Triple

T2947098
Position Surface form Disambiguated ID Type / Status
Subject Northern Spain E79528 entity
Predicate hasRiver P165 FINISHED
Object Nalón
The Nalón is a major river in Asturias, northern Spain, known for flowing through mountainous landscapes and historically supporting regional industry and mining.
 E313081 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: Nalón | Statement: [Northern Spain, hasRiver, Nalón]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nalón
Context triple: [Northern Spain, hasRiver, Nalón]
  • A. Caleruega
    Caleruega is a small town in the province of Burgos, Spain, best known as the birthplace of Saint Dominic, founder of the Dominican Order.
  • B. Alba de Tormes
    Alba de Tormes is a historic town in the province of Salamanca, Spain, known for its association with the noble House of Alba and as the burial place of Saint Teresa of Ávila.
  • C. Laínez
    Laínez is a Spanish surname most notably associated with Diego Laínez, a 16th-century Jesuit priest and second Superior General of the Society of Jesus.
  • D. Fuentealbilla
    Fuentealbilla is a small municipality in the province of Albacete, Spain, best known as the hometown of footballer Andrés Iniesta.
  • E. Espín
    Espín is a Spanish surname notably borne by Cuban revolutionary and feminist leader Vilma Espín.
  • 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: Nalón
Triple: [Northern Spain, hasRiver, Nalón]
Generated description
The Nalón is a major river in Asturias, northern Spain, known for flowing through mountainous landscapes and historically supporting regional industry and mining.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nalón
Target entity description: The Nalón is a major river in Asturias, northern Spain, known for flowing through mountainous landscapes and historically supporting regional industry and mining.
  • A. Caleruega
    Caleruega is a small town in the province of Burgos, Spain, best known as the birthplace of Saint Dominic, founder of the Dominican Order.
  • B. Alba de Tormes
    Alba de Tormes is a historic town in the province of Salamanca, Spain, known for its association with the noble House of Alba and as the burial place of Saint Teresa of Ávila.
  • C. Laínez
    Laínez is a Spanish surname most notably associated with Diego Laínez, a 16th-century Jesuit priest and second Superior General of the Society of Jesus.
  • D. Fuentealbilla
    Fuentealbilla is a small municipality in the province of Albacete, Spain, best known as the hometown of footballer Andrés Iniesta.
  • E. Espín
    Espín is a Spanish surname notably borne by Cuban revolutionary and feminist leader Vilma Espín.
  • 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_69ad8b1089588190b74d9e2505e45762 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad98b5916c8190b1163bf0b7fa136a completed March 8, 2026, 3:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69b08695bea08190abce552493abda57 completed March 10, 2026, 9:01 p.m.
NEDg Description generation batch_69b0d4d08d688190888459d7d4fbd8d4 completed March 11, 2026, 2:34 a.m.
NED2 Entity disambiguation (via description) batch_69b0d5567e488190b5eee8a494433ae4 completed March 11, 2026, 2:37 a.m.
Created at: March 8, 2026, 2:57 p.m.