Triple

T16687223
Position Surface form Disambiguated ID Type / Status
Subject Bruyères-le-Châtel E405493 entity
Predicate department P1467 FINISHED
Object Essonne E45084 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: Essonne | Statement: [Bruyères-le-Châtel, department, Essonne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Essonne
Context triple: [Bruyères-le-Châtel, department, Essonne]
  • A. Essonne chosen
    Essonne is a department in northern France that forms part of the Paris metropolitan region and includes a mix of suburban communities, research centers, and rural areas.
  • B. Seine-et-Oise
    Seine-et-Oise was a former department of France surrounding Paris, abolished in 1968 and divided into several new departments including Yvelines.
  • C. Loir-et-Cher
    Loir-et-Cher is a department in central France known for its historic châteaux, including parts of the Loire Valley UNESCO World Heritage site.
  • D. Eure-et-Loir
    Eure-et-Loir is a department in north-central France, located in the Centre-Val de Loire region and known for including the historic city of Chartres.
  • E. Seine-et-Marne
    Seine-et-Marne is a largely rural department in north-central France east of Paris, known for its historic towns, agricultural landscapes, and attractions such as the Château de Fontainebleau and Disneyland Paris.
  • 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_69d8838c28748190b3f5967c743940ab completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e37ea63b7081908a055036172f9683 completed April 18, 2026, 12:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a008a45af7c8190bfe09dd0e0573573 completed May 10, 2026, 1:38 p.m.
Created at: April 10, 2026, 5:19 a.m.