19.733 (2) | 19.600 (3) | 39.333 row 4 : 4 | russia | 19.600 (4) | 19.566 (4) | 39.166 row 5 : 5 | ukraine | 19.366 (5) | 15.500 (5) | 38.866 row 6 : 6 | japan | 19.166 (6) | 19.333 (7) | 38.499 row 7 : 7 | italy | 19.066 (7) | 19.400 (6) | 38.466 row 8 : 8 | hungary | 18.766 (8) | 19.166 (8) | 37.932
|
table_csv/204_979.csv
| 0 |
{
"column_index": [
1,
1,
1,
1,
1,
1,
1
],
"row_index": [
0,
1,
2,
4,
5,
6,
7
]
}
|
[
"which countries other than russia participated in the championship?"
] | 1 |
bulgaria, spain, belarus, ukraine, japan, italy, hungary
|
[
"Place",
"Nation",
"5 Hoops",
"3 Balls, 2 Ribbons",
"Total"
] |
which countries other than russia participated in the championship?
|
[
[
"1",
"Bulgaria",
"19.800 (1)",
"19.800 (1)",
"39.6"
],
[
"2",
"Spain",
"19.700 (3)",
"19.700 (2)",
"39.4"
],
[
"3",
"Belarus",
"19.733 (2)",
"19.600 (3)",
"39.333"
],
[
"4",
"Russia",
"19.600 (4)",
"19.566 (4)",
"39.166"
],
[
"5",
"Ukraine",
"19.366 (5)",
"15.500 (5)",
"38.866"
],
[
"6",
"Japan",
"19.166 (6)",
"19.333 (7)",
"38.499"
],
[
"7",
"Italy",
"19.066 (7)",
"19.400 (6)",
"38.466"
],
[
"8",
"Hungary",
"18.766 (8)",
"19.166 (8)",
"37.932"
]
] |
[
"Bulgaria",
"Spain",
"Belarus",
"Ukraine",
"Japan",
"Italy",
"Hungary"
] |
which countries other than russia participated in the championship? ||
|
nt-13946
|
col : place | nation | 5 hoops | 3 balls, 2 ribbons | total row 1 : 1 | bulgaria | 19.800 (1) | 19.800 (1) | 39.6 row 2 : 2 | spain | 19.700 (3) | 19.700 (2) | 39.4 row 3 : 3 | belarus | 19.733 (2) | 19.600 (3) | 39.333 row 4 : 4 | russia | 19.600 (4) | 19.566 (4) | 39.166 row 5 : 5 | ukraine | 19.366 (5) | 15.500 (5) | 38.866 row 6 : 6 | japan | 19.166 (6) | 19.333 (7) | 38.499 row 7 : 7 | italy | 19.066 (7) | 19.400 (6) | 38.466 row 8 : 8 | hungary | 18.766 (8) | 19.166 (8) | 37.932
|
table_csv/204_979.csv
| 1 |
{
"column_index": [
1,
1,
1
],
"row_index": [
0,
1,
2
]
}
|
[
"which countries other than russia participated in the championship?",
"of these countries which scored higher than russia?"
] | 1 |
bulgaria, spain, belarus
|
[
"Place",
"Nation",
"5 Hoops",
"3 Balls, 2 Ribbons",
"Total"
] |
of these countries which scored higher than russia?
|
[
[
"1",
"Bulgaria",
"19.800 (1)",
"19.800 (1)",
"39.6"
],
[
"2",
"Spain",
"19.700 (3)",
"19.700 (2)",
"39.4"
],
[
"3",
"Belarus",
"19.733 (2)",
"19.600 (3)",
"39.333"
],
[
"4",
"Russia",
"19.600 (4)",
"19.566 (4)",
"39.166"
],
[
"5",
"Ukraine",
"19.366 (5)",
"15.500 (5)",
"38.866"
],
[
"6",
"Japan",
"19.166 (6)",
"19.333 (7)",
"38.499"
],
[
"7",
"Italy",
"19.066 (7)",
"19.400 (6)",
"38.466"
],
[
"8",
"Hungary",
"18.766 (8)",
"19.166 (8)",
"37.932"
]
] |
[
"Bulgaria",
"Spain",
"Belarus"
] |
of these countries which scored higher than russia? || which countries other than russia participated in the championship?
