Triple
T15831206
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lyon bus network |
E383874
|
entity |
| Predicate | hasServiceArea |
P82
|
FINISHED |
| Object | Caluire-et-Cuire |
E487489
|
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: Caluire-et-Cuire | Statement: [Lyon bus network, hasServiceArea, Caluire-et-Cuire]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caluire-et-Cuire Context triple: [Lyon bus network, hasServiceArea, Caluire-et-Cuire]
-
A.
Caluire-et-Cuire
chosen
Caluire-et-Cuire is a suburban commune in eastern France’s Auvergne-Rhône-Alpes region, forming part of the northern outskirts of Lyon.
-
B.
Cuiseaux
Cuiseaux is a small commune in eastern France, notable as the birthplace of the painter Édouard Vuillard.
-
C.
Bédarieux
Bédarieux is a commune in southern France’s Hérault department, known for its location in the Orb valley at the foothills of the Massif Central.
-
D.
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.
-
E.
Aiguillon
Aiguillon is a commune in southwestern France, known for its strategic location at the confluence of the Lot and Garonne rivers.
- 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_69d86da34c888190976e06c4019d415a |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e11e6433ac8190a3d3e0d573673ea3 |
completed | April 16, 2026, 5:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00aae0f87c819085ebc7d475ebe8ba |
completed | May 10, 2026, 3:57 p.m. |
Created at: April 10, 2026, 4:49 a.m.