Triple

T3817633
Position Surface form Disambiguated ID Type / Status
Subject President of the Czech Republic E84294 entity
Predicate location 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: [President of the Czech Republic, location, Prague]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Prague
Context triple: [President of the Czech Republic, location, 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_69aed931f5908190be2c07af66d4df25 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aee8e1852c8190b61c507a7128d4c6 completed March 9, 2026, 3:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4fb373ce4819082e2d6d6c204c392 completed March 14, 2026, 6:07 a.m.
Created at: March 9, 2026, 3:17 p.m.