Triple
T2210096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles de Gaulle |
E50893
|
entity |
| Predicate | airWingPersonnel |
P37494
|
FINISHED |
| Object | approximately 600 |
—
|
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 600 | Statement: [Charles de Gaulle, airWingPersonnel, approximately 600]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: airWingPersonnel Context triple: [Charles de Gaulle, airWingPersonnel, approximately 600]
-
A.
crewType
Indicates the specific role or category of crew associated with an entity, such as the type of personnel assigned to operate or support it.
-
B.
aerialUnit
Indicates that the related entity functions as or is classified as an aerial unit, typically operating or acting in the air rather than on the ground or sea.
-
C.
paramilitaryWing
Indicates that one entity functions as the paramilitary or armed wing associated with another entity, typically an organization or movement.
-
D.
personnelType
Indicates the classification or role category assigned to a person within an organization or system.
-
E.
JapaneseAirCommander
Indicates that one entity serves as a Japanese air force commander in relation to another entity or context.
- 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc1baa0948190b07ffc347a4f714e |
completed | March 7, 2026, 6:12 a.m. |
| PD | Predicate disambiguation | batch_69abbda8a6dc8190aa855ce2d17194b1 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abc1b912c08190b9d7bc9230e49d1d |
completed | March 7, 2026, 6:12 a.m. |
Created at: March 4, 2026, 7:46 p.m.