Triple

T12718366
Position Surface form Disambiguated ID Type / Status
Subject Nilsa Castro Espín E303907 entity
Predicate givenName P17 FINISHED
Object Nilsa
Nilsa is a feminine given name of Spanish origin, often used in Latin American countries.
 E999681 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: Nilsa | Statement: [Nilsa Castro Espín, givenName, Nilsa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nilsa
Context triple: [Nilsa Castro Espín, givenName, Nilsa]
  • A. Nina
    Nina is a central character in the British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
  • B. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • C. Nina
    Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
  • D. Nenê
    Nenê is a Brazilian professional basketball player and longtime NBA center known for his physical interior play and key contributions to both the Denver Nuggets and Washington Wizards.
  • E. Naila
    Naila is a small town in northern Bavaria, Germany, known for its location near the Franconian Forest and its traditional Upper Franconian character.
  • 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: Nilsa
Triple: [Nilsa Castro Espín, givenName, Nilsa]
Generated description
Nilsa is a feminine given name of Spanish origin, often used in Latin American countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nilsa
Target entity description: Nilsa is a feminine given name of Spanish origin, often used in Latin American countries.
  • A. Nina
    Nina is a central character in the British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
  • B. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • C. Nina
    Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
  • D. Nenê
    Nenê is a Brazilian professional basketball player and longtime NBA center known for his physical interior play and key contributions to both the Denver Nuggets and Washington Wizards.
  • E. Naila
    Naila is a small town in northern Bavaria, Germany, known for its location near the Franconian Forest and its traditional Upper Franconian character.
  • 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_69d7bdf084148190ab9d513dc0735af4 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9625d9da48190ab377f9328a0e1f5 completed April 10, 2026, 8:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69f67c802e248190865a6561ad1bf0b6 completed May 2, 2026, 10:36 p.m.
NEDg Description generation batch_69f67d64ed3481908d434c20796866f9 completed May 2, 2026, 10:40 p.m.
NED2 Entity disambiguation (via description) batch_69f67e82e35081909c4b5fad7e941610 completed May 2, 2026, 10:45 p.m.
Created at: April 9, 2026, 5:23 p.m.