Triple

T21909762
Position Surface form Disambiguated ID Type / Status
Subject South Asian cinema E541028 entity
Predicate hasSubindustry P105701 FINISHED
Object Dhallywood (Bangladeshi cinema) NE NERFINISHED

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: Dhallywood (Bangladeshi cinema) | Statement: [South Asian cinema, hasSubindustry, Dhallywood (Bangladeshi cinema)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dhallywood (Bangladeshi cinema)
Context triple: [South Asian cinema, hasSubindustry, Dhallywood (Bangladeshi cinema)]
  • A. Dhallywood chosen
    Dhallywood is the Bangladeshi Bengali-language film industry centered in Dhaka, known for producing mainstream commercial cinema.
  • B. Bengali cinema
    Bengali cinema is the film industry producing movies in the Bengali language, renowned for its rich artistic tradition and influential auteurs such as Satyajit Ray.
  • 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. 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 (2 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_69e0c47c4b9c8190a5586a75f5f36453 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f121d9796881909fc1454a7b61b8fe completed April 28, 2026, 9:08 p.m.
Created at: April 16, 2026, 7:40 p.m.