Triple

T7772276
Position Surface form Disambiguated ID Type / Status
Subject Soumitra Chatterjee E179099 entity
Predicate workedWith P398 FINISHED
Object Aparna Sen E173486 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: Aparna Sen | Statement: [Soumitra Chatterjee, workedWith, Aparna Sen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aparna Sen
Context triple: [Soumitra Chatterjee, workedWith, Aparna Sen]
  • A. Aparna Sen chosen
    Aparna Sen is an acclaimed Indian filmmaker, screenwriter, and actress known for her pioneering and nuanced work in Bengali cinema.
  • B. Suchitra Sen
    Suchitra Sen was a legendary Indian film actress renowned for her powerful performances in Bengali cinema and as the first Indian actress to receive an international film award.
  • C. Sharmila Tagore
    Sharmila Tagore is an acclaimed Indian actress known for her influential work in both Bengali art cinema and mainstream Hindi films since the 1960s.
  • D. Gita Sen
    Gita Sen is an Indian actress known for her frequent collaborations with her husband, acclaimed filmmaker Mrinal Sen, in Bengali parallel cinema.
  • E. Smita Patil
    Smita Patil was a critically acclaimed Indian actress known for her powerful performances in parallel cinema during the 1970s and 1980s.
  • 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_69c69f30602c819082ab52cd4af5c592 completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c7046048688190a6cbc64e82b58eca completed March 27, 2026, 10:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8f30ecf808190a19d57a814d45a42 completed March 29, 2026, 9:38 a.m.
Created at: March 27, 2026, 4:11 p.m.