Triple
T5074742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isère |
E114365
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Voiron |
E338503
|
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: Voiron | Statement: [Isère, containsTown, Voiron]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Voiron Context triple: [Isère, containsTown, Voiron]
-
A.
Voiron
chosen
Voiron is a commune in southeastern France known for its historical town center and proximity to the Chartreuse Mountains.
-
B.
Saint-Véran
Saint-Véran is a French wine appellation in southern Burgundy known for its high-quality Chardonnay-based white wines.
-
C.
Gueugnon
Gueugnon is a small commune in eastern France known historically for its steel industry and location in the Bourgogne-Franche-Comté region.
-
D.
Brioude
Brioude is a historic town in south-central France known for its Romanesque Basilica of Saint-Julien and its location in the Haute-Loire department of the Auvergne region.
-
E.
Peisey-Vallandry
Peisey-Vallandry is a French Alpine ski resort and traditional mountain village area in the Savoie region, known for its access to the Paradiski ski domain.
- 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_69bd443cf28c8190ad371d603563dbdd |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd74d0be1c819081b26235fe602a30 |
completed | March 20, 2026, 4:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf9535f8008190b91cf823e9484b4e |
completed | March 22, 2026, 7:07 a.m. |
Created at: March 20, 2026, 1:39 p.m.