Triple

T20314338
Position Surface form Disambiguated ID Type / Status
Subject DB Schenker E510340 entity
Predicate headquartersLocation P62 FINISHED
Object Essen, 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: Essen, Germany | Statement: [DB Schenker, headquartersLocation, Essen, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Essen, Germany
Context triple: [DB Schenker, headquartersLocation, Essen, Germany]
  • A. Essen chosen
    Essen is a major industrial and cultural city in western Germany, historically known as a coal and steel center and now home to several large corporations and universities.
  • B. Hamm, Germany
    Hamm is a city in the German state of North Rhine-Westphalia, known as an industrial and transportation hub in the eastern Ruhr area.
  • 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. Brühl, Germany
    Brühl, Germany is a town in North Rhine-Westphalia known for its UNESCO-listed Augustusburg and Falkenlust palaces and its proximity to Cologne.
  • E. Bochum, Germany
    Bochum, Germany is an industrial city in the Ruhr region of western Germany known for its automotive manufacturing heritage and cultural institutions.
  • 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_69e0b4c7491c8190961113c4283b10b0 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e67745e2448190b5611382fe338bb2 completed April 20, 2026, 6:58 p.m.
Created at: April 16, 2026, 11:19 a.m.