Triple

T22840411
Position Surface form Disambiguated ID Type / Status
Subject Dietland E566063 entity
Predicate portrayedBy P1507 FINISHED
Object Tamara Tunie 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: Tamara Tunie | Statement: [Dietland, portrayedBy, Tamara Tunie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tamara Tunie
Context triple: [Dietland, portrayedBy, Tamara Tunie]
  • A. Tamara Tunie chosen
    Tamara Tunie is an American actress and director best known for her long-running role as medical examiner Melinda Warner on the television series "Law & Order: Special Victims Unit."
  • B. Tyne Daly
    Tyne Daly is an American actress acclaimed for her powerful performances in television dramas, film, and theater, including her iconic role in the series "Cagney & Lacey."
  • C. Laurie Durning
    Laurie Durning is an American filmmaker and costume designer best known for her long-term relationship and later marriage to Pink Floyd co-founder Roger Waters.
  • D. Lorna Raver
    Lorna Raver is an American character actress best known for her chilling performance as the vengeful Mrs. Ganush in the horror film "Drag Me to Hell."
  • E. Heidi Mark
    Heidi Mark is an American model and actress best known for her work with Playboy and appearances in various television shows and films in the 1990s and early 2000s.
  • 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_69e245869e188190a196584f36e682da completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17e83fa48819084568264ef45c833 completed April 29, 2026, 3:44 a.m.
Created at: April 17, 2026, 3:35 p.m.