Triple
T1750346
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fighter Wing 52 |
E38424
|
entity |
| Predicate | airVictoryCount |
P12250
|
FINISHED |
| Object | over 10,000 aerial victories |
—
|
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: over 10,000 aerial victories | Statement: [Fighter Wing 52, airVictoryCount, over 10,000 aerial victories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airVictoryCount Context triple: [Fighter Wing 52, airVictoryCount, over 10,000 aerial victories]
-
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.
airSuperiorityContribution
Indicates the extent to which an entity contributes to achieving or maintaining control of the airspace over a given area or conflict.
-
D.
aircraftLosses
Indicates the number or occurrence of aircraft that have been destroyed, damaged beyond repair, or otherwise lost.
-
E.
alliedVictoryIn
Indicates that the subject participated in or contributed to a military or strategic victory achieved by an alliance or coalition in the specified context.
- 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_69a8862bdb2081908aefe831c8aa8017 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aba6a63f588190b53b39c6b97d74f4 |
completed | March 7, 2026, 4:16 a.m. |
| PD | Predicate disambiguation | batch_69aa61c7ef4c8190abec87c96a787d82 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:31 p.m.