Triple

T12397695
Position Surface form Disambiguated ID Type / Status
Subject AJ Auxerre E296159 entity
Predicate rival P437 FINISHED
Object Dijon FCO E296160 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: Dijon FCO | Statement: [AJ Auxerre, rival, Dijon FCO]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dijon FCO
Context triple: [AJ Auxerre, rival, Dijon FCO]
  • A. Dijon FCO chosen
    Dijon FCO is a French professional football club based in Dijon, known for competing in the country’s top divisions and maintaining regional rivalries, including with AJ Auxerre.
  • B. Dijon-Bourgogne Airport
    Dijon-Bourgogne Airport is a regional airport in eastern France serving the city of Dijon and the surrounding Burgundy (Bourgogne) area.
  • C. Lyon–Bron Airport
    Lyon–Bron Airport is a regional airport in eastern France that primarily handles business, general aviation, and some charter flights for the Lyon area.
  • D. Franca Airport
    Franca Airport is a regional public airport serving the city of Franca in the state of São Paulo, Brazil.
  • E. Vinci Airports
    Vinci Airports is a global airport operator that manages and develops a large network of airports across multiple countries as part of the Vinci Group’s transport infrastructure portfolio.
  • 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_69d6ad9e653c8190b1473c860ee53dae completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d93fd448f08190af425a569d7ed158 completed April 10, 2026, 6:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f63480a8bc8190885130f63a3ec761 completed May 2, 2026, 5:29 p.m.
Created at: April 8, 2026, 9:54 p.m.