Triple
T22721781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antoine Richepanse |
E561879
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Richepanse |
—
|
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: Richepanse | Statement: [Antoine Richepanse, familyName, Richepanse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Richepanse Context triple: [Antoine Richepanse, familyName, Richepanse]
-
A.
Richepanse
chosen
Richepanse is a French surname most notably borne by Antoine Richepanse, a general of the French Revolutionary and Napoleonic Wars.
-
B.
Landais
Landais is a regional variety of the Gascon Occitan language traditionally spoken in parts of southwestern France.
-
C.
Dagneux
Dagneux is a commune in eastern France’s Ain department, known for its residential character and proximity to the Lyon metropolitan area.
-
D.
Roquebillière
Roquebillière is a small commune in southeastern France, situated in the Alpes-Maritimes department in the Provence-Alpes-Côte d’Azur region.
-
E.
Farciennes
Farciennes is an industrial town in the Walloon region of Belgium, historically associated with coal mining and heavy industry.
- 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_69e2454fc984819088213b58ee87a002 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f17926ae0c8190af8493cab6b15261 |
completed | April 29, 2026, 3:21 a.m. |
Created at: April 17, 2026, 3:20 p.m.