Triple

T9522345
Position Surface form Disambiguated ID Type / Status
Subject Province of Almería E229672 entity
Predicate hasMunicipalities P747 FINISHED
Object Vera
Vera is a historic coastal town and municipality in Spain’s Andalusian province of Almería, known for its beaches and traditional whitewashed architecture.
 E803911 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: Vera | Statement: [Province of Almería, hasMunicipalities, Vera]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vera
Context triple: [Province of Almería, hasMunicipalities, Vera]
  • A. Vera
    Vera is a memorable supporting character from the 1989 Eddie Murphy film "Harlem Nights," known for her tough, comedic persona.
  • B. Vera
    Vera Rubin was an influential American astronomer whose pioneering work on galaxy rotation curves provided key evidence for the existence of dark matter.
  • C. Vera
    Vera is a feminine given name of Slavic origin, commonly used in Russian and other Eastern European cultures, meaning "faith."
  • D. Vera Savina
    Vera Savina was the wife of renowned Russian choreographer and ballet dancer Léonide Massine, associated with the world of early 20th-century ballet.
  • E. Veronika
    Veronika is the troubled young protagonist of Paulo Coelho's novel "Veronika Decides to Die," whose suicide attempt leads her to a transformative stay in a mental institution.
  • 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: Vera
Triple: [Province of Almería, hasMunicipalities, Vera]
Generated description
Vera is a historic coastal town and municipality in Spain’s Andalusian province of Almería, known for its beaches and traditional whitewashed architecture.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vera
Target entity description: Vera is a historic coastal town and municipality in Spain’s Andalusian province of Almería, known for its beaches and traditional whitewashed architecture.
  • A. Vera
    Vera Rubin was an influential American astronomer whose pioneering work on galaxy rotation curves provided key evidence for the existence of dark matter.
  • B. Vera
    Vera is a memorable supporting character from the 1989 Eddie Murphy film "Harlem Nights," known for her tough, comedic persona.
  • C. Vera
    Vera is a feminine given name of Slavic origin, commonly used in Russian and other Eastern European cultures, meaning "faith."
  • D. Vera Savina
    Vera Savina was the wife of renowned Russian choreographer and ballet dancer Léonide Massine, associated with the world of early 20th-century ballet.
  • E. Veronika
    Veronika is the troubled young protagonist of Paulo Coelho's novel "Veronika Decides to Die," whose suicide attempt leads her to a transformative stay in a mental institution.
  • 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_69ca847870a881909d8d751a7d29da39 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd989788e4819086c235bf37a56b04 completed April 1, 2026, 10:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69d13a5cd2d881908b27649b396c50a1 completed April 4, 2026, 4:20 p.m.
NEDg Description generation batch_69d13be79b1c8190a9110312ae25cf32 completed April 4, 2026, 4:27 p.m.
NED2 Entity disambiguation (via description) batch_69d13ca165b88190b4d629df0e079b3b completed April 4, 2026, 4:30 p.m.
Created at: March 30, 2026, 7:59 p.m.