Triple

T14990698
Position Surface form Disambiguated ID Type / Status
Subject Vega E373825 entity
Predicate hasNotableBearer P458 FINISHED
Object María José Vega
María José Vega is a Spanish scholar and literary critic known for her work on Renaissance and Baroque literature and the history of literary theory.
 E1215812 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: María José Vega | Statement: [Vega, hasNotableBearer, María José Vega]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: María José Vega
Context triple: [Vega, hasNotableBearer, María José Vega]
  • A. María Carrasco
    María Carrasco is a Spanish flamenco-pop singer known for her emotive vocal style and early success as a child artist.
  • B. María Valenzuela
    María Valenzuela is an Argentine actress known for her extensive work in television, film, and theater across several decades.
  • C. María Pinto
    María Pinto is a rural commune and town in central Chile known for its agricultural activities and location within the Santiago Metropolitan Region.
  • D. María Romo
    María Romo is a Spanish actress known for her work in film and television.
  • E. Catalina Álvarez del Casal
    Catalina Álvarez del Casal was a Colombian woman best known as the mother of independence leader Antonio Nariño.
  • 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: María José Vega
Triple: [Vega, hasNotableBearer, María José Vega]
Generated description
María José Vega is a Spanish scholar and literary critic known for her work on Renaissance and Baroque literature and the history of literary theory.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: María José Vega
Target entity description: María José Vega is a Spanish scholar and literary critic known for her work on Renaissance and Baroque literature and the history of literary theory.
  • A. María Carrasco
    María Carrasco is a Spanish flamenco-pop singer known for her emotive vocal style and early success as a child artist.
  • B. María Valenzuela
    María Valenzuela is an Argentine actress known for her extensive work in television, film, and theater across several decades.
  • C. María Pinto
    María Pinto is a rural commune and town in central Chile known for its agricultural activities and location within the Santiago Metropolitan Region.
  • D. María Romo
    María Romo is a Spanish actress known for her work in film and television.
  • E. Catalina Álvarez del Casal
    Catalina Álvarez del Casal was a Colombian woman best known as the mother of independence leader Antonio Nariño.
  • 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_69d85ccc84388190aa151e5173370c8d completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded715db408190b44e8a8452c79764 completed April 15, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_6a004f3548b48190aec852723654bd35 completed May 10, 2026, 9:26 a.m.
NEDg Description generation batch_6a00509164cc8190a381ba0a1de95ed1 completed May 10, 2026, 9:32 a.m.
NED2 Entity disambiguation (via description) batch_6a005447f1948190a939c0051891e444 completed May 10, 2026, 9:47 a.m.
Created at: April 10, 2026, 2:53 a.m.