Triple

T4149751
Position Surface form Disambiguated ID Type / Status
Subject Villa Tugendhat E89873 entity
Predicate owner P347 FINISHED
Object City of Brno E47149 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: City of Brno | Statement: [Villa Tugendhat, owner, City of Brno]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Brno
Context triple: [Villa Tugendhat, owner, City of Brno]
  • A. Brno chosen
    Brno is the second-largest city in the Czech Republic, known as a major cultural, educational, and industrial center in the historical region of Moravia.
  • B. Kolín
    Kolín is a historic industrial town and important transport hub on the Elbe River in the Central Bohemian Region of the Czech Republic.
  • C. Olomouc
    Olomouc is a historic city in the eastern Czech Republic known for its well-preserved old town, Baroque architecture, and UNESCO-listed Holy Trinity Column.
  • D. České Budějovice
    České Budějovice is a historic city in the Czech Republic known for its medieval architecture and as the original home of Budweiser Budvar beer.
  • E. Nymburk
    Nymburk is a historic town in the Czech Republic known for its medieval fortifications and location on the Elbe River.
  • 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_69aed95a59a881909b26e70b42c6811a completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af0273a038819087db092da234e767 completed March 9, 2026, 5:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5f5a4f75481909236504eaf81ce33 completed March 14, 2026, 11:56 p.m.
Created at: March 9, 2026, 3:43 p.m.