Triple

T484586
Position Surface form Disambiguated ID Type / Status
Subject Virginia Military Institute E9846 entity
Predicate abbreviation P43 FINISHED
Object VMI
VMI is a public military college in Lexington, Virginia, known for its rigorous academic and military training programs.
 E60381 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: VMI | Statement: [Virginia Military Institute, abbreviation, VMI]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VMI
Context triple: [Virginia Military Institute, abbreviation, VMI]
  • A. VU
    VU is a major research university in Amsterdam, Netherlands, known for its wide range of academic programs and emphasis on interdisciplinary and socially engaged scholarship.
  • B. VMS
    VMS is a regional public transport association in the Chemnitz area of Germany that coordinates and manages integrated fares and services across multiple transit operators.
  • C. VP&S
    VP&S is the commonly used abbreviation for Columbia University Vagelos College of Physicians and Surgeons, a leading medical school in New York City.
  • D. VGIK
    VGIK is Russia’s renowned national film school and one of the world’s oldest film institutes, known for training influential filmmakers such as Sergei Eisenstein.
  • E. VNM
    VNM is the three-letter ISO 3166-1 alpha-3 country code assigned to Vietnam.
  • 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: VMI
Triple: [Virginia Military Institute, abbreviation, VMI]
Generated description
VMI is a public military college in Lexington, Virginia, known for its rigorous academic and military training programs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: VMI
Target entity description: VMI is a public military college in Lexington, Virginia, known for its rigorous academic and military training programs.
  • A. VU
    VU is a major research university in Amsterdam, Netherlands, known for its wide range of academic programs and emphasis on interdisciplinary and socially engaged scholarship.
  • B. VMS
    VMS is a regional public transport association in the Chemnitz area of Germany that coordinates and manages integrated fares and services across multiple transit operators.
  • C. VP&S
    VP&S is the commonly used abbreviation for Columbia University Vagelos College of Physicians and Surgeons, a leading medical school in New York City.
  • D. VGIK
    VGIK is Russia’s renowned national film school and one of the world’s oldest film institutes, known for training influential filmmakers such as Sergei Eisenstein.
  • E. VNM
    VNM is the three-letter ISO 3166-1 alpha-3 country code assigned to Vietnam.
  • 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_69a2e802e2908190ab17c9479e0b6412 completed Feb. 28, 2026, 1:05 p.m.
NER Named-entity recognition batch_69a2f0ba310c81909645ef7e8a20b52f completed Feb. 28, 2026, 1:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69a471205b9081908e75db702e9b3530 completed March 1, 2026, 5:02 p.m.
NEDg Description generation batch_69a47180628c8190b801210ec5edf071 completed March 1, 2026, 5:04 p.m.
NED2 Entity disambiguation (via description) batch_69a4721291a08190bc0b6f3aaadf8b71 completed March 1, 2026, 5:06 p.m.
Created at: Feb. 28, 2026, 1:12 p.m.