Triple
T6511916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Millau |
E150156
|
entity |
| Predicate | locatedInHistoricalRegion |
P915
|
FINISHED |
| Object | Rouergue |
E111644
|
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: Rouergue | Statement: [Millau, locatedInHistoricalRegion, Rouergue]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rouergue Context triple: [Millau, locatedInHistoricalRegion, Rouergue]
-
A.
Rouergue
chosen
Rouergue is a historic cultural region in southern France, centered around the present-day Aveyron department and known for its rural landscapes, medieval towns, and Occitan heritage.
-
B.
Drôme
Drôme is a department in southeastern France known for its diverse landscapes, historic towns, and location between the Alps and the Rhône Valley.
-
C.
Languedoc
Languedoc is a historic region in southern France known for its Occitan culture, medieval towns, and long-standing wine-making tradition.
-
D.
Touraine
Touraine is a historic region in central France, famed for its Loire Valley châteaux, wine production, and role as a former royal heartland.
-
E.
Auvergne
Auvergne is a historic region in central France known for its volcanic landscapes, rural character, and Romanesque heritage.
- 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_69c687ef291081909d437f035eef1cda |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c69f3c5eb88190a56723acd8096dd8 |
completed | March 27, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6eeca8ab88190aa8464671bdde2ec |
completed | March 27, 2026, 8:55 p.m. |
Created at: March 27, 2026, 1:44 p.m.