Triple

T1947747
Position Surface form Disambiguated ID Type / Status
Subject Soviet Navy E42093 entity
Predicate shortName P43 FINISHED
Object VMF
VMF is the Russian abbreviation for the Soviet Navy, the maritime branch of the former Soviet Union’s armed forces.
 E218192 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: VMF | Statement: [Soviet Navy, shortName, VMF]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VMF
Context triple: [Soviet Navy, shortName, VMF]
  • A. 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.
  • B. MVFC
    MVFC is a collegiate athletic conference that sponsors NCAA Division I Football Championship Subdivision (FCS) football programs primarily in the Midwestern United States.
  • C. VS
    VS is the two-letter abbreviation commonly used for the Swiss canton of Valais.
  • D. HVF
    HVF is a data-focused startup and innovation lab created by entrepreneur Max Levchin to explore and build companies around large-scale data problems.
  • E. MF 77
    MF 77 is a steel-wheeled electric multiple unit train used on several lines of the Paris Métro, introduced in the late 1970s to modernize the network’s rolling stock.
  • 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: VMF
Triple: [Soviet Navy, shortName, VMF]
Generated description
VMF is the Russian abbreviation for the Soviet Navy, the maritime branch of the former Soviet Union’s armed forces.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: VMF
Target entity description: VMF is the Russian abbreviation for the Soviet Navy, the maritime branch of the former Soviet Union’s armed forces.
  • A. 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.
  • B. MVFC
    MVFC is a collegiate athletic conference that sponsors NCAA Division I Football Championship Subdivision (FCS) football programs primarily in the Midwestern United States.
  • C. VS
    VS is the two-letter abbreviation commonly used for the Swiss canton of Valais.
  • D. HVF
    HVF is a data-focused startup and innovation lab created by entrepreneur Max Levchin to explore and build companies around large-scale data problems.
  • E. MF 77
    MF 77 is a steel-wheeled electric multiple unit train used on several lines of the Paris Métro, introduced in the late 1970s to modernize the network’s rolling stock.
  • 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_69a8870e08fc8190a319cbf2600db15f completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb33040c881908f42e80cbe1b1aca completed March 7, 2026, 5:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69adfbbf724081909b24680d483edbd1 completed March 8, 2026, 10:44 p.m.
NEDg Description generation batch_69adfc6aa96c81909ae3cff6c7ab7f79 completed March 8, 2026, 10:47 p.m.
NED2 Entity disambiguation (via description) batch_69adfcebbc808190a74f9082636bce11 completed March 8, 2026, 10:49 p.m.
Created at: March 4, 2026, 7:36 p.m.