Triple

T17767419
Position Surface form Disambiguated ID Type / Status
Subject Sigüenza E443543 entity
Predicate hasSubdivision P747 FINISHED
Object Guijosa
Guijosa is a small village and administrative subdivision within the municipality of Sigüenza in the province of Guadalajara, Spain.
 E1286799 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: Guijosa | Statement: [Sigüenza, hasSubdivision, Guijosa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Guijosa
Context triple: [Sigüenza, hasSubdivision, Guijosa]
  • A. Saborío
    Saborío is a Spanish-language surname most notably associated with Costa Rican footballer Álvaro Saborío.
  • B. Malasaña
    Malasaña is a vibrant central Madrid neighborhood known for its bohemian atmosphere, nightlife, and alternative cultural scene.
  • C. Gachalá
    Gachalá is a small Colombian town in the Cundinamarca Department, known for its emerald mining and scenic Andean landscapes.
  • D. Almagro
    Almagro is a Spanish surname borne by various notable figures, including politicians, athletes, and artists from Spanish-speaking countries.
  • E. Almagro
    Almagro is a traditional middle-class neighborhood in central Buenos Aires, Argentina, known for its historic tango culture, cafes, and densely populated residential streets.
  • 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: Guijosa
Triple: [Sigüenza, hasSubdivision, Guijosa]
Generated description
Guijosa is a small village and administrative subdivision within the municipality of Sigüenza in the province of Guadalajara, Spain.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Guijosa
Target entity description: Guijosa is a small village and administrative subdivision within the municipality of Sigüenza in the province of Guadalajara, Spain.
  • A. Saborío
    Saborío is a Spanish-language surname most notably associated with Costa Rican footballer Álvaro Saborío.
  • B. Malasaña
    Malasaña is a vibrant central Madrid neighborhood known for its bohemian atmosphere, nightlife, and alternative cultural scene.
  • C. Gachalá
    Gachalá is a small Colombian town in the Cundinamarca Department, known for its emerald mining and scenic Andean landscapes.
  • D. Almagro
    Almagro is a Spanish surname borne by various notable figures, including politicians, athletes, and artists from Spanish-speaking countries.
  • E. Almagro
    Almagro is a traditional middle-class neighborhood in central Buenos Aires, Argentina, known for its historic tango culture, cafes, and densely populated residential streets.
  • 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_69d8b9edf16c8190a59ebd245d378f4f completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e485fccb9881908923564bf319f3c1 completed April 19, 2026, 7:36 a.m.
NED1 Entity disambiguation (via context triple) batch_6a02efc14808819099ecbb8a752aff24 completed May 12, 2026, 9:15 a.m.
NEDg Description generation batch_6a02f096fc648190a1ff19f2704a4cef completed May 12, 2026, 9:19 a.m.
NED2 Entity disambiguation (via description) batch_6a02f1d0a4708190bbf51356da7e965e completed May 12, 2026, 9:24 a.m.
Created at: April 10, 2026, 10:11 a.m.