Triple

T22163392
Position Surface form Disambiguated ID Type / Status
Subject Vis a vis E547727 entity
Predicate hasMainCharacter P1183 FINISHED
Object Zulema Zahir 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: Zulema Zahir | Statement: [Vis a vis, hasMainCharacter, Zulema Zahir]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zulema Zahir
Context triple: [Vis a vis, hasMainCharacter, Zulema Zahir]
  • A. Zulema Zahir chosen
    Zulema Zahir is a ruthless and cunning inmate who becomes one of the central antagonists in the Spanish prison drama series "Vis a Vis" ("Locked Up").
  • B. Zara Kaleel
    Zara Kaleel is the central character in Kia Abdullah’s legal thriller series, a British-Muslim barrister known for her fierce pursuit of justice in complex, emotionally charged court cases.
  • C. Zana Khan
    Zana Khan is a town in Ghazni Province, Afghanistan, serving as the administrative center of Zana Khan District.
  • D. Tammea Ziya
    Tammea Ziya is known as the spouse of Canadian actor Adam Beach.
  • E. Riza Aziz
    Riza Aziz is a Malaysian film producer and co-founder of Red Granite Pictures, known for financing high-profile Hollywood films and being embroiled in the 1MDB corruption scandal.
  • 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_69e11e3c4c5c81908d336165816b12e0 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f12a2f2f90819080b5bb73a6052c24 completed April 28, 2026, 9:44 p.m.
Created at: April 16, 2026, 8:34 p.m.