Triple

T2243990
Position Surface form Disambiguated ID Type / Status
Subject León E49458 entity
Predicate hasFestival P3113 FINISHED
Object San Froilán
San Froilán is a traditional religious and cultural festival held in León, Spain, honoring the region’s patron saint with processions, folklore, and local gastronomy.
 E247046 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 Froilán | Statement: [León, hasFestival, San Froilán]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: San Froilán
Context triple: [León, hasFestival, San Froilán]
  • A. Íñigo
    Íñigo is the Basque given name of Ignatius of Loyola, the 16th-century Spanish priest who founded the Society of Jesus (Jesuits).
  • B. Baltasar
    Baltasar is a variant of the name Belshazzar, historically associated with the last king of Babylon mentioned in the biblical Book of Daniel.
  • C. Guillermo
    Guillermo is the Spanish form of the given name William, commonly used in Spanish-speaking countries.
  • D. Azaña
    Azaña is the surname of Manuel Azaña, a prominent Spanish politician and writer who served as President of the Second Spanish Republic.
  • E. ميرامار
    "ميرامار" هي رواية عربية شهيرة لنجيب محفوظ تدور أحداثها في بنسيون بالإسكندرية وتستعرض تحولات المجتمع المصري في ستينيات القرن العشرين من خلال وجهات نظر متعددة.
  • 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 Froilán
Triple: [León, hasFestival, San Froilán]
Generated description
San Froilán is a traditional religious and cultural festival held in León, Spain, honoring the region’s patron saint with processions, folklore, and local gastronomy.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: San Froilán
Target entity description: San Froilán is a traditional religious and cultural festival held in León, Spain, honoring the region’s patron saint with processions, folklore, and local gastronomy.
  • A. Íñigo
    Íñigo is the Basque given name of Ignatius of Loyola, the 16th-century Spanish priest who founded the Society of Jesus (Jesuits).
  • B. Baltasar
    Baltasar is a variant of the name Belshazzar, historically associated with the last king of Babylon mentioned in the biblical Book of Daniel.
  • C. Guillermo
    Guillermo is the Spanish form of the given name William, commonly used in Spanish-speaking countries.
  • D. Azaña
    Azaña is the surname of Manuel Azaña, a prominent Spanish politician and writer who served as President of the Second Spanish Republic.
  • E. ميرامار
    "ميرامار" هي رواية عربية شهيرة لنجيب محفوظ تدور أحداثها في بنسيون بالإسكندرية وتستعرض تحولات المجتمع المصري في ستينيات القرن العشرين من خلال وجهات نظر متعددة.
  • 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_69a88aa979788190ad6500f1d8eee2fc completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc0c157e88190a5bc876d9591a24b completed March 7, 2026, 6:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae6b10c38c8190af7d6d99f9377df1 completed March 9, 2026, 6:39 a.m.
NEDg Description generation batch_69ae6bbdef14819084b96389435ca080 completed March 9, 2026, 6:42 a.m.
NED2 Entity disambiguation (via description) batch_69ae6c2cfac48190b0425088e79cd122 completed March 9, 2026, 6:43 a.m.
Created at: March 4, 2026, 7:47 p.m.