Triple

T10229935
Position Surface form Disambiguated ID Type / Status
Subject School of Economics and Management, Lund University E243311 entity
Predicate region P40 FINISHED
Object Scania E138516 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 | Statement: [School of Economics and Management, Lund University, region, Scania]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Scania
Context triple: [School of Economics and Management, Lund University, region, Scania]
  • A. Scania chosen
    Scania is a historical province in southern Sweden known for its fertile farmland, coastal landscapes, and former status as part of Denmark.
  • B. Scania
    Scania is a Swedish manufacturer renowned for its heavy trucks, buses, and industrial and marine engines.
  • 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. Neoplan
    Neoplan is a German bus and coach manufacturer renowned for its innovative, high-end touring and city buses.
  • E. Volvo Cars
    Volvo Cars is a Swedish automotive manufacturer known for its focus on safety, practical design, and premium vehicles.
  • 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_69d381b0f97c819085c9b45799a5fb7c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d1fcb1d081908173033594a6bfc9 completed April 7, 2026, 9:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69d6f73610fc8190965c4e45a9deeac6 completed April 9, 2026, 12:47 a.m.
Created at: April 6, 2026, 11:19 a.m.