Triple

T22895318
Position Surface form Disambiguated ID Type / Status
Subject Kreis Kamenz E568156 entity
Predicate borderedBy P224 FINISHED
Object Kreis Riesa 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: Kreis Riesa | Statement: [Kreis Kamenz, borderedBy, Kreis Riesa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kreis Riesa
Context triple: [Kreis Kamenz, borderedBy, Kreis Riesa]
  • A. Kreis Riesa chosen
    Kreis Riesa was a former administrative district in the German Democratic Republic, located in the region around the town of Riesa in what is now the state of Saxony.
  • B. Kreis Torgau
    Kreis Torgau was a former rural district in the German Democratic Republic, centered on the town of Torgau in what is now the state of Saxony.
  • C. Kreis Gransee
    Kreis Gransee was a rural administrative district in the former East German state structure, located in the northern part of what is now the federal state of Brandenburg.
  • D. Kreis Sebnitz
    Kreis Sebnitz was a former rural district in the German Democratic Republic, located in the Bezirk Dresden region of Saxony and centered around the town of Sebnitz.
  • E. Kreis Meißen
    Kreis Meißen was a rural district in the former East German administrative region of Bezirk Dresden, centered around the historic town of Meißen in Saxony.
  • 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_69e2458c23ec81908fa2570692c6614f completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f17fc83d688190a8ab5ea0aad1e7ec completed April 29, 2026, 3:49 a.m.
Created at: April 17, 2026, 3:40 p.m.