Triple

T14286565
Position Surface form Disambiguated ID Type / Status
Subject Chantho E354190 entity
Predicate employer P7 FINISHED
Object Professor Yana E354195 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: Professor Yana | Statement: [Chantho, employer, Professor Yana]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Professor Yana
Context triple: [Chantho, employer, Professor Yana]
  • A. Professor Yana chosen
    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.
  • B. Professor Siletsky
    Professor Siletsky is a Nazi spy and antagonist in the 1942 satirical film "To Be or Not to Be."
  • C. Professor Utonium
    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.
  • D. Professor LeBlanc
    Professor LeBlanc is a recurring comedic character from the classic American radio and television series "The Jack Benny Program."
  • E. 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.
  • 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_69d8278e17088190b328c5a9d4be74ff completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de697ef40c8190bea37724b28c2e99 completed April 14, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd3d1c4d988190b595e6a33ef96c28 completed May 8, 2026, 1:32 a.m.
Created at: April 10, 2026, 1:11 a.m.