Triple

T6084939
Position Surface form Disambiguated ID Type / Status
Subject Usha Mangeshkar E135613 entity
Predicate placeOfActivity 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: [Usha Mangeshkar, placeOfActivity, Bollywood]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bollywood
Context triple: [Usha Mangeshkar, placeOfActivity, 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. Pollywood
    Pollywood is the regional film industry based in the Indian state of Punjab, producing Punjabi-language movies and entertainment content.
  • D. 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.
  • E. Lollywood
    Lollywood is the Pakistani film industry based in Lahore, historically known for producing Punjabi- and Urdu-language movies.
  • 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_69c0087bcc788190b20f093d3a6c60ec completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c057891dc88190997c4e32b2261fb3 completed March 22, 2026, 8:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69c20d4e28b48190bb44675c5c035bd3 completed March 24, 2026, 4:04 a.m.
Created at: March 22, 2026, 4:12 p.m.