Triple

T2025133
Position Surface form Disambiguated ID Type / Status
Subject Fürth E44190 entity
Predicate locatedOn P40 FINISHED
Object Regnitz River E124915 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: Regnitz River | Statement: [Fürth, locatedOn, Regnitz River]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Regnitz River
Context triple: [Fürth, locatedOn, Regnitz River]
  • A. Regnitz chosen
    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.
  • B. Würm River
    The Würm River is a small river in Bavaria, Germany, known for flowing north from Lake Starnberg through towns such as Gauting and Starnberg before joining the Amper River.
  • 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. Kinzig
    The Kinzig is a river in southwestern Germany that flows through the Black Forest region before joining the Rhine.
  • E. Neckar
    The Neckar is a significant river in southwestern Germany that flows through cities like Stuttgart and Heidelberg before joining the Rhine.
  • 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_69a8891201bc8190aca837be6de41579 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb8f3faa08190a48ae1355d6e009f completed March 7, 2026, 5:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69af5cbcf8dc8190a3319bdf58dce307 completed March 9, 2026, 11:50 p.m.
Created at: March 4, 2026, 7:38 p.m.