Triple

T14125735
Position Surface form Disambiguated ID Type / Status
Subject Wupper basin E340025 entity
Predicate containsTown P847 FINISHED
Object Velbert E740419 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: Velbert | Statement: [Wupper basin, containsTown, Velbert]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Velbert
Context triple: [Wupper basin, containsTown, Velbert]
  • A. Velbert chosen
    Velbert is a German city in North Rhine-Westphalia known for its metal and lock manufacturing industry and its location between Düsseldorf, Essen, and Wuppertal.
  • B. Erkrath
    Erkrath is a town in the German state of North Rhine-Westphalia, situated near Düsseldorf in the district of Mettmann.
  • C. Stadelhofen
    Stadelhofen is a village and district of the town of Oberkirch in the Ortenau region of Baden-Württemberg, Germany.
  • D. Troisdorf
    Troisdorf is a town in North Rhine-Westphalia, Germany, located between Cologne and Bonn and known as an important industrial and commuter hub in the Rhine-Sieg district.
  • E. Walldorf
    Walldorf is a town in southwestern Germany best known as the headquarters of software giant SAP and for its strong economic base in the technology sector.
  • 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_69d81c6a95b481909e39111e0c1f31ee completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de6096976481909dc79066c5165a50 completed April 14, 2026, 3:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcf7e2b0248190ba40aa8395355052 completed May 7, 2026, 8:36 p.m.
Created at: April 9, 2026, 10:22 p.m.