Triple

T4179732
Position Surface form Disambiguated ID Type / Status
Subject Stadium de Toulouse E86564 entity
Predicate operator P179 FINISHED
Object City of 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: City of Toulouse | Statement: [Stadium de Toulouse, operator, City of Toulouse]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Toulouse
Context triple: [Stadium de Toulouse, operator, City of 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. Montauban
    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.
  • C. Troyes
    Troyes is a historic city in northeastern France, known for its well-preserved medieval old town, half-timbered houses, and Gothic churches.
  • D. Albi
    Albi is a historic city in southern France renowned for its red-brick medieval architecture and the UNESCO-listed Episcopal City centered around Sainte-Cécile Cathedral.
  • E. Montpellier
    Montpellier is a major city in southern France known for its medieval old town, vibrant university life, and proximity to the Mediterranean coast.
  • 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_69aed93de98c8190ad838ce507b77c8a completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af0302972c819082f6e1938ed417c4 completed March 9, 2026, 5:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5d055a1888190a188a8dba7d8af23 completed March 14, 2026, 9:17 p.m.
Created at: March 9, 2026, 3:45 p.m.