|
nt-13946
|
col : place | nation | 5 hoops | 3 balls, 2 ribbons | total row 1 : 1 | bulgaria | 19.800 (1) | 19.800 (1) | 39.6 row 2 : 2 | spain | 19.700 (3) | 19.700 (2) | 39.4 row 3 : 3 | belarus | 19.733 (2) | 19.600 (3) | 39.333 row 4 : 4 | russia | 19.600 (4) | 19.566 (4) | 39.166 row 5 : 5 | ukraine | 19.366 (5) | 15.500 (5) | 38.866 row 6 : 6 | japan | 19.166 (6) | 19.333 (7) | 38.499 row 7 : 7 | italy | 19.066 (7) | 19.400 (6) | 38.466 row 8 : 8 | hungary | 18.766 (8) | 19.166 (8) | 37.932
|
table_csv/204_979.csv
| 2 |
{
"column_index": [
1
],
"row_index": [
2
]
}
|
[
"which countries other than russia participated in the championship?",
"of these countries which scored higher than russia?",
"of these three countries which one had an overall score of 39.33?"
] | 1 |
belarus
|
[
"Place",
"Nation",
"5 Hoops",
"3 Balls, 2 Ribbons",
"Total"
] |
of these three countries which one had an overall score of 39.33?
|
[
[
"1",
"Bulgaria",
"19.800 (1)",
"19.800 (1)",
"39.6"
],
[
"2",
"Spain",
"19.700 (3)",
"19.700 (2)",
"39.4"
],
[
"3",
"Belarus",
"19.733 (2)",
"19.600 (3)",
"39.333"
],
[
"4",
"Russia",
"19.600 (4)",
"19.566 (4)",
"39.166"
],
[
"5",
"Ukraine",
"19.366 (5)",
"15.500 (5)",
"38.866"
],
[
"6",
"Japan",
"19.166 (6)",
"19.333 (7)",
"38.499"
],
[
"7",
"Italy",
"19.066 (7)",
"19.400 (6)",
"38.466"
],
[
"8",
"Hungary",
"18.766 (8)",
"19.166 (8)",
"37.932"
]
] |
[
"Belarus"
] |
of these three countries which one had an overall score of 39.33? || of these countries which scored higher than russia? | which countries other than russia participated in the championship?
|
nt-13946
|
col : place | nation | 5 hoops | 3 balls, 2 ribbons | total row 1 : 1 | bulgaria | 19.800 (1) | 19.800 (1) | 39.6 row 2 : 2 | spain | 19.700 (3) | 19.700 (2) | 39.4 row 3 : 3 | belarus | 19.733 (2) | 19.600 (3) | 39.333 row 4 : 4 | russia | 19.600 (4) | 19.566 (4) | 39.166 row 5 : 5 | ukraine | 19.366 (5) | 15.500 (5) | 38.866 row 6 : 6 | japan | 19.166 (6) | 19.333 (7) | 38.499 row 7 : 7 | italy | 19.066 (7) | 19.400 (6) | 38.466 row 8 : 8 | hungary | 18.766 (8) | 19.166 (8) | 37.932
|
table_csv/204_979.csv
| 0 |
{
"column_index": [
4
],
"row_index": [
3
]
}
|
[
"how many points did russia score overall?"
] | 2 |
39.166
|
[
"Place",
"Nation",
"5 Hoops",
"3 Balls, 2 Ribbons",
"Total"
] |
how many points did russia score overall?
