Triple

T21662568
Position Surface form Disambiguated ID Type / Status
Subject Rinke Khanna E534628 entity
Predicate name P16 FINISHED
Object Rinke Khanna NE NERFINISHED

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: Rinke Khanna | Statement: [Rinke Khanna, name, Rinke Khanna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rinke Khanna
Context triple: [Rinke Khanna, name, Rinke Khanna]
  • A. Rinke Khanna chosen
    Rinke Khanna is an Indian former film actress and the younger daughter of Bollywood stars Rajesh Khanna and Dimple Kapadia.
  • B. Sharan Narang
    Sharan Narang is a machine learning researcher known for his work on large-scale natural language processing models, including contributions to the development of the T5 transformer architecture.
  • C. Jyoti Bansal
    Jyoti Bansal is an Indian-American entrepreneur and technologist best known for founding the application performance management company AppDynamics, which was acquired by Cisco for billions of dollars.
  • D. Aseem Sinha
    Aseem Sinha is a film editor known for his work on the acclaimed Hindi film "Suraj Ka Satvan Ghoda."
  • E. Janhavi Nilekani
    Janhavi Nilekani is an Indian writer and environmentalist known for her work on water conservation and for being the daughter of Infosys co-founder Nandan Nilekani.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c467e1f48190af2650b19175abc4 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef6c0956e0819093b4794418efe052 completed April 27, 2026, 2 p.m.
Created at: April 16, 2026, 6:36 p.m.