Triple
T2878413
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Château de Boncourt |
E56934
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Boncourt |
E56934
|
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: Boncourt | Statement: [Château de Boncourt, locatedIn, Boncourt]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Boncourt Context triple: [Château de Boncourt, locatedIn, Boncourt]
-
A.
Boncourt
chosen
Boncourt is a locality known for its historic Château de Boncourt, reflecting its cultural and architectural heritage.
-
B.
Couvet
Couvet is a village in the Val-de-Travers district of the canton of Neuchâtel in western Switzerland, known historically as the birthplace of the jurist Emer de Vattel.
-
C.
Ducos
Ducos is a French surname most notably borne by Pierre-Roger Ducos, a political figure during the French Revolution and the Consulate.
-
D.
Brière
Brière is a French-language surname most prominently associated with former NHL player and current hockey executive Daniel Brière.
-
E.
Olbreuse
Olbreuse is a small locality in western France historically notable as the ancestral seat of the noble d’Olbreuse family.
- 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_69ab4a4ced288190ab6d3e062d10f7f6 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abe008925c81909683d0ebc6227e5e |
completed | March 7, 2026, 8:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3608166888190a4bf75f865e42f45 |
completed | March 13, 2026, 12:55 a.m. |
Created at: March 6, 2026, 10:03 p.m.