Triple

T21445768
Position Surface form Disambiguated ID Type / Status
Subject Elizabeth Karlsen E529070 entity
Predicate hasNotableCollaboration P8554 FINISHED
Object Phyllis Nagy 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: Phyllis Nagy | Statement: [Elizabeth Karlsen, hasNotableCollaboration, Phyllis Nagy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Phyllis Nagy
Context triple: [Elizabeth Karlsen, hasNotableCollaboration, Phyllis Nagy]
  • A. Phyllis Nagy chosen
    Phyllis Nagy is an American screenwriter, director, and playwright best known for her Academy Award–nominated adaptation of Patricia Highsmith’s novel into the film "Carol."
  • B. Elizabeth Swados
    Elizabeth Swados was an American composer, writer, and director known for her innovative, socially engaged musical theater works on and off Broadway.
  • C. Phil Parmet
    Phil Parmet is an American cinematographer known for his work on genre films, including Rob Zombie’s horror movie "The Devil’s Rejects."
  • D. Joan Porter
    Joan Porter is known as the wife of William A. Porter, the co-founder of the online brokerage firm E*TRADE.
  • E. Louise Dahl-Wolfe
    Louise Dahl-Wolfe was a pioneering American fashion photographer renowned for her innovative use of natural light and location shooting, which helped redefine modern fashion imagery in the mid-20th century.
  • 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_69e0c457579481909db68053ed99750c completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69e8b707ecd88190b3576b8923840870 completed April 22, 2026, 11:54 a.m.
Created at: April 16, 2026, 6:05 p.m.