Triple

T9215145
Position Surface form Disambiguated ID Type / Status
Subject arrondissement of Grasse E221223 entity
Predicate contains P35 FINISHED
Object Valbonne E399970 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: Valbonne | Statement: [arrondissement of Grasse, contains, Valbonne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Valbonne
Context triple: [arrondissement of Grasse, contains, 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 (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_69ca83eae42c8190a0ea9e040710a277 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccda0830a8819096a186ed2e976cba completed April 1, 2026, 8:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0e33fa3d48190bc5f4ba72b422b85 completed April 4, 2026, 10:09 a.m.
Created at: March 30, 2026, 7:27 p.m.