Triple
T24963414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Praia de Maceió |
E624670
|
entity |
| Predicate | tipoDeMar |
P32346
|
FINISHED |
| Object | mar calmo em grande parte do ano |
—
|
LITERAL 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: mar calmo em grande parte do ano | Statement: [Praia de Maceió, tipoDeMar, mar calmo em grande parte do ano]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tipoDeMar Context triple: [Praia de Maceió, tipoDeMar, mar calmo em grande parte do ano]
-
A.
seaType
chosen
Indicates the specific classification or category of a sea associated with an entity.
-
B.
seaName
Indicates that an entity is identified by or associated with a particular sea’s name.
-
C.
seaOf
Indicates a relationship where one entity is metaphorically or literally surrounded or filled by another like a vast sea, emphasizing overwhelming abundance or expansiveness.
-
D.
marineArea
Indicates a relationship where an entity is located in, associated with, or relevant to a specific marine or oceanic area.
-
E.
oceanographicType
Indicates the specific oceanographic classification or category associated with an entity, such as a type of ocean feature, condition, or measurement.
- F. None of above.
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_69e2ff23a3a88190b1b9743fe5e15f94 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f4490283c481908c18246dc7125eec |
completed | May 1, 2026, 6:32 a.m. |
| PD | Predicate disambiguation | batch_69f442c0c2e88190acd7f170f10ccef6 |
completed | May 1, 2026, 6:05 a.m. |
Created at: April 18, 2026, 5:59 a.m.