Triple

T2019043
Position Surface form Disambiguated ID Type / Status
Subject Toruń E44061 entity
Predicate twinnedWith P1072 FINISHED
Object České Budějovice E186483 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: České Budějovice | Statement: [Toruń, twinnedWith, České Budějovice]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: České Budějovice
Context triple: [Toruń, twinnedWith, České Budějovice]
  • A. České Budějovice chosen
    České Budějovice is a historic city in the Czech Republic known for its medieval architecture and as the original home of Budweiser Budvar beer.
  • B. Plzeň
    Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
  • C. Liberec
    Liberec is a city in the northern Czech Republic known for its textile industry heritage, mountainous surroundings, and the landmark Ještěd Tower.
  • D. Kolín
    Kolín is a historic industrial town and important transport hub on the Elbe River in the Central Bohemian Region of the Czech Republic.
  • E. Ústí nad Labem
    Ústí nad Labem is an industrial city in the north of the Czech Republic, known as a major transport hub and river port in the Bohemian region.
  • 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_69a8891201bc8190aca837be6de41579 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb8cfa5c88190b55bce5db968665b completed March 7, 2026, 5:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69af989af058819082c35bee706d0ea0 completed March 10, 2026, 4:05 a.m.
Created at: March 4, 2026, 7:38 p.m.