Triple

T10317162
Position Surface form Disambiguated ID Type / Status
Subject Burlington, Colorado E242046 entity
Predicate county P75 FINISHED
Object Kit Carson County E242050 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: Kit Carson County | Statement: [Burlington, Colorado, county, Kit Carson County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kit Carson County
Context triple: [Burlington, Colorado, county, Kit Carson County]
  • A. Kit Carson County chosen
    Kit Carson County is a rural county in the High Plains region of eastern Colorado known for its agricultural economy and historic carousel in the county seat of Burlington.
  • B. Carson County
    Carson County is a rural county in the Texas Panhandle known for its agricultural economy and small-town communities.
  • C. Wasco County
    Wasco County is a county in north-central Oregon known for its historic role in regional transportation and agriculture along the Columbia River.
  • D. Fremont County
    Fremont County is a rural county in the southwestern corner of Iowa known for its agricultural landscape and small communities.
  • E. Fremont County
    Fremont County is a county in central Colorado known for its rugged terrain, Royal Gorge attractions, and the city of Cañon City as its county seat.
  • 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_69d381ac38808190a8ca7457c85b625b completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d35cf8cc819084dd472f22d604be completed April 7, 2026, 9:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69d89f45e8c0819091f9397619354882 completed April 10, 2026, 6:57 a.m.
Created at: April 6, 2026, 11:49 a.m.