Triple

T8993248
Position Surface form Disambiguated ID Type / Status
Subject DOS/VS E214839 entity
Predicate successor P78 FINISHED
Object VSE/AF
VSE/AF is an IBM mainframe operating system in the VSE family, designed as a follow-on to DOS/VS for batch and transaction processing on smaller System/370-class systems.
 E771683 NE FINISHED

How this triple was built (4 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: VSE/AF | Statement: [DOS/VS, successor, VSE/AF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VSE/AF
Context triple: [DOS/VS, successor, VSE/AF]
  • A. VSE/ESA
    VSE/ESA is an IBM mainframe operating system in the VSE family, designed for smaller System/390 and z/Architecture environments to support batch and transaction processing workloads.
  • B. AFS
    AFS is the National Rail station code for Ashford (Surrey) railway station in England.
  • C. VŠE
    VŠE is the commonly used abbreviation for the University of Economics in Prague, a leading Czech institution specializing in economics and business studies.
  • D. VFU
    VFU is the commonly used abbreviation for Varna Free University, a private higher education institution in Varna, Bulgaria.
  • E. VFA
    VFA is the commonly used abbreviation for the Victorian Football Association, a historic Australian rules football competition based in the state of Victoria.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: VSE/AF
Triple: [DOS/VS, successor, VSE/AF]
Generated description
VSE/AF is an IBM mainframe operating system in the VSE family, designed as a follow-on to DOS/VS for batch and transaction processing on smaller System/370-class systems.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: VSE/AF
Target entity description: VSE/AF is an IBM mainframe operating system in the VSE family, designed as a follow-on to DOS/VS for batch and transaction processing on smaller System/370-class systems.
  • A. VSE/ESA
    VSE/ESA is an IBM mainframe operating system in the VSE family, designed for smaller System/390 and z/Architecture environments to support batch and transaction processing workloads.
  • B. AFS
    AFS is the National Rail station code for Ashford (Surrey) railway station in England.
  • C. VŠE
    VŠE is the commonly used abbreviation for the University of Economics in Prague, a leading Czech institution specializing in economics and business studies.
  • D. VFU
    VFU is the commonly used abbreviation for Varna Free University, a private higher education institution in Varna, Bulgaria.
  • E. VFA
    VFA is the commonly used abbreviation for the Victorian Football Association, a historic Australian rules football competition based in the state of Victoria.
  • F. None of above. chosen

Provenance (5 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.
NEDg Description generation batch_69cfd27680408190a5ebcebacf9e4303 completed April 3, 2026, 2:45 p.m.
NED2 Entity disambiguation (via description) batch_69cfd2dbf3cc81909589bd9467239573 completed April 3, 2026, 2:46 p.m.
Created at: March 30, 2026, 7:04 p.m.