Triple

T12787752
Position Surface form Disambiguated ID Type / Status
Subject Bron E305674 entity
Predicate adjacentTo P224 FINISHED
Object Villeurbanne E96069 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: Villeurbanne | Statement: [Bron, adjacentTo, Villeurbanne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Villeurbanne
Context triple: [Bron, adjacentTo, Villeurbanne]
  • A. Villeurbanne chosen
    Villeurbanne is a major suburban city adjacent to Lyon in eastern France, known for its dense urban character and role as part of the Lyon metropolitan area.
  • B. Bois-Colombes
    Bois-Colombes is a suburban commune in the northwestern outskirts of Paris, France, known for its residential character and proximity to the capital.
  • C. Saint-Priest
    Saint-Priest is a suburban commune in eastern France that forms part of the metropolitan area of Lyon.
  • D. Firminy
    Firminy is a commune in central France known for its notable modernist architecture, including works by Le Corbusier such as the Maison de la Culture.
  • E. Vénissieux
    Vénissieux is a suburban commune in eastern France that forms part of the urban area of Lyon and is known for its industrial heritage and diverse population.
  • 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_69d7bdf366888190a8cccb982606889c completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96e5dbdb88190a1b06721ada51627 completed April 10, 2026, 9:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd466577508190b1926c475b7c49dc completed May 8, 2026, 2:11 a.m.
Created at: April 9, 2026, 5:29 p.m.