Triple
T24963399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Praia de Maceió |
E624670
|
entity |
| Predicate | economiaLocalRelacionadaA |
P154638
|
FINISHED |
| Object | pesca artesanal |
—
|
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: pesca artesanal | Statement: [Praia de Maceió, economiaLocalRelacionadaA, pesca artesanal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: economiaLocalRelacionadaA Context triple: [Praia de Maceió, economiaLocalRelacionadaA, pesca artesanal]
-
A.
partOfLocalEconomy
chosen
Indicates that an entity contributes to, participates in, or is integrated within the economic activities of a specific local area or community.
-
B.
regionalEconomyActivity
Indicates the type or level of economic activity occurring within a specific geographic region.
-
C.
localEconomyImpact
Indicates the effect that an action, event, or entity has on the economic conditions, activities, or performance of a specific local area or community.
-
D.
nearbyEconomicActivity
Indicates that there is economic activity occurring in close physical proximity to the referenced entity.
-
E.
regionalEconomyType
Indicates the type or classification of an economy associated with a specific region.
- 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_69f464b4c9b0819085daa00c7c3b8b76 |
completed | May 1, 2026, 8:30 a.m. |
| PD | Predicate disambiguation | batch_69f45cf017a88190b4985b11159c907d |
completed | May 1, 2026, 7:57 a.m. |
Created at: April 18, 2026, 5:59 a.m.