Triple

T5334205
Position Surface form Disambiguated ID Type / Status
Subject Veszprém County E123785 entity
Predicate containsCity P294 FINISHED
Object Balatonfüred E166290 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: Balatonfüred | Statement: [Veszprém County, containsCity, Balatonfüred]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Balatonfüred
Context triple: [Veszprém County, containsCity, Balatonfüred]
  • A. Balatonfüred chosen
    Balatonfüred is a historic Hungarian resort town and spa destination on the northern shore of Lake Balaton, known for its promenades, sailing, and mineral springs.
  • B. Balatonlelle
    Balatonlelle is a popular Hungarian holiday town on the southern shore of Lake Balaton, known for its beaches, family-friendly attractions, and lakeside resorts.
  • C. Balatonalmádi
    Balatonalmádi is a popular Hungarian resort town on the northern shore of Lake Balaton, known for its beaches, holiday facilities, and scenic surroundings.
  • D. Keszthely
    Keszthely is a historic town in western Hungary known for its lakeside resort atmosphere, cultural heritage, and proximity to Lake Balaton.
  • E. Törökbálint
    Törökbálint is a town in Pest County, Hungary, located just southwest of Budapest and known as a suburban residential area with growing commercial and industrial zones.
  • 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_69bd464b07f8819095aa76577c9829e4 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd85ae52c08190968a5567b7e6b794 completed March 20, 2026, 5:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf18baeca081909acc11d0c6c89f6d completed March 21, 2026, 10:16 p.m.
Created at: March 20, 2026, 2 p.m.