Triple

T16659916
Position Surface form Disambiguated ID Type / Status
Subject Neil K. Garg E404827 entity
Predicate doctoralAdvisor P167 FINISHED
Object Brian Stoltz E479985 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: Brian Stoltz | Statement: [Neil K. Garg, doctoralAdvisor, Brian Stoltz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brian Stoltz
Context triple: [Neil K. Garg, doctoralAdvisor, Brian Stoltz]
  • A. Kevin Stacom
    Kevin Stacom is a former American professional basketball guard best known for his college career at Providence and for playing in the NBA during the 1970s, including winning a championship with the Boston Celtics.
  • B. Steve Blum
    Steve Blum is a prolific American voice actor best known for his deep, distinctive performances in anime, video games, and animated series, including roles like Spike Spiegel in "Cowboy Bebop" and Wolverine in various Marvel projects.
  • C. Brian M. Stoltz chosen
    Brian M. Stoltz is an American organic chemist known for his work in synthetic methodology and complex natural product synthesis.
  • D. Mike Vogel
    Mike Vogel is an American actor known for his roles in films like "Cloverfield" and "The Help" as well as TV series such as "Under the Dome."
  • E. Matt Dabner
    Matt Dabner is a film industry professional associated with the Australian production company Blue-Tongue Films, known for its work in independent cinema.
  • 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_69d8838b5fbc81908c6575c132b82e80 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e37bfe0fb081909f2de38df0ed59d7 completed April 18, 2026, 12:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0084ce7cf0819091e7a4de2cc010ea completed May 10, 2026, 1:14 p.m.
Created at: April 10, 2026, 5:18 a.m.