Triple
T13548368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cambridge Regional College |
E323573
|
entity |
| Predicate | hasApprenticeshipProvision |
P33629
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Cambridge Regional College, hasApprenticeshipProvision, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApprenticeshipProvision Context triple: [Cambridge Regional College, hasApprenticeshipProvision, yes]
-
A.
offersApprenticeshipTraining
chosen
Indicates that one entity provides apprenticeship-based training opportunities or programs to another entity.
-
B.
hasWorkProgram
Indicates that an entity offers, participates in, or is associated with a specific work-related program or scheme.
-
C.
hasProvisionOn
Indicates that one entity contains, specifies, or includes a particular provision, clause, or stipulation concerning another entity or subject.
-
D.
hasProfessionalRequirement
Indicates that one entity imposes or specifies a professional qualification, credential, or condition that must be met by another entity.
-
E.
hasImprovementProgram
Indicates that an entity is associated with, participates in, or is covered by a program specifically designed to improve its performance, condition, or quality.
- 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_69d8076776248190bdf0d4fa1f85a5fc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbbb9ee3f081909056dc1a92c40b7a |
completed | April 12, 2026, 3:34 p.m. |
| PD | Predicate disambiguation | batch_69dbae13bec4819084c1770638c00ed9 |
completed | April 12, 2026, 2:37 p.m. |
Created at: April 9, 2026, 9:45 p.m.