Triple

T8930102
Position Surface form Disambiguated ID Type / Status
Subject Maschinenbau-AG Nürnberg E212630 entity
Predicate shortName P43 FINISHED
Object M.A.N. E766230 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: M.A.N. | Statement: [Maschinenbau-AG Nürnberg, shortName, M.A.N.]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: M.A.N.
Context triple: [Maschinenbau-AG Nürnberg, shortName, M.A.N.]
  • A. MAN Nutzfahrzeuge AG chosen
    MAN Nutzfahrzeuge AG was the former name of the German commercial vehicle manufacturer now known as MAN Truck & Bus, a major producer of trucks and buses in Europe.
  • B. DAF Trucks
    DAF Trucks is a Dutch manufacturer of commercial vehicles and heavy-duty trucks known for its reliable long-haul and distribution trucks across Europe and beyond.
  • C. Neoplan
    Neoplan is a German bus and coach manufacturer renowned for its innovative, high-end touring and city buses.
  • D. Deutz AG
    Deutz AG is a German manufacturer best known for producing internal combustion engines, particularly for industrial and agricultural applications.
  • E. Scania
    Scania is a Swedish manufacturer renowned for its heavy trucks, buses, and industrial and marine engines.
  • 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_69ca8395c438819087d7cb844ab5990c completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6676d5d881908ce78cbb5561a68b completed April 1, 2026, 12:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc93587b081908e23c2a8c01b9516 completed April 3, 2026, 2:05 p.m.
Created at: March 30, 2026, 6:57 p.m.