Triple
T2720354
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State of São Paulo |
E60066
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Osasco
Osasco is a major industrial and commercial city in the metropolitan region of São Paulo, Brazil.
|
E310616
|
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: Osasco | Statement: [State of São Paulo, hasCity, Osasco]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Osasco Context triple: [State of São Paulo, hasCity, Osasco]
-
A.
Guarulhos
Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
-
B.
São Bernardo do Campo
São Bernardo do Campo is a major industrial city in Brazil known as a key center of the automotive industry within the São Paulo metropolitan area.
-
C.
Santo André
Santo André is a major industrial and residential city in the São Paulo metropolitan region of Brazil.
-
D.
Campinas
Campinas is a major city in the state of São Paulo, Brazil, known as an important industrial, technological, and transportation hub in the country.
-
E.
Sorocaba
Sorocaba is a major industrial and commercial city in southeastern Brazil, located in the interior of the state of São Paulo.
- 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: Osasco Triple: [State of São Paulo, hasCity, Osasco]
Generated description
Osasco is a major industrial and commercial city in the metropolitan region of São Paulo, Brazil.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Osasco Target entity description: Osasco is a major industrial and commercial city in the metropolitan region of São Paulo, Brazil.
-
A.
Guarulhos
Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
-
B.
São Bernardo do Campo
São Bernardo do Campo is a major industrial city in Brazil known as a key center of the automotive industry within the São Paulo metropolitan area.
-
C.
Santo André
Santo André is a major industrial and residential city in the São Paulo metropolitan region of Brazil.
-
D.
Campinas
Campinas is a major city in the state of São Paulo, Brazil, known as an important industrial, technological, and transportation hub in the country.
-
E.
Sorocaba
Sorocaba is a major industrial and commercial city in southeastern Brazil, located in the interior of the state of São Paulo.
- 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_69ab4b746d248190958e052045c09255 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdab06d388190acf690787fe58ab5 |
completed | March 7, 2026, 7:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b055bd41108190920b7397c16d15f5 |
completed | March 10, 2026, 5:32 p.m. |
| NEDg | Description generation | batch_69b05d246a60819088510c8fa87402c2 |
completed | March 10, 2026, 6:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b062a6241c81909f3217a4a33aa4c6 |
completed | March 10, 2026, 6:27 p.m. |
Created at: March 6, 2026, 9:55 p.m.