Triple
T20134979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arpitanie |
E491001
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Lyonnais |
—
|
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: Lyonnais | Statement: [Arpitanie, hasPart, Lyonnais]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lyonnais Context triple: [Arpitanie, hasPart, Lyonnais]
-
A.
Lyonnais
chosen
Lyonnais is a historical region in east-central France centered around the city of Lyon, known for its rich cultural heritage, gastronomy, and role as a major economic hub.
-
B.
Lyonnet
Lyonnet is a French surname most notably associated with professional dancer Grégoire Lyonnet.
-
C.
Grenoblois
Grenoblois is the French demonym for inhabitants of the city of Grenoble in southeastern France.
-
D.
Roannais
Roannais is a natural region in central France known for its rolling countryside, agricultural landscapes, and proximity to the upper Loire River.
-
E.
Sebastiannaise
Sebastiannaise is the French demonym referring to a female inhabitant of the town of Saint-Sébastien-sur-Loire in western France.
- 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_69da62651a0c8190a3e05e95e056a66b |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66766e46c81908721fd47066dc9f8 |
completed | April 20, 2026, 5:50 p.m. |
Created at: April 11, 2026, 11:32 p.m.