|
[
[
"1",
"Bulgaria",
"19.800 (1)",
"19.800 (1)",
"39.6"
],
[
"2",
"Spain",
"19.700 (3)",
"19.700 (2)",
"39.4"
],
[
"3",
"Belarus",
"19.733 (2)",
"19.600 (3)",
"39.333"
],
[
"4",
"Russia",
"19.600 (4)",
"19.566 (4)",
"39.166"
],
[
"5",
"Ukraine",
"19.366 (5)",
"15.500 (5)",
"38.866"
],
[
"6",
"Japan",
"19.166 (6)",
"19.333 (7)",
"38.499"
],
[
"7",
"Italy",
"19.066 (7)",
"19.400 (6)",
"38.466"
],
[
"8",
"Hungary",
"18.766 (8)",
"19.166 (8)",
"37.932"
]
] |
[
"39.166"
] |
how many points did russia score overall? ||
|
nt-13946
|
col : place | nation | 5 hoops | 3 balls, 2 ribbons | total row 1 : 1 | bulgaria | 19.800 (1) | 19.800 (1) | 39.6 row 2 : 2 | spain | 19.700 (3) | 19.700 (2) | 39.4 row 3 : 3 | belarus | 19.733 (2) | 19.600 (3) | 39.333 row 4 : 4 | russia | 19.600 (4) | 19.566 (4) | 39.166 row 5 : 5 | ukraine | 19.366 (5) | 15.500 (5) | 38.866 row 6 : 6 | japan | 19.166 (6) | 19.333 (7) | 38.499 row 7 : 7 | italy | 19.066 (7) | 19.400 (6) | 38.466 row 8 : 8 | hungary | 18.766 (8) | 19.166 (8) | 37.932
|
table_csv/204_979.csv
| 1 |
{
"column_index": [
4,
4,
4
],
"row_index": [
0,
1,
2
]
}
|
[
"how many points did russia score overall?",
"what are the scores that are higher than russia's score?"
] | 2 |
39.600, 39.400, 39.333
|
[
"Place",
"Nation",
"5 Hoops",
"3 Balls, 2 Ribbons",
"Total"
] |
what are the scores that are higher than russia's score?
|
[
[
"1",
"Bulgaria",
"19.800 (1)",
"19.800 (1)",
"39.6"
],
[
"2",
"Spain",
"19.700 (3)",
"19.700 (2)",
"39.4"
],
[
"3",
"Belarus",
"19.733 (2)",
"19.600 (3)",
"39.333"
],
[
"4",
"Russia",
"19.600 (4)",
"19.566 (4)",
"39.166"
],
[
"5",
"Ukraine",
"19.366 (5)",
"15.500 (5)",
"38.866"
],
[
"6",
"Japan",
"19.166 (6)",
"19.333 (7)",
"38.499"
],
[
"7",
"Italy",
"19.066 (7)",
"19.400 (6)",
"38.466"
],
[
"8",
"Hungary",
"18.766 (8)",
"19.166 (8)",
"37.932"
]
] |
[
"39.600",
"39.400",
"39.333"
] |
what are the scores that are higher than russia's score? || how many points did russia score overall?
|
nt-13946
|
col : place | nation | 5 hoops | 3 balls, 2 ribbons | total row 1 : 1 | bulgaria | 19.800 (1) | 19.800 (1) | 39.6 row 2 : 2 | spain | 19.700 (3) | 19.700 (2) | 39.4 row 3 : 3 | belarus | 19.733 (2) | 19.600 (3) | 39.333 row 4 : 4 | russia | 19.600 (4) | 19.566 (4) | 39.166 row 5 : 5 | ukraine | 19.366 (5) | 15.500 (5) | 38.866 row 6 : 6 | japan | 19.166 (6) | 19.333 (7) | 38.499 row 7 : 7 | italy | 19.066 (7) | 19.400 (6) | 38.466 row 8 : 8 | hungary | 18.766 (8) | 19.166 (8) | 37.932
|
table_csv/204_979.csv
| 2 |
{
"column_index": [
1
],
"row_index": [
2
]
}
|
[
"how many points did russia score overall?",
"what are the scores that are higher than russia's score?",
"what is the name of one of the countries with one of the higher scores?"
] | 2 |
belarus
|
[
"Place",
"Nation",
"5 Hoops",
"3 Balls, 2 Ribbons",
"Total"
] |
what is the name of one of the countries with one of the higher scores?
