Triple

T8388232
Position Surface form Disambiguated ID Type / Status
Subject Sophia Lillis E197873 entity
Predicate characterIn P12208 FINISHED
Object Gretel & Hansel E446886 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: Gretel & Hansel | Statement: [Sophia Lillis, characterIn, Gretel & Hansel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gretel & Hansel
Context triple: [Sophia Lillis, characterIn, Gretel & Hansel]
  • A. Hansel & Gretel chosen
    Hansel & Gretel is a popular ballet adaptation of the classic Brothers Grimm fairy tale about two siblings who outwit a witch in a magical forest.
  • B. Hansel & Gretel: Witch Hunters
    Hansel & Gretel: Witch Hunters is a 2013 dark fantasy action film that reimagines the classic fairy-tale siblings as adult bounty hunters who specialize in tracking and killing witches.
  • C. Holda in Gretel & Hansel
    Holda in "Gretel & Hansel" is the sinister witch who lures children to her enchanted forest home and serves as the film’s primary antagonist.
  • D. Onkel Hans
    Onkel Hans is the jovial, lederhosen-clad mascot character symbolizing the Bavarian-themed spirit of the Kitchener-Waterloo Oktoberfest in Ontario, Canada.
  • E. Klaus
    Klaus is a masculine given name of German origin commonly used in German-speaking countries.
  • 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_69ca82f749388190bffbea6dfb509016 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb81090f688190a3a8d1680383c361 completed March 31, 2026, 8:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce02cb3a1481908d30993d47c70039 completed April 2, 2026, 5:46 a.m.
Created at: March 30, 2026, 6:03 p.m.