Triple

T3829204
Position Surface form Disambiguated ID Type / Status
Subject DHL E88768 entity
Predicate headquartersLocation P62 FINISHED
Object Bonn, Germany E23133 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: Bonn, Germany | Statement: [DHL, headquartersLocation, Bonn, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bonn, Germany
Context triple: [DHL, headquartersLocation, Bonn, Germany]
  • A. Bonn chosen
    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.
  • B. Bergen, Germany
    Bergen, Germany is a small town in Lower Saxony best known for its proximity to the former Bergen-Belsen concentration camp and its historical military significance.
  • C. Nassau, Germany
    Nassau, Germany is a historic town on the Lahn River in the state of Rhineland-Palatinate, known as the ancestral seat of the House of Nassau.
  • D. 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.
  • E. Rheinbach, Germany
    Rheinbach is a small town in the Rhein-Sieg district of North Rhine-Westphalia, western Germany, known for its glassmaking tradition and proximity to Bonn.
  • 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_69aed9538cf881909d9ce8ca4ac7c18c completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeeb683c2081908ffa6e759a3470fe completed March 9, 2026, 3:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4fb54636c8190a46224e0a8215e26 completed March 14, 2026, 6:08 a.m.
Created at: March 9, 2026, 3:17 p.m.