Triple

T14258790
Position Surface form Disambiguated ID Type / Status
Subject Women of the House E353455 entity
Predicate stars P1956 FINISHED
Object Teri Garr E152918 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: Teri Garr | Statement: [Women of the House, stars, Teri Garr]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Teri Garr
Context triple: [Women of the House, stars, Teri Garr]
  • A. Teri Garr chosen
    Teri Garr is an American actress known for her versatile performances in films such as "Young Frankenstein," "Close Encounters of the Third Kind," and "Tootsie."
  • B. Lisa Dillman
    Lisa Dillman is an American playwright known for her contemporary stage works and as a notable alumna of the Playwrights Workshop.
  • C. Jami Gertz
    Jami Gertz is an American actress known for her roles in 1980s films and television series, including standout performances in movies like "The Lost Boys" and "Quicksilver."
  • D. Danielle Kaye
    Danielle Kaye is known as the spouse of British film director and music video creator Tony Kaye.
  • E. Peggy Ann Garner
    Peggy Ann Garner was an American child actress best known for her acclaimed performance as Francie Nolan in the film adaptation of "A Tree Grows in Brooklyn."
  • 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_69d8278c43e08190824146f4632b89a5 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de6352611c819090d062fe3079cd03 completed April 14, 2026, 3:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd8aa2b7908190831e6c07abcc091d completed May 8, 2026, 7:02 a.m.
Created at: April 10, 2026, 1:09 a.m.