Triple

T13791857
Position Surface form Disambiguated ID Type / Status
Subject Daddy's Little Girls E331416 entity
Predicate editor P1954 FINISHED
Object Maysie Hoy E391345 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: Maysie Hoy | Statement: [Daddy's Little Girls, editor, Maysie Hoy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maysie Hoy
Context triple: [Daddy's Little Girls, editor, Maysie Hoy]
  • A. Maysie Hoy chosen
    Maysie Hoy is a Canadian film editor known for her work on numerous feature films, including collaborations with prominent directors such as Tyler Perry.
  • B. Molly Blane
    Molly Blane is a key member of the covert military team in the television series "The Unit," known for her resilience and role within the soldiers’ family network.
  • C. Shirley Owens
    Shirley Owens is an American singer best known as the lead vocalist of the girl group The Shirelles, pioneers of the early 1960s pop and R&B sound.
  • D. Maxine Albro
    Maxine Albro was an American muralist and painter associated with the New Deal era, best known for her vibrant frescoes and contributions to public art in San Francisco.
  • E. Mary Wickes
    Mary Wickes was an American character actress known for her sharp-tongued, comedic roles in film and television across several decades.
  • 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_69d81c58feb08190a77bca8bf7d6d20f completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de0258a1408190a837d17c6d6a2bd4 completed April 14, 2026, 9:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69fd192957008190b525778430b56ca0 completed May 7, 2026, 10:58 p.m.
Created at: April 9, 2026, 10:11 p.m.