Triple

T11794713
Position Surface form Disambiguated ID Type / Status
Subject Fables E280474 entity
Predicate mainCharacter P1183 FINISHED
Object Prince Charming E62934 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: Prince Charming | Statement: [Fables, mainCharacter, Prince Charming]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Prince Charming
Context triple: [Fables, mainCharacter, Prince Charming]
  • A. Prince Charming chosen
    Prince Charming is the idealized fairytale prince known for rescuing and marrying Cinderella in the classic Disney story.
  • B. Prince Hans
    Prince Hans is the charming yet treacherous antagonist from Disney's Frozen, who deceitfully schemes to seize control of the kingdom of Arendelle.
  • C. Prince Eric
    Prince Eric is the brave and kind-hearted human prince who becomes Ariel’s love interest in Disney’s animated film "The Little Mermaid."
  • D. Prince Humperdinck
    Prince Humperdinck is the scheming, cowardly prince and primary antagonist in the fantasy romance film and novel "The Princess Bride."
  • E. The Grand Duke (Disney's "Cinderella")
    The Grand Duke in Disney's "Cinderella" is the King’s fussy, long-suffering advisor tasked with organizing the royal ball and later tracking down the mysterious girl who captured the Prince’s heart.
  • 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_69d6ab258b808190b1735835c841e3a4 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a5a082d08190a42541396a06ed98 completed April 10, 2026, 7:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69f09115c66c8190b0a3e775bdf575c1 completed April 28, 2026, 10:51 a.m.
Created at: April 8, 2026, 9:42 p.m.