Triple

T3244651
Position Surface form Disambiguated ID Type / Status
Subject SPARC E68040 entity
Predicate usedBy P260 FINISHED
Object Fujitsu E267007 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: Fujitsu | Statement: [SPARC, usedBy, Fujitsu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fujitsu
Context triple: [SPARC, usedBy, Fujitsu]
  • A. Fujitsu Limited chosen
    Fujitsu Limited is a major Japanese multinational information and communications technology company known for its computing products, IT services, and solutions.
  • B. NEC Corporation
    NEC Corporation is a Japanese multinational information technology and electronics company known for providing IT services, network solutions, and telecommunications equipment worldwide.
  • C. Toshiba
    Toshiba is a major Japanese multinational conglomerate known for its electronics, semiconductors, and information technology products and services.
  • D. Hitachi
    Hitachi is a Japanese multinational conglomerate known for its wide range of businesses spanning information technology, infrastructure, industrial systems, and consumer electronics.
  • E. Sharp Corporation
    Sharp Corporation is a Japanese multinational electronics manufacturer known for its consumer electronics, display technologies, and home 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_69ad858e4c708190aa31d486cfee8a6a completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69adaf1982448190b3d60c9e4471421f completed March 8, 2026, 5:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2775be7c88190ba60b191f1c51e19 completed March 12, 2026, 8:20 a.m.
Created at: March 8, 2026, 3:08 p.m.