Triple

T11131833
Position Surface form Disambiguated ID Type / Status
Subject Náchod E263301 entity
Predicate hasNearbyTown P3883 FINISHED
Object Hronov E899902 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: Hronov | Statement: [Náchod, hasNearbyTown, Hronov]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hronov
Context triple: [Náchod, hasNearbyTown, Hronov]
  • A. Hronov chosen
    Hronov is a small town in the Hradec Králové Region of the Czech Republic, known as the birthplace of writer Alois Jirásek and for its traditional cultural events.
  • B. Trebišov
    Trebišov is a town in eastern Slovakia known as an administrative and cultural center of the Trebišov District.
  • C. Borohrádek
    Borohrádek is a small town in the Hradec Králové Region of the Czech Republic.
  • D. Bruntál
    Bruntál is a historic town in the Moravian-Silesian Region of the Czech Republic, known as one of the oldest towns in the country and a gateway to the Jeseníky Mountains.
  • E. Ružomberok
    Ružomberok is a town in northern Slovakia known for its location in the Liptov region and its historical and cultural significance.
  • 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_69d6aa9c0ba08190bbd19c217489b755 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e831f4808190afabdaa0e97bbe32 completed April 9, 2026, 5:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69f61e3185fc81908c1b838e6883de2f completed May 2, 2026, 3:54 p.m.
Created at: April 8, 2026, 9:28 p.m.