Triple

T15656732
Position Surface form Disambiguated ID Type / Status
Subject Double Cherry E376460 entity
Predicate usableByCharacter P31799 FINISHED
Object Rosalina E370306 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: Rosalina | Statement: [Double Cherry, usableByCharacter, Rosalina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rosalina
Context triple: [Double Cherry, usableByCharacter, Rosalina]
  • A. Rosalina chosen
    Rosalina is a celestial princess and guardian of the cosmos in Nintendo's Super Mario series, known for caring for the star-like Lumas and piloting the Comet Observatory.
  • B. Rosalinda
    Rosalinda is a feminine given name of Spanish and Italian origin, often interpreted to mean "beautiful rose."
  • C. Rosabella
    Rosabella is the shy, kind-hearted waitress who becomes the central romantic heroine in Frank Loesser’s Broadway musical "The Most Happy Fella."
  • D. Luisa
    Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
  • E. Lisette
    Lisette is a character in Giacomo Puccini's opera "La rondine," serving as the maid and comic counterpart to the heroine, Magda.
  • 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_69d85cd1564c8190991adda63bfab4b0 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04ef1f83c8190bbf65eed162cbd55 completed April 16, 2026, 2:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff997e13e4819080a39f59172ab99c completed May 9, 2026, 8:30 p.m.
Created at: April 10, 2026, 4:15 a.m.