Triple

T9464128
Position Surface form Disambiguated ID Type / Status
Subject Elbe Tunnel (Hamburg) E228225 entity
Predicate crosses P416 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: [Elbe Tunnel (Hamburg), crosses, River Elbe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: River Elbe
Context triple: [Elbe Tunnel (Hamburg), crosses, 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_69ca846fee388190a6ec273fd644b88b completed March 30, 2026, 2:10 p.m.
NER Named-entity recognition batch_69cd7fcec2d88190b93b6e4d881c85c6 completed April 1, 2026, 8:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1bcaaeaa08190b90ca5600deb84a2 completed April 5, 2026, 1:36 a.m.
Created at: March 30, 2026, 7:53 p.m.