Triple
T7455627
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nevers |
E172114
|
entity |
| Predicate | historicalRegion |
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: [Nevers, historicalRegion, Nivernais]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nivernais Context triple: [Nevers, historicalRegion, 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.
Tournaisis
Tournaisis is a historical region in present-day Belgium centered around the city of Tournai, known for its medieval political significance and rich cultural heritage.
-
C.
Vendômois
Vendômois is the French demonym referring to inhabitants or natives of the town of Vendôme in central France.
-
D.
Vosgien
Vosgien is a regional dialect of the Lorrain language spoken in the Vosges area of northeastern France.
-
E.
Brionnais
Brionnais is a historic rural region in eastern France known for its Romanesque churches, traditional stone villages, and Charolais cattle farming.
- 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_69c68a66554c8190add75c65942c0317 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f3af58dc819093fb0482482779a3 |
completed | March 27, 2026, 9:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c827c39a848190bc275468362ce3bc |
completed | March 28, 2026, 7:10 p.m. |
Created at: March 27, 2026, 3:15 p.m.