Triple

T16528204
Position Surface form Disambiguated ID Type / Status
Subject Tom Alter E401492 entity
Predicate workedWithLanguageIndustry P123895 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: [Tom Alter, workedWithLanguageIndustry, Bollywood]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bollywood
Context triple: [Tom Alter, workedWithLanguageIndustry, 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. Kollywood
    Kollywood is the Tamil-language film industry based in Chennai, India, known for its prolific output of commercial and artistic cinema.
  • 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. Pollywood
    Pollywood is the regional film industry based in the Indian state of Punjab, producing Punjabi-language movies and entertainment content.
  • E. 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.
  • 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: workedWithLanguageIndustry
Context triple: [Tom Alter, workedWithLanguageIndustry, Bollywood]
  • A. hasWorkedInLanguage
    Indicates that an entity has performed work or professional activities using a particular language.
  • B. usesWorkingLanguagesOf
    Indicates that one entity employs or operates using the working languages associated with another entity.
  • C. languagesUsed
    Indicates that one entity uses, employs, or is expressed in one or more languages associated with the other entity.
  • D. workLanguageOfTitle
    Indicates the language in which a specific work or title is expressed or written.
  • E. hasIndustryRole
    Indicates that an entity holds or performs a specific role, function, or position within a particular industry or sector.
  • F. None of above. chosen

Provenance (5 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_69d883838abc8190bc79cb2d41733ce2 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e32ed57be481908625d4c5aab0940c completed April 18, 2026, 7:12 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00a5047a7c8190bac0ac9888547d16 completed May 10, 2026, 3:32 p.m.
PD Predicate disambiguation batch_69e296995d388190b88ebe189dce890d completed April 17, 2026, 8:22 p.m.
PDg Predicate description generation batch_69e2d7f97e548190a474691a152bd8e8 completed April 18, 2026, 1:01 a.m.
Created at: April 10, 2026, 5:14 a.m.