Triple
T22307358
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frédéric Bricout |
E551414
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Cambrai |
—
|
NE NERFINISHED |
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: Cambrai | Statement: [Frédéric Bricout, residence, Cambrai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cambrai Context triple: [Frédéric Bricout, residence, Cambrai]
-
A.
Cambrai
chosen
Cambrai is a historic city in northern France known for its medieval heritage, role in World War I, and traditional confectionery.
-
B.
Saint-Omer
Saint-Omer is a historic town in northern France known for its medieval architecture, strategic military importance, and role in Franco-Spanish conflicts.
-
C.
Arras
Arras is a historic city in northern France renowned for its Flemish-Baroque architecture, grand squares, and role as a strategic site in both World Wars.
-
D.
Thérouanne
Thérouanne is a historic town in northern France that once served as an important medieval religious center and episcopal seat.
-
E.
Valenciennes
Valenciennes is a historic industrial city in northern France near the Belgian border, known for its former coal and steel industries and its rich artistic and architectural heritage.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e46c0188190800181a4233f28fe |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1574bccb08190a6236dd14cf0fc5b |
completed | April 29, 2026, 12:56 a.m. |
Created at: April 16, 2026, 8:41 p.m.