Triple

T2730977
Position Surface form Disambiguated ID Type / Status
Subject Vilma Espín E60311 entity
Predicate givenName P17 FINISHED
Object Vilma
Vilma is a feminine given name used in various cultures, often as a variant of Wilma or Vilhelmina.
 E293417 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: Vilma | Statement: [Vilma Espín, givenName, Vilma]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vilma
Context triple: [Vilma Espín, givenName, Vilma]
  • A. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • B. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • C. Consuelo
    Consuelo is a feminine given name of Spanish origin, historically associated with figures such as American socialite Consuelo Vanderbilt.
  • D. Gregoria
    Gregoria is a feminine given name derived from the masculine name Gregory, commonly used in various European languages.
  • E. Lorena
    Lorena is a city in the state of São Paulo, Brazil, known for hosting a campus of the University of São Paulo.
  • 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: Vilma
Triple: [Vilma Espín, givenName, Vilma]
Generated description
Vilma is a feminine given name used in various cultures, often as a variant of Wilma or Vilhelmina.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vilma
Target entity description: Vilma is a feminine given name used in various cultures, often as a variant of Wilma or Vilhelmina.
  • A. María
    "María" is a film featuring actress Taryn Power in a significant role.
  • B. María
    María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
  • C. Consuelo
    Consuelo is a feminine given name of Spanish origin, historically associated with figures such as American socialite Consuelo Vanderbilt.
  • D. Gregoria
    Gregoria is a feminine given name derived from the masculine name Gregory, commonly used in various European languages.
  • E. Lorena
    Lorena is a city in the state of São Paulo, Brazil, known for hosting a campus of the University of São Paulo.
  • 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_69ab4b75cd908190b691ef0d1801acda completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdaee29088190bc4c734e48995794 completed March 7, 2026, 7:59 a.m.
NED1 Entity disambiguation (via context triple) batch_69afb69c9f648190bbbcfa42ab68c6f2 completed March 10, 2026, 6:13 a.m.
NEDg Description generation batch_69afb75f5a8c81908648149d27ef7a5a completed March 10, 2026, 6:17 a.m.
NED2 Entity disambiguation (via description) batch_69afb7cdadd08190a7c47e38eee43f90 completed March 10, 2026, 6:18 a.m.
Created at: March 6, 2026, 9:56 p.m.