Triple
T46907
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NHS organisations |
E918
|
entity |
| Predicate | trainingRole |
P268
|
FINISHED |
| Object | clinical training for doctors |
—
|
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: clinical training for doctors | Statement: [NHS organisations, trainingRole, clinical training for doctors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingRole Context triple: [NHS organisations, trainingRole, clinical training for doctors]
-
A.
role
chosen
Indicates the function, position, or responsibility that one entity holds in relation to another within a given context.
-
B.
notableTrain
Indicates that there is a train or rail service associated with the subject that is considered notable or significant in some way.
-
C.
educationalModel
Indicates that one entity serves as an educational model, framework, or paradigm that guides or structures the teaching, learning, or training practices of another entity.
-
D.
coachOf
Indicates that one entity serves as the coach (trainer or manager) of another entity, typically a person or team.
-
E.
target
Indicates that one entity is the intended object, goal, or focus of another entity’s action or attention.
- 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_69a2480baefc81909951b14058479aa2 |
completed | Feb. 28, 2026, 1:42 a.m. |
| NER | Named-entity recognition | batch_69a24b1bf2c081908f20e13939b713ff |
completed | Feb. 28, 2026, 1:55 a.m. |
| PD | Predicate disambiguation | batch_69a24abd07508190a83ffba5368c1c79 |
completed | Feb. 28, 2026, 1:54 a.m. |
Created at: Feb. 28, 2026, 1:47 a.m.