Triple

T11531489
Position Surface form Disambiguated ID Type / Status
Subject Gary Cohn E273431 entity
Predicate boardMemberOf P10 FINISHED
Object IBM E1102 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: IBM | Statement: [Gary Cohn, boardMemberOf, IBM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: IBM
Context triple: [Gary Cohn, boardMemberOf, IBM]
  • A. IBM chosen
    IBM is a multinational technology and consulting company known for its pioneering work in computer hardware, software, and enterprise services.
  • B. IBM AT
    The IBM AT (Advanced Technology) is a mid-1980s IBM personal computer that introduced the 80286 processor and became a widely adopted standard for business PCs.
  • C. Hewlett-Packard
    Hewlett-Packard is a pioneering American technology company known for its innovations in computing, printers, and enterprise IT solutions.
  • D. Unisys
    Unisys is an American global information technology company known for providing IT services, software, and infrastructure solutions to government and commercial clients.
  • E. Computer Sciences Corporation
    Computer Sciences Corporation was a major American multinational IT services and consulting company that provided technology and professional services to government and commercial clients worldwide.
  • 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_69d6aae3fbec8190a14632a5df2538b6 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8839878948190b170e64629d6f2db completed April 10, 2026, 4:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69e6856341b481909d2ee71893e6117b completed April 20, 2026, 7:58 p.m.
Created at: April 8, 2026, 9:37 p.m.