Triple

T15204450
Position Surface form Disambiguated ID Type / Status
Subject Llano County E363353 entity
Predicate borders P224 FINISHED
Object San Saba County E689201 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: San Saba County | Statement: [Llano County, borders, San Saba County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: San Saba County
Context triple: [Llano County, borders, San Saba County]
  • A. San Saba County chosen
    San Saba County is a rural county in central Texas known for its ranching, pecan production, and small-town communities.
  • B. Mayes County
    Mayes County is a county in northeastern Oklahoma known for its mix of small towns, agricultural areas, and recreational lakes.
  • C. Fisher County
    Fisher County is a rural county in west-central Texas known for its agricultural economy and small, sparsely populated communities.
  • D. Harding County
    Harding County is a sparsely populated rural county in northeastern New Mexico known for its ranching landscape and wide-open high plains.
  • E. Briscoe County
    Briscoe County is a rural county in the Texas Panhandle known for its agricultural economy and proximity to the scenic Caprock Canyons region.
  • 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e006b693a48190a6230b7b52bc8cd3 completed April 15, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffb02f505c81908e3982b67456e81c completed May 9, 2026, 10:07 p.m.
Created at: April 10, 2026, 3:11 a.m.