Triple

T5693891
Position Surface form Disambiguated ID Type / Status
Subject Irrfan Khan E125488 entity
Predicate workedInIndustry P17879 FINISHED
Object Bollywood E31769 NE FINISHED

How this triple was built (3 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: [Irrfan Khan, workedInIndustry, Bollywood]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bollywood
Context triple: [Irrfan Khan, workedInIndustry, 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.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: workedInIndustry
Context triple: [Irrfan Khan, workedInIndustry, Bollywood]
  • A. workedAs
    Indicates that an entity held a particular job, role, or position, performing work in that capacity.
  • B. hasWorkedIn chosen
    Indicates that a person has been employed or has performed work within a particular organization, location, or domain for some period of time.
  • C. workedInMedium
    Indicates that an entity carried out work or activities within a particular medium or channel of expression or communication.
  • D. workedUnder
    Indicates that one entity was hierarchically subordinate to and performed work under the supervision or authority of another entity.
  • E. isPartOfIndustry
    Indicates that one entity belongs to, operates within, or is categorized under a particular industry sector.
  • F. None of above.

Provenance (4 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_69c0082bb19c8190823a4facd3cba79b completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c029014588819094a2a0f6f9b66bab completed March 22, 2026, 5:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07dd76f008190970c3b17ec8cbfd8 completed March 22, 2026, 11:40 p.m.
PD Predicate disambiguation batch_69c021c0e0408190ab6c3cd3f907e80f completed March 22, 2026, 5:07 p.m.
Created at: March 22, 2026, 3:44 p.m.