Triple
T37969532
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francis S. Gabreski |
E947240
|
entity |
| Predicate | numberOfAerialVictoriesKoreanWar |
P12250
|
FINISHED |
| Object | 6.5 |
—
|
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: 6.5 | Statement: [Francis S. Gabreski, numberOfAerialVictoriesKoreanWar, 6.5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfAerialVictoriesKoreanWar Context triple: [Francis S. Gabreski, numberOfAerialVictoriesKoreanWar, 6.5]
-
A.
numberOfAerialVictories
chosen
Indicates the count of successful aerial combat victories achieved by an entity over opposing aircraft.
-
B.
estimatedAerialVictories
Indicates an approximate count of aerial combat victories attributed to an entity, rather than an exact, confirmed total.
-
C.
battleStarsKoreanWar
Indicates that an entity received battle stars for its participation in the Korean War.
-
D.
sideInKoreanWar
Indicates that an entity participated as a belligerent on a particular side in the Korean War.
-
E.
numberOfDeploymentsToKorea
Indicates the count of times an entity has been deployed to Korea.
- F. None of above.
Provenance (3 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_69f76ef7db908190bba6086673a32300 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a192a008190a9917688a9e804f4 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:20 p.m.