Triple
T982432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Gettysburg |
E21198
|
entity |
| Predicate | UnionStrength |
P21922
|
FINISHED |
| Object | approximately 93,000 soldiers |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: approximately 93,000 soldiers | Statement: [Battle of Gettysburg, UnionStrength, approximately 93,000 soldiers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: UnionStrength Context triple: [Battle of Gettysburg, UnionStrength, approximately 93,000 soldiers]
-
A.
laborUnion
Indicates that an entity is a labor union representing workers in collective employment-related matters.
-
B.
union
Indicates that two or more sets are combined into a single set containing all elements that belong to at least one of them.
-
C.
associatedWithTradeUnions
Indicates that there is a relationship of involvement, connection, or affiliation between an entity and one or more trade unions.
-
D.
hasTradeUnionWing
Indicates that an organization has an affiliated trade union branch or division that represents workers’ interests.
-
E.
tradeUnionWing
Indicates that one entity functions as the trade union wing or labor-organizing arm of another entity.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a493c383dc8190a03257f22d4b4183 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b49284ac8190b1d7c828d728893c |
completed | March 1, 2026, 9:50 p.m. |
| PD | Predicate disambiguation | batch_69a4b2aa219081908a6b0ef786b4aa52 |
completed | March 1, 2026, 9:42 p.m. |
| PDg | Predicate description generation | batch_69a4b345f890819092b51bf8128da461 |
completed | March 1, 2026, 9:44 p.m. |
Created at: March 1, 2026, 7:41 p.m.