|
[
[
"1",
"Bulgaria",
"19.800 (1)",
"19.800 (1)",
"39.6"
],
[
"2",
"Spain",
"19.700 (3)",
"19.700 (2)",
"39.4"
],
[
"3",
"Belarus",
"19.733 (2)",
"19.600 (3)",
"39.333"
],
[
"4",
"Russia",
"19.600 (4)",
"19.566 (4)",
"39.166"
],
[
"5",
"Ukraine",
"19.366 (5)",
"15.500 (5)",
"38.866"
],
[
"6",
"Japan",
"19.166 (6)",
"19.333 (7)",
"38.499"
],
[
"7",
"Italy",
"19.066 (7)",
"19.400 (6)",
"38.466"
],
[
"8",
"Hungary",
"18.766 (8)",
"19.166 (8)",
"37.932"
]
] |
[
"Belarus"
] |
what is the name of one of the countries with one of the higher scores? || what are the scores that are higher than russia's score? | how many points did russia score overall?
|
nt-5056
|
col : date | opponent | score | result | location | attendance row 1 : december 3, 1948 | butler | 67-62 | win | champaign, il | - row 2 : december 8, 1948 | notre dame | 59-58 | win (ot) | notre dame, in | - row 3 : december 11, 1948 | depaul | 50-60 | loss | chicago, il | 17,189 row 4 : december 13, 1948 | oklahoma | 73-68 | win | champaign, il | 6,902 row 5 : december 18, 1948 | pennsylvania | 80-61 | win | champaign, il | 3,943 row 6 : december 20, 1948 | depaul | 89-51 | win | champaign, il | 6,013 row 7 : december 21, 1948 | cornell | 71-47 | win | champaign, il | 3,042 row 8 : december 29, 1948 | colgate | 77-54 | win | champaign, il | 4,541 row 9 : december 30, 1948 | colgate | 85-55 | win | champaign, il | 3,880 row 10 : january 3, 1949 | wisconsin | 62-50 | win | champaign, il | - row 11 : january 8, 1949 | indiana | 44-42 | win (2ot) | bloomington, in | 10,000 row 12 : january 10, 1949 | ohio state | 64-63 | win | columbus, oh | 6,958 row 13 : january 15, 1949 | creighton | 96-30 | win | champaign, il | 6,958 row 14 : january 29, 1949 | minnesota (ranked #4) | 45-44 | win | champaign, il | 6,905 row 15 : january 31, 1949 | purdue | 53-55 | loss | west lafayette, in | 10,000 row 16 : february 5, 1949 | wisconsin | 61-54 | win | madison, wi | 13,5000 row 17 : february 7, 1949 | northwestern | 85-66 | win | champaign, il | - row 18 : february 12, 1949 | ohio state | 64-49 | win | champaign, il | 6,905 row 19 : february 21, 1949 | iowa | 80-49 | win | champaign, il | - row 20 : february 26, 1949 | northwestern | 81-64 | win | chicago, il | 17,905 row 21 : february 28, 1949 | indiana | 91-28 | win | champaign, il | - row 22 : march 7, 1949 | michigan | 53-70 | loss | ann arbor, mi | -
|
table_csv/204_795.csv
| 0 |
{
"column_index": [
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21
]
}
|
[
"which dates were the games played on?"
] | 0 |
december 3, 1948, december 8, 1948, december 11, 1948, december 13, 1948, december 18, 1948, december 20, 1948, december 21, 1948, december 29, 1948, december 30, 1948, january 3, 1949, january 8, 1949, january 10, 1949, january 15, 1949, january 29, 1949, january 31, 1949, february 5, 1949, february 7, 1949, february 12, 1949, february 21, 1949, february 26, 1949, february 28, 1949, march 7, 1949
|
[
"Date",
"Opponent",
"Score",
"Result",
"Location",
"Attendance"
] |
which dates were the games played on?
