Triple

T6769721
Position Surface form Disambiguated ID Type / Status
Subject Vehicle and Operator Services Agency E155012 entity
Predicate shortName P43 FINISHED
Object 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.
 E617260 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: VOSA | Statement: [Vehicle and Operator Services Agency, shortName, VOSA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VOSA
Context triple: [Vehicle and Operator Services Agency, shortName, VOSA]
  • A. VOI
    VOI is the ICAO airline designator used to identify Volaris, a Mexican low-cost carrier, in aviation operations and communications.
  • B. VASCO
    VASCO is a regional airline in Vietnam that operates domestic flights, often serving smaller airports and routes on behalf of Vietnam Airlines.
  • C. VABO
    VABO is the ICAO airport code assigned to Vadodara Airport in Gujarat, India.
  • D. VOHS
    VOHS is the ICAO airport code for Rajiv Gandhi International Airport serving Hyderabad, India.
  • E. VUSAC
    VUSAC is the student government representing and advocating for students at Victoria University in the University of Toronto.
  • 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: VOSA
Triple: [Vehicle and Operator Services Agency, shortName, VOSA]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: VOSA
Target entity description: 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.
  • A. VOI
    VOI is the ICAO airline designator used to identify Volaris, a Mexican low-cost carrier, in aviation operations and communications.
  • B. VASCO
    VASCO is a regional airline in Vietnam that operates domestic flights, often serving smaller airports and routes on behalf of Vietnam Airlines.
  • C. VABO
    VABO is the ICAO airport code assigned to Vadodara Airport in Gujarat, India.
  • D. VOHS
    VOHS is the ICAO airport code for Rajiv Gandhi International Airport serving Hyderabad, India.
  • E. VUSAC
    VUSAC is the student government representing and advocating for students at Victoria University in the University of Toronto.
  • 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_69c68812ef7c819099369f51febb725c completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d2347fb48190a44c03317b5ecfd7 completed March 27, 2026, 6:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69c712c46b70819097401afab991c808 completed March 27, 2026, 11:29 p.m.
NEDg Description generation batch_69c713853bf88190a8a07fd9f4ea1687 completed March 27, 2026, 11:32 p.m.
NED2 Entity disambiguation (via description) batch_69c713ead2a48190bbf95caf2ca8d997 completed March 27, 2026, 11:34 p.m.
Created at: March 27, 2026, 2:13 p.m.