Triple

T4863316
Position Surface form Disambiguated ID Type / Status
Subject Jacques-Pierre Brissot E108710 entity
Predicate birthPlace P1 FINISHED
Object Chartres E153197 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: Chartres | Statement: [Jacques-Pierre Brissot, birthPlace, Chartres]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chartres
Context triple: [Jacques-Pierre Brissot, birthPlace, Chartres]
  • A. Chartres chosen
    Chartres is a historic city in northern France renowned for its well-preserved medieval old town and its UNESCO-listed Gothic cathedral, famed for its stained glass windows.
  • B. Cholet
    Cholet is a town in western France’s Maine-et-Loire department, known historically for its textile industry and as part of the Pays de la Loire region.
  • C. Bourges
    Bourges is a historic city in central France known for its well-preserved medieval architecture and its UNESCO-listed Gothic cathedral, Saint-Étienne.
  • D. Lisieux
    Lisieux is a town and commune in the Calvados department of Normandy in northwestern France, known as a major Catholic pilgrimage site associated with Saint Thérèse of Lisieux.
  • E. Angoulême
    Angoulême is a historic city in southwestern France known for its hilltop old town, medieval ramparts, and status as a major center of the French comics industry.
  • 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_69bd440b965081908b0557721cae6338 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6d60e47c819094b5fbe883db4c15 completed March 20, 2026, 3:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69be5cfdb3248190a16a5f3fb97d4950 completed March 21, 2026, 8:55 a.m.
Created at: March 20, 2026, 1:26 p.m.