Triple
T6261829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | São Francisco River mouth |
E140317
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object |
Brejo Grande
Brejo Grande is a small coastal municipality in the Brazilian state of Sergipe, known for its location at the mouth of the São Francisco River.
|
E582783
|
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: Brejo Grande | Statement: [São Francisco River mouth, nearCity, Brejo Grande]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brejo Grande Context triple: [São Francisco River mouth, nearCity, Brejo Grande]
-
A.
Brejo da Guabiraba
Brejo da Guabiraba is a neighborhood located in the northern zone of Recife, in the state of Pernambuco, Brazil.
-
B.
Brejo de Beberibe
Brejo de Beberibe is a neighborhood within the city of Recife in the state of Pernambuco, Brazil.
-
C.
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.
-
D.
Arapiraca
Arapiraca is a major city in the Brazilian state of Alagoas, known as an important regional commercial and agricultural center.
-
E.
Caieiras
Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
- 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: Brejo Grande Triple: [São Francisco River mouth, nearCity, Brejo Grande]
Generated description
Brejo Grande is a small coastal municipality in the Brazilian state of Sergipe, known for its location at the mouth of the São Francisco River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Brejo Grande Target entity description: Brejo Grande is a small coastal municipality in the Brazilian state of Sergipe, known for its location at the mouth of the São Francisco River.
-
A.
Brejo da Guabiraba
Brejo da Guabiraba is a neighborhood located in the northern zone of Recife, in the state of Pernambuco, Brazil.
-
B.
Brejo de Beberibe
Brejo de Beberibe is a neighborhood within the city of Recife in the state of Pernambuco, Brazil.
-
C.
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.
-
D.
Arapiraca
Arapiraca is a major city in the Brazilian state of Alagoas, known as an important regional commercial and agricultural center.
-
E.
Caieiras
Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
- 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_69c008c95c5c819084bd3dd56133d84d |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c06386a7b48190b032edd12078c5bc |
completed | March 22, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5192b99d4819083ab6e6f2092547b |
completed | March 26, 2026, 11:31 a.m. |
| NEDg | Description generation | batch_69c51b04fa688190a366d5c90150a530 |
completed | March 26, 2026, 11:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c583978758819094aebde9d410f849 |
completed | March 26, 2026, 7:05 p.m. |
Created at: March 22, 2026, 4:25 p.m.