Triple

T15864504
Position Surface form Disambiguated ID Type / Status
Subject Zwickau district E384673 entity
Predicate hasUrbanCenter P2106 FINISHED
Object Crimmitschau E1129650 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: Crimmitschau | Statement: [Zwickau district, hasUrbanCenter, Crimmitschau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Crimmitschau
Context triple: [Zwickau district, hasUrbanCenter, Crimmitschau]
  • A. Crimmitschau chosen
    Crimmitschau is a town in the German state of Saxony, historically known for its textile industry and located within the broader Leipzig metropolitan area.
  • B. Glauchau
    Glauchau is a town in the Free State of Saxony in eastern Germany, known for its historic castles and location in the industrial region of Zwickau.
  • C. Riesa
    Riesa is a town in the German state of Saxony, situated on the Elbe River and known historically as an important regional railway and industrial center.
  • D. Bischofswerda
    Bischofswerda is a small town in the Saxony region of eastern Germany, known as a local commercial and transport hub near the city of Dresden.
  • E. Zittau
    Zittau is a historic town in the southeastern corner of Germany, known for its proximity to both the Czech and Polish borders and its well-preserved medieval architecture.
  • 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_69d86da4e86481909f1325fdc971b5ec completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1555e4ee48190a3b27b4ab9bdb1c8 completed April 16, 2026, 9:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00aae0f87c819085ebc7d475ebe8ba completed May 10, 2026, 3:57 p.m.
Created at: April 10, 2026, 4:50 a.m.