Triple
T10331289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TER Auvergne-Rhône-Alpes services |
E242878
|
entity |
| Predicate | connectsCity |
P4245
|
FINISHED |
| Object | Oyonnax |
E160056
|
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: Oyonnax | Statement: [TER Auvergne-Rhône-Alpes services, connectsCity, Oyonnax]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oyonnax Context triple: [TER Auvergne-Rhône-Alpes services, connectsCity, Oyonnax]
-
A.
Oyonnax
chosen
Oyonnax is a town in eastern France’s Ain department, known historically for its plastics industry and its role in the French Resistance during World War II.
-
B.
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.
-
C.
Vaujours
Vaujours is a small suburban commune in the northeastern outskirts of Paris, France.
-
D.
Sorgues
Sorgues is a commune in southeastern France’s Vaucluse department, known for its location near Avignon and its position at the confluence of the Ouvèze and Sorgue rivers.
-
E.
Gros-Caillou
Gros-Caillou is a residential and historically bourgeois neighborhood in Paris’s 7th arrondissement, situated near landmarks like the Eiffel Tower and the Champ de Mars.
- 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_69d381af787481908bc401325c760a88 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d7fcaf4c8190bdcfcb953d88ea2f |
completed | April 7, 2026, 10:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9336139088190bd9ea3e2333c59cb |
completed | April 10, 2026, 5:29 p.m. |
Created at: April 6, 2026, 11:52 a.m.