Triple

T16887040
Position Surface form Disambiguated ID Type / Status
Subject Szombathely E421565 entity
Predicate locatedIn P40 FINISHED
Object Vas County E412769 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: Vas County | Statement: [Szombathely, locatedIn, Vas County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vas County
Context triple: [Szombathely, locatedIn, Vas County]
  • A. Vas County chosen
    Vas County is an administrative region in western Hungary known for its historic towns, thermal spas, and proximity to the Austrian and Slovenian borders.
  • B. McKenzie County
    McKenzie County is a sparsely populated county in western North Dakota known for its oil production, ranching, and access to outdoor recreation along Lake Sakakawea and the Badlands.
  • C. Manas County
    Manas County is an administrative division in Xinjiang, China, known for its agricultural production and proximity to Manas Lake.
  • D. Tandora County
    Tandora County is a cadastral land division in New South Wales, Australia, used primarily for property and land title purposes.
  • E. Dawson County
    Dawson County is a rural county in the western part of Texas known for its agriculture and oil production.
  • 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_69d889d470fc8190b4aec199636c0c56 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e3bbc1f42481909dcf595358c23497 completed April 18, 2026, 5:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00c2befaa88190ba83dc17aa66b541 completed May 10, 2026, 5:39 p.m.
Created at: April 10, 2026, 5:29 a.m.