Triple

T10484610
Position Surface form Disambiguated ID Type / Status
Subject Richard Strickland E247263 entity
Predicate createdBy P806 FINISHED
Object Vanessa Taylor E195697 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: Vanessa Taylor | Statement: [Richard Strickland, createdBy, Vanessa Taylor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vanessa Taylor
Context triple: [Richard Strickland, createdBy, Vanessa Taylor]
  • A. Vanessa Taylor chosen
    Vanessa Taylor is an American screenwriter and producer known for co-writing the Academy Award–winning film "The Shape of Water" and her work on television series such as "Game of Thrones."
  • B. Vanessa Haywood
    Vanessa Haywood is a South African actress and model best known for her role in the science fiction film "District 9."
  • C. Vanessa Lampley
    Vanessa Lampley is a character in Stephen King and Owen King's novel "Sleeping Beauties," involved in the unfolding crisis when women around the world fall into a mysterious sleep.
  • D. Emily Taylor
    Emily Taylor is the central protagonist of the psychological thriller film "Side Effects," around whom the story’s exploration of medication, mental health, and deception revolves.
  • E. Vanessa Loring
    Vanessa Loring is a key supporting character in the film "Juno," portrayed as a woman longing to adopt a child and struggling with the complexities of marriage and motherhood.
  • 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_69d381c309b88190af78aa681cf6a4c2 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d50968a0bc8190a18ba24eb37431d9 completed April 7, 2026, 1:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69d8a03c647c81909521fee4a66ec8ac completed April 10, 2026, 7:01 a.m.
Created at: April 6, 2026, 12:22 p.m.