Triple

T7819116
Position Surface form Disambiguated ID Type / Status
Subject Christine E181083 entity
Predicate hasVariant P455 FINISHED
Object Kristin E608731 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: Kristin | Statement: [Christine, hasVariant, Kristin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kristin
Context triple: [Christine, hasVariant, Kristin]
  • A. Kristin
    Kristin is the central female protagonist of Sigrid Undset’s historical novel trilogy "Kristin Lavransdatter," set in medieval Norway.
  • B. Kristin chosen
    Kristin is a feminine given name commonly used in English-speaking and Scandinavian countries, often considered a variant of Christine or Christina.
  • C. Kristin
    Kristin is one of the official mascots of the 1994 Winter Olympics held in Lillehammer, Norway.
  • D. Kristin
    Kristin is the given name of the acclaimed British-French actress Kristin Scott Thomas, known for her roles in films such as "The English Patient" and "Four Weddings and a Funeral."
  • E. Kristen
    Kristen is a central female character in the romantic comedy film "Think Like a Man," whose love life and personal growth are explored through the movie’s ensemble relationship dynamics.
  • 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_69ca828153f48190bdb27ac46f8e0745 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69caf97247c481908b18287eb7ee0a53 completed March 30, 2026, 10:30 p.m.
NED1 Entity disambiguation (via context triple) batch_69cb5a5c24908190847b612a56a1abf5 completed March 31, 2026, 5:23 a.m.
Created at: March 30, 2026, 4:40 p.m.