Triple

T5248900
Position Surface form Disambiguated ID Type / Status
Subject Scaniarinken E118531 entity
Predicate namedAfter P63 FINISHED
Object Scania (company) E37748 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: Scania (company) | Statement: [Scaniarinken, namedAfter, Scania (company)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Scania (company)
Context triple: [Scaniarinken, namedAfter, Scania (company)]
  • A. Scania chosen
    Scania is a Swedish manufacturer renowned for its heavy trucks, buses, and industrial and marine engines.
  • B. Scania
    Scania is a historical province in southern Sweden known for its fertile farmland, coastal landscapes, and former status as part of Denmark.
  • C. Volvo Group
    Volvo Group is a Swedish multinational manufacturing company best known for producing trucks, buses, construction equipment, and marine and industrial engines.
  • D. Volvo Cars
    Volvo Cars is a Swedish automotive manufacturer known for its focus on safety, practical design, and premium vehicles.
  • E. Saab Automobile
    Saab Automobile was a Swedish car manufacturer known for its innovative engineering, turbocharged engines, and distinctive, safety-focused designs.
  • 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_69bd4468aacc8190a8196f71855cdf4f completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd7b787b34819081af96de9355bb4f completed March 20, 2026, 4:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69bef83998f881909fef2746f5c496af completed March 21, 2026, 7:57 p.m.
Created at: March 20, 2026, 1:50 p.m.