Triple

T23333527
Position Surface form Disambiguated ID Type / Status
Subject Aussig an der Elbe E591510 entity
Predicate hasAlternativeName P39 FINISHED
Object Aussig 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: Aussig | Statement: [Aussig an der Elbe, hasAlternativeName, Aussig]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Aussig
Context triple: [Aussig an der Elbe, hasAlternativeName, Aussig]
  • A. Aussig an der Elbe chosen
    Aussig an der Elbe is the German name for the Czech industrial and port city of Ústí nad Labem, located on the Elbe River in northern Bohemia.
  • B. Löcknitz
    Löcknitz is a river in eastern Germany that flows through Brandenburg and is associated with the town of Erkner.
  • C. Meißner
    Meißner is a municipality in the Werra-Meißner district of the German state of Hesse, known for its proximity to the Meißner mountain range.
  • D. Rechlin
    Rechlin is a small municipality in northeastern Germany’s Mecklenburg Lake District, known as a gateway to Müritz National Park and the surrounding lake landscape.
  • E. Oßling
    Oßling is a small municipality in the German state of Saxony, located in the eastern part of the country.
  • 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_69e25d20156c81908c5c53195bd9c738 completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f197efd98c819083635a2b8440f3eb completed April 29, 2026, 5:32 a.m.
Created at: April 17, 2026, 5:16 p.m.