Triple

T7402321
Position Surface form Disambiguated ID Type / Status
Subject Zákupy E170777 entity
Predicate regionCapital P16248 FINISHED
Object Liberec E188165 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: Liberec | Statement: [Zákupy, regionCapital, Liberec]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Liberec
Context triple: [Zákupy, regionCapital, Liberec]
  • A. Liberec chosen
    Liberec is a city in the northern Czech Republic known for its textile industry heritage, mountainous surroundings, and the landmark Ještěd Tower.
  • B. Plzeň
    Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
  • C. Pardubice
    Pardubice is a city in the Czech Republic known for its ice hockey tradition, historic center, and as the hometown of legendary NHL goaltender Dominik Hašek.
  • 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. Žatec
    Žatec is a historic Czech town in the Ústí nad Labem Region renowned for its long-standing hop-growing tradition and beer production.
  • 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_69c68a6010108190925e5284de022660 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f26ea27c8190a55e0e0314b463d8 completed March 27, 2026, 9:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69c81110d7648190a8938db7061be454 completed March 28, 2026, 5:34 p.m.
Created at: March 27, 2026, 3:10 p.m.