Triple

T15864822
Position Surface form Disambiguated ID Type / Status
Subject Vanessa Zima E384683 entity
Predicate name P16 FINISHED
Object Vanessa Zima E384683 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: Vanessa Zima | Statement: [Vanessa Zima, name, Vanessa Zima]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vanessa Zima
Context triple: [Vanessa Zima, name, Vanessa Zima]
  • A. Vanessa Zima chosen
    Vanessa Zima is an American actress known for her roles in films such as "Ulee's Gold" and "The Brain."
  • B. Vanessa Brown
    Vanessa Brown was an Austrian-born American actress known for her work in mid-20th-century Hollywood films, radio, and stage productions.
  • C. Vanessa Roth
    Vanessa Roth is an Academy Award-winning American documentary filmmaker known for her socially conscious films and work in education and social justice.
  • D. Vanessa Ferlito
    Vanessa Ferlito is an American actress known for her roles in films like "Death Proof" and TV series such as "CSI: NY" and "NCIS: New Orleans."
  • E. Vanessa Loring
    Vanessa Loring is a key supporting character in the film "Juno," portrayed as a woman longing to adopt a child and struggling with the complexities of marriage and motherhood.
  • 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_69d86da4e86481909f1325fdc971b5ec completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1555e4ee48190a3b27b4ab9bdb1c8 completed April 16, 2026, 9:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffa945d9808190a65f5182db341393 completed May 9, 2026, 9:38 p.m.
Created at: April 10, 2026, 4:50 a.m.