Triple

T1850655
Position Surface form Disambiguated ID Type / Status
Subject Hill Country of Texas E41386 entity
Predicate hasCounty P285 FINISHED
Object Hays County E102985 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: Hays County | Statement: [Hill Country of Texas, hasCounty, Hays County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hays County
Context triple: [Hill Country of Texas, hasCounty, Hays County]
  • A. Hays County chosen
    Hays County is a rapidly growing county in Central Texas, located just southwest of Austin and known for its Hill Country landscapes and suburban communities.
  • B. Gillespie County
    Gillespie County is a central Texas county known for its scenic Hill Country landscapes, German heritage, and the historic town of Fredericksburg.
  • C. Hastings County
    Hastings County is a large, predominantly rural county in eastern Ontario, Canada, known for its forests, lakes, and outdoor recreation opportunities.
  • D. Dougherty County
    Dougherty County is a county in southwestern Georgia best known for encompassing the city of Albany, a regional hub for commerce, education, and healthcare.
  • E. McLennan County
    McLennan County is a county in central Texas best known for encompassing the city of Waco, home to Baylor University.
  • 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_69a88648cd44819093303206d96d76ad completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb066bd6881909c8d6a6b63cb0ee5 completed March 7, 2026, 4:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69afbba693f08190aead3b593f081c62 completed March 10, 2026, 6:35 a.m.
Created at: March 4, 2026, 7:33 p.m.