Triple

T10197331
Position Surface form Disambiguated ID Type / Status
Subject Bo Peep E238796 entity
Predicate voiceActor P1507 FINISHED
Object Annie Potts E240001 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: Annie Potts | Statement: [Bo Peep, voiceActor, Annie Potts]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Annie Potts
Context triple: [Bo Peep, voiceActor, Annie Potts]
  • A. Annie Potts chosen
    Annie Potts is an American actress best known for her roles in films like "Ghostbusters" and "Pretty in Pink" and for voicing Bo Peep in the "Toy Story" animated franchise.
  • B. Cindy Williams
    Cindy Williams was an American actress best known for her role as Shirley Feeney on the hit television sitcom "Laverne & Shirley."
  • C. Teri Garr
    Teri Garr is an American actress known for her versatile performances in films such as "Young Frankenstein," "Close Encounters of the Third Kind," and "Tootsie."
  • D. Jami Gertz
    Jami Gertz is an American actress known for her roles in 1980s films and television series, including standout performances in movies like "The Lost Boys" and "Quicksilver."
  • E. Marilu Henner
    Marilu Henner is an American actress and author best known for her role as Elaine Nardo on the TV sitcom "Taxi" and for her appearances in numerous film and television projects.
  • 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_69ca84e1ea088190b38162e43d4cfa8f completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdedcaf6688190938d8e56e29493eb completed April 2, 2026, 4:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69d369b043608190a740621a2eeb49ed completed April 6, 2026, 8:07 a.m.
Created at: March 30, 2026, 9:13 p.m.