Triple

T10012497
Position Surface form Disambiguated ID Type / Status
Subject Baranya County E199407 entity
Predicate containsTown P847 FINISHED
Object Kozármisleny E505529 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: Kozármisleny | Statement: [Baranya County, containsTown, Kozármisleny]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kozármisleny
Context triple: [Baranya County, containsTown, Kozármisleny]
  • A. Kozármisleny chosen
    Kozármisleny is a small town in southern Hungary, near Pécs, known for its growing residential character and local sports culture.
  • B. Komárno
    Komárno is a historic town and river port in southern Slovakia, situated at the confluence of the Danube and Váh rivers on the border with Hungary.
  • C. Ružomberok
    Ružomberok is a town in northern Slovakia known for its location in the Liptov region and its historical and cultural significance.
  • D. Sedlčany
    Sedlčany is a small historic town in the Czech Republic known for its traditional cheese production and location on the Mastník River.
  • E. Zalakomár
    Zalakomár is a village in southwestern Hungary known for its rural character and proximity to the Zala River and nearby wetlands.
  • 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_69ca8315a1a08190ab310f25620f362b completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cdcd3cf5b881908f5318e55bdd22b6 completed April 2, 2026, 1:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2cb8b92b0819081e5ac52e2f4f27e completed April 5, 2026, 8:52 p.m.
Created at: March 30, 2026, 8:52 p.m.