Triple

T5430451
Position Surface form Disambiguated ID Type / Status
Subject Goodbye Marie E121472 entity
Predicate hasTitleCharacter P5716 FINISHED
Object Marie E27948 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: Marie | Statement: [Goodbye Marie, hasTitleCharacter, Marie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marie
Context triple: [Goodbye Marie, hasTitleCharacter, Marie]
  • A. Marie chosen
    Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
  • B. Marie Christine
    Marie Christine, better known as Princess Michael of Kent, is a member of the British royal family, an author, and the wife of Prince Michael of Kent, a first cousin of King Charles III.
  • C. Françoise
    Françoise is the given name of Louise de La Vallière, a 17th-century French noblewoman best known as a mistress of King Louis XIV.
  • D. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • E. Madeleine
    Madeleine is a feminine given name, commonly used in French and English, derived from Magdalene and often associated with literary and cultural figures.
  • 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_69bd463c65f0819082ee6483ab4b466a completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd883e5e10819091e159dfd245e94d completed March 20, 2026, 5:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf70d2a5b481908a6fdc28bb2d2e11 completed March 22, 2026, 4:32 a.m.
Created at: March 20, 2026, 2:06 p.m.