Triple

T2643991
Position Surface form Disambiguated ID Type / Status
Subject Disney Princess franchise E62941 entity
Predicate notableCharacter P1481 FINISHED
Object Jasmine E285209 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: Jasmine | Statement: [Disney Princess franchise, notableCharacter, Jasmine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jasmine
Context triple: [Disney Princess franchise, notableCharacter, Jasmine]
  • A. Jasmine chosen
    Jasmine is the independent and strong-willed princess of Agrabah from Disney's Aladdin, known for challenging tradition and seeking freedom beyond palace walls.
  • B. Rosa
    Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
  • C. Rosa
    Rosa is the birth name of Linda Christian, a Mexican film actress known as the first "Bond girl" for her role in the 1954 television adaptation of Casino Royale.
  • D. Hoa
    The Hoa are an ethnic Chinese minority community in Vietnam, particularly concentrated in the Mekong Delta and major urban centers.
  • E. Hana
    Hana is a compassionate Canadian army nurse in Michael Ondaatje's novel "The English Patient," who cares for a badly burned man in an abandoned Italian villa during 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_69ab4c3f2dcc819082df80f5e032f690 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abd90046dc81908bab3440733f1e98 completed March 7, 2026, 7:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69afa050cc408190b2fa81a0f2da06eb completed March 10, 2026, 4:38 a.m.
Created at: March 6, 2026, 9:53 p.m.