Triple

T16527965
Position Surface form Disambiguated ID Type / Status
Subject Rajkumar Santoshi E401487 entity
Predicate hasWorkedWith P9615 FINISHED
Object Karisma Kapoor E262551 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: Karisma Kapoor | Statement: [Rajkumar Santoshi, hasWorkedWith, Karisma Kapoor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Karisma Kapoor
Context triple: [Rajkumar Santoshi, hasWorkedWith, Karisma Kapoor]
  • A. Karisma Kapoor chosen
    Karisma Kapoor is an acclaimed Indian film actress best known for her leading roles in popular Hindi movies of the 1990s and early 2000s.
  • B. Kajol
    Kajol is a renowned Indian film actress celebrated for her powerful performances and iconic roles in Hindi cinema since the 1990s.
  • C. Seema Kapoor
    Seema Kapoor is an Indian television and film actress and director, known for her work in Hindi entertainment and her marriage to the late actor Om Puri.
  • D. Sridevi
    Sridevi was a legendary Indian actress celebrated for her versatile performances across Tamil, Hindi, and other regional cinemas, and is widely regarded as one of the greatest and most influential actresses in Indian film history.
  • E. Rani Mukerji
    Rani Mukerji is an acclaimed Indian film actress known for her versatile performances in numerous successful Hindi movies since the late 1990s.
  • 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_69d883838abc8190bc79cb2d41733ce2 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e32ed57be481908625d4c5aab0940c completed April 18, 2026, 7:12 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0067a7616c8190af486bef3331e115 completed May 10, 2026, 11:10 a.m.
Created at: April 10, 2026, 5:14 a.m.