|
[
[
"December 3, 1948",
"Butler",
"67-62",
"Win",
"Champaign, IL",
"-"
],
[
"December 8, 1948",
"Notre Dame",
"59-58",
"Win (OT)",
"Notre Dame, IN",
"-"
],
[
"December 11, 1948",
"DePaul",
"50-60",
"Loss",
"Chicago, IL",
"17,189"
],
[
"December 13, 1948",
"Oklahoma",
"73-68",
"Win",
"Champaign, IL",
"6,902"
],
[
"December 18, 1948",
"Pennsylvania",
"80-61",
"Win",
"Champaign, IL",
"3,943"
],
[
"December 20, 1948",
"DePaul",
"89-51",
"Win",
"Champaign, IL",
"6,013"
],
[
"December 21, 1948",
"Cornell",
"71-47",
"Win",
"Champaign, IL",
"3,042"
],
[
"December 29, 1948",
"Colgate",
"77-54",
"Win",
"Champaign, IL",
"4,541"
],
[
"December 30, 1948",
"Colgate",
"85-55",
"Win",
"Champaign, IL",
"3,880"
],
[
"January 3, 1949",
"Wisconsin",
"62-50",
"Win",
"Champaign, IL",
"-"
],
[
"January 8, 1949",
"Indiana",
"44-42",
"Win (2ot)",
"Bloomington, IN",
"10,000"
],
[
"January 10, 1949",
"Ohio State",
"64-63",
"Win",
"Columbus, OH",
"6,958"
],
[
"January 15, 1949",
"Creighton",
"96-30",
"Win",
"Champaign, IL",
"6,958"
],
[
"January 29, 1949",
"Minnesota (ranked #4)",
"45-44",
"Win",
"Champaign, IL",
"6,905"
],
[
"January 31, 1949",
"Purdue",
"53-55",
"Loss",
"West Lafayette, IN",
"10,000"
],
[
"February 5, 1949",
"Wisconsin",
"61-54",
"Win",
"Madison, WI",
"13,5000"
],
[
"February 7, 1949",
"Northwestern",
"85-66",
"Win",
"Champaign, IL",
"-"
],
[
"February 12, 1949",
"Ohio State",
"64-49",
"Win",
"Champaign, IL",
"6,905"
],
[
"February 21, 1949",
"Iowa",
"80-49",
"Win",
"Champaign, IL",
"-"
],
[
"February 26, 1949",
"Northwestern",
"81-64",
"Win",
"Chicago, IL",
"17,905"
],
[
"February 28, 1949",
"Indiana",
"91-28",
"Win",
"Champaign, IL",
"-"
],
[
"March 7, 1949",
"Michigan",
"53-70",
"Loss",
"Ann Arbor, MI",
"-"
]
] |
[
"December 3, 1948",
"December 8, 1948",
"December 11, 1948",
"December 13, 1948",
"December 18, 1948",
"December 20, 1948",
"December 21, 1948",
"December 29, 1948",
"December 30, 1948",
"January 3, 1949",
"January 8, 1949",
"January 10, 1949",
"January 15, 1949",
"January 29, 1949",
"January 31, 1949",
"February 5, 1949",
"February 7, 1949",
"February 12, 1949",
"February 21, 1949",
"February 26, 1949",
"February 28, 1949",
"March 7, 1949"
] |
which dates were the games played on? ||
|
nt-5056
|
col : date | opponent | score | result | location | attendance row 1 : december 3, 1948 | butler | 67-62 | win | champaign, il | - row 2 : december 8, 1948 | notre dame | 59-58 | win (ot) | notre dame, in | - row 3 : december 11, 1948 | depaul | 50-60 | loss | chicago, il | 17,189 row 4 : december 13, 1948 | oklahoma | 73-68 | win | champaign, il | 6,902 row 5 : december 18, 1948 | pennsylvania | 80-61 | win | champaign, il | 3,943 row 6 : december 20, 1948 | depaul | 89-51 | win | champaign, il | 6,013 row 7 : december 21, 1948 | cornell | 71-47 | win | champaign, il | 3,042 row 8 : december 29, 1948 | colgate | 77-54 | win | champaign, il | 4,541 row 9 : december 30, 1948 | colgate | 85-55 | win | champaign, il | 3,880 row 10 : january 3, 1949 | wisconsin | 62-50 | win | champaign, il | - row 11 : january 8, 1949 | indiana | 44-42 | win (2ot) | bloomington, in | 10,000 row 12 : january 10, 1949 | ohio state | 64-63 | win | columbus, oh | 6,958 row 13 : january 15, 1949 | creighton | 96-30 | win | champaign, il | 6,958 row 14 : january 29, 1949 | minnesota (ranked #4) | 45-44 | win | champaign, il | 6,905 row 15 : january 31, 1949 | purdue | 53-55 | loss | west lafayette, in | 10,000 row 16 : february 5, 1949 | wisconsin | 61-54 | win | madison, wi | 13,5000 row 17 : february 7, 1949 | northwestern | 85-66 | win | champaign, il | - row 18 : february 12, 1949 | ohio state | 64-49 | win | champaign, il | 6,905 row 19 : february 21, 1949 | iowa | 80-49 | win | champaign, il | - row 20 : february 26, 1949 | northwestern | 81-64 | win | chicago, il | 17,905 row 21 : february 28, 1949 | indiana | 91-28 | win | champaign, il | - row 22 : march 7, 1949 | michigan | 53-70 | loss | ann arbor, mi | -
|
table_csv/204_795.csv
| 1 |
{
"column_index": [
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5,
5
],
"row_index": [
0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21
]
}
|
[
"which dates were the games played on?",
"and was was the attendance during those games?"
