Triple

T291355
Position Surface form Disambiguated ID Type / Status
Subject Volkswagen Group E6000 entity
Predicate owns P347 FINISHED
Object Scania
Scania is a Swedish manufacturer renowned for its heavy trucks, buses, and industrial and marine engines.
 E37748 NE FINISHED

How this triple was built (4 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: Scania | Statement: [Volkswagen Group, owns, Scania]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Scania
Context triple: [Volkswagen Group, owns, Scania]
  • A. Saab Automobile
    Saab Automobile was a Swedish car manufacturer known for its innovative engineering, turbocharged engines, and distinctive, safety-focused designs.
  • B. Saab AB
    Saab AB is a Swedish aerospace and defense company known for developing military aircraft, advanced defense systems, and security solutions.
  • C. New Holland
    New Holland was the name given by the Dutch to their 17th-century colonial possessions in northeastern Brazil, known historically as Dutch Brazil.
  • D. Saab 95
    The Saab 95 is a mid-size executive car produced by Swedish automaker Saab, known for its distinctive Scandinavian design, advanced safety features, and turbocharged performance.
  • E. Saab 96
    The Saab 96 is a compact Swedish car produced from 1960 to 1980, known for its distinctive aerodynamic shape, front-wheel drive, and success in international rallying.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Scania
Triple: [Volkswagen Group, owns, Scania]
Generated description
Scania is a Swedish manufacturer renowned for its heavy trucks, buses, and industrial and marine engines.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Scania
Target entity description: Scania is a Swedish manufacturer renowned for its heavy trucks, buses, and industrial and marine engines.
  • A. Saab Automobile
    Saab Automobile was a Swedish car manufacturer known for its innovative engineering, turbocharged engines, and distinctive, safety-focused designs.
  • B. Saab AB
    Saab AB is a Swedish aerospace and defense company known for developing military aircraft, advanced defense systems, and security solutions.
  • C. New Holland
    New Holland was the name given by the Dutch to their 17th-century colonial possessions in northeastern Brazil, known historically as Dutch Brazil.
  • D. Saab 95
    The Saab 95 is a mid-size executive car produced by Swedish automaker Saab, known for its distinctive Scandinavian design, advanced safety features, and turbocharged performance.
  • E. Saab 96
    The Saab 96 is a compact Swedish car produced from 1960 to 1980, known for its distinctive aerodynamic shape, front-wheel drive, and success in international rallying.
  • F. None of above. chosen

Provenance (5 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_69a2e79114b081909490b3bf5a5dbb51 completed Feb. 28, 2026, 1:03 p.m.
NER Named-entity recognition batch_69a2e975d2c0819082bbf6a0f3d928af completed Feb. 28, 2026, 1:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3a33be60481908016ef90c44498a7 completed March 1, 2026, 2:23 a.m.
NEDg Description generation batch_69a3a4068610819086b8a58a0a9198b7 completed March 1, 2026, 2:27 a.m.
NED2 Entity disambiguation (via description) batch_69a3a49050ec8190b81afc1407187e3f completed March 1, 2026, 2:29 a.m.
Created at: Feb. 28, 2026, 1:06 p.m.