Triple

T8565168
Position Surface form Disambiguated ID Type / Status
Subject Balu Mahendra E202783 entity
Predicate activeIn P1560 FINISHED
Object Hindi cinema E31769 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: Hindi cinema | Statement: [Balu Mahendra, activeIn, Hindi cinema]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hindi cinema
Context triple: [Balu Mahendra, activeIn, Hindi cinema]
  • A. Bollywood cinema chosen
    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.
  • B. Indian cinema
    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.
  • C. Kollywood
    Kollywood is the Tamil-language film industry based in Chennai, India, known for its prolific output of commercial and artistic cinema.
  • D. Pollywood
    Pollywood is the regional film industry based in the Indian state of Punjab, producing Punjabi-language movies and entertainment content.
  • E. 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.
  • 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_69ca8327b0a881908606ff860713964d completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe9d2331881909d92ddde90f580e9 completed March 31, 2026, 3:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69cebb84a6988190ba6852f72c8918ca completed April 2, 2026, 6:55 p.m.
Created at: March 30, 2026, 6:20 p.m.