Triple

T7323584
Position Surface form Disambiguated ID Type / Status
Subject Lutherstadt Wittenberg station E168811 entity
Predicate locatedNear P294 FINISHED
Object River Elbe E16410 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: River Elbe | Statement: [Lutherstadt Wittenberg station, locatedNear, River Elbe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: River Elbe
Context triple: [Lutherstadt Wittenberg station, locatedNear, River Elbe]
  • A. Elbe chosen
    The Elbe is one of Central Europe's major rivers, flowing from the Czech Republic through Germany to the North Sea and serving as an important waterway for transport, industry, and agriculture.
  • B. Saale
    The Saale is a major river in central Germany that flows through the states of Thuringia, Saxony-Anhalt, and Bavaria before joining the Elbe.
  • C. Weser
    The Weser is a major river in northwestern Germany that flows through several federal states before emptying into the North Sea.
  • D. Regnitz
    The Regnitz is a river in the German state of Bavaria that flows through cities such as Erlangen and Bamberg before joining the Main River.
  • E. Unstrut River
    The Unstrut River is a tributary of the Saale in central Germany, flowing through Thuringia and Saxony-Anhalt and known for its scenic valleys, vineyards, and historic towns.
  • 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_69c68a54cacc81908e3b773441f19566 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f046b93c8190a80dd48ee409ec5d completed March 27, 2026, 9:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8ac7fdaec8190a014513b8b60977b completed March 29, 2026, 4:37 a.m.
Created at: March 27, 2026, 3:03 p.m.