Triple

T8993249
Position Surface form Disambiguated ID Type / Status
Subject DOS/VS E214839 entity
Predicate successor P78 FINISHED
Object VSE/ESA E724109 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: VSE/ESA | Statement: [DOS/VS, successor, VSE/ESA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: VSE/ESA
Context triple: [DOS/VS, successor, VSE/ESA]
  • A. VSE/ESA chosen
    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. ESE
    ESE is a highly competitive Indian national-level examination conducted by the Union Public Service Commission to recruit engineers for prestigious technical and managerial positions in various government departments and public sector organizations.
  • C. VOSA
    VOSA was an executive agency of the UK government responsible for enforcing vehicle safety and environmental standards, and regulating operators of heavy goods and public service vehicles.
  • D. VSI
    VSI is the commonly used abbreviation for the "Very Short Introductions" series of concise, authoritative books published by Oxford University Press on a wide range of subjects.
  • E. VŠE
    VŠE is the commonly used abbreviation for the University of Economics in Prague, a leading Czech institution specializing in economics and business studies.
  • 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.