Triple

T21945310
Position Surface form Disambiguated ID Type / Status
Subject Farah Naaz E541917 entity
Predicate workedWith P398 FINISHED
Object Anil Kapoor 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: Anil Kapoor | Statement: [Farah Naaz, workedWith, Anil Kapoor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Anil Kapoor
Context triple: [Farah Naaz, workedWith, Anil Kapoor]
  • A. Anil Kapoor chosen
    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."
  • B. Anupam Kher
    Anupam Kher is an acclaimed Indian actor known for his extensive work in Hindi cinema and notable roles in international films.
  • C. Rishi Kapoor
    Rishi Kapoor was a prominent Indian film actor and director, best known for his romantic lead roles in Hindi cinema from the 1970s onward and as a member of the influential Kapoor film family.
  • D. 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.
  • E. Randeep Hooda
    Randeep Hooda is an Indian film actor known for his intense performances in Hindi cinema across critically acclaimed and commercially successful films.
  • 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_69e0c47e2e5c81909a7f74ce3de50911 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f12427c2b48190949c41bd3be2d9f3 completed April 28, 2026, 9:18 p.m.
Created at: April 16, 2026, 7:56 p.m.