Triple
T20127169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fifteen Restaurant |
E490788
|
entity |
| Predicate | hasApprenticeshipProgram |
P33629
|
FINISHED |
| Object | chef apprenticeship scheme |
—
|
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: chef apprenticeship scheme | Statement: [Fifteen Restaurant, hasApprenticeshipProgram, chef apprenticeship scheme]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasApprenticeshipProgram Context triple: [Fifteen Restaurant, hasApprenticeshipProgram, chef apprenticeship scheme]
-
A.
hasEducationalProgram
Indicates that an entity offers, runs, or is associated with a specific educational program.
-
B.
hasWorkProgram
Indicates that an entity offers, participates in, or is associated with a specific work-related program or scheme.
-
C.
offersApprenticeshipTraining
chosen
Indicates that one entity provides apprenticeship-based training opportunities or programs to another entity.
-
D.
hasCadetProgram
Indicates that an organization or entity offers or operates a cadet program for training or development.
-
E.
hasIndustryProgram
Indicates that an entity offers, participates in, or is associated with a structured program involving collaboration or engagement with industry organizations or sectors.
- 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_69da62651a0c8190a3e05e95e056a66b |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66743494c81908e63a5efca3aa3ea |
completed | April 20, 2026, 5:49 p.m. |
| PD | Predicate disambiguation | batch_69e54cfb0d0081908e789b9b57e96668 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 11, 2026, 11:31 p.m.