Triple

T23044147
Position Surface form Disambiguated ID Type / Status
Subject Fernando Chueca Goitia E573827 entity
Predicate givenName P17 FINISHED
Object Fernando
Fernando is a masculine given name of Spanish and Portuguese origin, commonly used across the Iberian Peninsula and Latin America.
 E410234 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: Fernando | Statement: [Fernando Chueca Goitia, givenName, Fernando]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Fernando
Context triple: [Fernando Chueca Goitia, givenName, Fernando]
  • A. Fernando
    Fernando is the given name of Salgueiro Maia, a key Portuguese military officer who played a leading role in the Carnation Revolution.
  • B. Fernando
    Fernando was the given name of the Duke of Alba who served as governor-general, a prominent Spanish noble and military leader.
  • C. Fernando
    Fernando is the given name of Fernando Primo de Rivera, a 19th-century Spanish general and politician who briefly served as Prime Minister of Spain.
  • D. Fernando
    Fernando is a fictional character portrayed by actor Jake T. Austin, likely in a television or film role.
  • E. Fernando
    "Fernando" is a popular 1976 ballad by Swedish pop group ABBA, known for its nostalgic, storytelling lyrics and melodic harmonies.
  • 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: Fernando
Triple: [Fernando Chueca Goitia, givenName, Fernando]
Generated description
Fernando is a masculine given name of Spanish and Portuguese origin, commonly used across the Iberian Peninsula and Latin America.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Fernando
Target entity description: Fernando is a masculine given name of Spanish and Portuguese origin, commonly used across the Iberian Peninsula and Latin America.
  • A. Fernando chosen
    Fernando is a masculine given name of Spanish and Portuguese origin, commonly used in many Spanish-speaking and Lusophone countries.
  • B. Fernando
    Fernando is the given name of Fernando Primo de Rivera, a 19th-century Spanish general and politician who briefly served as Prime Minister of Spain.
  • C. Fernando
    Fernando was the given name of the Duke of Alba who served as governor-general, a prominent Spanish noble and military leader.
  • D. Fernando
    Fernando is the given name of Salgueiro Maia, a key Portuguese military officer who played a leading role in the Carnation Revolution.
  • E. Fernando
    "Fernando" is a popular 1976 ballad by Swedish pop group ABBA, known for its nostalgic, storytelling lyrics and melodic harmonies.
  • F. None of above.

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_69e245b9c11481909d06c872214d21af completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f18517083c8190a0850da5440e0a73 completed April 29, 2026, 4:12 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0c3f31c9f08190a90b1d8cb3c493c8 completed May 19, 2026, 10:45 a.m.
NEDg Description generation batch_6a0c43740a7c81909e9af7a9974a5700 completed May 19, 2026, 11:03 a.m.
NED2 Entity disambiguation (via description) batch_6a0c44d05c748190a5f4ab5f76dfcaab completed May 19, 2026, 11:09 a.m.
Created at: April 17, 2026, 3:54 p.m.