Triple

T11403297
Position Surface form Disambiguated ID Type / Status
Subject Azuay Province E270169 entity
Predicate hasImportantTown P14082 FINISHED
Object San Fernando
San Fernando is a small Andean town in Ecuador’s Azuay Province, known for its rural highland landscapes and traditional agricultural communities.
 E923848 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 Fernando | Statement: [Azuay Province, hasImportantTown, San Fernando]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: San Fernando
Context triple: [Azuay Province, hasImportantTown, San Fernando]
  • A. San Fernando
    San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
  • B. San Fernando
    San Fernando is a coastal municipality located in the island province of Romblon in the Philippines.
  • C. 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.
  • D. San Fernando
    San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
  • E. 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.
  • 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 Fernando
Triple: [Azuay Province, hasImportantTown, San Fernando]
Generated description
San Fernando is a small Andean town in Ecuador’s Azuay Province, known for its rural highland landscapes and traditional agricultural communities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: San Fernando
Target entity description: San Fernando is a small Andean town in Ecuador’s Azuay Province, known for its rural highland landscapes and traditional agricultural communities.
  • A. San Fernando
    San Fernando is a coastal municipality in the Philippine province of Masbate, known for its rural communities and fishing-based local economy.
  • B. San Fernando
    San Fernando is a municipality located in the Morazán Department of northeastern El Salvador, known for its rural character and mountainous surroundings.
  • C. San Fernando
    San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
  • D. San Fernando
    San Fernando is a Philippine city on the island of Luzon known as a regional commercial and administrative center.
  • E. 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.
  • 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_69d6aaddeaa8819088b30ef7b50598c9 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8014ab46881909fa1d425926c617b completed April 9, 2026, 7:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69e58d244870819091e8331eb3bd792d completed April 20, 2026, 2:19 a.m.
NEDg Description generation batch_69e59777b1208190a33a50da286535ee completed April 20, 2026, 3:03 a.m.
NED2 Entity disambiguation (via description) batch_69e5a3cf9d388190944340af484b3a54 completed April 20, 2026, 3:55 a.m.
Created at: April 8, 2026, 9:34 p.m.