Triple
T6859987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | USS Laffey |
E158249
|
entity |
| Predicate | battleStarsKoreanWar |
P74158
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [USS Laffey, battleStarsKoreanWar, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: battleStarsKoreanWar Context triple: [USS Laffey, battleStarsKoreanWar, 2]
-
A.
battleStars
Indicates that two entities are engaged in or associated through a competitive or combative confrontation, often implying a contest of skill, strength, or status.
-
B.
roleInKoreanWar
Indicates the specific function, position, or involvement an entity had during the Korean War.
-
C.
battleDepicted
Indicates that a work or representation visually portrays a specific battle or combat event.
-
D.
capturedInWar
Indicates that one entity was taken prisoner or seized by another entity as a result of armed conflict or wartime actions.
-
E.
battlefieldOf
Indicates that a location is the site where a particular battle or military engagement took place.
- 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_69c68830cdbc8190a8301c7a9d9f651a |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6da3ce95081909a424ac04bc7fa07 |
completed | March 27, 2026, 7:27 p.m. |
| PD | Predicate disambiguation | batch_69c6d7b168908190b2f7c724b1bc7fc9 |
completed | March 27, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69c6da3b5510819093c5893ea92ac025 |
completed | March 27, 2026, 7:27 p.m. |
Created at: March 27, 2026, 2:21 p.m.