Triple

T6969379
Position Surface form Disambiguated ID Type / Status
Subject Mythri Movie Makers E161563 entity
Predicate collaboratedWith P435 FINISHED
Object Chiranjeevi E161559 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: Chiranjeevi | Statement: [Mythri Movie Makers, collaboratedWith, Chiranjeevi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Chiranjeevi
Context triple: [Mythri Movie Makers, collaboratedWith, Chiranjeevi]
  • A. Chiranjeevi chosen
    Chiranjeevi is a legendary Indian film actor and former politician, widely regarded as one of the biggest and most influential stars in Telugu cinema.
  • B. Allu Venkatesh
    Allu Venkatesh is an Indian film producer and member of the prominent Allu family in the Telugu cinema industry.
  • C. Vijayakanth
    Vijayakanth is an Indian actor-turned-politician best known for his leading roles in Tamil cinema and for founding the Desiya Murpokku Dravida Kazhagam (DMDK) party.
  • D. Rajinikanth
    Rajinikanth is an iconic Indian film actor and cultural phenomenon, best known for his charismatic performances and larger-than-life roles primarily in Tamil cinema.
  • E. Allu Aravind
    Allu Aravind is a prominent Indian film producer and distributor, best known for founding the production company Geetha Arts and producing numerous successful Telugu and Hindi films.
  • 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_69c68853cff881908439d488924a8283 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6db152b2081909271493a5d1469fb completed March 27, 2026, 7:31 p.m.
NED1 Entity disambiguation (via context triple) batch_69c794318fb08190a9a89570b2a6999b completed March 28, 2026, 8:41 a.m.
Created at: March 27, 2026, 2:30 p.m.