Triple

T11982818
Position Surface form Disambiguated ID Type / Status
Subject Disney Villains E285201 entity
Predicate hasKeyCharacter P70239 FINISHED
Object The Evil Queen E173047 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: The Evil Queen | Statement: [Disney Villains, hasKeyCharacter, The Evil Queen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: The Evil Queen
Context triple: [Disney Villains, hasKeyCharacter, The Evil Queen]
  • A. The Evil Queen chosen
    The Evil Queen is the vain and power-hungry royal villain from Disney’s Snow White, infamous for her jealousy and use of dark magic to eliminate her rival.
  • B. Carabosse
    Carabosse is the malevolent fairy or sorceress who curses Princess Aurora in the classic fairy tale and ballet "The Sleeping Beauty."
  • C. the Witch from Rapunzel
    The Witch from Rapunzel is the powerful, overprotective sorceress who imprisons Rapunzel in a tower and serves as the primary antagonist in the classic fairy tale.
  • D. Mother Gothel
    Mother Gothel is the manipulative, vain antagonist in Disney's "Tangled" who kidnaps Rapunzel to exploit her magical healing hair and maintain her own youth.
  • E. Queen Red
    Queen Red was one of the designated assault sub-sectors of Sword Beach used by Allied forces during the D-Day landings in 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_69d6ab44a77c8190a652f4b27164e4ef completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903973c848190aac871d6dfecc74b completed April 10, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69f49d0791348190a2f6c0e808af3aea completed May 1, 2026, 12:31 p.m.
Created at: April 8, 2026, 9:46 p.m.