Triple

T10031101
Position Surface form Disambiguated ID Type / Status
Subject Night Time, My Time E204852 entity
Predicate hasTrack P3284 FINISHED
Object Kristine E368433 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: Kristine | Statement: [Night Time, My Time, hasTrack, Kristine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kristine
Context triple: [Night Time, My Time, hasTrack, Kristine]
  • A. Kristine chosen
    Kristine is a feminine given name commonly considered a variant of Christine or Christina, used in various European and English-speaking countries.
  • B. Kristin
    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 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."
  • D. Kristin
    Kristin is one of the official mascots of the 1994 Winter Olympics held in Lillehammer, Norway.
  • E. Kristin
    Kristin is the central female protagonist of Sigrid Undset’s historical novel trilogy "Kristin Lavransdatter," set in medieval Norway.
  • 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_69ca834d77188190ad645e33e8ca3200 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cdcde7ec088190845656cc2529c771 completed April 2, 2026, 2:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2823e63488190bef7633b1a755df8 completed April 5, 2026, 3:39 p.m.
Created at: March 30, 2026, 8:54 p.m.