Triple

T13876093
Position Surface form Disambiguated ID Type / Status
Subject Comal County E333586 entity
Predicate bordersOn P224 FINISHED
Object Blanco County E361293 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: Blanco County | Statement: [Comal County, bordersOn, Blanco County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Blanco County
Context triple: [Comal County, bordersOn, Blanco County]
  • A. Blanco County chosen
    Blanco County is a rural county in central Texas known for its scenic Hill Country landscapes, small towns, and outdoor recreation along the Blanco River.
  • B. Greenwood County
    Greenwood County is a county in western South Carolina known for its mix of small-city life, manufacturing, and agricultural communities centered around the city of Greenwood.
  • C. Mitchell County
    Mitchell County is a rural county in west-central Texas known for its ranching, wind energy production, and the city of Colorado City as its county seat.
  • D. Mitchell County
    Mitchell County is a rural county in northern Iowa known for its small farming communities and agricultural landscape.
  • E. Llano County
    Llano County is a rural county in central Texas known for its scenic Hill Country landscapes, granite outcrops, and outdoor recreation around lakes and rivers.
  • 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_69d81c5ced9c8190b0e9bcc6effe5959 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de0be556708190bbcf0b3583f677e3 completed April 14, 2026, 9:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69fda9009ce88190b77c8f02f38107e1 completed May 8, 2026, 9:12 a.m.
Created at: April 9, 2026, 10:15 p.m.