Triple
T9224005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Praia de Monte Clérigo |
E221633
|
entity |
| Predicate | isLessUrbanizedThan |
P87673
|
FINISHED |
| Object | central Algarve beaches |
—
|
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: central Algarve beaches | Statement: [Praia de Monte Clérigo, isLessUrbanizedThan, central Algarve beaches]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isLessUrbanizedThan Context triple: [Praia de Monte Clérigo, isLessUrbanizedThan, central Algarve beaches]
-
A.
isUrbanized
Indicates that a place or area has been developed with dense human settlement, infrastructure, and built environment characteristic of a city or town.
-
B.
isUrbanizedAround
Indicates that an area or region has developed urban characteristics or infrastructure surrounding a particular location or feature.
-
C.
isPredominantlyRural
Indicates that a place or region is characterized mainly by rural features, such as low population density and extensive non-urban land use.
-
D.
isRuralOrUrban
Indicates whether an entity is classified as being in a rural area or an urban area.
-
E.
isInRuralAreaOf
Indicates that one entity is located within the rural area or countryside region associated with another entity.
- F. None of above. chosen
Provenance (4 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_69ca83ec8db08190a9110df8232885d2 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccda9c71c4819089dcc3689f322529 |
completed | April 1, 2026, 8:43 a.m. |
| PD | Predicate disambiguation | batch_69cc7a3daeb481908b0abde3fbc1f1f0 |
completed | April 1, 2026, 1:51 a.m. |
| PDg | Predicate description generation | batch_69cc95597be081908ece2491dd2f0f74 |
completed | April 1, 2026, 3:47 a.m. |
Created at: March 30, 2026, 7:28 p.m.