Triple

T22023258
Position Surface form Disambiguated ID Type / Status
Subject Blood and Water E543893 entity
Predicate executiveProducer P7225 FINISHED
Object Anita Kapila 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: Anita Kapila | Statement: [Blood and Water, executiveProducer, Anita Kapila]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Anita Kapila
Context triple: [Blood and Water, executiveProducer, Anita Kapila]
  • A. Anita Kapila chosen
    Anita Kapila is a television writer and producer best known for creating the Canadian teen drama series "Blood and Water."
  • B. Anita Bhalla
    Anita Bhalla is a British media executive and former BBC journalist known for her leadership roles in broadcasting and public service in the UK.
  • C. Anita Kanwar
    Anita Kanwar is an Indian actress best known for her acclaimed roles in parallel cinema and television, particularly the landmark TV series "Buniyaad."
  • D. Anita Bhogle
    Anita Bhogle is an Indian sports marketing professional, author, and co-founder of the consultancy Prosearch, known for her work on applying sports-based insights to business and leadership.
  • E. Rukmini Haran
    Rukmini Haran is a devotional Assamese literary work by the saint-scholar Srimanta Sankardev, narrating the story of Rukmini and her marriage to Krishna.
  • 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_69e11e2e8ea4819084210fe06d3a1b8d completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f127c9959481908da6bed356199f75 completed April 28, 2026, 9:34 p.m.
Created at: April 16, 2026, 8:23 p.m.