Triple

T1723933
Position Surface form Disambiguated ID Type / Status
Subject Dresden E37454 entity
Predicate locatedOnRiver P165 FINISHED
Object 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: Elbe | Statement: [Dresden, locatedOnRiver, Elbe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elbe
Context triple: [Dresden, locatedOnRiver, 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. Rhine
    The Rhine is one of Europe's most important rivers, historically serving as a vital trade route and cultural boundary from the Alps through Germany to the North Sea.
  • E. 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.
  • 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_69a8861acab88190bb43cde203429399 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa635a5c8c8190b3fbf977a93838fd completed March 6, 2026, 5:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69af9040c20c8190aaae6f22fc893032 completed March 10, 2026, 3:30 a.m.
Created at: March 4, 2026, 7:30 p.m.