Triple

T7730128
Position Surface form Disambiguated ID Type / Status
Subject Srijit Mukherji E175226 entity
Predicate hasWorkedWith P9615 FINISHED
Object Prosenjit Chatterjee E201866 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: Prosenjit Chatterjee | Statement: [Srijit Mukherji, hasWorkedWith, Prosenjit Chatterjee]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Prosenjit Chatterjee
Context triple: [Srijit Mukherji, hasWorkedWith, Prosenjit Chatterjee]
  • A. Prosenjit Chatterjee chosen
    Prosenjit Chatterjee is a leading Indian actor and producer, widely regarded as one of the most prominent and influential stars in contemporary Bengali cinema.
  • B. Subrata Chatterjee
    Subrata Chatterjee is an Indian actor known for his work in Bengali cinema, including roles in classic films of the 1960s and 1970s.
  • C. Barin Ghosh
    Barin Ghosh, also known as Barindra Kumar Ghosh, was an Indian revolutionary associated with the early nationalist and anti-colonial movement against British rule.
  • D. Amar Lahiri
    Amar Lahiri is the father of Pulitzer Prize–winning author Jhumpa Lahiri and a key figure in her Bengali-Indian immigrant family background.
  • E. Shekhar Chatterjee
    Shekhar Chatterjee was an Indian actor known for his work in Bengali cinema and theatre.
  • 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_69c6995e912c81909a49a2657103f786 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c703358cf881909df8496d943d6de7 completed March 27, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69c966100ffc8190873c6246759b0aa0 completed March 29, 2026, 5:49 p.m.
Created at: March 27, 2026, 4:06 p.m.