Triple

T7257024
Position Surface form Disambiguated ID Type / Status
Subject Spanglish E157746 entity
Predicate castMember P1668 FINISHED
Object Téa Leoni E289044 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: Téa Leoni | Statement: [Spanglish, castMember, Téa Leoni]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Téa Leoni
Context triple: [Spanglish, castMember, Téa Leoni]
  • A. Téa Leoni chosen
    Téa Leoni is an American actress and producer best known for her leading roles in film and television, including the political drama series "Madam Secretary."
  • B. Elizabeth Berkley
    Elizabeth Berkley is an American actress best known for her roles in the TV series "Saved by the Bell" and the film "Showgirls."
  • C. Annabella Sciorra
    Annabella Sciorra is an American actress known for her work in film and television, including acclaimed roles in movies like "Jungle Fever" and the TV series "The Sopranos."
  • D. Laura Kugler
    Laura Kugler was the wife of Victor Kugler, one of the helpers who hid Anne Frank and her family during World War II.
  • E. Virginia Madsen
    Virginia Madsen is an American actress known for her versatile film and television roles, including acclaimed performances in movies such as "Sideways" and "Candyman."
  • 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_69c6882d81d4819085f7ff862951ee4f completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6eaa274bc8190b017b71583711453 completed March 27, 2026, 8:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7fa6ad54081908a0be2d1f7d6505e completed March 28, 2026, 3:57 p.m.
Created at: March 27, 2026, 2:57 p.m.