Triple

T750894
Position Surface form Disambiguated ID Type / Status
Subject Otelo Saraiva de Carvalho E15444 entity
Predicate placeOfBirth P1 FINISHED
Object Lourenço Marques
Lourenço Marques is the former name of Maputo, the capital city and main port of Mozambique.
 E95848 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: Lourenço Marques | Statement: [Otelo Saraiva de Carvalho, placeOfBirth, Lourenço Marques]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lourenço Marques
Context triple: [Otelo Saraiva de Carvalho, placeOfBirth, Lourenço Marques]
  • A. Beira
    Beira is a major port city in central Mozambique, serving as a key commercial and transport hub for the region.
  • B. Salvador
    Salvador is the given name of the renowned Spanish surrealist artist Salvador Dalí.
  • C. Belém
    Belém is a historic riverside district of Lisbon, Portugal, known for its monuments of the Age of Discoveries, including the Belém Tower and Jerónimos Monastery.
  • D. Olinda
    Olinda is a historic coastal city in northeastern Brazil renowned for its well-preserved colonial architecture and vibrant Carnival celebrations.
  • E. Salvador, Bahia, Brazil
    Salvador, the capital of Brazil’s Bahia state, is a major coastal city known for its Afro-Brazilian culture, colonial architecture, and historic role as the country’s first capital.
  • 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: Lourenço Marques
Triple: [Otelo Saraiva de Carvalho, placeOfBirth, Lourenço Marques]
Generated description
Lourenço Marques is the former name of Maputo, the capital city and main port of Mozambique.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lourenço Marques
Target entity description: Lourenço Marques is the former name of Maputo, the capital city and main port of Mozambique.
  • A. Beira
    Beira is a major port city in central Mozambique, serving as a key commercial and transport hub for the region.
  • B. Salvador
    Salvador is the given name of the renowned Spanish surrealist artist Salvador Dalí.
  • C. Belém
    Belém is a historic riverside district of Lisbon, Portugal, known for its monuments of the Age of Discoveries, including the Belém Tower and Jerónimos Monastery.
  • D. Olinda
    Olinda is a historic coastal city in northeastern Brazil renowned for its well-preserved colonial architecture and vibrant Carnival celebrations.
  • E. Salvador, Bahia, Brazil
    Salvador, the capital of Brazil’s Bahia state, is a major coastal city known for its Afro-Brazilian culture, colonial architecture, and historic role as the country’s first capital.
  • 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_69a493599a0081908da65f3407af1ef2 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a64adf2c81908e48090be35dd9d9 completed March 1, 2026, 8:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69a76d767e5c8190918d55df7528f465 completed March 3, 2026, 11:23 p.m.
NEDg Description generation batch_69a7727d46f08190a634f4fecb00b33c completed March 3, 2026, 11:45 p.m.
NED2 Entity disambiguation (via description) batch_69a774080e24819085b7cdd8e80c74b6 completed March 3, 2026, 11:51 p.m.
Created at: March 1, 2026, 7:37 p.m.