Triple

T17243525
Position Surface form Disambiguated ID Type / Status
Subject Zala County E418562 entity
Predicate borders P224 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: [Zala County, borders, Vas County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vas County
Context triple: [Zala County, borders, 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_69d886d8e96081909870bff6c3d0bf09 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e42e21bb5c8190ad960f231fe54665 completed April 19, 2026, 1:21 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0170f388608190b709b1c228a7ba29 completed May 11, 2026, 6:02 a.m.
Created at: April 10, 2026, 5:39 a.m.