Triple

T9207110
Position Surface form Disambiguated ID Type / Status
Subject Moravian-Silesian Region E221010 entity
Predicate hasMajorCity P316 FINISHED
Object Opava E194100 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: Opava | Statement: [Moravian-Silesian Region, hasMajorCity, Opava]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Opava
Context triple: [Moravian-Silesian Region, hasMajorCity, Opava]
  • A. Opava chosen
    Opava is a historic city in the Czech Republic’s Silesian region, known as a former political and cultural center of Silesia.
  • B. Plzeň
    Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
  • C. Ostrava
    Ostrava is a major industrial and cultural city in the northeastern Czech Republic, near the borders with Poland and Slovakia.
  • 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_69ca83e9d0e081908bdb71097201a06c completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccd9b0b6788190908bee67a0c5d48f completed April 1, 2026, 8:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69d61b0c9dec8190af5a38413d4861c2 completed April 8, 2026, 9:08 a.m.
Created at: March 30, 2026, 7:26 p.m.