Triple

T8140984
Position Surface form Disambiguated ID Type / Status
Subject Calas affair E190094 entity
Predicate location P40 FINISHED
Object Toulouse E16066 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: Toulouse | Statement: [Calas affair, location, Toulouse]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Toulouse
Context triple: [Calas affair, location, Toulouse]
  • A. Toulouse chosen
    Toulouse is a major city in southwestern France known for its aerospace industry, historic pink-brick architecture, and vibrant university and cultural life.
  • B. Montpellier
    Montpellier is a major city in southern France known for its medieval old town, vibrant university life, and proximity to the Mediterranean coast.
  • C. Toulouse Métropole
    Toulouse Métropole is an intercommunal metropolitan authority in southwestern France that coordinates urban planning, transportation, and public services for Toulouse and its surrounding communes.
  • D. Rodez
    Rodez is a historic cathedral city in southern France that serves as the capital of the Aveyron department in the Occitanie region.
  • E. Béziers
    Béziers is a historic city in southern France known for its wine production, ancient Roman heritage, and the famous Feria de Béziers festival.
  • 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_69ca82bd9900819099477cdc2eb4244f completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb4442299881909db56f7475cbb99a completed March 31, 2026, 3:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69ccbe4ca4cc8190a664968334225087 completed April 1, 2026, 6:42 a.m.
Created at: March 30, 2026, 5:36 p.m.