Triple

T14249069
Position Surface form Disambiguated ID Type / Status
Subject State of Maranhão E353208 entity
Predicate contains P35 FINISHED
Object Belém
Belém is a major city in northern Brazil known for its Amazon River port, rich colonial history, and vibrant Amazonian culture and cuisine.
 E234072 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: Belém | Statement: [State of Maranhão, contains, Belém]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Belém
Context triple: [State of Maranhão, contains, Belém]
  • 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. São Luís
    São Luís is the historic capital of the Brazilian state of Maranhão, known for its well-preserved colonial architecture and rich Afro-Brazilian cultural heritage.
  • D. São Luís
    São Luís is a civil parish in the municipality of Odemira, located in Portugal’s Alentejo region.
  • E. Manaus
    Manaus is a major Brazilian city and capital of the state of Amazonas, known as a key gateway to the Amazon rainforest and an important industrial and cultural center in the region.
  • 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: Belém
Triple: [State of Maranhão, contains, Belém]
Generated description
Belém is a major city in northern Brazil known for its Amazon River port, rich colonial history, and vibrant Amazonian culture and cuisine.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Belém
Target entity description: Belém is a major city in northern Brazil known for its Amazon River port, rich colonial history, and vibrant Amazonian culture and cuisine.
  • 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 chosen
    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. São Luís
    São Luís is the historic capital of the Brazilian state of Maranhão, known for its well-preserved colonial architecture and rich Afro-Brazilian cultural heritage.
  • D. São Luís
    São Luís is a civil parish in the municipality of Odemira, located in Portugal’s Alentejo region.
  • E. Manaus
    Manaus is a major Brazilian city and capital of the state of Amazonas, known as a key gateway to the Amazon rainforest and an important industrial and cultural center in the region.
  • 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_69d8278c43e08190824146f4632b89a5 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de6295ef9081909cfb0c1283bca21a completed April 14, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd647b37a8819095fe0bb62e0373fb completed May 8, 2026, 4:20 a.m.
NEDg Description generation batch_69fd6539793881909d1fd3985c171837 completed May 8, 2026, 4:23 a.m.
NED2 Entity disambiguation (via description) batch_69fd65c06fc88190a7d2a84d7858b20f completed May 8, 2026, 4:25 a.m.
Created at: April 10, 2026, 1:08 a.m.