Triple

T9481517
Position Surface form Disambiguated ID Type / Status
Subject Quindío Department E228650 entity
Predicate hasMunicipality P847 FINISHED
Object Génova
Génova is a small municipality in Colombia’s Quindío Department, known for its coffee-growing traditions and Andean rural landscapes.
 E804783 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: Génova | Statement: [Quindío Department, hasMunicipality, Génova]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Génova
Context triple: [Quindío Department, hasMunicipality, Génova]
  • A. Genoa
    Genoa is the codename for AMD’s fourth-generation EPYC server processors based on the Zen 4 architecture and the SP5 platform.
  • B. Genoa
    Genoa is a historic port city in northwestern Italy known for its significant maritime heritage, trade, and role as a major economic hub on the Ligurian coast.
  • C. Livorno
    Livorno is a port city on Italy’s western coast, historically notable for its diverse communities and significant Jewish population.
  • D. La Spezia
    La Spezia is a port city in northwestern Italy known as a major naval base and gateway to the Cinque Terre on the Ligurian coast.
  • E. Turin
    Turin is a small town located in Coweta County in the U.S. state of Georgia.
  • 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: Génova
Triple: [Quindío Department, hasMunicipality, Génova]
Generated description
Génova is a small municipality in Colombia’s Quindío Department, known for its coffee-growing traditions and Andean rural landscapes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Génova
Target entity description: Génova is a small municipality in Colombia’s Quindío Department, known for its coffee-growing traditions and Andean rural landscapes.
  • A. Genoa
    Genoa is a historic port city in northwestern Italy known for its significant maritime heritage, trade, and role as a major economic hub on the Ligurian coast.
  • B. Genoa
    Genoa is the codename for AMD’s fourth-generation EPYC server processors based on the Zen 4 architecture and the SP5 platform.
  • C. Livorno
    Livorno is a port city on Italy’s western coast, historically notable for its diverse communities and significant Jewish population.
  • D. La Spezia
    La Spezia is a port city in northwestern Italy known as a major naval base and gateway to the Cinque Terre on the Ligurian coast.
  • E. Turin
    Turin is a small town located in Coweta County in the U.S. state of Georgia.
  • 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_69ca84730a5081908de282651019bf2f completed March 30, 2026, 2:10 p.m.
NER Named-entity recognition batch_69cd8018645c8190823d82a93635b345 completed April 1, 2026, 8:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69d14bf604d4819092fc25f487866834 completed April 4, 2026, 5:35 p.m.
NEDg Description generation batch_69d14ca535588190aa2ff77f0658226b completed April 4, 2026, 5:38 p.m.
NED2 Entity disambiguation (via description) batch_69d14d29d32c8190a8615b3450492abc completed April 4, 2026, 5:40 p.m.
Created at: March 30, 2026, 7:55 p.m.