Triple

T13325852
Position Surface form Disambiguated ID Type / Status
Subject Rovira i Virgili University E317439 entity
Predicate shortName P43 FINISHED
Object URV
URV is the commonly used abbreviation for Rovira i Virgili University, a public university based in Tarragona, Catalonia, Spain.
E1034013 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: URV | Statement: [Rovira i Virgili University, shortName, URV]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: URV
Context triple: [Rovira i Virgili University, shortName, URV]
  • A. UR
    UR is the vehicle registration code used on license plates for vehicles registered in the Swiss canton of Uri.
  • B. UR
    UR is the commonly used abbreviation for the University of Redlands, a private liberal arts university in Redlands, California.
  • C. UR
    UR (Utbildningsradion) is Sweden’s public educational broadcasting company, producing and distributing educational radio, TV, and digital content.
  • D. ULV
    ULV is the IATA airport code for Ulyanovsk Baratayevka Airport in Ulyanovsk, Russia.
  • E. URU
    URU is the FIFA country code used to represent the Uruguay national football team in international competitions and rankings.
  • 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: URV
Triple: [Rovira i Virgili University, shortName, URV]
Generated description
URV is the commonly used abbreviation for Rovira i Virgili University, a public university based in Tarragona, Catalonia, Spain.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: URV
Target entity description: URV is the commonly used abbreviation for Rovira i Virgili University, a public university based in Tarragona, Catalonia, Spain.
  • A. UR
    UR is the vehicle registration code used on license plates for vehicles registered in the Swiss canton of Uri.
  • B. UR
    UR is the commonly used abbreviation for the University of Redlands, a private liberal arts university in Redlands, California.
  • C. UR
    UR (Utbildningsradion) is Sweden’s public educational broadcasting company, producing and distributing educational radio, TV, and digital content.
  • D. ULV
    ULV is the IATA airport code for Ulyanovsk Baratayevka Airport in Ulyanovsk, Russia.
  • E. URU
    URU is the FIFA country code used to represent the Uruguay national football team in international competitions and rankings.
  • 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_69d806b4d62c81908d4ced1665414be5 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d9992d3b0881909732fbb8db98e44c completed April 11, 2026, 12:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69f71f2f69a88190b11e61a922786fc4 completed May 3, 2026, 10:10 a.m.
NEDg Description generation batch_69f71fe2c128819096cc31c9cbb739b5 completed May 3, 2026, 10:13 a.m.
NED2 Entity disambiguation (via description) batch_69f72090b6b081908870801fdb679f57 completed May 3, 2026, 10:16 a.m.
Created at: April 9, 2026, 9:30 p.m.