Triple

T22824337
Position Surface form Disambiguated ID Type / Status
Subject Roman Catholic Diocese of Vác E565311 entity
Predicate locatedIn P40 FINISHED
Object Vác NE NERFINISHED

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: Vác | Statement: [Roman Catholic Diocese of Vác, locatedIn, Vác]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vác
Context triple: [Roman Catholic Diocese of Vác, locatedIn, Vác]
  • A. Vác chosen
    Vác is a historic town on the Danube in northern Hungary, known for its Baroque architecture and role as a regional cultural and religious center.
  • B. Nitra
    Nitra is one of the oldest cities in Slovakia, known for its historic castle, early Christian heritage, and role as a cultural and academic center.
  • C. Zvolen
    Zvolen is a historic town in central Slovakia known for its medieval castle and role as a regional transport and cultural hub.
  • D. Vsetín
    Vsetín is a town in the eastern Czech Republic known as an industrial and cultural center of the Moravian Wallachia region.
  • E. Vysočany
    Vysočany is a district in the northeastern part of Prague, Czech Republic, known for its mix of residential areas, industrial heritage, and modern commercial development.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e2458426188190b58b8ab4844fe420 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17dd34a248190881f2bccdd9aedce completed April 29, 2026, 3:41 a.m.
Created at: April 17, 2026, 3:34 p.m.