Triple

T744896
Position Surface form Disambiguated ID Type / Status
Subject Seattle metropolitan area E15319 entity
Predicate hasCounty P285 FINISHED
Object Mason County E44266 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: Mason County | Statement: [Seattle metropolitan area, hasCounty, Mason County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mason County
Context triple: [Seattle metropolitan area, hasCounty, Mason County]
  • A. Mason County chosen
    Mason County is a county in western Washington State known for its forests, waterways, and location along the southern reaches of Puget Sound.
  • B. Pike County
    Pike County is a county in west-central Georgia, United States, known for its rural character and location within the Atlanta metropolitan area’s broader region.
  • C. Madison County
    Madison County is a county in central Mississippi, located in the Jackson metropolitan area and known for its rapidly growing suburban communities.
  • D. Lincoln County
    Lincoln County is a sparsely populated rural county in southeastern Nevada known for its vast desert landscapes, public lands, and small ranching communities.
  • E. Sullivan County
    Sullivan County is a rural county in southeastern New York State, known for its Catskill Mountains scenery, outdoor recreation, and historic role as a popular vacation destination.
  • 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_69a49358aa308190adbc9b5a0a2adcf9 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a61217b881908592096b1edacb8a completed March 1, 2026, 8:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69acb2ede6208190ba0857f4b0209fcf completed March 7, 2026, 11:21 p.m.
Created at: March 1, 2026, 7:37 p.m.