Triple

T20390443
Position Surface form Disambiguated ID Type / Status
Subject Nikita E498068 entity
Predicate mainProtagonist P9202 FINISHED
Object Nikita Mears 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: Nikita Mears | Statement: [Nikita, mainProtagonist, Nikita Mears]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nikita Mears
Context triple: [Nikita, mainProtagonist, Nikita Mears]
  • A. Nikita Mears chosen
    Nikita Mears is the highly skilled rogue assassin and central protagonist of the action-thriller TV series "Nikita."
  • B. Nikki Alexander
    Nikki Alexander is a central forensic pathologist character in the British crime drama television series "Silent Witness."
  • C. Makenzy Doniak
    Makenzy Doniak is an American professional soccer forward known for her standout collegiate career at the University of Virginia and subsequent play in the National Women's Soccer League.
  • D. Kayla Fenech
    Kayla Fenech is the daughter of Australian former world champion boxer Jeff Fenech.
  • E. Mikaela Banes
    Mikaela Banes is a skilled, street-smart mechanic and the primary human female protagonist in the early live-action Transformers films, portrayed by Megan Fox.
  • 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_69e0b4a71ebc8190b153a36c738730f4 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6790f8d9c819093038f6bb6f47a92 completed April 20, 2026, 7:05 p.m.
Created at: April 16, 2026, 11:28 a.m.