Triple

T361232
Position Surface form Disambiguated ID Type / Status
Subject Düsseldorf school of painting E7858 entity
Predicate location P40 FINISHED
Object Düsseldorf E36993 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: Düsseldorf | Statement: [Düsseldorf school of painting, location, Düsseldorf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Düsseldorf
Context triple: [Düsseldorf school of painting, location, Düsseldorf]
  • A. Düsseldorf chosen
    Düsseldorf is a major German city on the Rhine River known for its fashion and art scenes, modern architecture, and status as an important economic and financial center.
  • B. 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.
  • C. 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.
  • D. Bonn
    Bonn is a historic German city on the Rhine River, best known for being the birthplace of Ludwig van Beethoven and the former seat of the federal government before reunification.
  • E. Frankfurt am Main
    Frankfurt am Main is a major German financial and transportation hub on the River Main, known for hosting the European Central Bank and one of Europe’s busiest airports.
  • 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_69a2e7e880008190a6ad7e06e5d03007 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ebce64c88190a0a8edcc7095f78b completed Feb. 28, 2026, 1:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac995e2f808190a324f2da7a79b836 completed March 7, 2026, 9:32 p.m.
Created at: Feb. 28, 2026, 1:08 p.m.