Triple

T10544862
Position Surface form Disambiguated ID Type / Status
Subject Cheyenne County, Colorado E248790 entity
Predicate countySeat P383 FINISHED
Object Cheyenne Wells, Colorado E871465 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: Cheyenne Wells, Colorado | Statement: [Cheyenne County, Colorado, countySeat, Cheyenne Wells, Colorado]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cheyenne Wells, Colorado
Context triple: [Cheyenne County, Colorado, countySeat, Cheyenne Wells, Colorado]
  • A. Cheyenne Wells, Colorado chosen
    Cheyenne Wells, Colorado is a small rural town in eastern Colorado that serves as the administrative and commercial hub of Cheyenne County.
  • B. Parker Laramie
    Parker Laramie is an editor known for working on the film "Jockey."
  • C. Kim, Colorado
    Kim, Colorado is a small rural town in southeastern Colorado known for its ranching community and remote High Plains setting.
  • D. Rocky Ford, Colorado
    Rocky Ford, Colorado is a small agricultural city in Otero County best known for its melon production and role in southern Colorado’s farming economy.
  • E. Rosita, Colorado
    Rosita, Colorado is a historic former silver-mining town in Custer County that is now largely a ghost town and unincorporated community.
  • 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_69d381c733c08190ab1dd6239f5f34ae completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d519128cac819086c93f3bab854ac2 completed April 7, 2026, 2:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69d94b23b2988190b536d5ecb76298ff completed April 10, 2026, 7:10 p.m.
Created at: April 6, 2026, 12:33 p.m.