Triple

T8450572
Position Surface form Disambiguated ID Type / Status
Subject For Colored Girls E199787 entity
Predicate editedBy 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: [For Colored Girls, editedBy, Maysie Hoy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maysie Hoy
Context triple: [For Colored Girls, editedBy, 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_69ca8318231881908fd1bc1c4d45d286 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe44815488190a912d63512e19af0 completed March 31, 2026, 3:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce6cf9368081909cad61cdf6156a0e completed April 2, 2026, 1:19 p.m.
Created at: March 30, 2026, 6:09 p.m.