Triple
T4430985
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nièvre |
E95326
|
entity |
| Predicate | historicProvince |
P915
|
FINISHED |
| Object | Nivernais |
E150661
|
NE 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: Nivernais | Statement: [Nièvre, historicProvince, Nivernais]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nivernais Context triple: [Nièvre, historicProvince, Nivernais]
-
A.
Nivernais
chosen
Nivernais is a historic province in central France, centered around the town of Nevers and known for its rural landscapes and traditional agriculture.
-
B.
Brionnais
Brionnais is a historic rural region in eastern France known for its Romanesque churches, traditional stone villages, and Charolais cattle farming.
-
C.
Aube
Aube is a department in northeastern France known for its historic towns, Champagne vineyards, and rural landscapes.
-
D.
Chalonnais
Chalonnais is the French demonym for inhabitants of the town of Chalonnes-sur-Loire in western France.
-
E.
Vivarois
Vivarois is a Romance dialect of the Occitan language traditionally spoken in parts of southeastern France.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69b3453c2a0c8190926b574c90766db9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3556b71448190a3fab938853f8b87 |
completed | March 13, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b627f7a8c881908a04b64d43c7b908 |
completed | March 15, 2026, 3:31 a.m. |
Created at: March 12, 2026, 11:31 p.m.