Triple

T4003344
Position Surface form Disambiguated ID Type / Status
Subject Wesel E89464 entity
Predicate locatedAtConfluenceOf P11842 FINISHED
Object Rhine and Lippe E148047 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: Rhine and Lippe | Statement: [Wesel, locatedAtConfluenceOf, Rhine and Lippe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rhine and Lippe
Context triple: [Wesel, locatedAtConfluenceOf, Rhine and Lippe]
  • A. Lippe
    Lippe is a historical region in northwestern Germany that once formed a small principality and later a Free State within the German Reich.
  • B. Lippe chosen
    The Lippe is a river in western Germany that flows through North Rhine-Westphalia and is a right-bank tributary of the Rhine.
  • C. Rheine
    Rheine is a German city in the state of North Rhine-Westphalia, known for its historical town center and location along the River Ems.
  • D. Wupper
    The Wupper is a river in North Rhine-Westphalia, Germany, known for flowing through the industrial city of Wuppertal and its surrounding region.
  • E. Lower Rhine
    The Lower Rhine is the downstream section of the Rhine River flowing from roughly Bonn through Germany and the Netherlands to the North Sea, known for its dense population, industry, and historical river landscapes.
  • 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_69aed9585e788190bec2d39deba3750f completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefa5de98c8190a95b21a75fffdef3 completed March 9, 2026, 4:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69be031d16a08190b84524b7153f7f85 completed March 21, 2026, 2:31 a.m.
Created at: March 9, 2026, 3:34 p.m.