Triple

T913043
Position Surface form Disambiguated ID Type / Status
Subject Franz Kafka Prize E19705 entity
Predicate presentedBy P83 FINISHED
Object City of 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: City of Prague | Statement: [Franz Kafka Prize, presentedBy, City of Prague]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Prague
Context triple: [Franz Kafka Prize, presentedBy, City of 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. Hradec Králové
    Hradec Králové is a historic city in the Czech Republic known for its educational institutions, modernist architecture, and role as a regional cultural and economic center.
  • 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. Plzeň
    Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
  • E. Ústí nad Labem
    Ústí nad Labem is an industrial city in the north of the Czech Republic, known as a major transport hub and river port in the Bohemian region.
  • 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_69a4939f91a08190ba68c2c81eab90fe completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b2df9ba88190824437026796586f completed March 1, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac762e945c8190a4716a2115689b20 completed March 7, 2026, 7:02 p.m.
Created at: March 1, 2026, 7:39 p.m.