Triple

T14381013
Position Surface form Disambiguated ID Type / Status
Subject Siemens EuroSprinter E356600 entity
Predicate manufacturer P490 FINISHED
Object Siemens E49800 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: Siemens | Statement: [Siemens EuroSprinter, manufacturer, Siemens]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siemens
Context triple: [Siemens EuroSprinter, manufacturer, Siemens]
  • A. Siemens chosen
    Siemens is a major German multinational conglomerate best known for its leading roles in industrial manufacturing, energy, healthcare technology, and infrastructure solutions worldwide.
  • B. S7 Group
    S7 Group is a Russian aviation holding company best known for owning and operating S7 Airlines and related air transport businesses.
  • C. Siemens Avanto
    Siemens Avanto is a family of light rail and tram-train vehicles developed by Siemens for urban and regional public transport systems.
  • D. Siemens Energy
    Siemens Energy is a global energy technology company specializing in power generation, transmission, and related services for conventional and renewable energy systems.
  • E. Bosch
    Bosch is a multinational engineering and technology company best known for its automotive components, industrial products, and household appliances.
  • 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_69d8279163a081908aec45c0e3f1e02f completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de900bbfb08190a1e56f281a2374c0 completed April 14, 2026, 7:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd5511c9e4819089dcbf089ca0dc6a completed May 8, 2026, 3:14 a.m.
Created at: April 10, 2026, 1:16 a.m.