Triple

T3547472
Position Surface form Disambiguated ID Type / Status
Subject South Estonian language E75030 entity
Predicate primaryRegion P1103 FINISHED
Object Valga County E367592 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: Valga County | Statement: [South Estonian language, primaryRegion, Valga County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Valga County
Context triple: [South Estonian language, primaryRegion, Valga County]
  • A. Saare County
    Saare County is a county in western Estonia that encompasses the islands of Saaremaa and several smaller islands in the Baltic Sea.
  • B. Hiiu County
    Hiiu County is an administrative region of Estonia encompassing the island of Hiiumaa and its surrounding islets in the Baltic Sea.
  • C. Põlva County chosen
    Põlva County is a rural administrative region in southeastern Estonia known for its forests, lakes, and strong South Estonian cultural and linguistic heritage.
  • D. Harju County
    Harju County is a northern Estonian county on the Gulf of Finland that includes the nation’s capital, Tallinn, and serves as its main political and economic hub.
  • E. Upson County
    Upson County is a county in central Georgia, United States, known for its seat in Thomaston and its mix of rural communities and small-town industry.
  • 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_69ad85d33c6c819081d5ac1df13b5680 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbfd0eb6081908f1380db4cfade87 completed March 8, 2026, 6:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3bb8ec8948190a0ae799ad20f42b3 completed March 13, 2026, 7:23 a.m.
Created at: March 8, 2026, 3:20 p.m.