Triple

T10461442
Position Surface form Disambiguated ID Type / Status
Subject Western Sudetes E246682 entity
Predicate hasSettlement P1068 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: [Western Sudetes, hasSettlement, Liberec]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Liberec
Context triple: [Western Sudetes, hasSettlement, 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_69d381c16c248190a2fe5b471e584e9c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d50883b62c819082711b8c9fd968e3 completed April 7, 2026, 1:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69f12f646ec88190ab4745c52798b599 completed April 28, 2026, 10:06 p.m.
Created at: April 6, 2026, 12:19 p.m.