Triple

T21110851
Position Surface form Disambiguated ID Type / Status
Subject Girish Kasaravalli E520165 entity
Predicate activeIn P1560 FINISHED
Object Kannada 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: Kannada cinema | Statement: [Girish Kasaravalli, activeIn, Kannada cinema]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kannada cinema
Context triple: [Girish Kasaravalli, activeIn, Kannada cinema]
  • A. Kannada cinema chosen
    Kannada cinema is the segment of Indian film industry that produces movies in the Kannada language, primarily based in the state of Karnataka.
  • B. Pollywood
    Pollywood is the regional film industry based in the Indian state of Punjab, producing Punjabi-language movies and entertainment content.
  • C. Mollywood
    Mollywood is the Malayalam-language film industry based in the Indian state of Kerala, known for its content-driven cinema and strong storytelling traditions.
  • D. Tollywood
    Tollywood is the Bengali-language film industry based primarily in Kolkata, India, known for its rich artistic and literary cinematic tradition.
  • E. South Indian cinema
    South Indian cinema is the collective film industry of the southern states of India, encompassing Tamil, Telugu, Malayalam, and Kannada films known for their distinctive storytelling, music, and star-driven productions.
  • 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_69e0b509a318819092fbbcb21d1fe603 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e72101f7308190beb202a052ff04d2 completed April 21, 2026, 7:02 a.m.
Created at: April 16, 2026, 2:54 p.m.