Triple

T9930492
Position Surface form Disambiguated ID Type / Status
Subject Niederrhein University of Applied Sciences E192634 entity
Predicate hasCampusIn P4623 FINISHED
Object Krefeld E398502 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: Krefeld | Statement: [Niederrhein University of Applied Sciences, hasCampusIn, Krefeld]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Krefeld
Context triple: [Niederrhein University of Applied Sciences, hasCampusIn, Krefeld]
  • A. Krefeld chosen
    Krefeld is a city in western Germany near the Rhine River, known historically for its textile and silk industry.
  • B. Düsseldorf
    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.
  • 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. Remscheid
    Remscheid is a city in North Rhine-Westphalia, Germany, known historically for its metalworking industry and as the birthplace of physicist Wilhelm Röntgen.
  • E. Mülheim an der Ruhr
    Mülheim an der Ruhr is a city in western Germany’s Ruhr area, known for its industrial heritage, riverside setting on the Ruhr River, and role as a regional economic and cultural center.
  • 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_69ca82dd978c8190947124ab0d3315ac completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb5b4196881909a004091a4203c45 completed April 2, 2026, 12:17 a.m.
NED1 Entity disambiguation (via context triple) batch_69e4963545f481909ecc360480b1fc37 completed April 19, 2026, 8:45 a.m.
Created at: March 30, 2026, 8:43 p.m.