Triple
T8403043
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pinar del Río |
E198421
|
entity |
| Predicate | tourismAttractionTypeNearby |
P3449
|
FINISHED |
| Object | rural tourism |
—
|
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 tourism | Statement: [Pinar del Río, tourismAttractionTypeNearby, rural tourism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tourismAttractionTypeNearby Context triple: [Pinar del Río, tourismAttractionTypeNearby, rural tourism]
-
A.
touristAttractionIn
Indicates that a place functions as a tourist attraction located within a specified geographic area or entity.
-
B.
heritageSiteTypeNearby
Indicates that a particular type of heritage site is located in close proximity to the referenced entity.
-
C.
nearbyVenue
Indicates that one venue is located close to another venue in physical space.
-
D.
nearbyResortArea
Indicates that a resort area is located close to or within a short distance of a specified place or entity.
-
E.
hasAttractionNearby
chosen
Indicates that one entity is located close to another entity that serves as an attraction or point of interest.
- 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_69ca8310df9c8190b25f16161cca3e41 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cb82505e0c81909549db59b7c4eb00 |
completed | March 31, 2026, 8:14 a.m. |
| PD | Predicate disambiguation | batch_69cb70d473dc8190af8ea81ee5aa970d |
completed | March 31, 2026, 6:59 a.m. |
Created at: March 30, 2026, 6:04 p.m.