Triple
T2720398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State of São Paulo |
E60066
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Cubatão
Cubatão is an industrial city in southeastern Brazil known for its major petrochemical and steel complexes and its location near the port of Santos in the state of São Paulo.
|
E359555
|
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: Cubatão | Statement: [State of São Paulo, hasCity, Cubatão]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cubatão Context triple: [State of São Paulo, hasCity, Cubatão]
-
A.
Itapetininga
Itapetininga is a municipality in southeastern Brazil known for its agricultural activities and regional commercial importance within the state of São Paulo.
-
B.
Barueri
Barueri is a rapidly developing municipality in the São Paulo metropolitan area of Brazil, known for its strong commercial sector and high standard of living.
-
C.
Jundiaí
Jundiaí is a mid-sized industrial and logistics city in southeastern Brazil known for its strong economy and high quality of life.
-
D.
Guarujá
Guarujá is a coastal resort city in southeastern Brazil known for its popular beaches and tourism.
-
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: Cubatão Triple: [State of São Paulo, hasCity, Cubatão]
Generated description
Cubatão is an industrial city in southeastern Brazil known for its major petrochemical and steel complexes and its location near the port of Santos in the state of São Paulo.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Cubatão Target entity description: Cubatão is an industrial city in southeastern Brazil known for its major petrochemical and steel complexes and its location near the port of Santos in the state of São Paulo.
-
A.
Itapetininga
Itapetininga is a municipality in southeastern Brazil known for its agricultural activities and regional commercial importance within the state of São Paulo.
-
B.
Barueri
Barueri is a rapidly developing municipality in the São Paulo metropolitan area of Brazil, known for its strong commercial sector and high standard of living.
-
C.
Jundiaí
Jundiaí is a mid-sized industrial and logistics city in southeastern Brazil known for its strong economy and high quality of life.
-
D.
Guarujá
Guarujá is a coastal resort city in southeastern Brazil known for its popular beaches and tourism.
-
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_69b3608166888190a4bf75f865e42f45 |
completed | March 13, 2026, 12:55 a.m. |
| NEDg | Description generation | batch_69b3614702348190bd35c37d2059312f |
completed | March 13, 2026, 12:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b362451b848190a2fe80a17ab8f9c3 |
completed | March 13, 2026, 1:03 a.m. |
Created at: March 6, 2026, 9:55 p.m.