Triple

T9096764
Position Surface form Disambiguated ID Type / Status
Subject Halmstad E218044 entity
Predicate locatedIn P40 FINISHED
Object Halland County E340055 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: Halland County | Statement: [Halmstad, locatedIn, Halland County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Halland County
Context triple: [Halmstad, locatedIn, Halland County]
  • A. Halland County chosen
    Halland County is a coastal county in southwestern Sweden known for its beaches along the Kattegat, agriculture, and proximity to the city of Gothenburg.
  • B. Kalmar County
    Kalmar County is an administrative region in southeastern Sweden that includes parts of the mainland and the island of Öland, known for its coastal landscapes and historical sites.
  • C. Viken county
    Viken county is an administrative region in southeastern Norway that includes several municipalities and borders Sweden and the Oslofjord.
  • D. Rice County
    Rice County is a county in southeastern Minnesota known for its mix of agricultural communities and college towns, including the city of Northfield.
  • E. Whitman County
    Whitman County is a largely rural county in southeastern Washington State known for its agricultural economy and the presence of Washington State University in Pullman.
  • 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_69ca83d9844081908e561e367fda6d45 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cc96b7d0d48190a3b15f35bef087e3 completed April 1, 2026, 3:53 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0479a58c48190acd4a4af21aa01c3 completed April 3, 2026, 11:04 p.m.
Created at: March 30, 2026, 7:15 p.m.