Triple

T16235875
Position Surface form Disambiguated ID Type / Status
Subject Rani Mukerji E394109 entity
Predicate name P16 FINISHED
Object Rani Mukerji E394109 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: Rani Mukerji | Statement: [Rani Mukerji, name, Rani Mukerji]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rani Mukerji
Context triple: [Rani Mukerji, name, Rani Mukerji]
  • A. Rani Mukerji chosen
    Rani Mukerji is an acclaimed Indian film actress known for her versatile performances in numerous successful Hindi movies since the late 1990s.
  • B. Urmila Matondkar
    Urmila Matondkar is an Indian film actress known for her acclaimed performances in Hindi cinema, particularly in the 1990s and early 2000s, and for her later work as a television personality and politician.
  • C. Madhuri Mukherjee
    Madhuri Mukherjee, better known by her stage name Madhabi Mukherjee, is a renowned Indian Bengali film actress celebrated for her work in classic art-house cinema, including collaborations with director Satyajit Ray.
  • D. Kajol
    Kajol is a renowned Indian film actress celebrated for her powerful performances and iconic roles in Hindi cinema since the 1990s.
  • E. Karisma Kapoor
    Karisma Kapoor is an acclaimed Indian film actress best known for her leading roles in popular Hindi movies of the 1990s and early 2000s.
  • 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_69d87f204df88190a8f88923decf9835 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2455abc608190ba3308c15c9e8a23 completed April 17, 2026, 2:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a002d9de7548190974851dc54465f0b completed May 10, 2026, 7:02 a.m.
Created at: April 10, 2026, 5:04 a.m.