Triple

T14887708
Position Surface form Disambiguated ID Type / Status
Subject Villingen-Schwenningen E359670 entity
Predicate twinTown P1072 FINISHED
Object Zittau E445817 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: Zittau | Statement: [Villingen-Schwenningen, twinTown, Zittau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zittau
Context triple: [Villingen-Schwenningen, twinTown, Zittau]
  • A. Zittau chosen
    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.
  • B. Bautzen
    Bautzen is a historic town in eastern Germany known for its well-preserved medieval architecture and as a cultural center of the Sorbian minority.
  • C. Liebenwalde
    Liebenwalde is a small town and municipality in the Oberhavel district of the German state of Brandenburg.
  • D. Crimmitschau
    Crimmitschau is a town in the German state of Saxony, historically known for its textile industry and located within the broader Leipzig metropolitan area.
  • E. Görlitz
    Görlitz is a historic city in eastern Germany on the Lusatian Neisse River, known for its well-preserved old town and role as a popular film location.
  • 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_69d827980cbc8190a0c569ae3940a1d9 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69ded5f5b1c88190815f3585770cb135 completed April 15, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff56afa5ec8190a058574dff7431dc completed May 9, 2026, 3:45 p.m.
Created at: April 10, 2026, 2:08 a.m.