Triple

T2169375
Position Surface form Disambiguated ID Type / Status
Subject CNES E46986 entity
Predicate mainTechnicalCenterLocation P3231 FINISHED
Object Toulouse, France 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, France | Statement: [CNES, mainTechnicalCenterLocation, Toulouse, France]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Toulouse, France
Context triple: [CNES, mainTechnicalCenterLocation, Toulouse, France]
  • A. Montpellier, France
    Montpellier, France is a historic and vibrant city in southern France near the Mediterranean coast, known for its medieval architecture, large student population, and role as a regional cultural and economic center.
  • B. 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.
  • C. Toulon
    Toulon is a major port city on France’s Mediterranean coast that serves as the principal base of the French Navy.
  • D. Montpellier
    Montpellier is a major city in southern France known for its medieval old town, vibrant university life, and proximity to the Mediterranean coast.
  • E. Sochaux, France
    Sochaux, France is an industrial town in eastern France best known as the historic home of the Peugeot automobile manufacturing complex.
  • 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_69a88a184cbc8190877791f6552c2484 completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abc5af20808190902031d8c0bba376 completed March 7, 2026, 6:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae58f511a08190880fbde8900d59df completed March 9, 2026, 5:21 a.m.
Created at: March 4, 2026, 7:45 p.m.