Triple

T19786149
Position Surface form Disambiguated ID Type / Status
Subject Frankenberg, Hesse E475272 entity
Predicate hasTwinTown P919 FINISHED
Object Mladá Boleslav NE NERFINISHED

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: Mladá Boleslav | Statement: [Frankenberg, Hesse, hasTwinTown, Mladá Boleslav]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mladá Boleslav
Context triple: [Frankenberg, Hesse, hasTwinTown, Mladá Boleslav]
  • A. Mladá Boleslav chosen
    Mladá Boleslav is a Czech city best known as an important industrial center and the headquarters of the Škoda Auto automobile manufacturer.
  • B. Nymburk
    Nymburk is a historic town in the Czech Republic known for its medieval fortifications and location on the Elbe River.
  • C. Plzeň
    Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
  • 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. Pardubice
    Pardubice is a city in the Czech Republic known for its ice hockey tradition, historic center, and as the hometown of legendary NHL goaltender Dominik Hašek.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e51b014081908b263e167370529a completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e65387d3348190a31f9c2f9bc1c6d9 completed April 20, 2026, 4:25 p.m.
Created at: April 10, 2026, 1:49 p.m.