Triple

T13590491
Position Surface form Disambiguated ID Type / Status
Subject Agen E324679 entity
Predicate hasAirport P105 FINISHED
Object Agen La Garenne Airport E327952 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: Agen La Garenne Airport | Statement: [Agen, hasAirport, Agen La Garenne Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Agen La Garenne Airport
Context triple: [Agen, hasAirport, Agen La Garenne Airport]
  • A. Agen La Garenne Airport chosen
    Agen La Garenne Airport is a regional airport in southwestern France serving the city of Agen with domestic flights and general aviation services.
  • B. Bourges Airport
    Bourges Airport is a regional public airport serving the city of Bourges in central France, handling general aviation and limited commercial traffic.
  • C. Nancy-Essey Airport
    Nancy-Essey Airport is a regional airport serving the city of Nancy and its surrounding area in northeastern France.
  • D. Gillot Airport
    Gillot Airport is the former name of Roland Garros Airport, the main international airport serving Réunion Island in the Indian Ocean.
  • E. Valence-Chabeuil Airport
    Valence-Chabeuil Airport is a regional airport in southeastern France serving the Valence area and surrounding communes in the Drôme department.
  • 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_69d80769eaf081909d82f44e484d6113 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb055cc98819091fab597b69e5e3e completed April 12, 2026, 2:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69f77f8ffc508190a7bc69745c43a644 completed May 3, 2026, 5:02 p.m.
Created at: April 9, 2026, 9:49 p.m.