Triple

T1755425
Position Surface form Disambiguated ID Type / Status
Subject Dianne Wiest E38535 entity
Predicate name P16 FINISHED
Object Dianne Wiest E38535 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: Dianne Wiest | Statement: [Dianne Wiest, name, Dianne Wiest]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dianne Wiest
Context triple: [Dianne Wiest, name, Dianne Wiest]
  • A. Dianne Wiest chosen
    Dianne Wiest is an acclaimed American actress known for her versatile performances in film, television, and theater, including multiple award-winning supporting roles.
  • B. Jessica Walter
    Jessica Walter was an American actress best known for her sharp, comedic portrayal of Lucille Bluth on the television series "Arrested Development."
  • C. Christine Baranski
    Christine Baranski is an American actress known for her sharp-witted, sophisticated roles in film, television, and theater, including acclaimed performances in works like "The Good Wife," "Mamma Mia!," and numerous stage productions.
  • D. Anne McDonnell
    Anne McDonnell was an American socialite best known as the first wife of industrialist Henry Ford II.
  • E. Candice Bergen
    Candice Bergen is an American actress and former fashion model best known for her Emmy-winning role as the sharp-tongued journalist Murphy Brown on the hit television sitcom of the same name.
  • 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_69a8862bdb2081908aefe831c8aa8017 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa643b623081908064be75758ec5de completed March 6, 2026, 5:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae0ab3a140819081dbb7b19e33051c completed March 8, 2026, 11:48 p.m.
Created at: March 4, 2026, 7:31 p.m.