Triple
T2720388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State of São Paulo |
E60066
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Mogi Guaçu
Mogi Guaçu is a municipality in the interior of Brazil’s São Paulo state, known for its industrial activity and the Mogi Guaçu River that runs through it.
|
E350286
|
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: Mogi Guaçu | Statement: [State of São Paulo, hasCity, Mogi Guaçu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mogi Guaçu Context triple: [State of São Paulo, hasCity, Mogi Guaçu]
-
A.
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.
-
B.
Jundiaí
Jundiaí is a mid-sized industrial and logistics city in southeastern Brazil known for its strong economy and high quality of life.
-
C.
Magé
Magé is a municipality in the state of Rio de Janeiro, Brazil, located in the metropolitan region of Rio de Janeiro and known for its coastal setting and historical significance.
-
D.
Guarulhos
Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
-
E.
Guarujá
Guarujá is a coastal resort city in southeastern Brazil known for its popular beaches and tourism.
- 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: Mogi Guaçu Triple: [State of São Paulo, hasCity, Mogi Guaçu]
Generated description
Mogi Guaçu is a municipality in the interior of Brazil’s São Paulo state, known for its industrial activity and the Mogi Guaçu River that runs through it.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mogi Guaçu Target entity description: Mogi Guaçu is a municipality in the interior of Brazil’s São Paulo state, known for its industrial activity and the Mogi Guaçu River that runs through it.
-
A.
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.
-
B.
Jundiaí
Jundiaí is a mid-sized industrial and logistics city in southeastern Brazil known for its strong economy and high quality of life.
-
C.
Magé
Magé is a municipality in the state of Rio de Janeiro, Brazil, located in the metropolitan region of Rio de Janeiro and known for its coastal setting and historical significance.
-
D.
Guarulhos
Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
-
E.
Guarujá
Guarujá is a coastal resort city in southeastern Brazil known for its popular beaches and tourism.
- 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_69b324c5ed1c8190a7b42abe24785654 |
completed | March 12, 2026, 8:40 p.m. |
| NEDg | Description generation | batch_69b325a0de7c8190a33ae611b8450b5a |
completed | March 12, 2026, 8:44 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b326862e848190bf1bea74b6ab0b36 |
completed | March 12, 2026, 8:48 p.m. |
Created at: March 6, 2026, 9:55 p.m.