Triple

T7165307
Position Surface form Disambiguated ID Type / Status
Subject Nayak E167052 entity
Predicate leadActor P1507 FINISHED
Object Uttam Kumar E175228 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: Uttam Kumar | Statement: [Nayak, leadActor, Uttam Kumar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Uttam Kumar
Context triple: [Nayak, leadActor, Uttam Kumar]
  • A. Uttam Kumar chosen
    Uttam Kumar was a legendary Indian actor and cultural icon, widely regarded as the greatest star of Bengali cinema.
  • B. Dulal Dutta
    Dulal Dutta was an Indian film editor best known for his long-standing collaboration with director Satyajit Ray on several landmark Bengali films.
  • C. Soumitra Chatterjee
    Soumitra Chatterjee was a legendary Indian actor best known for his long and acclaimed collaboration with filmmaker Satyajit Ray, particularly in the Apu film series and numerous other Bengali classics.
  • D. Dilip Kumar
    Dilip Kumar was a legendary Indian film actor, celebrated as the "Tragedy King" of Hindi cinema and renowned for his intense, nuanced performances in classic Bollywood films.
  • E. Sanjeev Kumar
    Sanjeev Kumar was a highly acclaimed Indian film actor known for his versatile performances in both mainstream and parallel cinema during the 1960s and 1970s.
  • 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_69c68888c10c819095e0383020225758 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e832d2548190aacff0de80dbc268 completed March 27, 2026, 8:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7fa640f0081909a538d4705ca95bc completed March 28, 2026, 3:57 p.m.
Created at: March 27, 2026, 2:47 p.m.