Triple
T16217605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cebu diving circuit |
E393632
|
entity |
| Predicate | hasTrainingOpportunities |
P2489
|
FINISHED |
| Object | entry-level dive courses |
—
|
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: entry-level dive courses | Statement: [Cebu diving circuit, hasTrainingOpportunities, entry-level dive courses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrainingOpportunities Context triple: [Cebu diving circuit, hasTrainingOpportunities, entry-level dive courses]
-
A.
hasDevelopmentOpportunity
Indicates that an entity offers or possesses a potential for growth, improvement, or advancement, typically in skills, performance, or capabilities.
-
B.
offersApprenticeshipTraining
Indicates that one entity provides apprenticeship-based training opportunities or programs to another entity.
-
C.
hasTrainingRole
Indicates that an entity holds or is assigned a specific role within a training or instructional context.
-
D.
hasEducationalProgram
chosen
Indicates that an entity offers, runs, or is associated with a specific educational program.
-
E.
leadsToTrainingAt
Indicates that one entity causes, results in, or serves as a pathway to another entity undergoing training.
- 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_69d87f1f5bd08190bd01cac0d5b9d2ef |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e227f76f748190831d230d32c18611 |
completed | April 17, 2026, 12:30 p.m. |
| PD | Predicate disambiguation | batch_69e219e94a448190b73a4e6aa374eb4a |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:03 a.m.