Triple

T8214755
Position Surface form Disambiguated ID Type / Status
Subject Shanghai Maglev Train E191905 entity
Predicate builtBy P972 FINISHED
Object ThyssenKrupp E489151 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: ThyssenKrupp | Statement: [Shanghai Maglev Train, builtBy, ThyssenKrupp]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ThyssenKrupp
Context triple: [Shanghai Maglev Train, builtBy, ThyssenKrupp]
  • A. ThyssenKrupp AG chosen
    ThyssenKrupp AG is a major German multinational conglomerate specializing in industrial engineering and steel production, with significant operations in areas such as elevators, automotive components, and plant technology.
  • B. ArcelorMittal
    ArcelorMittal is a multinational steel manufacturing corporation and one of the world’s largest steel producers, headquartered in Luxembourg.
  • C. Tata Steel
    Tata Steel is one of India’s largest and oldest steel manufacturing companies, known for its integrated steel plants, global operations, and pioneering role in the country’s industrial development.
  • D. Corus Group
    Corus Group was a major British-Dutch steel company formed from the merger of British Steel and Koninklijke Hoogovens, later acquired by Tata Steel.
  • E. Borsigwerke
    Borsigwerke is a Berlin U-Bahn station on line U6 serving the Tegel district in the city’s northwest.
  • 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_69ca82c8c054819087fedd9a5436b8a3 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb776c5cd081908259b1c3d12285de completed March 31, 2026, 7:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69ccedf320c08190ada5ae5c47d059eb completed April 1, 2026, 10:05 a.m.
Created at: March 30, 2026, 5:44 p.m.