Triple

T15250643
Position Surface form Disambiguated ID Type / Status
Subject Meissen district E364508 entity
Predicate containsMunicipality P852 FINISHED
Object Wülknitz E1035716 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: Wülknitz | Statement: [Meissen district, containsMunicipality, Wülknitz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wülknitz
Context triple: [Meissen district, containsMunicipality, Wülknitz]
  • A. Wülknitz chosen
    Wülknitz is a small municipality in the German state of Saxony that forms part of the broader Leipzig metropolitan area.
  • B. Wanzleben
    Wanzleben is a small town in the German state of Saxony-Anhalt, historically part of the former East German administrative district of Magdeburg.
  • C. Zwönitz
    Zwönitz is a river in Saxony, Germany, that serves as one of the headstreams of the Chemnitz River.
  • D. Ückeritz
    Ückeritz is a seaside village and holiday resort on the island of Usedom in northeastern Germany, known for its beaches and natural surroundings.
  • E. 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.
  • 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_69d85a0dde7481908fc64d1e82d5d20d completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e007f62b9c8190b9ad40e2d1912b63 completed April 15, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff755e0f2c819088293d8a55d7883a completed May 9, 2026, 5:56 p.m.
Created at: April 10, 2026, 3:13 a.m.