Triple

T13891866
Position Surface form Disambiguated ID Type / Status
Subject Universidad de Oviedo E333991 entity
Predicate locatedIn P40 FINISHED
Object Mieres
Mieres is a town in the Asturias region of northern Spain known for its industrial and mining heritage and as a local educational hub.
 E1069143 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: Mieres | Statement: [Universidad de Oviedo, locatedIn, Mieres]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mieres
Context triple: [Universidad de Oviedo, locatedIn, Mieres]
  • A. Segorbe
    Segorbe is a historic town in eastern Spain known for its medieval architecture and traditional festivals, located in the Valencian Community.
  • B. Brunete
    Brunete is a town in the Community of Madrid, Spain, historically notable as a major battleground of the Spanish Civil War.
  • C. Sarria
    Sarria is a historic town in the province of Lugo, Galicia, Spain, known today as a major starting point on the Camino de Santiago pilgrimage route.
  • D. La Bañeza
    La Bañeza is a small historic city in northwestern Spain known for its cultural festivals and traditional architecture.
  • E. Ripollet
    Ripollet is a municipality in the comarca of Vallès Occidental in Catalonia, northeastern Spain, forming part of the Barcelona metropolitan area.
  • 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: Mieres
Triple: [Universidad de Oviedo, locatedIn, Mieres]
Generated description
Mieres is a town in the Asturias region of northern Spain known for its industrial and mining heritage and as a local educational hub.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mieres
Target entity description: Mieres is a town in the Asturias region of northern Spain known for its industrial and mining heritage and as a local educational hub.
  • A. Segorbe
    Segorbe is a historic town in eastern Spain known for its medieval architecture and traditional festivals, located in the Valencian Community.
  • B. Brunete
    Brunete is a town in the Community of Madrid, Spain, historically notable as a major battleground of the Spanish Civil War.
  • C. Sarria
    Sarria is a historic town in the province of Lugo, Galicia, Spain, known today as a major starting point on the Camino de Santiago pilgrimage route.
  • D. La Bañeza
    La Bañeza is a small historic city in northwestern Spain known for its cultural festivals and traditional architecture.
  • E. Ripollet
    Ripollet is a municipality in the comarca of Vallès Occidental in Catalonia, northeastern Spain, forming part of the Barcelona metropolitan area.
  • 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_69d81c5dd2d48190b7a5fc1e009de936 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de23a537d4819093c2bae2a244816a completed April 14, 2026, 11:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7ce71dcd481908f732542dfb1c3e3 completed May 3, 2026, 10:38 p.m.
NEDg Description generation batch_69f7cf37cd7c81908f4da2495403bc6c completed May 3, 2026, 10:42 p.m.
NED2 Entity disambiguation (via description) batch_69f7cfee80a881909de648b20043bf6d completed May 3, 2026, 10:45 p.m.
Created at: April 9, 2026, 10:15 p.m.