Triple

T2558083
Position Surface form Disambiguated ID Type / Status
Subject GANA E56774 entity
Predicate hasAbbreviation P43 FINISHED
Object GANA
GANA is an acronym that can refer to various organizations or entities depending on the context, such as political parties, associations, or initiatives in different countries.
 E278283 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: GANA | Statement: [GANA, hasAbbreviation, GANA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GANA
Context triple: [GANA, hasAbbreviation, GANA]
  • A. Bustamante
    Bustamante is a Spanish-origin surname borne by numerous notable figures in politics, arts, and sports across the Spanish-speaking world.
  • B. Consuela
    Consuela is a recurring character on the animated TV series "Family Guy," known as a stubborn, heavily accented Latina maid who often says "No, no, no."
  • C. Gabo
    Gabo is the affectionate diminutive nickname commonly used for the renowned Colombian writer Gabriel García Márquez.
  • D. Rojas
    Rojas is a Spanish surname historically associated with prominent noble families and political figures in Spain.
  • E. Garzón
    Garzón is a municipality and town in south-central Colombia known as an agricultural center within the Huila Department.
  • 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: GANA
Triple: [GANA, hasAbbreviation, GANA]
Generated description
GANA is an acronym that can refer to various organizations or entities depending on the context, such as political parties, associations, or initiatives in different countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: GANA
Target entity description: GANA is an acronym that can refer to various organizations or entities depending on the context, such as political parties, associations, or initiatives in different countries.
  • A. Bustamante
    Bustamante is a Spanish-origin surname borne by numerous notable figures in politics, arts, and sports across the Spanish-speaking world.
  • B. Consuela
    Consuela is a recurring character on the animated TV series "Family Guy," known as a stubborn, heavily accented Latina maid who often says "No, no, no."
  • C. Gabo
    Gabo is the affectionate diminutive nickname commonly used for the renowned Colombian writer Gabriel García Márquez.
  • D. Rojas
    Rojas is a Spanish surname historically associated with prominent noble families and political figures in Spain.
  • E. Garzón
    Garzón is a municipality and town in south-central Colombia known as an agricultural center within the Huila Department.
  • 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_69ab4a4bfec081908039988ec4c86e28 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd33153fc8190aa106e23ee645f63 completed March 7, 2026, 7:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69af5d2061108190b6250d2943736ae4 completed March 9, 2026, 11:52 p.m.
NEDg Description generation batch_69af5e0b40e0819098b6cbe31152a18c completed March 9, 2026, 11:55 p.m.
NED2 Entity disambiguation (via description) batch_69af5e69ac3481908f48ad0efcd13767 completed March 9, 2026, 11:57 p.m.
Created at: March 6, 2026, 9:48 p.m.