Triple

T2367160
Position Surface form Disambiguated ID Type / Status
Subject Ministry of the Revolutionary Armed Forces E46008 entity
Predicate shortName P43 FINISHED
Object MINFAR
MINFAR is the acronym for Cuba’s Ministry of the Revolutionary Armed Forces, the government body responsible for overseeing the country’s military.
E261449 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: MINFAR | Statement: [Ministry of the Revolutionary Armed Forces, shortName, MINFAR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MINFAR
Context triple: [Ministry of the Revolutionary Armed Forces, shortName, MINFAR]
  • A. Manf
    Manf is the Arabic name for the ancient Egyptian city of Memphis, a historically significant capital near modern-day Cairo.
  • B. MIN
    MIN is the standard NHL abbreviation for the Minnesota Wild professional ice hockey team.
  • C. MIN
    MIN is the standard abbreviation used for the Minnesota Twins Major League Baseball team.
  • D. MIN
    MIN is the standard NBA abbreviation for the Minnesota Timberwolves basketball team.
  • E. MINISDEF
    MINISDEF is the official abbreviation for Spain’s Ministry of Defence, the government department responsible for national defense and the armed forces.
  • 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: MINFAR
Triple: [Ministry of the Revolutionary Armed Forces, shortName, MINFAR]
Generated description
MINFAR is the acronym for Cuba’s Ministry of the Revolutionary Armed Forces, the government body responsible for overseeing the country’s military.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: MINFAR
Target entity description: MINFAR is the acronym for Cuba’s Ministry of the Revolutionary Armed Forces, the government body responsible for overseeing the country’s military.
  • A. Manf
    Manf is the Arabic name for the ancient Egyptian city of Memphis, a historically significant capital near modern-day Cairo.
  • B. MINISDEF
    MINISDEF is the official abbreviation for Spain’s Ministry of Defence, the government department responsible for national defense and the armed forces.
  • C. Nuffar
    Nuffar is the modern name for the archaeological mound that marks the site of the ancient Sumerian city of Nippur in present-day Iraq.
  • D. Minginish
    Minginish is a rugged peninsula on the Isle of Skye in Scotland, known for its dramatic coastal scenery and proximity to the Cuillin mountains.
  • E. Mangina
    Mangina is a town in North Kivu Province in the eastern Democratic Republic of the Congo that gained international attention as a focal point of the 2018–2020 Kivu Ebola epidemic.
  • 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_69a88a145268819083e2736cb835c696 completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abc74b6bdc8190a12b2bcaa2dd7616 completed March 7, 2026, 6:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69aea89ae4688190be2e0825f0875ed3 completed March 9, 2026, 11:01 a.m.
NEDg Description generation batch_69aeac4b9b108190b440c991342df9b6 completed March 9, 2026, 11:17 a.m.
NED2 Entity disambiguation (via description) batch_69aeacc8445481908a3ae8bd62493413 completed March 9, 2026, 11:19 a.m.
Created at: March 4, 2026, 7:56 p.m.