Triple

T7437733
Position Surface form Disambiguated ID Type / Status
Subject Amapá E171661 entity
Predicate capital P234 FINISHED
Object Macapá
Macapá is a Brazilian city located on the northern bank of the Amazon River, known for being one of the few state capitals in the world situated directly on the equator.
 E676849 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: Macapá | Statement: [Amapá, capital, Macapá]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Macapá
Context triple: [Amapá, capital, Macapá]
  • A. 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.
  • B. Belém do Pará
    Belém do Pará is a major port city in northern Brazil, known as the gateway to the Amazon region and an important cultural and economic center.
  • C. Lourenço Marques
    Lourenço Marques is the former name of Maputo, the capital city and main port of Mozambique.
  • D. Aracaju
    Aracaju is a coastal city in northeastern Brazil known for its planned urban layout, beaches, and role as an administrative and economic center.
  • E. Maceió
    Maceió is a coastal city in northeastern Brazil known for its white-sand beaches, turquoise waters, and vibrant tourism industry.
  • 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: Macapá
Triple: [Amapá, capital, Macapá]
Generated description
Macapá is a Brazilian city located on the northern bank of the Amazon River, known for being one of the few state capitals in the world situated directly on the equator.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Macapá
Target entity description: Macapá is a Brazilian city located on the northern bank of the Amazon River, known for being one of the few state capitals in the world situated directly on the equator.
  • A. 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.
  • B. Belém do Pará
    Belém do Pará is a major port city in northern Brazil, known as the gateway to the Amazon region and an important cultural and economic center.
  • C. Lourenço Marques
    Lourenço Marques is the former name of Maputo, the capital city and main port of Mozambique.
  • D. Aracaju
    Aracaju is a coastal city in northeastern Brazil known for its planned urban layout, beaches, and role as an administrative and economic center.
  • E. Maceió
    Maceió is a coastal city in northeastern Brazil known for its white-sand beaches, turquoise waters, and vibrant tourism industry.
  • 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_69c68a64228c8190affaec2a8127ce7b completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f34aa3388190ac300cf934042d78 completed March 27, 2026, 9:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8682c7c64819081bc1110a3a6e305 completed March 28, 2026, 11:45 p.m.
NEDg Description generation batch_69c8699f5c008190be32da0a096cbb29 completed March 28, 2026, 11:51 p.m.
NED2 Entity disambiguation (via description) batch_69c86abe53b08190b9fbd17d83cbe7c3 completed March 28, 2026, 11:56 p.m.
Created at: March 27, 2026, 3:13 p.m.