Triple
T37778277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Município de Santa Maria de Jetibá |
E941749
|
entity |
| Predicate | tipoDePaisagem |
P9701
|
FINISHED |
| Object | rural |
—
|
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: rural | Statement: [Município de Santa Maria de Jetibá, tipoDePaisagem, rural]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tipoDePaisagem Context triple: [Município de Santa Maria de Jetibá, tipoDePaisagem, rural]
-
A.
landscapeType
chosen
Indicates the kind or category of natural terrain or scenery that characterizes a place or area.
-
B.
scenicCategory
Indicates the classification of a place or route based on its visual appeal or scenic qualities.
-
C.
terrainFeature
Indicates a relationship where one entity is a natural or constructed landform or surface characteristic associated with a given location or area.
-
D.
landscapeStyle
Indicates the design style or aesthetic approach applied to a landscape or outdoor environment.
-
E.
landerType
Indicates the specific kind or category of lander involved in the relationship or action.
- 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_69f76ee4431881908f87e8892a9f39f3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbaf45bd40819090879114ec90db3e |
completed | May 6, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69fbadf632ec8190b14991c971258307 |
completed | May 6, 2026, 9:09 p.m. |
Created at: May 3, 2026, 4:19 p.m.