Triple

T2082323
Position Surface form Disambiguated ID Type / Status
Subject Spaceship Earth E45270 entity
Predicate sponsor P67 FINISHED
Object Siemens USA 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 USA | Statement: [Spaceship Earth, sponsor, Siemens USA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Siemens USA
Context triple: [Spaceship Earth, sponsor, Siemens USA]
  • 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. 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.
  • C. S7 Group
    S7 Group is a Russian aviation holding company best known for owning and operating S7 Airlines and related air transport businesses.
  • D. Rockwell Automation
    Rockwell Automation is a leading American industrial automation and information technology company known for providing control systems, software, and services to manufacturing and industrial clients worldwide.
  • 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_69a8891869c88190a02643e3bb746f59 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abba5097ac8190a723a8af2982238c completed March 7, 2026, 5:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae518752148190bd7524872d70da7e completed March 9, 2026, 4:50 a.m.
Created at: March 4, 2026, 7:41 p.m.