Triple

T13170025
Position Surface form Disambiguated ID Type / Status
Subject A Dog’s Journey E312951 entity
Predicate screenwriter P2831 FINISHED
Object Maya Forbes E488234 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: Maya Forbes | Statement: [A Dog’s Journey, screenwriter, Maya Forbes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maya Forbes
Context triple: [A Dog’s Journey, screenwriter, Maya Forbes]
  • A. Maya Forbes chosen
    Maya Forbes is an American screenwriter, director, and producer known for films such as "Infinitely Polar Bear" and for her work on television series like "The Larry Sanders Show."
  • B. Maya Bishop
    Maya Bishop is a driven and skilled firefighter and former Olympic athlete who serves as a central protagonist and eventual captain on the television drama "Station 19."
  • C. Maya Imhoof
    Maya Imhoof is a film producer best known for her work on the acclaimed Swiss drama "The Boat Is Full."
  • D. Maya Vidal
    Maya Vidal is the troubled teenage protagonist of Isabel Allende’s novel "Maya’s Notebook," whose coming-of-age journey unfolds through her candid, reflective diary entries.
  • E. Maia Wilson
    Maia Wilson is an American singer and actress best known for her work in musical theatre and as a voice performer in film and television.
  • 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_69d806ac3ee081909b2fd27d060aa974 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98c2e03c481909909b8f10c7e8ffc completed April 10, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f72660f8d48190b437a57f2a75f6ae completed May 3, 2026, 10:41 a.m.
Created at: April 9, 2026, 9:13 p.m.