Triple

T17243526
Position Surface form Disambiguated ID Type / Status
Subject Zala County E418562 entity
Predicate borders P224 FINISHED
Object Veszprém County E123785 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: Veszprém County | Statement: [Zala County, borders, Veszprém County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Veszprém County
Context triple: [Zala County, borders, Veszprém County]
  • A. Veszprém County chosen
    Veszprém County is an administrative region in western Hungary known for its historic city of Veszprém and its location along the northern shore of Lake Balaton.
  • B. Heves County
    Heves County is an administrative region in northern Hungary known for its natural landscapes, including part of the Mátra mountain range, and historic towns such as Eger.
  • C. Trencsén County
    Trencsén County was a historic administrative county of the Kingdom of Hungary, located in what is now northwestern Slovakia.
  • D. Pozsony County
    Pozsony County was a historic administrative region of the Kingdom of Hungary centered on the city now known as Bratislava.
  • E. Zala County
    Zala County is an administrative region in southwestern Hungary known for its rolling hills, thermal spas, and proximity to Lake Balaton and the Croatian border.
  • 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.