Triple
T15093352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ciego de Ávila |
E360475
|
entity |
| Predicate | hasNearbyTourismActivity |
P85424
|
FINISHED |
| Object | beach 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: beach tourism | Statement: [Ciego de Ávila, hasNearbyTourismActivity, beach tourism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyTourismActivity Context triple: [Ciego de Ávila, hasNearbyTourismActivity, beach tourism]
-
A.
nearbyActivity
chosen
Indicates that an activity occurs close in space or proximity to a specified entity or location.
-
B.
hasAttractionNearby
Indicates that one entity is located close to another entity that serves as an attraction or point of interest.
-
C.
nearbyEvent
Indicates that one event occurs close in space or time to another event.
-
D.
nearbyCurrent
Indicates that one entity is located close to another entity at the present moment or in the current context.
-
E.
hasNearbyPilgrimageSite
Indicates that one entity is located close to a place used as a pilgrimage site for religious or spiritual journeys.
- 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_69d85a035aa88190b52a139d3a1b7b6d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0054571a48190a57055c0d6e90f82 |
completed | April 15, 2026, 9:38 p.m. |
| PD | Predicate disambiguation | batch_69deb9645b9c8190a5712456dbd78029 |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:04 a.m.