Triple

T22033529
Position Surface form Disambiguated ID Type / Status
Subject Drishyam 2 E544144 entity
Predicate producer P490 FINISHED
Object Kumar Mangat Pathak 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: Kumar Mangat Pathak | Statement: [Drishyam 2, producer, Kumar Mangat Pathak]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kumar Mangat Pathak
Context triple: [Drishyam 2, producer, Kumar Mangat Pathak]
  • A. Kumar Mangat Pathak chosen
    Kumar Mangat Pathak is an Indian film producer and industry figure known for backing numerous Bollywood projects through his production banner.
  • B. Kumar Mohan
    Kumar Mohan is a film producer best known for his work on the Indian movie "Haasil."
  • C. Madan Lal Khurana
    Madan Lal Khurana was an Indian politician from the Bharatiya Janata Party who played a key role in Delhi’s political landscape and also served in various national-level positions.
  • D. Anant Singh
    Anant Singh is a prominent South African film producer of Indian origin, best known for producing critically acclaimed anti-apartheid and socially conscious films.
  • E. Sahib Singh Verma
    Sahib Singh Verma was an Indian politician from the Bharatiya Janata Party who served in both state and national politics, including a term as a Union Cabinet minister.
  • 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_69e11e2f98c8819083e11eab90942a78 completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f127ef97348190b8dcdcad11694ebe completed April 28, 2026, 9:34 p.m.
Created at: April 16, 2026, 8:24 p.m.