Triple
T2860911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Douglas Bader |
E63316
|
entity |
| Predicate | hasMilitaryVictories |
P12250
|
FINISHED |
| Object | over 20 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 20 aerial victories | Statement: [Douglas Bader, hasMilitaryVictories, over 20 aerial victories]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMilitaryVictories Context triple: [Douglas Bader, hasMilitaryVictories, over 20 aerial victories]
-
A.
hasSignificantBattle
Indicates that a major or decisive battle occurred involving the related entities.
-
B.
hasNotableWar
Indicates that an entity is associated with a significant or historically important war.
-
C.
yearOfMajorVictory
Indicates the specific year in which a major victory associated with the subject occurred.
-
D.
numberOfAerialVictories
chosen
Indicates the count of successful aerial combat victories achieved by an entity over opposing aircraft.
-
E.
militaryCampaignsIn
Indicates that a military campaign took place within, or was conducted in, a specified geographic or political area.
- 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_69ab4c41e8c08190a9e8f5249cc12610 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf8c676c8190ab29f89d50bd09c3 |
completed | March 7, 2026, 8:19 a.m. |
| PD | Predicate disambiguation | batch_69abdd10aef88190b750aae07e7df4dc |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:02 p.m.