Triple

T2022346
Position Surface form Disambiguated ID Type / Status
Subject Jurassic World E44132 entity
Predicate starring P1507 FINISHED
Object Irrfan Khan E125488 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: Irrfan Khan | Statement: [Jurassic World, starring, Irrfan Khan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Irrfan Khan
Context triple: [Jurassic World, starring, Irrfan Khan]
  • A. Irrfan Khan chosen
    Irrfan Khan was a critically acclaimed Indian actor known for his powerful performances in both Bollywood and international films such as "The Namesake," "Slumdog Millionaire," and "Life of Pi."
  • B. Sanjeev Bhaskar
    Sanjeev Bhaskar is a British comedian, actor, and writer best known for his work on the sketch show "Goodness Gracious Me" and the sitcom "The Kumars at No. 42."
  • 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. Himesh Patel
    Himesh Patel is a British actor best known for his breakout lead role in the film "Yesterday" and supporting performances in major productions like "Tenet" and the series "Station Eleven."
  • E. Anupam Kher
    Anupam Kher is an acclaimed Indian actor known for his extensive work in Hindi cinema and notable roles in international films.
  • 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_69a8891201bc8190aca837be6de41579 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb8efbe148190901d3650aa60408a completed March 7, 2026, 5:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae0af7e5b0819088e2221ddc0a38ce completed March 8, 2026, 11:49 p.m.
Created at: March 4, 2026, 7:38 p.m.