Triple

T9710893
Position Surface form Disambiguated ID Type / Status
Subject Vyškov Military Academy E235018 entity
Predicate locatedIn P40 FINISHED
Object Vyškov E438638 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: Vyškov | Statement: [Vyškov Military Academy, locatedIn, Vyškov]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vyškov
Context triple: [Vyškov Military Academy, locatedIn, Vyškov]
  • A. Vyškov chosen
    Vyškov is a historic town in the South Moravian Region of the Czech Republic, known for its Renaissance chateau, zoo and dinosaur park, and its role as a local administrative and cultural center.
  • B. Přerov
    Přerov is a city in the Olomouc Region of the Czech Republic, known as an important industrial and transport hub on the Bečva River.
  • C. Harrachov
    Harrachov is a Czech mountain town in the Krkonoše range known as a major ski and winter sports resort near the Polish border.
  • D. Bražec
    Bražec is a small village and administrative part of the town of Náchod in the Hradec Králové Region of the Czech Republic.
  • E. Hradec Králové
    Hradec Králové is a historic city in the Czech Republic known for its educational institutions, modernist architecture, and role as a regional cultural and economic center.
  • 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_69ca84cd8fa0819090a5e243ceb37003 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9e0591208190aa57cc9e2aebafb7 completed April 1, 2026, 10:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69de54a30b748190bb791078e9dde442 completed April 14, 2026, 2:52 p.m.
Created at: March 30, 2026, 8:19 p.m.