Triple

T2890715
Position Surface form Disambiguated ID Type / Status
Subject Virginia Sports & Entertainment Law Journal E63814 entity
Predicate hasAbbreviation P43 FINISHED
Object VSELJ
VSELJ is a student-edited law journal at the University of Virginia School of Law focusing on legal issues in sports and entertainment.
E308637 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: VSELJ | Statement: [Virginia Sports & Entertainment Law Journal, hasAbbreviation, VSELJ]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: VSELJ
Context triple: [Virginia Sports & Entertainment Law Journal, hasAbbreviation, VSELJ]
  • A. VLKSM
    VLKSM was the Russian abbreviation for the All-Union Leninist Young Communist League, the Soviet Union’s official youth organization affiliated with the Communist Party.
  • B. Vlašim
    Vlašim is a small Czech town known for its historic château, English-style park, and location in the Central Bohemian Region southeast of Prague.
  • C. VEKU
    VEKU is the ICAO airport code assigned to Silchar Airport in Assam, India.
  • D.
    Vé is a Norse god, one of Odin’s brothers, associated with the creation of the world in Norse mythology.
  • E. Vestli
    Vestli is a residential neighborhood in the Stovner borough of Oslo, Norway, known for being served by the Oslo Metro.
  • 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: VSELJ
Triple: [Virginia Sports & Entertainment Law Journal, hasAbbreviation, VSELJ]
Generated description
VSELJ is a student-edited law journal at the University of Virginia School of Law focusing on legal issues in sports and entertainment.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: VSELJ
Target entity description: VSELJ is a student-edited law journal at the University of Virginia School of Law focusing on legal issues in sports and entertainment.
  • A. VLKSM
    VLKSM was the Russian abbreviation for the All-Union Leninist Young Communist League, the Soviet Union’s official youth organization affiliated with the Communist Party.
  • B. Vlašim
    Vlašim is a small Czech town known for its historic château, English-style park, and location in the Central Bohemian Region southeast of Prague.
  • C. VEKU
    VEKU is the ICAO airport code assigned to Silchar Airport in Assam, India.
  • D.
    Vé is a Norse god, one of Odin’s brothers, associated with the creation of the world in Norse mythology.
  • E. Vestli
    Vestli is a residential neighborhood in the Stovner borough of Oslo, Norway, known for being served by the Oslo Metro.
  • 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_69ab4c45822c8190830c5f2bb97bcfd0 completed March 6, 2026, 9:51 p.m.
NER Named-entity recognition batch_69abe05f98848190a33344fa780a3597 completed March 7, 2026, 8:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69b03179d7448190bcdbea164856aaa2 completed March 10, 2026, 2:58 p.m.
NEDg Description generation batch_69b03f0c5bac81909aa21d5963a86c92 completed March 10, 2026, 3:55 p.m.
NED2 Entity disambiguation (via description) batch_69b044c1ea3c8190a9ae7c1431d3a3f2 completed March 10, 2026, 4:20 p.m.
Created at: March 6, 2026, 10:07 p.m.