Triple

T9759765
Position Surface form Disambiguated ID Type / Status
Subject A Very Curious Girl E236638 entity
Predicate leadCharacter P1668 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: [A Very Curious Girl, leadCharacter, Marie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Marie
Context triple: [A Very Curious Girl, leadCharacter, 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. Marie Émilie
    Marie Émilie is a French noblewoman best known as the morganatic wife of Louis, Grand Dauphin of France, during the late 17th and early 18th centuries.
  • D. 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.
  • E. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • 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_69ca84d64f6c8190a4ed4e9f5936eda5 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cda049995c81908569ec61805642b2 completed April 1, 2026, 10:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1c41022908190a5f55291a2323691 completed April 5, 2026, 2:08 a.m.
Created at: March 30, 2026, 8:24 p.m.