Triple
T1842050
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chief of Staff of the French Army |
E41197
|
entity |
| Predicate | rankOfOfficeholder |
P32760
|
FINISHED |
| Object | Général d’armée |
—
|
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: Général d’armée | Statement: [Chief of Staff of the French Army, rankOfOfficeholder, Général d’armée]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankOfOfficeholder Context triple: [Chief of Staff of the French Army, rankOfOfficeholder, Général d’armée]
-
A.
rankHeldByOfficeholder
chosen
Indicates the specific rank or level associated with an office or position that is held by a particular officeholder.
-
B.
officeHolderOf
Indicates that a person holds or has held an official position or role within a specified organization, institution, or office.
-
C.
officeHolderRoleFor
Indicates that a specific role or position is held by an office holder within an organization or governing body.
-
D.
ordinalInOffice
Indicates the numerical order or rank of an individual’s term or tenure in a particular office or position.
-
E.
typeOfOffice
Indicates the specific category or kind of office that an office entity belongs to (e.g., executive, legislative, judicial, or other office types).
- 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_69a88648cd44819093303206d96d76ad |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb32d35508190bf1c487dffbecaf0 |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abafdb0d2c8190a67f584e67979fa3 |
completed | March 7, 2026, 4:55 a.m. |
Created at: March 4, 2026, 7:33 p.m.