Triple

T17945069
Position Surface form Disambiguated ID Type / Status
Subject Buttercup E448682 entity
Predicate guardian P28704 FINISHED
Object Professor Utonium NE NERFINISHED

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: Professor Utonium | Statement: [Buttercup, guardian, Professor Utonium]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Professor Utonium
Context triple: [Buttercup, guardian, Professor Utonium]
  • A. Professor Utonium chosen
    Professor Utonium is the kind-hearted scientist and father figure who accidentally created and now cares for the superhero trio known as the Powerpuff Girls.
  • B. Professor Yana
    Professor Yana is a human guise adopted by the Master in the Doctor Who television story "Utopia," later revealed as a Time Lord and one of the Doctor’s greatest enemies.
  • C. Professor Marius
    Professor Marius is a character in the Doctor Who universe known as the scientist who created the robotic dog K-9.
  • D. Professor Nemur
    Professor Nemur is the ambitious but ethically conflicted scientist in "Flowers for Algernon" who oversees the experimental intelligence-enhancing surgery on the protagonist, Charlie Gordon.
  • E. Professor Unrat
    Professor Unrat is a satirical novel by Heinrich Mann that critiques bourgeois morality through the story of a repressed schoolteacher whose obsession with a cabaret singer leads to his downfall.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9f8cca8819099836916c56b7c95 completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e4ad990b948190a5e6fd50a15f64e3 completed April 19, 2026, 10:25 a.m.
Created at: April 10, 2026, 10:21 a.m.