Triple
T2720370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State of São Paulo |
E60066
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Taboão da Serra
Taboão da Serra is a densely populated municipality in the São Paulo metropolitan area in southeastern Brazil.
|
E293508
|
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: Taboão da Serra | Statement: [State of São Paulo, hasCity, Taboão da Serra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taboão da Serra Context triple: [State of São Paulo, hasCity, Taboão da Serra]
-
A.
Caucaia
Caucaia is a coastal municipality in northeastern Brazil known for its beaches and proximity to the state capital, Fortaleza.
-
B.
Parnamirim
Parnamirim is a rapidly growing city in northeastern Brazil known for its proximity to Natal and its historical role in World War II aviation.
-
C.
Santo Amaro
Santo Amaro is a central neighborhood in Recife, Brazil, known for its mix of residential areas, commerce, and important urban infrastructure.
-
D.
Pau dos Ferros
Pau dos Ferros is a municipality in the interior of Brazil’s Rio Grande do Norte state, known as a regional commercial and educational hub in the Alto Oeste Potiguar region.
-
E.
Afogados
Afogados is a populous neighborhood in the Brazilian city of Recife, known for its busy commercial areas and dense urban character.
- 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: Taboão da Serra Triple: [State of São Paulo, hasCity, Taboão da Serra]
Generated description
Taboão da Serra is a densely populated municipality in the São Paulo metropolitan area in southeastern Brazil.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Taboão da Serra Target entity description: Taboão da Serra is a densely populated municipality in the São Paulo metropolitan area in southeastern Brazil.
-
A.
Caucaia
Caucaia is a coastal municipality in northeastern Brazil known for its beaches and proximity to the state capital, Fortaleza.
-
B.
Parnamirim
Parnamirim is a rapidly growing city in northeastern Brazil known for its proximity to Natal and its historical role in World War II aviation.
-
C.
Santo Amaro
Santo Amaro is a central neighborhood in Recife, Brazil, known for its mix of residential areas, commerce, and important urban infrastructure.
-
D.
Pau dos Ferros
Pau dos Ferros is a municipality in the interior of Brazil’s Rio Grande do Norte state, known as a regional commercial and educational hub in the Alto Oeste Potiguar region.
-
E.
Afogados
Afogados is a populous neighborhood in the Brazilian city of Recife, known for its busy commercial areas and dense urban character.
- 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_69afb6914f70819099482893d026f34b |
completed | March 10, 2026, 6:13 a.m. |
| NEDg | Description generation | batch_69afb726182081909570e4cb7a364e4d |
completed | March 10, 2026, 6:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afb78f9d08819087d6f31fe1e4e61c |
completed | March 10, 2026, 6:17 a.m. |
Created at: March 6, 2026, 9:55 p.m.