Triple

T6530464
Position Surface form Disambiguated ID Type / Status
Subject Thyssen-Bornemisza family E152216 entity
Predicate associatedCompany P629 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: [Thyssen-Bornemisza family, associatedCompany, ThyssenKrupp]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ThyssenKrupp
Context triple: [Thyssen-Bornemisza family, associatedCompany, 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. 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.
  • D. Borsigwerke
    Borsigwerke is a Berlin U-Bahn station on line U6 serving the Tegel district in the city’s northwest.
  • E. Krupp (company)
    Krupp (company) was a major German industrial conglomerate best known for its steel production and armaments manufacturing, playing a central role in both World Wars and in the development of heavy industry in Germany.
  • 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_69c688048ec8819093a47f7d332e12ec completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6adac53b0819097fece48a75cc48f completed March 27, 2026, 4:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6e41908f0819096e6e432509afe7d completed March 27, 2026, 8:10 p.m.
Created at: March 27, 2026, 1:46 p.m.