Triple

T2926227
Position Surface form Disambiguated ID Type / Status
Subject Selke E78850 entity
Predicate flowsThrough P225 FINISHED
Object Saxony-Anhalt E48552 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: Saxony-Anhalt | Statement: [Selke, flowsThrough, Saxony-Anhalt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saxony-Anhalt
Context triple: [Selke, flowsThrough, Saxony-Anhalt]
  • A. Saxony-Anhalt chosen
    Saxony-Anhalt is a federal state in central Germany known for its rich cultural heritage, including numerous UNESCO World Heritage Sites such as the Bauhaus in Dessau and the historic towns of Quedlinburg and Wittenberg.
  • B. Thuringia
    Thuringia is a federal state in central Germany known for its forested landscapes, historic cities like Weimar and Erfurt, and its rich cultural and intellectual heritage.
  • C. Upper Saxony
    Upper Saxony is a historical region in central Europe that formed part of the Electorate of Saxony and encompassed areas including the city of Dresden.
  • D. Lower Saxony
    Lower Saxony is a large federal state in northwestern Germany known for its diverse landscapes, strong industrial base, and historic cities such as Hanover and Göttingen.
  • E. Mecklenburg-Vorpommern
    Mecklenburg-Vorpommern is a federal state in northeastern Germany known for its Baltic Sea coastline, numerous lakes, and relatively low population density.
  • 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_69ad8b0d40b481908bc2a5fa2e73c3fb completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad97c1e9c08190bcec80bc3262697a completed March 8, 2026, 3:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf832772d08190842b1e1c1a87ea3a completed March 22, 2026, 5:50 a.m.
Created at: March 8, 2026, 2:55 p.m.