Triple

T22012347
Position Surface form Disambiguated ID Type / Status
Subject Rang Rasiya E543607 entity
Predicate producer P490 FINISHED
Object Ketan Mehta 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: Ketan Mehta | Statement: [Rang Rasiya, producer, Ketan Mehta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ketan Mehta
Context triple: [Rang Rasiya, producer, Ketan Mehta]
  • A. Ketan Mehta chosen
    Ketan Mehta is an Indian film director known for his socially and politically charged cinema, including acclaimed works in both mainstream and parallel Bollywood.
  • B. Govind Nihalani
    Govind Nihalani is an acclaimed Indian cinematographer and filmmaker known for his work in parallel cinema and socially conscious films.
  • C. Hansal Mehta
    Hansal Mehta is an Indian filmmaker and director known for critically acclaimed films and series such as "Shahid," "Aligarh," and "Scam 1992."
  • D. Hrishikesh Mukherjee
    Hrishikesh Mukherjee was a celebrated Indian film director and editor, best known for his warm, middle-class family dramas and comedies in Hindi cinema from the 1960s to the 1980s.
  • E. M.S. Sathyu
    M.S. Sathyu is an acclaimed Indian film director best known for his socially conscious and politically charged works, including the landmark film "Garm Hava," which helped define the parallel cinema movement.
  • 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_69e11e2db934819095556760c7d85e4d completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f127a5e624819082ed5beeb4bc82fa completed April 28, 2026, 9:33 p.m.
Created at: April 16, 2026, 8:22 p.m.