Triple
T9290963
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cambrésis |
E223513
|
entity |
| Predicate | notableTown |
P14082
|
FINISHED |
| Object | Caudry |
E790687
|
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: Caudry | Statement: [Cambrésis, notableTown, Caudry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caudry Context triple: [Cambrésis, notableTown, Caudry]
-
A.
Caudry
chosen
Caudry is a French town in the Nord department, historically known as a center of lace and textile production.
-
B.
Choully
Choully is a small wine-producing village in the commune of Satigny in the canton of Geneva, Switzerland.
-
C.
Tanguy
Tanguy is a French surname most notably associated with Yves Tanguy, a prominent 20th-century Surrealist painter.
-
D.
Rendeux
Rendeux is a small rural municipality and village in the Ardennes region of Belgium, known for its natural landscapes and outdoor tourism.
-
E.
Crevant-Laveine
Crevant-Laveine is a small commune in central France’s Puy-de-Dôme department, characterized by its rural setting and traditional Auvergne landscape.
- 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_69ca8422ddf881908a3f8f876c9f53aa |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd0865a7108190b807afd259980db2 |
completed | April 1, 2026, 11:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0c766fb408190a9f073f033652b6f |
completed | April 4, 2026, 8:10 a.m. |
Created at: March 30, 2026, 7:35 p.m.