Triple
T20153603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jean Cavaillès |
E491497
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Deux-Sèvres |
—
|
NE NERFINISHED |
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: Deux-Sèvres | Statement: [Jean Cavaillès, placeOfBirth, Deux-Sèvres]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Deux-Sèvres Context triple: [Jean Cavaillès, placeOfBirth, Deux-Sèvres]
-
A.
Deux-Sèvres
chosen
Deux-Sèvres is a department in western France known for its rural landscapes, historic towns such as Niort, and location within the Nouvelle-Aquitaine region.
-
B.
Maine-et-Loire
Maine-et-Loire is a department in western France known for its historic towns, châteaux, and vineyards along the Loire River.
-
C.
Mayenne
Mayenne is a river in western France that flows through the regions of Normandy and Pays de la Loire before joining other waterways to form the Loire basin.
-
D.
Mayenne
Mayenne is a department in northwestern France known for its rural landscapes, historic towns, and location within the former province of Maine.
-
E.
Loir-et-Cher
Loir-et-Cher is a department in central France known for its historic châteaux, including parts of the Loire Valley UNESCO World Heritage site.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6265f8f0819080b29c752a574088 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e667de9bec8190836887c86dbcf28d |
completed | April 20, 2026, 5:52 p.m. |
Created at: April 11, 2026, 11:34 p.m.