Triple

T4059559
Position Surface form Disambiguated ID Type / Status
Subject Unbreakable Kimmy Schmidt E84775 entity
Predicate executiveProducer P7225 FINISHED
Object Tina Fey E11671 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: Tina Fey | Statement: [Unbreakable Kimmy Schmidt, executiveProducer, Tina Fey]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tina Fey
Context triple: [Unbreakable Kimmy Schmidt, executiveProducer, Tina Fey]
  • A. Tina Fey chosen
    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."
  • B. 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."
  • 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. Kristen Wiig
    Kristen Wiig is an American comedian, actress, and writer best known for her work on Saturday Night Live and films such as Bridesmaids.
  • E. 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.
  • 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_69aed933bec881909edfa28ebb69c634 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefbd32c0c8190bc575974ccf831b3 completed March 9, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69b627b362508190905af163e9dbc1b4 completed March 15, 2026, 3:29 a.m.
Created at: March 9, 2026, 3:38 p.m.