Triple

T5693321
Position Surface form Disambiguated ID Type / Status
Subject Chris Messina E125476 entity
Predicate notableWork P4 FINISHED
Object 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: Devil | Statement: [Chris Messina, notableWork, Devil]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Devil
Context triple: [Chris Messina, notableWork, Devil]
  • A. Šatan
    Šatan is a Slovak surname most famously borne by Miroslav Šatan, a prominent former professional ice hockey player and national team star.
  • B. 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.
  • C. Deabolis
    Deabolis was a medieval Balkan town of strategic importance in the Byzantine–Norman conflicts, known as the site where the Treaty of Devol was concluded.
  • D. Shatana
    Shatana is a character from the Nart sagas, the traditional epic cycle of the North Caucasus peoples.
  • E. El Diablo
    El Diablo is the famous nickname of Bolivian football legend Marco Etcheverry, a creative attacking midfielder renowned for his playmaking skills.
  • 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_69c0082bb19c8190823a4facd3cba79b completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c023e7dbe48190850b501f223614e3 completed March 22, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a4f2bfc8190bc56c094f9ae9ce1 completed March 22, 2026, 9:08 p.m.
Created at: March 22, 2026, 3:44 p.m.