Triple

T6831709
Position Surface form Disambiguated ID Type / Status
Subject Wilhelm Grimm E157151 entity
Predicate workSubject P7040 FINISHED
Object Snow White E357344 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: Snow White | Statement: [Wilhelm Grimm, workSubject, Snow White]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Snow White
Context triple: [Wilhelm Grimm, workSubject, Snow White]
  • A. Snowy White
    Snowy White is a British blues and rock guitarist known for his work with Thin Lizzy, Pink Floyd-related projects, and his own solo career.
  • B. Cinderella
    Cinderella is a musical by Andrew Lloyd Webber that reimagines the classic fairy tale with a modern twist in story, character, and score.
  • C. Rapunzel
    Rapunzel is a classic fairy-tale princess best known for her extraordinarily long hair and her story of captivity in a tower and eventual escape.
  • D. The Sleeping Beauty
    The Sleeping Beauty is a landmark classical ballet, originally choreographed by Marius Petipa to Tchaikovsky’s score, renowned for its grand style, technical precision, and prominence in the Mariinsky Theatre tradition.
  • E. Blancanieves chosen
    Blancanieves is a 2012 Spanish silent black-and-white fantasy drama film that reimagines the Snow White fairy tale in 1920s Spain.
  • 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_69c6882a5b5c8190917a7db9ed36bad1 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d62992908190996efab71cbf70f0 completed March 27, 2026, 7:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69c723fab0a4819080d7cd9cb4dddd33 completed March 28, 2026, 12:42 a.m.
Created at: March 27, 2026, 2:18 p.m.