Triple

T236768
Position Surface form Disambiguated ID Type / Status
Subject Bengal E4840 entity
Predicate knownFor P22 FINISHED
Object Bengali cinema E5055 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: Bengali cinema | Statement: [Bengal, knownFor, Bengali cinema]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bengali cinema
Context triple: [Bengal, knownFor, Bengali cinema]
  • A. Punjabi cinema
    Punjabi cinema is the film industry that produces motion pictures in the Punjabi language, primarily based in the Punjab regions of India and Pakistan.
  • 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. Bengali chosen
    Bengali is an Indo-Aryan language spoken primarily in the Bengal region of South Asia and serving as the official and most widely used language of Bangladesh and the Indian state of West Bengal.
  • D. Star of India
    The Star of India is a historic emblem and chivalric order created by the British Crown to honor loyalty and service among princes and officials in colonial India.
  • E. Odia
    Odia is an Indo-Aryan language spoken primarily in the Indian state of Odisha, known for its rich literary tradition and classical language status.
  • 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_69a257c3d0708190b0871c4269d273e6 completed Feb. 28, 2026, 2:49 a.m.
NER Named-entity recognition batch_69a25ccc5d548190b505bf1d99db41bd completed Feb. 28, 2026, 3:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3673474548190aea1f43318d15a71 completed Feb. 28, 2026, 10:07 p.m.
Created at: Feb. 28, 2026, 2:53 a.m.