Triple

T10182639
Position Surface form Disambiguated ID Type / Status
Subject Colusa E236824 entity
Predicate county P75 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: [Colusa, county, Colusa County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Colusa County
Context triple: [Colusa, county, 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. Gem County
    Gem County is a county in southwestern Idaho that forms part of the Boise metropolitan area.
  • C. 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.
  • D. Elgin County
    Elgin County is a predominantly rural county in southwestern Ontario, Canada, known for its agricultural communities, Lake Erie shoreline, and the city of St. Thomas as its largest urban center.
  • E. Harney County
    Harney County is a sparsely populated county in southeastern Oregon known for its vast high desert landscapes, ranching, and wildlife refuges.
  • 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_69ca84d7260c8190bfbec36762943f37 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cded3408b88190a7d981a6dcea48d9 completed April 2, 2026, 4:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69d3179b9de88190ba3beb7d6dbf7ad3 completed April 6, 2026, 2:16 a.m.
Created at: March 30, 2026, 9:12 p.m.