Triple

T154671
Position Surface form Disambiguated ID Type / Status
Subject Lisbon E3151 entity
Predicate hasDistrict P459 FINISHED
Object 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.
 E18930 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: [Lisbon, hasDistrict, Belém]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Belém
Context triple: [Lisbon, hasDistrict, Belém]
  • A. Rio de Janeiro
    Rio de Janeiro is a major Brazilian coastal city famed for its stunning beaches, dramatic landscape, Carnival festival, and iconic Christ the Redeemer statue.
  • B. São Paulo
    São Paulo is Brazil’s largest city and a major global financial, cultural, and industrial center in South America.
  • C. Guayaquil
    Guayaquil is a major Pacific port city in southwestern Ecuador and the country’s principal commercial and industrial center.
  • D. Lima
    Lima is the capital and largest city of Peru, known as a major political, economic, and cultural center on South America's Pacific coast.
  • E. Buenos Aires
    Buenos Aires is the capital and largest city of Argentina, known for its rich European-influenced culture, tango music and dance, and vibrant urban life.
  • 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: [Lisbon, hasDistrict, Belém]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Belém
Target entity description: 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.
  • A. Rio de Janeiro
    Rio de Janeiro is a major Brazilian coastal city famed for its stunning beaches, dramatic landscape, Carnival festival, and iconic Christ the Redeemer statue.
  • B. São Paulo
    São Paulo is Brazil’s largest city and a major global financial, cultural, and industrial center in South America.
  • C. Guayaquil
    Guayaquil is a major Pacific port city in southwestern Ecuador and the country’s principal commercial and industrial center.
  • D. Lima
    Lima is the capital and largest city of Peru, known as a major political, economic, and cultural center on South America's Pacific coast.
  • E. Buenos Aires
    Buenos Aires is the capital and largest city of Argentina, known for its rich European-influenced culture, tango music and dance, and vibrant urban life.
  • 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_69a2527757ec819090b8becb2cf1a862 completed Feb. 28, 2026, 2:27 a.m.
NER Named-entity recognition batch_69a2582d25448190931c2d785e678a8a completed Feb. 28, 2026, 2:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2ce3867688190bd6c32a2da7d67b3 completed Feb. 28, 2026, 11:15 a.m.
NEDg Description generation batch_69a2ceb2bd48819084fa2f198af74712 completed Feb. 28, 2026, 11:17 a.m.
NED2 Entity disambiguation (via description) batch_69a2cf98fc4881909e3e7cf0b90ae5ce completed Feb. 28, 2026, 11:20 a.m.
Created at: Feb. 28, 2026, 2:31 a.m.