Triple

T12968527
Position Surface form Disambiguated ID Type / Status
Subject Kamenz E321330 entity
Predicate hasTwinTown P919 FINISHED
Object Kolín E227472 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: Kolín | Statement: [Kamenz, hasTwinTown, Kolín]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kolín
Context triple: [Kamenz, hasTwinTown, Kolín]
  • A. Kolín chosen
    Kolín is a historic industrial town and important transport hub on the Elbe River in the Central Bohemian Region of the Czech Republic.
  • B. 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.
  • C. Plzeň
    Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
  • D. Liberec
    Liberec is a city in the northern Czech Republic known for its textile industry heritage, mountainous surroundings, and the landmark Ještěd Tower.
  • E. 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.
  • 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_69d80763bd6c819094437da5b20b01d2 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e407e5081909424fc0c22483c28 completed April 10, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd4c1ee7048190b2571364b25bd49d completed May 8, 2026, 2:36 a.m.
Created at: April 9, 2026, 8:33 p.m.