Triple
T5739657
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Special Class Railway Apprentices examination |
E126581
|
entity |
| Predicate | intake |
P65573
|
FINISHED |
| Object | small number of candidates selected annually |
—
|
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: small number of candidates selected annually | Statement: [Special Class Railway Apprentices examination, intake, small number of candidates selected annually]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: intake Context triple: [Special Class Railway Apprentices examination, intake, small number of candidates selected annually]
-
A.
intakeLocation
Indicates the place or facility where an entity is initially received, admitted, or taken in.
-
B.
airIntakeType
Indicates the type or configuration of the system or mechanism used to draw air into an engine or device.
-
C.
aspiration
Indicates that one entity has a strong desire, goal, or ambition directed toward achieving or becoming another entity or state.
-
D.
swallowed
Indicates that one entity caused another entity to pass from the mouth into the body, typically down the throat.
-
E.
consumes
Indicates that one entity eats, drinks, or otherwise uses up another entity as a resource or nourishment.
- F. None of above. chosen
Provenance (4 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_69c0083179548190b384b0bf3c08ca4d |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0255dc35c8190ab9ee5d269ce553a |
completed | March 22, 2026, 5:22 p.m. |
| PD | Predicate disambiguation | batch_69c021c8195481909419808b002628aa |
completed | March 22, 2026, 5:07 p.m. |
| PDg | Predicate description generation | batch_69c022a50c048190aff24c63e7039dd6 |
completed | March 22, 2026, 5:11 p.m. |
Created at: March 22, 2026, 3:48 p.m.