Triple

T19868474
Position Surface form Disambiguated ID Type / Status
Subject National School of Drama E477450 entity
Predicate notableAlumnus P304 FINISHED
Object Pankaj Kapur 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: Pankaj Kapur | Statement: [National School of Drama, notableAlumnus, Pankaj Kapur]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pankaj Kapur
Context triple: [National School of Drama, notableAlumnus, Pankaj Kapur]
  • A. Pankaj Kapur chosen
    Pankaj Kapur is an acclaimed Indian actor and director known for his powerful performances in film, television, and theatre.
  • B. Rajit Kapur
    Rajit Kapur is an Indian actor acclaimed for his nuanced performances in film, television, and theatre, notably in both parallel and mainstream cinema.
  • C. Vikas Khanna
    Vikas Khanna is an acclaimed Indian chef, restaurateur, cookbook author, and filmmaker known for his Michelin-starred cooking and appearances on culinary television shows.
  • D. Deepak Kapur
    Deepak Kapur is a computer scientist known for his influential work in automated reasoning and term rewriting systems.
  • E. Pankaj Tripathi
    Pankaj Tripathi is an acclaimed Indian actor known for his versatile character roles in Hindi films and web series such as Gangs of Wasseypur, Newton, and Mirzapur.
  • 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_69d8e51e7d948190aedbcd6c30361c39 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e658a168288190a2fbb735d1fd30a8 completed April 20, 2026, 4:47 p.m.
Created at: April 10, 2026, 1:51 p.m.