Triple
T13889452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BPS-1 |
E333930
|
entity |
| Predicate | jobSecurityCharacteristic |
P11918
|
FINISHED |
| Object | permanent or contract appointments |
—
|
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 or contract appointments | Statement: [BPS-1, jobSecurityCharacteristic, permanent or contract appointments]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: jobSecurityCharacteristic Context triple: [BPS-1, jobSecurityCharacteristic, permanent or contract appointments]
-
A.
careerSafeties
Indicates the total number of safeties a player has recorded over the course of their career.
-
B.
securityGuarantee
Indicates a commitment by one party to protect or defend another party against specified threats or risks.
-
C.
laborForceCharacteristic
Indicates a relationship where an entity is described or classified by a specific attribute or status related to its participation in the labor force.
-
D.
legalStatusOfWork
Indicates the legal classification or protection status that applies to a particular work (e.g., copyrighted, public domain, licensed).
-
E.
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).
- 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_69d81c5dd2d48190b7a5fc1e009de936 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de23a3a24881908d81d634622fbbcc |
completed | April 14, 2026, 11:23 a.m. |
| PD | Predicate disambiguation | batch_69dd464b1ab48190ae50bfc902bf6ef7 |
completed | April 13, 2026, 7:38 p.m. |
Created at: April 9, 2026, 10:15 p.m.