Triple

T9410097
Position Surface form Disambiguated ID Type / Status
Subject RWE E226682 entity
Predicate headquartersLocation P62 FINISHED
Object Essen, Germany E311580 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: Essen, Germany | Statement: [RWE, headquartersLocation, Essen, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Essen, Germany
Context triple: [RWE, 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. 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.
  • D. Friedberg, Germany
    Friedberg, Germany is a historic town in the state of Hesse known for its medieval architecture, including a well-preserved castle and old town center.
  • E. Minden, Germany
    Minden, Germany is a historic town in North Rhine-Westphalia known for its strategic location on the Weser River and its role in significant military events such as the Battle of Minden.
  • 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_69ca843280488190bc65600e843ef9e6 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd5255e4fc81908bf2f69c9a12ef83 completed April 1, 2026, 5:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69d107a5fcc081909a2d743e9673fe9e completed April 4, 2026, 12:44 p.m.
Created at: March 30, 2026, 7:47 p.m.