Triple

T3892837
Position Surface form Disambiguated ID Type / Status
Subject Agnes E88099 entity
Predicate hasVariant P455 FINISHED
Object Agnès E175041 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: Agnès | Statement: [Agnes, hasVariant, Agnès]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Agnès
Context triple: [Agnes, hasVariant, Agnès]
  • A. Clara Beranger
    Clara Beranger was an American screenwriter of the silent film era, known for her work with Paramount Pictures and her contributions to early Hollywood cinema.
  • B. Renée chosen
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • C. Lucia DeLury
    Lucia DeLury is a supporting character in the dark comedy film "The Opposite of Sex," involved in the tangled romantic and personal conflicts that drive the story.
  • D. Marguerite Courtot
    Marguerite Courtot was an American silent film actress known for her work in early 20th-century cinema, particularly in serials and adventure films.
  • E. Agnès de La Borde
    Agnès de La Borde was the wife of French diplomat and Suez Canal developer Ferdinand de Lesseps, known primarily through her marriage into his prominent family.
  • 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_69aed9466d548190939f5217a23ed4ac completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeecce860c8190b16eca2e14f6544f completed March 9, 2026, 3:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69b51c96ff648190b03807547930d51d completed March 14, 2026, 8:30 a.m.
Created at: March 9, 2026, 3:21 p.m.