Triple

T1762950
Position Surface form Disambiguated ID Type / Status
Subject Volkswagen Beetle E38697 entity
Predicate productionLocation P3992 FINISHED
Object Wolfsburg, Germany E74139 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: Wolfsburg, Germany | Statement: [Volkswagen Beetle, productionLocation, Wolfsburg, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wolfsburg, Germany
Context triple: [Volkswagen Beetle, productionLocation, Wolfsburg, Germany]
  • A. Wolfsburg chosen
    Wolfsburg is a German city best known as the headquarters and main production site of the Volkswagen automobile company.
  • 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. Krefeld, Germany
    Krefeld, Germany is an industrial city in North Rhine-Westphalia known historically for its textile and silk production.
  • 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. Weinheim, Germany
    Weinheim, Germany is a town in the state of Baden-Württemberg known for its historic old town, twin castles, and role as a regional economic and publishing 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_69a8862d562481908d7025a1c1f67c0d completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa6465245c8190b1ee84628c62c529 completed March 6, 2026, 5:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69adbf4db1c48190a96f137db3e2f32c completed March 8, 2026, 6:26 p.m.
Created at: March 4, 2026, 7:31 p.m.