Triple

T16443166
Position Surface form Disambiguated ID Type / Status
Subject Ghajini E399357 entity
Predicate starring P1507 FINISHED
Object Pradeep Rawat E850375 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: Pradeep Rawat | Statement: [Ghajini, starring, Pradeep Rawat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pradeep Rawat
Context triple: [Ghajini, starring, Pradeep Rawat]
  • A. Pradeep Rawat chosen
    Pradeep Rawat is an Indian actor best known for his villainous roles in films such as "Ghajini" and for character parts in major Hindi and regional cinema.
  • B. Vijay Maurya
    Vijay Maurya is an Indian actor, writer, and director known for his work in Hindi cinema and web series.
  • C. Raghuveer Chaudhari
    Raghuveer Chaudhari is an acclaimed Indian Gujarati writer and scholar renowned for his influential novels, poetry, and literary criticism.
  • D. Naresh Kumar
    Naresh Kumar is a senior Indian Administrative Service (IAS) officer who has served in top bureaucratic positions in the Government of Delhi.
  • E. Virendra Sharma
    Virendra Sharma is a British Labour Party politician who has served as the Member of Parliament for the London constituency of Ealing Southall.
  • 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_69d87f2c6778819080fcfae53be8f12a completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e32cd8d2988190acb5722a15623319 completed April 18, 2026, 7:03 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0060746c308190b67ff7c4646e10de completed May 10, 2026, 10:39 a.m.
Created at: April 10, 2026, 5:10 a.m.