Triple

T14647761
Position Surface form Disambiguated ID Type / Status
Subject arrondissement of Besançon E343897 entity
Predicate namedAfter P63 FINISHED
Object Besançon E64224 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: Besançon | Statement: [arrondissement of Besançon, namedAfter, Besançon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Besançon
Context triple: [arrondissement of Besançon, namedAfter, Besançon]
  • A. Besançon chosen
    Besançon is a historic city in eastern France, known for its well-preserved Vauban fortifications, rich cultural heritage, and role as a regional administrative and educational center.
  • B. Épinal
    Épinal is a historic town in northeastern France, known for its traditional image-printing industry and picturesque setting in the Vosges region.
  • C. Brioude
    Brioude is a historic town in south-central France known for its Romanesque Basilica of Saint-Julien and its location in the Haute-Loire department of the Auvergne region.
  • D. Bourg-en-Bresse
    Bourg-en-Bresse is a historic town in eastern France known as the capital of the Ain department, noted for its Renaissance architecture and the royal monastery of Brou.
  • E. Mulhouse
    Mulhouse is an industrial city in northeastern France near the Swiss and German borders, known for its textile heritage and major technical museums.
  • 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_69d822e1a2cc81908e5bb93cf61ce3cc completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb4ebe8048190a2935d00c9cfd8be completed April 14, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff3d379bc881908b7954c633787165 completed May 9, 2026, 1:57 p.m.
Created at: April 10, 2026, 1:26 a.m.