Triple
T6160795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nossa Senhora do Socorro |
E137435
|
entity |
| Predicate | isUrbanAreaOf |
P12103
|
FINISHED |
| Object | Greater Aracaju |
E166976
|
NE FINISHED |
How this triple was built (2 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: Greater Aracaju | Statement: [Nossa Senhora do Socorro, isUrbanAreaOf, Greater Aracaju]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Greater Aracaju Context triple: [Nossa Senhora do Socorro, isUrbanAreaOf, Greater Aracaju]
-
A.
Aracaju
chosen
Aracaju is a coastal city in northeastern Brazil known for its planned urban layout, beaches, and role as an administrative and economic center.
-
B.
Santo Amaro
Santo Amaro is a central neighborhood in Recife, Brazil, known for its mix of residential areas, commerce, and important urban infrastructure.
-
C.
Sete Lagoas
Sete Lagoas is a city in the state of Minas Gerais, Brazil, known for its industrial activity and automotive manufacturing sector.
-
D.
Itanhaém
Itanhaém is a coastal municipality in southeastern Brazil known for its beaches, historic colonial center, and tourism along the São Paulo state shoreline.
-
E.
Laranjal Paulista
Laranjal Paulista is a municipality in the state of São Paulo, Brazil, known for its riverside setting and regional agricultural activities.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c008a54fc88190b6ce4416490ca79d |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05d35b2f88190abb9b90b5971e6d7 |
completed | March 22, 2026, 9:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c14194d31081908e61a867f11117b4 |
completed | March 23, 2026, 1:35 p.m. |
Created at: March 22, 2026, 4:17 p.m.