] | 0 |
-, -, 17,189, 6,902, 3,943, 6,013, 3,042, 4,541, 3,880, -, 10,000, 6,958, 6,958, 6,905, 10,000, 13,5000, -, 6,905, -, 17,905, -, -
|
[
"Date",
"Opponent",
"Score",
"Result",
"Location",
"Attendance"
] |
and was was the attendance during those games?
|
[
[
"December 3, 1948",
"Butler",
"67-62",
"Win",
"Champaign, IL",
"-"
],
[
"December 8, 1948",
"Notre Dame",
"59-58",
"Win (OT)",
"Notre Dame, IN",
"-"
],
[
"December 11, 1948",
"DePaul",
"50-60",
"Loss",
"Chicago, IL",
"17,189"
],
[
"December 13, 1948",
"Oklahoma",
"73-68",
"Win",
"Champaign, IL",
"6,902"
],
[
"December 18, 1948",
"Pennsylvania",
"80-61",
"Win",
"Champaign, IL",
"3,943"
],
[
"December 20, 1948",
"DePaul",
"89-51",
"Win",
"Champaign, IL",
"6,013"
],
[
"December 21, 1948",
"Cornell",
"71-47",
"Win",
"Champaign, IL",
"3,042"
],
[
"December 29, 1948",
"Colgate",
"77-54",
"Win",
"Champaign, IL",
"4,541"
],
[
"December 30, 1948",
"Colgate",
"85-55",
"Win",
"Champaign, IL",
"3,880"
],
[
"January 3, 1949",
"Wisconsin",
"62-50",
"Win",
"Champaign, IL",
"-"
],
[
"January 8, 1949",
"Indiana",
"44-42",
"Win (2ot)",
"Bloomington, IN",
"10,000"
],
[
"January 10, 1949",
"Ohio State",
"64-63",
"Win",
"Columbus, OH",
"6,958"
],
[
"January 15, 1949",
"Creighton",
"96-30",
"Win",
"Champaign, IL",
"6,958"
],
[
"January 29, 1949",
"Minnesota (ranked #4)",
"45-44",
"Win",
"Champaign, IL",
"6,905"
],
[
"January 31, 1949",
"Purdue",
"53-55",
"Loss",
"West Lafayette, IN",
"10,000"
],
[
"February 5, 1949",
"Wisconsin",
"61-54",
"Win",
"Madison, WI",
"13,5000"
],
[
"February 7, 1949",
"Northwestern",
"85-66",
"Win",
"Champaign, IL",
"-"
],
[
"February 12, 1949",
"Ohio State",
"64-49",
"Win",
"Champaign, IL",
"6,905"
],
[
"February 21, 1949",
"Iowa",
"80-49",
"Win",
"Champaign, IL",
"-"
],
[
"February 26, 1949",
"Northwestern",
"81-64",
"Win",
"Chicago, IL",
"17,905"
],
[
"February 28, 1949",
"Indiana",
"91-28",
"Win",
"Champaign, IL",
"-"
],
[
"March 7, 1949",
"Michigan",
"53-70",
"Loss",
"Ann Arbor, MI",
"-"
]
] |
[
"-",
"-",
"17,189",
"6,902",
"3,943",
"6,013",
"3,042",
"4,541",
"3,880",
"-",
"10,000",
"6,958",
"6,958",
"6,905",
"10,000",
"13,5000",
"-",
"6,905",
"-",
"17,905",
"-",
"-"
] |
and was was the attendance during those games? || which dates were the games played on?
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.