Triple

T6712941
Position Surface form Disambiguated ID Type / Status
Subject Hansraj College E153191 entity
Predicate hasNotableAlumnus P51 FINISHED
Object Kirron Kher E392734 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: Kirron Kher | Statement: [Hansraj College, hasNotableAlumnus, Kirron Kher]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kirron Kher
Context triple: [Hansraj College, hasNotableAlumnus, Kirron Kher]
  • A. Kirron Kher chosen
    Kirron Kher is an Indian film and television actress and politician known for her powerful character roles in Hindi cinema and her work as a Member of Parliament.
  • B. Ekta Kapoor
    Ekta Kapoor is a prominent Indian television and film producer known for revolutionizing Hindi soap operas and co-founding Balaji Telefilms.
  • C. Syesha Kapoor
    Syesha Kapoor is the daughter of renowned Indian playback singer Alka Yagnik.
  • D. Riya Sen
    Riya Sen is an Indian actress and model known for her work in Hindi, Bengali, and other regional films, as well as for her prominent presence in Indian popular culture and fashion.
  • E. Rajat Kapoor
    Rajat Kapoor is an Indian actor, writer, and filmmaker known for his work in independent cinema and acclaimed films such as "Bheja Fry," "Mithya," and "Ankhon Dekhi."
  • 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_69c68809b4608190a2509ddb5ab87f05 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d121a92c8190a03f384a8aba84da completed March 27, 2026, 6:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69c700948788819087f9b466be337286 completed March 27, 2026, 10:11 p.m.
Created at: March 27, 2026, 2:07 p.m.