Triple

T5014413
Position Surface form Disambiguated ID Type / Status
Subject Radio Farda E112705 entity
Predicate hasHeadquartersLocation P62 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: [Radio Farda, hasHeadquartersLocation, Prague]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Prague
Context triple: [Radio Farda, hasHeadquartersLocation, 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. Praga
    Praga is a historic district on the eastern bank of the Vistula River in Warsaw, Poland, known for its older architecture, cultural life, and role in the city's wartime history.
  • C. Kolín
    Kolín is a historic industrial town and important transport hub on the Elbe River in the Central Bohemian Region of the Czech Republic.
  • D. 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.
  • E. Liberec
    Liberec is a city in the northern Czech Republic known for its textile industry heritage, mountainous surroundings, and the landmark Ještěd Tower.
  • 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_69bd4434acb8819086679dbeccc2fe54 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd7310c5b08190a5c9ab0f9fe9569f completed March 20, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69be923eac848190b8511b8027c87ff3 completed March 21, 2026, 12:42 p.m.
Created at: March 20, 2026, 1:35 p.m.