Triple

T14120036
Position Surface form Disambiguated ID Type / Status
Subject Peter and Wendy E339879 entity
Predicate featuresCharacter P626 FINISHED
Object Michael Darling E342309 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: Michael Darling | Statement: [Peter and Wendy, featuresCharacter, Michael Darling]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Darling
Context triple: [Peter and Wendy, featuresCharacter, Michael Darling]
  • A. Michael Darling chosen
    Michael Darling is the youngest of the Darling children in J.M. Barrie’s Peter Pan, known for his innocence, curiosity, and adventures in Neverland alongside his siblings and the Lost Boys.
  • B. Joe Dougherty
    Joe Dougherty was an American voice actor best known for originating the voice of the Warner Bros. cartoon character Porky Pig in the 1930s.
  • C. Phil Morrow
    Phil Morrow is a television producer known for his work in developing and producing various entertainment and factual programs.
  • D. Lynde Bradley
    Lynde Bradley was an American industrialist and co-founder of the company that became Rockwell Automation, known for pioneering work in industrial controls and automation.
  • E. Douglas Morrow
    Douglas Morrow was an American screenwriter best known for his Academy Award-winning work in mid-20th-century Hollywood cinema.
  • 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_69d81c6a95b481909e39111e0c1f31ee completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de60942a588190beff0058a92f7051 completed April 14, 2026, 3:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcf7e04184819081633f9cfc0ccab9 completed May 7, 2026, 8:36 p.m.
Created at: April 9, 2026, 10:22 p.m.