Triple

T12569821
Position Surface form Disambiguated ID Type / Status
Subject Domažlice District E295570 entity
Predicate administrativeCenter P1474 FINISHED
Object Domažlice E295569 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: Domažlice | Statement: [Domažlice District, administrativeCenter, Domažlice]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Domažlice
Context triple: [Domažlice District, administrativeCenter, Domažlice]
  • A. Domažlice chosen
    Domažlice is a historic town in the western Czech Republic known for its well-preserved medieval center and rich Chodové folk traditions.
  • B. Jizbice
    Jizbice is a small locality that forms part of the town of Náchod in the Hradec Králové Region of the Czech Republic.
  • C. Říčany
    Říčany is a town in the Czech Republic, located just southeast of Prague and known as a popular residential and commuter suburb with historical roots.
  • D. Ruzyně
    Ruzyně is a district in the western part of Prague, Czech Republic, best known as the location of the city’s main international airport.
  • E. Slaný
    Slaný is a historic town in the Czech Republic known for its medieval center and location northwest of Prague.
  • 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_69d6ad9cac2c81908e8a7bed82d1e21d completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d954a422c88190a22cc34d2eac00ce completed April 10, 2026, 7:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd191f84bc819096d6cc6167732a98 completed May 7, 2026, 10:58 p.m.
Created at: April 8, 2026, 11:50 p.m.