Triple

T14325594
Position Surface form Disambiguated ID Type / Status
Subject Angers – Loire Airport E355206 entity
Predicate cityServed P82 FINISHED
Object Angers E71733 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: Angers | Statement: [Angers – Loire Airport, cityServed, Angers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Angers
Context triple: [Angers – Loire Airport, cityServed, Angers]
  • A. Angers chosen
    Angers is a historic city in western France known for its medieval architecture, including the Château d'Angers and its famous Apocalypse Tapestry.
  • B. Bourges
    Bourges is a historic city in central France known for its well-preserved medieval architecture and its UNESCO-listed Gothic cathedral, Saint-Étienne.
  • C. Mâcon
    Mâcon is a historic town in eastern France’s Burgundy region, known for its wine production and picturesque setting along the Saône River.
  • D. Poitiers
    Poitiers is a historic city in western France known for its Romanesque architecture, medieval heritage, and role as a regional center in the Nouvelle-Aquitaine region.
  • E. Nantes
    Nantes is a historic port city in western France on the Loire River, known for its maritime heritage, cultural institutions, and vibrant arts scene.
  • 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_69d8278fa2108190bc0d0e7939c1eb03 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de883e6a288190b6c22f630a1eef3c completed April 14, 2026, 6:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff90815b64819082715292f6088c74 completed May 9, 2026, 7:52 p.m.
Created at: April 10, 2026, 1:13 a.m.