Triple

T7994739
Position Surface form Disambiguated ID Type / Status
Subject Aurach E186094 entity
Predicate mouthLocatedIn P417 FINISHED
Object Regnitz 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 | Statement: [Aurach, mouthLocatedIn, Regnitz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Regnitz
Context triple: [Aurach, mouthLocatedIn, Regnitz]
  • 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. 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. Wupper
    The Wupper is a river in North Rhine-Westphalia, Germany, known for flowing through the industrial city of Wuppertal and its surrounding region.
  • D. Neckar
    The Neckar is a significant river in southwestern Germany that flows through cities like Stuttgart and Heidelberg before joining the Rhine.
  • E. 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.
  • 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_69ca829c6c308190ab05b43d234c52b2 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3c73ba388190bcedc29fbdd22f3c completed March 31, 2026, 3:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69cde6dc7b248190b59187a80b4fe036 completed April 2, 2026, 3:47 a.m.
Created at: March 30, 2026, 5:17 p.m.