Triple

T12978113
Position Surface form Disambiguated ID Type / Status
Subject Glenn County E321581 entity
Predicate borderedBy P224 FINISHED
Object Colusa County E124422 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: Colusa County | Statement: [Glenn County, borderedBy, Colusa County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Colusa County
Context triple: [Glenn County, borderedBy, Colusa County]
  • A. Colusa County chosen
    Colusa County is a rural county in California’s Sacramento Valley known for its agricultural economy and small, historic communities.
  • B. Treasure County
    Treasure County is a sparsely populated rural county in south-central Montana known for its agricultural landscape along the Yellowstone River.
  • C. Gem County
    Gem County is a county in southwestern Idaho that forms part of the Boise metropolitan area.
  • D. Lewis and Clark County
    Lewis and Clark County is a county in west-central Montana, United States, known for encompassing the state capital city of Helena.
  • E. Fillmore County
    Fillmore County is a rural county in southeastern Minnesota known for its rolling farmland, small towns, and karst landscapes with caves and sinkholes.
  • 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_69d80763bd6c819094437da5b20b01d2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e59a4c88190907d05b8d57dae89 completed April 10, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6b8f0315c8190aae5908ba65d5867 completed May 3, 2026, 2:54 a.m.
Created at: April 9, 2026, 8:38 p.m.