Triple

T10803872
Position Surface form Disambiguated ID Type / Status
Subject Chandrabose (lyricist) E254912 entity
Predicate industry P71 FINISHED
Object Telugu film industry E31774 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: Telugu film industry | Statement: [Chandrabose (lyricist), industry, Telugu film industry]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Telugu film industry
Context triple: [Chandrabose (lyricist), industry, Telugu film industry]
  • A. Tollywood film industry chosen
    The Tollywood film industry is the segment of Indian cinema that produces movies in the Telugu language, primarily based in Hyderabad.
  • B. Tollywood
    Tollywood is the Bengali-language film industry based primarily in Kolkata, India, known for its rich artistic and literary cinematic tradition.
  • C. Pollywood
    Pollywood is the regional film industry based in the Indian state of Punjab, producing Punjabi-language movies and entertainment content.
  • D. Kannada cinema
    Kannada cinema is the segment of Indian film industry that produces movies in the Kannada language, primarily based in the state of Karnataka.
  • 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 (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_69d6aa61c15c8190a1839550c56e75e1 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d73370e7388190885b104fc883456e completed April 9, 2026, 5:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69de567a7ea0819088a2fa10f8367d89 completed April 14, 2026, 3 p.m.
Created at: April 8, 2026, 9:18 p.m.