Triple
T20013818
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Plascassier |
E494658
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Valbonne |
—
|
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: Valbonne | Statement: [Plascassier, locatedNear, Valbonne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Valbonne Context triple: [Plascassier, locatedNear, Valbonne]
-
A.
Valbonne
chosen
Valbonne is a picturesque village in southeastern France known for its preserved medieval old town and proximity to the technology hub of Sophia Antipolis.
-
B.
Ambert
Ambert is a small commune in central France known for its traditional paper mills and as a center of production for Fourme d'Ambert blue cheese.
-
C.
Bilhères
Bilhères is a small mountain village in southwestern France, situated in the Ossau Valley of the Pyrenees.
-
D.
Chiroubles
Chiroubles is a French appellation in the Beaujolais region known for producing light, aromatic red wines primarily from the Gamay grape.
-
E.
Moûtiers
Moûtiers is a small town in the French Alps that serves as a key gateway and transport hub for several major ski resorts in the Tarentaise region.
- 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_69da626bfd288190aa5d65098b6433ae |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6623a9bd48190a3344a07540170c7 |
completed | April 20, 2026, 5:28 p.m. |
Created at: April 11, 2026, 3:34 p.m.