Triple

T6834318
Position Surface form Disambiguated ID Type / Status
Subject Jill P. Mesirov E157411 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: [Jill P. Mesirov, employer, IBM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: IBM
Context triple: [Jill P. Mesirov, 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. Hewlett-Packard
    Hewlett-Packard is a pioneering American technology company known for its innovations in computing, printers, and enterprise IT solutions.
  • C. Unisys
    Unisys is an American global information technology company known for providing IT services, software, and infrastructure solutions to government and commercial clients.
  • D. 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.
  • E. Sun Microsystems
    Sun Microsystems was a pioneering American technology company best known for developing the Java programming language, the Solaris operating system, and high-performance networked computer systems.
  • 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_69c6882c53608190b99aebef079b23bd completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d67936288190829fedc3729aadd8 completed March 27, 2026, 7:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69c723eb23c8819085d2e8a8ce9c3c90 completed March 28, 2026, 12:42 a.m.
Created at: March 27, 2026, 2:18 p.m.