Triple

T10533048
Position Surface form Disambiguated ID Type / Status
Subject Province of León E248491 entity
Predicate contains P35 FINISHED
Object La Bañeza
La Bañeza is a small historic city in northwestern Spain known for its cultural festivals and traditional architecture.
 E879380 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: La Bañeza | Statement: [Province of León, contains, La Bañeza]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: La Bañeza
Context triple: [Province of León, contains, La Bañeza]
  • A. Baeza
    Baeza is a historic Andalusian town in southern Spain renowned for its well-preserved Renaissance architecture and status as a UNESCO World Heritage Site.
  • B. Berruecos
    Berruecos is a locality in southwestern Colombia historically known as the site where independence leader Antonio José de Sucre was assassinated in 1830.
  • C. Almendralejo
    Almendralejo is a town in the Spanish region of Extremadura known for its wine production and agricultural economy.
  • D. Vilalba
    Vilalba is a town in the province of Lugo in Galicia, northwestern Spain, known as the birthplace of several notable Galician political and cultural figures.
  • E. Brihuega
    Brihuega is a historic town in central Spain’s Castilla-La Mancha region, renowned for its medieval architecture and extensive lavender fields.
  • 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: La Bañeza
Triple: [Province of León, contains, La Bañeza]
Generated description
La Bañeza is a small historic city in northwestern Spain known for its cultural festivals and traditional architecture.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: La Bañeza
Target entity description: La Bañeza is a small historic city in northwestern Spain known for its cultural festivals and traditional architecture.
  • A. Baeza
    Baeza is a historic Andalusian town in southern Spain renowned for its well-preserved Renaissance architecture and status as a UNESCO World Heritage Site.
  • B. Berruecos
    Berruecos is a locality in southwestern Colombia historically known as the site where independence leader Antonio José de Sucre was assassinated in 1830.
  • C. Almendralejo
    Almendralejo is a town in the Spanish region of Extremadura known for its wine production and agricultural economy.
  • D. Vilalba
    Vilalba is a town in the province of Lugo in Galicia, northwestern Spain, known as the birthplace of several notable Galician political and cultural figures.
  • E. Brihuega
    Brihuega is a historic town in central Spain’s Castilla-La Mancha region, renowned for its medieval architecture and extensive lavender fields.
  • 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_69d381c5c7448190bec34bee7ec72bac completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d50a19b59c8190b00db7d5813ad37d completed April 7, 2026, 1:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69d98832b97c8190a11246e087674e57 completed April 10, 2026, 11:30 p.m.
NEDg Description generation batch_69d98ae8403c81908a229aa06bd0388a completed April 10, 2026, 11:42 p.m.
NED2 Entity disambiguation (via description) batch_69d98ce9ba0c8190a7c62fa670e23705 completed April 10, 2026, 11:51 p.m.
Created at: April 6, 2026, 12:30 p.m.