Triple

T14497487
Position Surface form Disambiguated ID Type / Status
Subject Brian A. Benczkowski E359539 entity
Predicate name P16 FINISHED
Object Brian A. Benczkowski 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: Brian A. Benczkowski | Statement: [Brian A. Benczkowski, name, Brian A. Benczkowski]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brian A. Benczkowski
Context triple: [Brian A. Benczkowski, name, Brian A. Benczkowski]
  • A. Brian A. Benczkowski chosen
    Brian A. Benczkowski is an American lawyer and former senior U.S. Department of Justice official who led the Criminal Division under the Trump administration.
  • B. Christopher D. Lozinski
    Christopher D. Lozinski is a film editor known for his work on the animated superhero movie "Batman: The Killing Joke" (2016).
  • C. Michael E. Bakich
    Michael E. Bakich is an American astronomy writer, editor, and popularizer of observational astronomy, long associated with Astronomy magazine.
  • D. Craig A. Stough
    Craig A. Stough is an American local government leader who serves as the mayor of Sylvania, Ohio.
  • E. Stephen M. Kellen
    Stephen M. Kellen was a prominent financier and philanthropist known for his leadership at Arnhold and S. Bleichroeder and his significant support of cultural and educational institutions.
  • 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_69d8279740308190af9df93a3af8592e completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de9311cc748190880c784f173b7f2b completed April 14, 2026, 7:18 p.m.
Created at: April 10, 2026, 1:21 a.m.