Triple

T10212989
Position Surface form Disambiguated ID Type / Status
Subject Taal E242375 entity
Predicate starring P1507 FINISHED
Object Amrish Puri E520153 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: Amrish Puri | Statement: [Taal, starring, Amrish Puri]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Amrish Puri
Context triple: [Taal, starring, Amrish Puri]
  • A. Amrish Puri chosen
    Amrish Puri was a renowned Indian actor best known for his powerful villainous roles in Hindi cinema and for playing the iconic antagonist Mola Ram in the film "Indiana Jones and the Temple of Doom."
  • B. Paresh Rawal
    Paresh Rawal is a renowned Indian actor and comedian celebrated for his versatile performances in Hindi cinema and theatre.
  • 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. Himanshu Rai
    Himanshu Rai was a prominent Indian actor and pioneering film producer-director, best known for co-founding Bombay Talkies and helping shape early Indian cinema.
  • E. Prakash Raj
    Prakash Raj is an acclaimed Indian actor, filmmaker, and producer known for his versatile performances across multiple South Indian film industries and Hindi cinema.
  • 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_69d381ae26c48190985abd0e25ee5d04 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d3aa23bce881909b5deac612ec22cb completed April 6, 2026, 12:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d6a7f6730081908b941eaeb6c00993 completed April 8, 2026, 7:09 p.m.
Created at: April 6, 2026, 11:03 a.m.