Triple

T9987073
Position Surface form Disambiguated ID Type / Status
Subject Volkswagen Karmann Ghia E196794 entity
Predicate assemblyLocation P40 FINISHED
Object Osnabrück, Germany E22113 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: Osnabrück, Germany | Statement: [Volkswagen Karmann Ghia, assemblyLocation, Osnabrück, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Osnabrück, Germany
Context triple: [Volkswagen Karmann Ghia, assemblyLocation, Osnabrück, Germany]
  • A. Osnabrück chosen
    Osnabrück is a historic city in Lower Saxony, Germany, known for its medieval architecture and role in the Peace of Westphalia.
  • B. Brunswick, Germany
    Brunswick, Germany is a historic city in Lower Saxony known for its medieval architecture, former status as a ducal residence, and role as an important commercial and cultural center in northern Germany.
  • C. Oldenburg, Germany
    Oldenburg, Germany is a historic city in northwestern Germany known for its former status as a grand duchy’s capital and its well-preserved old town.
  • 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. Giessen, Germany
    Giessen, Germany is a central German university town in the state of Hesse, known for its large student population and academic institutions.
  • 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_69ca82f1678c819093d06320a05f16a4 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdc79c8b80819091dc16ac8fd0c720 completed April 2, 2026, 1:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69d25810584c81908382cf6edd700530 completed April 5, 2026, 12:39 p.m.
Created at: March 30, 2026, 8:50 p.m.