Triple

T10778636
Position Surface form Disambiguated ID Type / Status
Subject Naval Support Activity Hampton Roads E254259 entity
Predicate location P40 FINISHED
Object Virginia E5410 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: Virginia | Statement: [Naval Support Activity Hampton Roads, location, Virginia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Virginia
Context triple: [Naval Support Activity Hampton Roads, location, Virginia]
  • A. Virginia chosen
    Virginia is a U.S. state in the Mid-Atlantic and Southeastern regions, known for its pivotal role in American history, including being home to several early presidents and key Revolutionary and Civil War sites.
  • B. Virginia
    Virginia is a small community located within the town of Georgina in Ontario, Canada.
  • C. Virginia
    Virginia is a character in the classic French farce "Il cappello di paglia di Firenze" ("The Florentine Straw Hat"), around whom part of the play’s romantic and comedic misunderstandings revolve.
  • D. Virginia
    Virginia is a coastal township in Montserrado County, Liberia, known for its beaches and proximity to the capital, Monrovia.
  • E. La Virginia
    La Virginia is a municipality in western Colombia known for its location along the Cauca River and its role as a commercial and transport hub in the Risaralda Department.
  • 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_69d6aa609f008190a294200aefcb7bd5 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d732c25478819093dedec0c8556777 completed April 9, 2026, 5:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69dbdbc3780c819092337924e2ae90f8 completed April 12, 2026, 5:52 p.m.
Created at: April 8, 2026, 9:16 p.m.