Triple

T5722455
Position Surface form Disambiguated ID Type / Status
Subject Gerd Binnig E126177 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: [Gerd Binnig, employer, IBM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: IBM
Context triple: [Gerd Binnig, 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_69c0082f723881908ce8bb13a0c0f8b7 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c024e6c444819089270b188e60cc67 completed March 22, 2026, 5:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a80aefc8190b6ea35ab405ea502 completed March 22, 2026, 9:09 p.m.
Created at: March 22, 2026, 3:46 p.m.