Triple

T17802233
Position Surface form Disambiguated ID Type / Status
Subject University of Franche-Comté E444458 entity
Predicate mainCampus P115 FINISHED
Object Besançon NE NERFINISHED

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: [University of Franche-Comté, mainCampus, Besançon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Besançon
Context triple: [University of Franche-Comté, mainCampus, 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 (2 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_69d8b9efe370819095cd219b143ae727 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e488005a288190b7a2cffa590d2557 completed April 19, 2026, 7:45 a.m.
Created at: April 10, 2026, 10:13 a.m.