Triple

T22012355
Position Surface form Disambiguated ID Type / Status
Subject Rang Rasiya E543607 entity
Predicate castMember P1668 FINISHED
Object Randeep Hooda 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: Randeep Hooda | Statement: [Rang Rasiya, castMember, Randeep Hooda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Randeep Hooda
Context triple: [Rang Rasiya, castMember, Randeep Hooda]
  • A. Randeep Hooda chosen
    Randeep Hooda is an Indian film actor known for his intense performances in Hindi cinema across critically acclaimed and commercially successful films.
  • B. Sameer Saran
    Sameer Saran is an Indian businessman best known as the husband of actress Rinke Khanna, daughter of Bollywood stars Rajesh Khanna and Dimple Kapadia.
  • 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. 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.
  • E. Anil Kapoor
    Anil Kapoor is a veteran Indian actor and producer known for his work in Hindi cinema and international films, recognized for his energetic screen presence and roles in movies like "Mr. India," "Dil Dhadakne Do," and the series "24."
  • 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_69e11e2db934819095556760c7d85e4d completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f127a5e624819082ed5beeb4bc82fa completed April 28, 2026, 9:33 p.m.
Created at: April 16, 2026, 8:22 p.m.