Triple

T21991262
Position Surface form Disambiguated ID Type / Status
Subject Vanessa Bayer E543093 entity
Predicate coStarredWith P14987 FINISHED
Object Kristen Wiig NE NERFINISHED

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: Kristen Wiig | Statement: [Vanessa Bayer, coStarredWith, Kristen Wiig]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kristen Wiig
Context triple: [Vanessa Bayer, coStarredWith, Kristen Wiig]
  • A. Kristen Wiig chosen
    Kristen Wiig is an American comedian, actress, and writer best known for her work on Saturday Night Live and films such as Bridesmaids.
  • B. Melissa McCarthy
    Melissa McCarthy is an American actress and comedian known for her breakout comedic role in "Bridesmaids" and subsequent work in film and television.
  • C. Maya Rudolph
    Maya Rudolph is an American actress and comedian known for her work on "Saturday Night Live" and in numerous film and animated voice roles.
  • D. Amy Poehler
    Amy Poehler is an American comedian, actress, writer, and producer best known for her work on "Saturday Night Live" and for starring as Leslie Knope on the sitcom "Parks and Recreation."
  • E. Tina Fey
    Tina Fey is an American comedian, writer, actress, and producer best known for her work on "Saturday Night Live" and creating the acclaimed sitcom "30 Rock."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c48136b081908831fa907cc02e18 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f1270d7cbc819086eea86be04a2ec0 completed April 28, 2026, 9:30 p.m.
Created at: April 16, 2026, 8:05 p.m.