Triple
T13924255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BPS-10 |
E334820
|
entity |
| Predicate | jobSecurityType |
P11918
|
FINISHED |
| Object | permanent civil service positions |
—
|
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: permanent civil service positions | Statement: [BPS-10, jobSecurityType, permanent civil service positions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: jobSecurityType Context triple: [BPS-10, jobSecurityType, permanent civil service positions]
-
A.
employmentType
chosen
Indicates the specific kind or category of employment relationship that exists between an individual and an employer (e.g., full-time, part-time, contract).
-
B.
legalStatusOfWork
Indicates the legal classification or protection status that applies to a particular work (e.g., copyrighted, public domain, licensed).
-
C.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
-
D.
salaryType
Indicates the classification or structure of compensation associated with an entity, such as whether pay is salaried, hourly, commission-based, or another type.
-
E.
careerSafeties
Indicates the total number of safeties a player has recorded over the course of their career.
- 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_69d81c5f739081908bc05b2461f54828 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2aa6cd9881908f652538f4613f37 |
completed | April 14, 2026, 11:53 a.m. |
| PD | Predicate disambiguation | batch_69de059e4ba881908554f72e889719fa |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:16 p.m.