Triple

T14703232
Position Surface form Disambiguated ID Type / Status
Subject Alan Tudyk E345359 entity
Predicate voicedCharacter P2000 FINISHED
Object King Candy E834620 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: King Candy | Statement: [Alan Tudyk, voicedCharacter, King Candy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: King Candy
Context triple: [Alan Tudyk, voicedCharacter, King Candy]
  • A. King Candy chosen
    King Candy is the main antagonist of Disney's animated film "Wreck-It Ralph," a duplicitous ruler of the Sugar Rush game world who hides a dark secret about his true identity.
  • B. Candy Kong
    Candy Kong is a recurring character in the Donkey Kong video game series, typically portrayed as Donkey Kong’s love interest and a friendly, music-loving Kong who often provides save points or support to the player.
  • C. Candy Kingdom Guard
    The Candy Kingdom Guard is the royal security force responsible for protecting Princess Bubblegum and maintaining order within the Candy Kingdom in the animated series "Adventure Time."
  • D. Sugar Rush
    Sugar Rush is a candy-themed kart racing video game world featured in Disney's animated film "Wreck-It Ralph."
  • E. Sugar Rush
    Sugar Rush is a British television drama series, based on Julie Burchill’s novel, that follows the coming-of-age and romantic entanglements of a teenage girl in Brighton.
  • 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_69d822e4a8c08190a155df736bb7bc13 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb6071e5c8190bb5509c859135c2d completed April 14, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe24a996708190834733bfc669c3d3 completed May 8, 2026, 6 p.m.
Created at: April 10, 2026, 1:28 a.m.