Triple

T21110941
Position Surface form Disambiguated ID Type / Status
Subject Nihalani E520167 entity
Predicate industry P71 FINISHED
Object Indian film industry 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: Indian film industry | Statement: [Nihalani, industry, Indian film industry]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Indian film industry
Context triple: [Nihalani, industry, Indian film industry]
  • A. Indian cinema chosen
    Indian cinema is the diverse and prolific film industry of India, encompassing multiple regional and language-based film sectors and producing some of the world's highest-volume and most influential movies.
  • B. Bollywood cinema
    Bollywood cinema is the mainstream Hindi-language film industry based in Mumbai, India, known for its song-and-dance musicals, melodrama, and massive cultural influence across South Asia and the global Indian diaspora.
  • C. Nollywood
    Nollywood is Nigeria’s prolific film industry, renowned as one of the largest movie producers in the world and a major cultural force across Africa.
  • D. Kollywood
    Kollywood is the Tamil-language film industry based in Chennai, India, known for its prolific output of commercial and artistic cinema.
  • E. Pollywood
    Pollywood is the regional film industry based in the Indian state of Punjab, producing Punjabi-language movies and entertainment content.
  • 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_69e0b509a318819092fbbcb21d1fe603 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e72101f7308190beb202a052ff04d2 completed April 21, 2026, 7:02 a.m.
Created at: April 16, 2026, 2:54 p.m.