Triple

T20093223
Position Surface form Disambiguated ID Type / Status
Subject Mount Lincoln E496329 entity
Predicate county P75 FINISHED
Object Park County 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: Park County | Statement: [Mount Lincoln, county, Park County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Park County
Context triple: [Mount Lincoln, county, Park County]
  • A. Park County
    Park County is a rural county in southwestern Montana known for its proximity to Yellowstone National Park and its scenic mountain landscapes.
  • B. Park County
    Park County is a county in northwestern Wyoming known for encompassing portions of Yellowstone National Park and its surrounding mountainous landscapes.
  • C. Custer County
    Custer County is a rural county in south-central Colorado known for its scenic Wet Mountain Valley, ranching heritage, and outdoor recreation opportunities in the surrounding Wet Mountains and Sangre de Cristo Range.
  • D. Custer County
    Custer County is a county in southwestern South Dakota known for its Black Hills scenery, outdoor recreation, and attractions such as Custer State Park and the Crazy Horse Memorial.
  • E. Custer County
    Custer County is a largely rural county in central Nebraska known for its agricultural landscape and small communities.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide. chosen

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_69da626eee3881909f3454986d4a6511 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e66668db8881908c43b1deef9af1d3 completed April 20, 2026, 5:46 p.m.
Created at: April 11, 2026, 11:22 p.m.