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"songwriter",
"Bob",
"Dylan",
"and",
"the",
"Grateful",
"Dead",
",",
"released",
"on",
"February",
"6",
... | [
[
{
"global_pos": [
0,
0
],
"index": "0_0",
"name": "Dylan & the Dead",
"pos": [
0,
4
],
"sent_id": 0,
"type": "MISC"
},
{
"global_pos": [
61,
61
],
"index": "0_1",
"name": "Dylan & th... | Dylan & the Dead | [
{
"evidence": [
0
],
"h": 2,
"r": "P27",
"t": 1
},
{
"evidence": [
0,
1,
6,
7
],
"h": 17,
"r": "P175",
"t": 2
},
{
"evidence": [
0,
1,
6,
7
],
"h": 17,
"r": "P495",
"t": 1
},
{
"evidence... |
[
[
"Hiram",
"is",
"an",
"unincorporated",
"community",
"in",
"eastern",
"Wayne",
"County",
",",
"Missouri",
",",
"United",
"States",
"."
],
[
"It",
"is",
"located",
"on",
"Missouri",
"Route",
"C",
"between",
"Clubb",
... | [
[
{
"global_pos": [
0,
0
],
"index": "0_0",
"name": "Hiram",
"pos": [
0,
1
],
"sent_id": 0,
"type": "LOC"
},
{
"global_pos": [
97,
97
],
"index": "0_1",
"name": "Hiram",
"pos": [... | Hiram, Missouri | [
{
"evidence": [
0
],
"h": 0,
"r": "P17",
"t": 3
},
{
"evidence": [
0
],
"h": 1,
"r": "P131",
"t": 2
},
{
"evidence": [
0
],
"h": 1,
"r": "P17",
"t": 3
},
{
"evidence": [
0
],
"h": 2,
"r": "P150",
"t":... |
[
[
"The",
"Pasteur",
"Institute",
"(",
")",
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"French",
"non",
"-",
"profit",
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"biology",
",",
"micro",
"-",
"organisms",
",",
"diseases",
... | [
[
{
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1,
1
],
"index": "0_0",
"name": "Pasteur Institute",
"pos": [
1,
3
],
"sent_id": 0,
"type": "ORG"
},
{
"global_pos": [
83,
83
],
"index": "0_1",
"name": "Institut P... | Pasteur Institute | [
{
"evidence": [
0,
2
],
"h": 0,
"r": "P571",
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},
{
"evidence": [
0,
4
],
"h": 0,
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},
{
"evidence": [
0
],
"h": 0,
"r": "P17",
"t": 1
},
{
"evidence": [],
"h": 12,
"r": "P527",
... |
[
[
"Michael",
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"is",
"a",
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"character",
"in",
"Gus",
"Van",
"Sant",
"'s",
"My",
"Own",
"Private",
"Idaho",
",",
"portrayed",
"by",
"River",
"Phoenix",
"."
],
[
"He",
"is",
"a",
"sensitive",
... | [
[
{
"global_pos": [
0,
0
],
"index": "0_0",
"name": "Michael Waters",
"pos": [
0,
2
],
"sent_id": 0,
"type": "PER"
},
{
"global_pos": [
64,
64
],
"index": "0_1",
"name": "Mike",
... | Mikey Waters | [
{
"evidence": [
0
],
"h": 0,
"r": "P1441",
"t": 2
},
{
"evidence": [
1
],
"h": 4,
"r": "P161",
"t": 5
},
{
"evidence": [
1
],
"h": 4,
"r": "P175",
"t": 5
},
{
"evidence": [
0
],
"h": 2,
"r": "P161",
"... |
[
[
"Yongxing",
"Island",
"Airport",
",",
"also",
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"as",
"Woody",
"Island",
"Airport",
",",
"is",
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"airport",
"located",
"on",
"Yongxing",
"(",
"Woody",
")",
"Island",
",",
"the",
"largest",
... | [
[
{
"global_pos": [
0,
0
],
"index": "0_0",
"name": "Yongxing Island Airport",
"pos": [
0,
3
],
"sent_id": 0,
"type": "LOC"
},
{
"global_pos": [
7,
7
],
"index": "0_1",
"name": "Woody ... | Yongxing Island Airport | [
{
"evidence": [
0
],
"h": 0,
"r": "P706",
"t": 3
},
{
"evidence": [
0,
1
],
"h": 0,
"r": "P17",
"t": 4
},
{
"evidence": [
0
],
"h": 0,
"r": "P131",
"t": 1
},
{
"evidence": [
0
],
"h": 2,
"r": "P206"... |
[
[
"Le",
"Reculet",
"is",
"the",
"second",
"-",
"highest",
"summit",
"in",
"the",
"Jura",
"Mountains",
"."
],
[
"It",
"is",
"located",
"in",
"the",
"Ain",
"department",
"in",
"France",
"."
],
[
"Its",
"altitud... | [
[
{
"global_pos": [
0,
0
],
"index": "0_0",
"name": "Le Reculet",
"pos": [
0,
2
],
"sent_id": 0,
"type": "LOC"
},
{
"global_pos": [
97,
97
],
"index": "0_1",
"name": "Le Reculet",
... | Le Reculet | [
{
"evidence": [
1
],
"h": 3,
"r": "P150",
"t": 2
},
{
"evidence": [
0,
3,
4
],
"h": 6,
"r": "P706",
"t": 1
},
{
"evidence": [
0,
6
],
"h": 0,
"r": "P361",
"t": 1
},
{
"evidence": [
0,
6
],... |
Subsets and Splits
Extract Triplets from Sentences
Extracts and displays meaningful triplets (subject, relation, object) from the dataset, helping to understand relational patterns within the text data.
Train Data with Sentences
The query selects titles, sentences, and labels from entries in the 'train' table, limiting the results to 100. It includes a condition to check for existence, but since it's querying the same table, the condition is redundant and doesn't add meaningful insight beyond basic retrieval.