Triple

T3931047
Position Surface form Disambiguated ID Type / Status
Subject Tarn-et-Garonne E90794 entity
Predicate capital P234 FINISHED
Object Montauban E112838 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: Montauban | Statement: [Tarn-et-Garonne, capital, Montauban]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Montauban
Context triple: [Tarn-et-Garonne, capital, Montauban]
  • A. Montauban chosen
    Montauban is a historic city in southern France known for its red-brick architecture and role as the capital of the Tarn-et-Garonne department.
  • B. Gradignan
    Gradignan is a suburban commune in southwestern France’s Gironde department, forming part of the Bordeaux metropolitan area and known for its green spaces and wine-growing surroundings.
  • C. Pont-Saint-Esprit
    Pont-Saint-Esprit is a historic commune in southern France, known for its medieval bridge over the Rhône River and its location in the Gard department of the Occitanie region.
  • D. La Grande-Motte
    La Grande-Motte is a seaside resort town on France’s Mediterranean coast, noted for its distinctive modernist pyramid-shaped architecture and beaches.
  • E. Épinal
    Épinal is a historic town in northeastern France, known for its traditional image-printing industry and picturesque setting in the Vosges region.
  • 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_69aed95f26e0819094b0e71974543a19 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeeda98058819094dd6ab223670860 completed March 9, 2026, 3:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69be10079a408190ae09d3df55ead73c completed March 21, 2026, 3:27 a.m.
Created at: March 9, 2026, 3:23 p.m.