Triple

T2999458
Position Surface form Disambiguated ID Type / Status
Subject Czechoslovak Air Force E81150 entity
Predicate garrisonLocation P40 FINISHED
Object Prague E14162 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: Prague | Statement: [Czechoslovak Air Force, garrisonLocation, Prague]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Prague
Context triple: [Czechoslovak Air Force, garrisonLocation, Prague]
  • A. Prague chosen
    Prague is the historic capital city of the Czech Republic, renowned for its well-preserved medieval architecture, iconic Charles Bridge and Prague Castle, and vibrant cultural life.
  • B. Kolín
    Kolín is a historic industrial town and important transport hub on the Elbe River in the Central Bohemian Region of the Czech Republic.
  • C. Brno
    Brno is the second-largest city in the Czech Republic, known as a major cultural, educational, and industrial center in the historical region of Moravia.
  • 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. Prague 6
    Prague 6 is a large municipal district of Prague, Czech Republic, known for its residential neighborhoods, diplomatic quarter, and proximity to Prague Castle and the airport.
  • 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_69ad8b187fc8819085914d3c9ea3142d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad99f8eb248190b50fd539a06a5a62 completed March 8, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b12dfe893c81909c79ea6cffb5bae6 completed March 11, 2026, 8:55 a.m.
Created at: March 8, 2026, 2:59 p.m.