Triple

T13063928
Position Surface form Disambiguated ID Type / Status
Subject Nicholas M. Donofrio E329269 entity
Predicate employer P7 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: [Nicholas M. Donofrio, employer, IBM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: IBM
Context triple: [Nicholas M. Donofrio, employer, 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. Computer Associates International
    Computer Associates International (now known as CA Technologies) was a major American enterprise software company recognized for its broad portfolio of mainframe, security, and IT management solutions.
  • E. Unisys
    Unisys is an American global information technology company known for providing IT services, software, and infrastructure solutions to government and commercial clients.
  • 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_69d80771749c81909a6d9197b9504872 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d980e9bdfc81908eb90fb50597df64 completed April 10, 2026, 10:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6cbe630808190a9a3481127bbaa86 completed May 3, 2026, 4:15 a.m.
Created at: April 9, 2026, 8:59 p.m.