Triple

T7694237
Position Surface form Disambiguated ID Type / Status
Subject Shah Rukh Khan E174331 entity
Predicate fullName P16 FINISHED
Object Shah Rukh Khan E174331 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: Shah Rukh Khan | Statement: [Shah Rukh Khan, fullName, Shah Rukh Khan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shah Rukh Khan
Context triple: [Shah Rukh Khan, fullName, Shah Rukh Khan]
  • A. Shah Rukh Khan chosen
    Shah Rukh Khan is a hugely influential Indian film actor and producer, often called the "King of Bollywood," known for his prolific career in Hindi cinema and global cultural impact.
  • B. Salman Khan
    Salman Khan is an American educator and entrepreneur best known as the founder of the online learning platform Khan Academy.
  • C. Aamir Khan
    Aamir Khan is a renowned Indian film actor, director, and producer known for his critically acclaimed and socially impactful movies in Bollywood.
  • D. Akshaye Khanna
    Akshaye Khanna is an Indian film actor known for his versatile performances in Hindi cinema across both commercial hits and critically acclaimed dramas.
  • E. Bo Derek
    Bo Derek is an American actress and model best known for her breakout role in the 1979 film "10," which made her a major sex symbol of the late 20th century.
  • 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_69c6995966348190939e6c37ba272c06 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c702459f988190bf7087bf51d5317f completed March 27, 2026, 10:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8aca5f3388190b25e70caa364d712 completed March 29, 2026, 4:37 a.m.
Created at: March 27, 2026, 4:02 p.m.