Triple

T14398011
Position Surface form Disambiguated ID Type / Status
Subject Multiplicity E357000 entity
Predicate character P662 FINISHED
Object Laura Kinney E735265 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: Laura Kinney | Statement: [Multiplicity, character, Laura Kinney]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Laura Kinney
Context triple: [Multiplicity, character, Laura Kinney]
  • A. Laura Kinney chosen
    Laura Kinney, also known as X-23, is a Marvel Comics mutant and clone-daughter of Wolverine who becomes a prominent member of the X-Men.
  • B. Roxanne Kowalski
    Roxanne Kowalski is the intelligent and kind-hearted female lead in the 1987 romantic comedy film "Roxanne," portrayed by Daryl Hannah opposite Steve Martin.
  • C. Rachel Kane
    Rachel Kane is a key CIA operative and mission handler in the video game Call of Duty: Black Ops III, guiding and assisting the player throughout much of the campaign.
  • D. Jennifer Kale
    Jennifer Kale is a powerful Marvel Comics sorceress often associated with occult storylines and supernatural teams like the Midnight Sons.
  • E. Virginia Troy
    Virginia Troy is a central female character in Evelyn Waugh’s "Sword of Honour" trilogy, representing moral integrity and emotional resilience amid the absurdities and tragedies of World War II.
  • 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_69d827927c988190ad98bb0360981783 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de9083f9d081908fe5c99655c410b3 completed April 14, 2026, 7:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd551cbdb08190a9ea53e607f2555b completed May 8, 2026, 3:14 a.m.
Created at: April 10, 2026, 1:17 a.m.