Triple
T1533666
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Creuse |
E32501
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Haute-Vienne |
E148669
|
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: Haute-Vienne | Statement: [Creuse, borders, Haute-Vienne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haute-Vienne Context triple: [Creuse, borders, Haute-Vienne]
-
A.
Haute-Vienne
chosen
Haute-Vienne is a department in west-central France, within the Nouvelle-Aquitaine region, known for its capital Limoges and its historic porcelain industry.
-
B.
Aveyron
Aveyron is a rural department in southern France known for its rugged landscapes, medieval villages, and traditional gastronomy including Roquefort cheese.
-
C.
Tarn-et-Garonne
Tarn-et-Garonne is a department in the Occitanie region of southern France, known for its agricultural landscapes, historic towns, and location along the Garonne and Tarn rivers.
-
D.
Dordogne
Dordogne is a major river in southwestern France known for flowing through the Dordogne valley, a region famed for its picturesque landscapes, historic towns, and prehistoric cave art.
-
E.
Essonne
Essonne is a department in northern France that forms part of the Paris metropolitan region and includes a mix of suburban communities, research centers, and rural areas.
- 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_69a885ea86308190998f6bc14bb91f8e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa61f8df00819086f34847e2170e12 |
completed | March 6, 2026, 5:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad30942dc481908de85bd2ca30c0bd |
completed | March 8, 2026, 8:17 a.m. |
Created at: March 4, 2026, 7:26 p.m.