Triple

T1963308
Position Surface form Disambiguated ID Type / Status
Subject Doctor Faustus E42633 entity
Predicate hasCharacter P2308 FINISHED
Object the Devil E38619 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 Devil | Statement: [Doctor Faustus, hasCharacter, the Devil]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: the Devil
Context triple: [Doctor Faustus, hasCharacter, the Devil]
  • A. the Devil chosen
    The Devil is a supernatural embodiment of evil and temptation, commonly depicted in religious and literary traditions as a powerful adversary who bargains for human souls.
  • B. Šatan
    Šatan is a Slovak surname most famously borne by Miroslav Šatan, a prominent former professional ice hockey player and national team star.
  • C. The Black Devil
    The Black Devil was the fearsome nickname of Erich Hartmann, the German World War II fighter ace who remains the highest-scoring fighter pilot in history.
  • D. Demons
    The Demons are the nickname of the Melbourne Football Club, one of the oldest and most storied teams in Australian rules football.
  • E. Demons
    Demons is a political and psychological novel by Fyodor Dostoevsky that explores radicalism, moral chaos, and the destructive consequences of nihilist ideology in 19th-century Russia.
  • 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_69a88711151c8190940b2572095059d7 completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb3ac31a08190abaecac8badc52c7 completed March 7, 2026, 5:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69adfbd32eb88190a2069b6490b12e5d completed March 8, 2026, 10:44 p.m.
Created at: March 4, 2026, 7:36 p.m.