Triple
T36904044
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Léonor d'Orléans, duc de Longueville |
E912720
|
entity |
| Predicate | heldMilitaryOffice |
P94220
|
FINISHED |
| Object | high military office under the French crown |
—
|
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: high military office under the French crown | Statement: [Léonor d'Orléans, duc de Longueville, heldMilitaryOffice, high military office under the French crown]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: heldMilitaryOffice Context triple: [Léonor d'Orléans, duc de Longueville, heldMilitaryOffice, high military office under the French crown]
-
A.
hadMilitaryPost
chosen
Indicates that an entity held an official position or assignment within a military organization.
-
B.
formerMilitaryRole
Indicates that an entity previously held, but no longer holds, a specific military role or position.
-
C.
heldPoliticalOfficeIn
Indicates that an entity served in a political office or position within a specified governmental body or jurisdiction.
-
D.
servedInRole
Indicates that one entity performed duties or held a position within a specified role or office in relation to another entity.
-
E.
hadMilitaryServiceFrom
Indicates that an entity performed or was engaged in military service starting from a specified point in time.
- 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_69f76e879768819085c2fb31a6a5b44b |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f9fda91e5c8190a06aeccc56992144 |
completed | May 5, 2026, 2:24 p.m. |
| PD | Predicate disambiguation | batch_69f7cf79ddb08190a083405cccc14137 |
completed | May 3, 2026, 10:43 p.m. |
Created at: May 3, 2026, 4:13 p.m.