Triple

T8993156
Position Surface form Disambiguated ID Type / Status
Subject OS/VS1 E214837 entity
Predicate supportsSubsystem P13398 FINISHED
Object VSAM E724117 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: VSAM | Statement: [OS/VS1, supportsSubsystem, VSAM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: VSAM
Context triple: [OS/VS1, supportsSubsystem, VSAM]
  • A. VSAM chosen
    VSAM (Virtual Storage Access Method) is an IBM file storage access method used on mainframe operating systems to organize and manage data in various indexed and sequential formats.
  • B. MVS
    MVS (Multiple Virtual Storage) is an IBM mainframe operating system that became the dominant successor to OS/360, providing advanced virtual memory and multitasking capabilities for enterprise computing.
  • C. MVS
    MVS is the commonly used Ukrainian abbreviation for the Ministry of Internal Affairs, the government body responsible for law enforcement and internal security in Ukraine.
  • D. VMS
    VMS is a multiuser, multitasking operating system originally created by Digital Equipment Corporation for its VAX minicomputers, known for its robustness, security features, and influence on later systems like Windows NT.
  • E. VMS
    VMS is a regional public transport association in the Chemnitz area of Germany that coordinates and manages integrated fares and services across multiple transit operators.
  • 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_69ca83a05c608190bdfdbdb25e994b39 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6876583081909d936dc3c3152587 completed April 1, 2026, 12:36 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfd0cd2b948190947a26fe11ad81bf completed April 3, 2026, 2:38 p.m.
Created at: March 30, 2026, 7:04 p.m.