Triple

T15761381
Position Surface form Disambiguated ID Type / Status
Subject Madeline Zima E382104 entity
Predicate siblingOccupation P13234 FINISHED
Object Vanessa Zima is an actress 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 is an actress | Statement: [Madeline Zima, siblingOccupation, Vanessa Zima is an actress]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vanessa Zima is an actress
Context triple: [Madeline Zima, siblingOccupation, Vanessa Zima is an actress]
  • 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 Slim Domit
    Vanessa Slim Domit is a Mexican businesswoman and philanthropist, known as a member of the prominent Slim family.
  • C. Vanessa Ray
    Vanessa Ray is an American actress best known for her role as Officer Edit "Eddie" Janko-Reagan on the television series "Blue Bloods."
  • D. Vanessa Vadim
    Vanessa Vadim is a French-American filmmaker and environmental activist, best known as the daughter of actress Jane Fonda and director Roger Vadim.
  • E. Vanessa Angel
    Vanessa Angel is an English actress and former model best known for her roles in the film "Kingpin" and the TV series "Weird Science."
  • 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_69d86d9e6b44819085d1f6a969ecb74c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e050b52c548190a0ffa4493a4eb15c completed April 16, 2026, 3 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff8774eda08190a6231b4fd5027e6f completed May 9, 2026, 7:13 p.m.
Created at: April 10, 2026, 4:47 a.m.