Triple

T3927613
Position Surface form Disambiguated ID Type / Status
Subject Schwarze Elster E93313 entity
Predicate partOf P40 FINISHED
Object Elbe river system 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: Elbe river system | Statement: [Schwarze Elster, partOf, Elbe river system]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elbe river system
Context triple: [Schwarze Elster, partOf, Elbe river system]
  • 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. Rhine–Danube watershed
    The Rhine–Danube watershed is the major European drainage divide separating river systems that flow into the North Sea via the Rhine from those that flow into the Black Sea via the Danube.
  • C. 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.
  • D. 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.
  • E. River Spree
    River Spree is a major river flowing through Berlin, Germany, known for shaping the city’s landscape and passing many historic and cultural landmarks.
  • 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_69aed96bfa1081908f7b30f2c647dee6 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aeeda4f9d481908dda1b5a826ab64d completed March 9, 2026, 3:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69b53387775881909479f4e1fcecdaca completed March 14, 2026, 10:08 a.m.
Created at: March 9, 2026, 3:23 p.m.