Triple

T13778854
Position Surface form Disambiguated ID Type / Status
Subject International Paralympic Committee E331083 entity
Predicate foundingLocation P40 FINISHED
Object Düsseldorf, Germany 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, Germany | Statement: [International Paralympic Committee, foundingLocation, Düsseldorf, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Düsseldorf, Germany
Context triple: [International Paralympic Committee, foundingLocation, Düsseldorf, Germany]
  • 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. Krefeld, Germany
    Krefeld, Germany is an industrial city in North Rhine-Westphalia known historically for its textile and silk production.
  • C. Dortmund
    Dortmund is a major city in western Germany known for its rich football culture, industrial heritage, and home club Borussia Dortmund.
  • D. Frankfort, Germany
    Frankfort, Germany is a German city whose name has been used for places abroad, including the village of Frankfort in Illinois, USA.
  • 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 (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_69d81c583b0081909e408a17db517a21 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de0239cbfc81909064ac2457fdfff5 completed April 14, 2026, 9 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7c7007d5c8190800456e32101903f completed May 3, 2026, 10:06 p.m.
Created at: April 9, 2026, 10:11 p.m.