Triple

T10468624
Position Surface form Disambiguated ID Type / Status
Subject Bride and Prejudice E246867 entity
Predicate castMember P1668 FINISHED
Object Naveen Andrews E49514 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: Naveen Andrews | Statement: [Bride and Prejudice, castMember, Naveen Andrews]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Naveen Andrews
Context triple: [Bride and Prejudice, castMember, Naveen Andrews]
  • A. Naveen Andrews chosen
    Naveen Andrews is a British actor best known for his roles in the television series "Lost" and films such as "The English Patient."
  • B. Jay Chaudhry
    Jay Chaudhry is an Indian-American entrepreneur and billionaire best known as the founder and CEO of the cloud security company Zscaler.
  • C. Asheem Chandna
    Asheem Chandna is a prominent venture capitalist known for investing in and advising leading enterprise technology and cybersecurity startups.
  • D. Harish Patel
    Harish Patel is an Indian character actor known for his extensive work in Hindi cinema and television, as well as for appearing in international projects such as Marvel’s Eternals.
  • E. Rohan Chand
    Rohan Chand is an American child actor best known for his lead role as Mowgli in the film "Mowgli: Legend of the Jungle" and appearances in movies like "Bad Words" and "Lone Survivor."
  • 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_69d381c16c248190a2fe5b471e584e9c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d5092ef810819093a4d1df83aeac09 completed April 7, 2026, 1:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69d89ff1cd948190a1ef331fb810bf26 completed April 10, 2026, 7 a.m.
Created at: April 6, 2026, 12:20 p.m.