Triple

T19216056
Position Surface form Disambiguated ID Type / Status
Subject Whit Babcock E480487 entity
Predicate employer P7 FINISHED
Object Virginia Tech 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: Virginia Tech | Statement: [Whit Babcock, employer, Virginia Tech]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Virginia Tech
Context triple: [Whit Babcock, employer, Virginia Tech]
  • A. Virginia Tech chosen
    Virginia Tech is a major public research university in Blacksburg, Virginia, known for its strong engineering programs, large campus community, and prominent athletics.
  • B. Virginia Commonwealth University
    Virginia Commonwealth University is a large public research university known for its diverse academic programs and strong arts and health sciences disciplines.
  • C. Virginia State University
    Virginia State University is a public historically Black land-grant university located in Petersburg, Virginia.
  • D. Old Dominion University
    Old Dominion University is a public research university known for its diverse academic programs and coastal campus in Norfolk, Virginia.
  • E. North Carolina State University
    North Carolina State University is a major public research university in Raleigh, North Carolina, known for its strong engineering, agriculture, and STEM programs and as a key member of the Research Triangle.
  • 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_69d8e8cb8c348190b52075823911c869 completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e5fa3a417c819083e2e276d44d4d89 completed April 20, 2026, 10:04 a.m.
Created at: April 10, 2026, 1:22 p.m.