Triple

T366216
Position Surface form Disambiguated ID Type / Status
Subject Armed Forces of Cuba E7964 entity
Predicate controls P760 FINISHED
Object GAESA
GAESA is a powerful Cuban military-owned business conglomerate that dominates key sectors of the country's economy, including tourism, retail, and real estate.
 E46010 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: GAESA | Statement: [Armed Forces of Cuba, controls, GAESA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GAESA
Context triple: [Armed Forces of Cuba, controls, GAESA]
  • A. EGA
    EGA is the common abbreviation for the Eagle, Globe, and Anchor emblem that symbolizes the United States Marine Corps.
  • B. GEKUT
    GEKUT is the UN/LOCODE identifier for the city of Kutaisi in Georgia, used in international trade and transport logistics.
  • C. Degesch
    Degesch was a German chemical company best known for producing the pesticide Zyklon B, which was infamously used in Nazi extermination camps during the Holocaust.
  • D. Kalorama
    Kalorama is an affluent, historic residential neighborhood in Northwest Washington, D.C., known for its embassies, stately mansions, and prominent political residents.
  • E. GAU
    GAU is an abbreviation commonly used for the University of Göttingen, a major research university in Göttingen, Germany.
  • 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: GAESA
Triple: [Armed Forces of Cuba, controls, GAESA]
Generated description
GAESA is a powerful Cuban military-owned business conglomerate that dominates key sectors of the country's economy, including tourism, retail, and real estate.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: GAESA
Target entity description: GAESA is a powerful Cuban military-owned business conglomerate that dominates key sectors of the country's economy, including tourism, retail, and real estate.
  • A. EGA
    EGA is the common abbreviation for the Eagle, Globe, and Anchor emblem that symbolizes the United States Marine Corps.
  • B. GEKUT
    GEKUT is the UN/LOCODE identifier for the city of Kutaisi in Georgia, used in international trade and transport logistics.
  • C. Degesch
    Degesch was a German chemical company best known for producing the pesticide Zyklon B, which was infamously used in Nazi extermination camps during the Holocaust.
  • D. Kalorama
    Kalorama is an affluent, historic residential neighborhood in Northwest Washington, D.C., known for its embassies, stately mansions, and prominent political residents.
  • E. GAU
    GAU is an abbreviation commonly used for the University of Göttingen, a major research university in Göttingen, Germany.
  • 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_69a2e7e880008190a6ad7e06e5d03007 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ebe7d4d0819083daeb7686ae1914 completed Feb. 28, 2026, 1:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3e867bd50819083023e6021b784f4 completed March 1, 2026, 7:19 a.m.
NEDg Description generation batch_69a3e8d3f88c8190b8ab2e9109bb7184 completed March 1, 2026, 7:20 a.m.
NED2 Entity disambiguation (via description) batch_69a3e97a2c6481909b2fdc596df0f522 completed March 1, 2026, 7:23 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.