Triple

T1548865
Position Surface form Disambiguated ID Type / Status
Subject Caldas Department E33040 entity
Predicate hasCity P316 FINISHED
Object San José
San José is a small municipality and town located in the Caldas Department of Colombia, known for its coffee-growing rural landscape in the Andean region.
 E210729 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: San José | Statement: [Caldas Department, hasCity, San José]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: San José
Context triple: [Caldas Department, hasCity, San José]
  • A. San José
    San José is the capital and largest city of Costa Rica, known for its political, economic, and cultural significance in Central America.
  • B. San Jose
    San Jose is a major technology and innovation hub in Silicon Valley and one of the largest cities in Northern California.
  • C. San Fernando
    San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
  • D. San Fernando
    San Fernando is a coastal city in the Province of Cádiz, Andalusia, Spain, known for its naval base, salt marshes, and historical role in the Spanish War of Independence.
  • E. San Fernando
    San Fernando is a major industrial and commercial city located in the southern part of Trinidad, known for its energy sector and bustling urban center.
  • 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: San José
Triple: [Caldas Department, hasCity, San José]
Generated description
San José is a small municipality and town located in the Caldas Department of Colombia, known for its coffee-growing rural landscape in the Andean region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: San José
Target entity description: San José is a small municipality and town located in the Caldas Department of Colombia, known for its coffee-growing rural landscape in the Andean region.
  • A. San José
    San José is the capital and largest city of Costa Rica, known for its political, economic, and cultural significance in Central America.
  • B. San Jose
    San Jose is a major technology and innovation hub in Silicon Valley and one of the largest cities in Northern California.
  • C. San Fernando
    San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
  • D. San Fernando
    San Fernando is a coastal city in the Province of Cádiz, Andalusia, Spain, known for its naval base, salt marshes, and historical role in the Spanish War of Independence.
  • E. San Fernando
    San Fernando is a major industrial and commercial city located in the southern part of Trinidad, known for its energy sector and bustling urban center.
  • 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_69a885ee6db8819099502bc5ce8af881 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90856642c81909d88a679eb265b10 completed March 5, 2026, 4:36 a.m.
NED1 Entity disambiguation (via context triple) batch_69addf2ab134819090727cc68ff5c02e completed March 8, 2026, 8:42 p.m.
NEDg Description generation batch_69ade30730b48190b854ebe44f38436e completed March 8, 2026, 8:58 p.m.
NED2 Entity disambiguation (via description) batch_69ade361ab208190bc42269534973273 completed March 8, 2026, 9 p.m.
Created at: March 4, 2026, 7:26 p.m.