Triple

T5524582
Position Surface form Disambiguated ID Type / Status
Subject Dimple Kapadia E144890 entity
Predicate workedIn P1527 FINISHED
Object Bollywood 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: Bollywood | Statement: [Dimple Kapadia, workedIn, Bollywood]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bollywood
Context triple: [Dimple Kapadia, workedIn, Bollywood]
  • 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. 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.
  • C. 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.
  • D. Lollywood
    Lollywood is the Pakistani film industry based in Lahore, historically known for producing Punjabi- and Urdu-language movies.
  • E. Tollywood
    Tollywood is the Bengali-language film industry based primarily in Kolkata, India, known for its rich artistic and literary cinematic tradition.
  • 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_69c008f873a481909b4d9f7e2db3c37d completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c01f874bd081909cccfc25767ee6fa completed March 22, 2026, 4:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69c04cd6ce3c8190ac5ef4c216190266 completed March 22, 2026, 8:11 p.m.
Created at: March 22, 2026, 3:34 p.m.