Triple

T17357227
Position Surface form Disambiguated ID Type / Status
Subject DEG Metro Stars E421969 entity
Predicate basedIn P40 FINISHED
Object Düsseldorf, Germany 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: Düsseldorf, Germany | Statement: [DEG Metro Stars, basedIn, Düsseldorf, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Düsseldorf, Germany
Context triple: [DEG Metro Stars, basedIn, 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. Mettmann, Germany
    Mettmann, Germany is a historic town in North Rhine-Westphalia known for its proximity to Düsseldorf and the nearby Neander Valley, where the famous Neanderthal fossils were discovered.
  • D. Dortmund
    Dortmund is a major city in western Germany known for its rich football culture, industrial heritage, and home club Borussia Dortmund.
  • E. Frankfort, Germany
    Frankfort, Germany is a German city whose name has been used for places abroad, including the village of Frankfort in Illinois, USA.
  • 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_69d889d520008190a26917a95bf1c2ea completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e43a4976788190b00c00f710be6c46 completed April 19, 2026, 2:13 a.m.
Created at: April 10, 2026, 5:44 a.m.