Triple

T1271141
Position Surface form Disambiguated ID Type / Status
Subject Salgueiro Maia E15711 entity
Predicate givenName P17 FINISHED
Object Fernando
Fernando is the given name of Salgueiro Maia, a key Portuguese military officer who played a leading role in the Carnation Revolution.
 E167378 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: [Salgueiro Maia, givenName, Fernando]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Fernando
Context triple: [Salgueiro Maia, givenName, Fernando]
  • A. Alfonso
    Alfonso is a masculine given name of Spanish and Italian origin historically borne by numerous kings, nobles, and notable figures across Europe.
  • B. Francisco
    Francisco is a masculine given name of Spanish and Portuguese origin, equivalent to Francis in English.
  • C. Manuel
    Manuel is the given name of Manny Ramirez, the former Major League Baseball star known for his powerful hitting and tenure with the Boston Red Sox.
  • D. Luís
    Luís is a common Portuguese male given name, historically associated with notable figures such as the poet Luís de Camões.
  • E. Guillermo
    Guillermo is the Spanish form of the given name William, commonly used in Spanish-speaking countries.
  • 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: [Salgueiro Maia, givenName, Fernando]
Generated description
Fernando is the given name of Salgueiro Maia, a key Portuguese military officer who played a leading role in the Carnation Revolution.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Fernando
Target entity description: Fernando is the given name of Salgueiro Maia, a key Portuguese military officer who played a leading role in the Carnation Revolution.
  • A. Alfonso
    Alfonso is a masculine given name of Spanish and Italian origin historically borne by numerous kings, nobles, and notable figures across Europe.
  • B. Francisco
    Francisco is a masculine given name of Spanish and Portuguese origin, equivalent to Francis in English.
  • C. Manuel
    Manuel is the given name of Manny Ramirez, the former Major League Baseball star known for his powerful hitting and tenure with the Boston Red Sox.
  • D. Luís
    Luís is a common Portuguese male given name, historically associated with notable figures such as the poet Luís de Camões.
  • E. Guillermo
    Guillermo is the Spanish form of the given name William, commonly used in 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_69a4935a94308190bb92555b79032824 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4c06ae7b88190a1e0b5232d84a7b1 completed March 1, 2026, 10:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad0e6027448190b10c65bcafe9fedf completed March 8, 2026, 5:51 a.m.
NEDg Description generation batch_69ad100fc7108190bf5211fb5817ca5f completed March 8, 2026, 5:58 a.m.
NED2 Entity disambiguation (via description) batch_69ad105a9bcc8190ba6d14df99ff2a7a completed March 8, 2026, 5:59 a.m.
Created at: March 1, 2026, 7:50 p.m.