Triple

T1781323
Position Surface form Disambiguated ID Type / Status
Subject Eurostar E39296 entity
Predicate rollingStockManufacturer P27543 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: [Eurostar, rollingStockManufacturer, Siemens]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siemens
Context triple: [Eurostar, rollingStockManufacturer, 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. Bosch
    Bosch is a multinational engineering and technology company best known for its automotive components, industrial products, and household appliances.
  • C. Schneider Electric
    Schneider Electric is a French multinational company specializing in energy management and industrial automation solutions for homes, buildings, data centers, infrastructure, and industry.
  • D. General Electric
    General Electric is a major American multinational conglomerate historically known for its leadership in industrial manufacturing, aviation, power, and healthcare technologies.
  • E. Mitsubishi Electric
    Mitsubishi Electric is a global Japanese electronics and electrical equipment manufacturer known for producing advanced technologies ranging from factory automation systems and power equipment to large-scale display and video board solutions.
  • 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_69a88630519c8190a17addd83c4a3ef4 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa64e22d6881909ba6ec120b320918 completed March 6, 2026, 5:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada99f52a08190854109d152c22be0 completed March 8, 2026, 4:53 p.m.
Created at: March 4, 2026, 7:31 p.m.