Triple

T13068648
Position Surface form Disambiguated ID Type / Status
Subject Nimarata Nikki Randhawa E329394 entity
Predicate middleName P143 FINISHED
Object Nikki E329393 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: Nikki | Statement: [Nimarata Nikki Randhawa, middleName, Nikki]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nikki
Context triple: [Nimarata Nikki Randhawa, middleName, Nikki]
  • A. Nikki
    Nikki is a seductive and ambitious burlesque performer featured as one of the central characters in the musical film "Burlesque."
  • B. Nikki
    Nikki is the estranged wife of Pat Solitano in the film "Silver Linings Playbook," whose separation from him drives much of the movie’s emotional conflict.
  • C. Nikki
    Nikki is the central protagonist of the 1993 coming-of-age sports comedy film "Airborne," known for his laid-back California surfer attitude and exceptional inline skating skills.
  • D. Nikki chosen
    Nikki is the commonly used first name of American politician and former U.S. Ambassador to the United Nations Nikki Haley.
  • E. Nikki
    Nikki is the main character of the 1994 platform video game "Pandemonium!", known for her acrobatic abilities and whimsical, magical adventures.
  • 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_69d80771749c81909a6d9197b9504872 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d980ec8ba48190baf52c7823482680 completed April 10, 2026, 10:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6e26e5d6881908663444bca67b01e completed May 3, 2026, 5:51 a.m.
Created at: April 9, 2026, 9 p.m.