Triple

T10341870
Position Surface form Disambiguated ID Type / Status
Subject Visa Information System E243144 entity
Predicate legalBasis P125 FINISHED
Object VIS Regulation
The VIS Regulation is the European Union law that establishes and governs the operation, data use, and security rules of the Visa Information System for processing Schengen visa applications.
 E858302 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: VIS Regulation | Statement: [Visa Information System, legalBasis, VIS Regulation]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VIS Regulation
Context triple: [Visa Information System, legalBasis, VIS Regulation]
  • A. VIS
    VIS is the IATA airport code for Visalia Municipal Airport in Visalia, California, United States.
  • B. Vis
    Vis is a Croatian island in the Adriatic Sea known for its unspoiled nature, historic towns, and former role as a strategic military base.
  • C. VIAG
    VIAG is the ICAO airport code for Agra Airport, a public and military airfield serving the city of Agra in Uttar Pradesh, India.
  • D. VIAG
    VIAG was a major German industrial and energy conglomerate that later merged into E.ON.
  • E. 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.
  • 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: VIS Regulation
Triple: [Visa Information System, legalBasis, VIS Regulation]
Generated description
The VIS Regulation is the European Union law that establishes and governs the operation, data use, and security rules of the Visa Information System for processing Schengen visa applications.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: VIS Regulation
Target entity description: The VIS Regulation is the European Union law that establishes and governs the operation, data use, and security rules of the Visa Information System for processing Schengen visa applications.
  • A. VIS
    VIS is the IATA airport code for Visalia Municipal Airport in Visalia, California, United States.
  • B. Vis
    Vis is a Croatian island in the Adriatic Sea known for its unspoiled nature, historic towns, and former role as a strategic military base.
  • C. VIAG
    VIAG is the ICAO airport code for Agra Airport, a public and military airfield serving the city of Agra in Uttar Pradesh, India.
  • D. VIAG
    VIAG was a major German industrial and energy conglomerate that later merged into E.ON.
  • E. 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.
  • 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_69d381af787481908bc401325c760a88 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e0a645348190af91450360a9abed completed April 7, 2026, 10:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69d75070c3ac8190b0d50a93d48c9bd9 completed April 9, 2026, 7:08 a.m.
NEDg Description generation batch_69d7618c9abc819080c4d6669dfb8320 completed April 9, 2026, 8:21 a.m.
NED2 Entity disambiguation (via description) batch_69d7702ae24481908b0f5319413e81d4 completed April 9, 2026, 9:23 a.m.
Created at: April 6, 2026, 11:55 a.m.