Triple

T17755079
Position Surface form Disambiguated ID Type / Status
Subject Autobahn A1 E443210 entity
Predicate servesCity P82 FINISHED
Object Wuppertal NE NERFINISHED

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: Wuppertal | Statement: [Autobahn A1, servesCity, Wuppertal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wuppertal
Context triple: [Autobahn A1, servesCity, Wuppertal]
  • A. Wuppertal chosen
    Wuppertal is a city in western Germany known for its steep slopes, extensive parks, and the unique suspended monorail Wuppertal Schwebebahn.
  • B. Duisburg
    Duisburg is a major industrial and port city in western Germany’s Ruhr region, known for its steel production and one of the world’s largest inland harbors.
  • C. Cologne
    Cologne is a historic German city on the Rhine River, renowned for its Gothic cathedral, vibrant cultural scene, and status as a major economic and media hub.
  • D. Cologne
    Cologne is an unincorporated community within Galloway Township in Atlantic County, New Jersey, known primarily as a small residential area in the region.
  • E. Krefeld
    Krefeld is a city in western Germany near the Rhine River, known historically for its textile and silk industry.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9edf16c8190a59ebd245d378f4f completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e4841e7050819083a4e638ca7484f7 completed April 19, 2026, 7:28 a.m.
Created at: April 10, 2026, 10:10 a.m.