Triple

T21961584
Position Surface form Disambiguated ID Type / Status
Subject María E542342 entity
Predicate hasDiminutive P456 FINISHED
Object Maruja
Maruja is a Spanish feminine given name, commonly used as a diminutive or affectionate form of María.
 E1511655 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: Maruja | Statement: [María, hasDiminutive, Maruja]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Maruja
Context triple: [María, hasDiminutive, Maruja]
  • A. Inés
    Inés is a feminine given name, especially common in Spanish-speaking countries, derived from the name Agnes.
  • B. Marisabel
    Marisabel is a feminine given name of Spanish origin, commonly used in Spanish-speaking countries.
  • C. Rosa Elena
    Rosa Elena is a Mexican public figure best known as the wife of former president Felipe Calderón and for her involvement in high-profile political and legal controversies.
  • D. Manuela
    Manuela is the given name of Maria Manuela, a 16th-century Portuguese princess who became Queen of Castile through marriage to King Philip II of Spain.
  • E. Pilar
    Pilar is a Spanish royal, known formally as Infanta Pilar, Duchess of Badajoz, and a member of the House of Bourbon.
  • 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: Maruja
Triple: [María, hasDiminutive, Maruja]
Generated description
Maruja is a Spanish feminine given name, commonly used as a diminutive or affectionate form of María.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Maruja
Target entity description: Maruja is a Spanish feminine given name, commonly used as a diminutive or affectionate form of María.
  • A. Inés
    Inés is a feminine given name, especially common in Spanish-speaking countries, derived from the name Agnes.
  • B. Marisabel
    Marisabel is a feminine given name of Spanish origin, commonly used in Spanish-speaking countries.
  • C. Rosa Elena
    Rosa Elena is a Mexican public figure best known as the wife of former president Felipe Calderón and for her involvement in high-profile political and legal controversies.
  • D. Manuela
    Manuela is the given name of Maria Manuela, a 16th-century Portuguese princess who became Queen of Castile through marriage to King Philip II of Spain.
  • E. Pilar
    Pilar is a Spanish feminine given name, often associated with religious devotion to Our Lady of the Pillar and traditionally used in Spain and Spanish-speaking countries.
  • 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_69e0c47fab1081908dc74a6545dbb051 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f124572738819098cc669aafa53cc6 completed April 28, 2026, 9:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a6d6f86248190a6fbfbc745d1e567 completed May 18, 2026, 1:37 a.m.
NEDg Description generation batch_6a0a6e3690e88190a8cabaa29a84799e completed May 18, 2026, 1:41 a.m.
NED2 Entity disambiguation (via description) batch_6a0a6ea775988190865a406893a467cc completed May 18, 2026, 1:43 a.m.
Created at: April 16, 2026, 8 p.m.