Triple

T16295381
Position Surface form Disambiguated ID Type / Status
Subject Mount Vernon, Texas E395632 entity
Predicate namedAfter P63 FINISHED
Object Mount Vernon, Virginia E3244 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: Mount Vernon, Virginia | Statement: [Mount Vernon, Texas, namedAfter, Mount Vernon, Virginia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mount Vernon, Virginia
Context triple: [Mount Vernon, Texas, namedAfter, Mount Vernon, Virginia]
  • A. Mount Vernon, Virginia chosen
    Mount Vernon, Virginia is a historic estate along the Potomac River best known as George Washington’s longtime plantation home and burial site.
  • B. Mount Vernon
    Mount Vernon is a small city in northwestern Washington State that serves as the administrative and commercial hub of Skagit County.
  • C. Mount Vernon
    Mount Vernon is a historic and culturally rich neighborhood in Baltimore, Maryland, known for its 19th-century architecture, arts institutions, and prominent monuments.
  • D. Mount Vernon
    Mount Vernon is a small city in eastern Iowa known for being home to Cornell College and its historic, tree-lined downtown.
  • E. Mount Vernon
    Mount Vernon is a small city in Posey County, Indiana, situated along the Ohio River and known for its regional industrial and agricultural activities.
  • 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_69d87f22c7248190a54c949738441e2e completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e25e2d08108190bab1b3325923af1d completed April 17, 2026, 4:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a001f9965b8819080278ccef15288aa completed May 10, 2026, 6:03 a.m.
Created at: April 10, 2026, 5:06 a.m.