Triple
T14456228
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Château Villette |
E358465
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Condécourt |
E1106221
|
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: Condécourt | Statement: [Château Villette, locatedIn, Condécourt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Condécourt Context triple: [Château Villette, locatedIn, Condécourt]
-
A.
Condécourt
chosen
Condécourt is a small commune in the Val-d'Oise department in the Île-de-France region of northern France.
-
B.
Calvé
Calvé is a well-known food brand, particularly recognized for its peanut butter and sauces, that forms part of Unilever’s global brand portfolio.
-
C.
Villeneuve-le-Comte
Villeneuve-le-Comte is a commune in the Seine-et-Marne department in the Île-de-France region of north-central France.
-
D.
Remigny
Remigny is a small wine-producing village in the Burgundy region of eastern France, situated near the renowned appellation of Santenay.
-
E.
Boncourt
Boncourt is a locality known for its historic Château de Boncourt, reflecting its cultural and architectural 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_69d82794dfa081909b9134ad2e32244b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91a9c0d48190ae015e5e0db806ca |
completed | April 14, 2026, 7:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff218b93d48190a7e16c3934828aa8 |
completed | May 9, 2026, 11:59 a.m. |
Created at: April 10, 2026, 1:19 a.m.