Triple
T90268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South America |
E1813
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
São Paulo
São Paulo is Brazil’s largest city and a major global financial, cultural, and industrial center in South America.
|
E9033
|
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: São Paulo | Statement: [South America, hasMajorCity, São Paulo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: São Paulo Context triple: [South America, hasMajorCity, São Paulo]
-
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.
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.
-
C.
Santiago
Santiago is the capital and primary economic, political, and cultural center of Chile, located in the country’s central valley.
-
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.
Lisbon
Lisbon is the coastal capital city of Portugal, renowned for its historic architecture, hilly landscape, and role as a major cultural and economic center in Europe.
- 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: São Paulo Triple: [South America, hasMajorCity, São Paulo]
Generated description
São Paulo is Brazil’s largest city and a major global financial, cultural, and industrial center in South America.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: São Paulo Target entity description: São Paulo is Brazil’s largest city and a major global financial, cultural, and industrial center in South America.
-
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.
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.
-
C.
Santiago
Santiago is the capital and primary economic, political, and cultural center of Chile, located in the country’s central valley.
-
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.
Lisbon
Lisbon is the coastal capital city of Portugal, renowned for its historic architecture, hilly landscape, and role as a major cultural and economic center in Europe.
- 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_69a24d1a97dc819094e6c021fe9b05a7 |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a24f6c29888190890caa7872d63ac6 |
completed | Feb. 28, 2026, 2:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a266ebb994819085fb84dd1d2d25ad |
completed | Feb. 28, 2026, 3:54 a.m. |
| NEDg | Description generation | batch_69a267b740388190a321023aa52a539a |
completed | Feb. 28, 2026, 3:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a2685dc64c8190bd611985d1bc27a3 |
completed | Feb. 28, 2026, 4 a.m. |
Created at: Feb. 28, 2026, 2:07 a.m.