Triple

T4797854
Position Surface form Disambiguated ID Type / Status
Subject Plzeň main railway station E106755 entity
Predicate connectsTo P845 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: [Plzeň main railway station, connectsTo, Prague]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Prague
Context triple: [Plzeň main railway station, connectsTo, 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_69bd43f591c881909e5a532388b0f3f3 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6632708c8190b627d99363ab062c completed March 20, 2026, 3:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69be43e8f9dc8190b4e932d179e2f097 completed March 21, 2026, 7:08 a.m.
Created at: March 20, 2026, 1:22 p.m.