Triple

T11043645
Position Surface form Disambiguated ID Type / Status
Subject Pierre Viret E261081 entity
Predicate deathPlace P21 FINISHED
Object Orthez E439400 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: Orthez | Statement: [Pierre Viret, deathPlace, Orthez]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Orthez
Context triple: [Pierre Viret, deathPlace, Orthez]
  • A. Orthez chosen
    Orthez is a historic town in southwestern France’s Pyrénées-Atlantiques department, known for its medieval architecture and its role in the Napoleonic Wars.
  • B. Ribérac
    Ribérac is a small historic town in southwestern France’s Dordogne department, known for its traditional markets and rural charm.
  • C. Gradignan
    Gradignan is a suburban commune in southwestern France’s Gironde department, forming part of the Bordeaux metropolitan area and known for its green spaces and wine-growing surroundings.
  • D. Saint-Céré
    Saint-Céré is a small historic town in the Lot department of southwestern France, known for its medieval architecture and picturesque setting in the Dordogne Valley.
  • E. Jonzac
    Jonzac is a small historic town in southwestern France known for its thermal spa resort, medieval château, and role as an administrative center in the Charente-Maritime department.
  • 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_69d6aa979bdc8190bf0e79104cc098c1 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7982d42bc81908ac10f54a7b43fb7 completed April 9, 2026, 12:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3c846d9f08190943d457ff6da6a9f completed April 18, 2026, 6:07 p.m.
Created at: April 8, 2026, 9:26 p.m.