Triple

T10359586
Position Surface form Disambiguated ID Type / Status
Subject Marie-Thérèse E244097 entity
Predicate relatedName P3889 FINISHED
Object Thérèse E195946 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: Thérèse | Statement: [Marie-Thérèse, relatedName, Thérèse]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Thérèse
Context triple: [Marie-Thérèse, relatedName, Thérèse]
  • A. Therese chosen
    Therese is a feminine given name of French origin, commonly associated with Christian saints and used in various European cultures.
  • B. Bénédicte
    Bénédicte is the given name of Louise Bénédicte de Bourbon, a French noblewoman of the House of Bourbon.
  • C. Thérèse Desqueyroux
    Thérèse Desqueyroux is a French drama film adaptation of François Mauriac’s novel, centered on a woman trapped in a stifling bourgeois marriage in 1920s provincial France.
  • D. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • E. Émilie
    Émilie is the given first name of the French-born American actress Claudette Colbert, a major Hollywood star of the 1930s and 1940s.
  • 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_69d381b22b8c8190aaed476be5f872a9 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e9609c4481908b7d72ecf1adaa73 completed April 7, 2026, 11:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69d89f53484881909fb976efb3882b9b completed April 10, 2026, 6:57 a.m.
Created at: April 6, 2026, 11:59 a.m.