Triple
T27728900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jobsinthemoney |
E697376
|
entity |
| Predicate | employmentTypeCovered |
P11918
|
FINISHED |
| Object | full-time 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: full-time positions | Statement: [Jobsinthemoney, employmentTypeCovered, full-time positions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employmentTypeCovered Context triple: [Jobsinthemoney, employmentTypeCovered, full-time 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.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
-
C.
employerStatus
Indicates the current employment relationship or condition between an employer and a worker, such as whether the person is actively employed, terminated, retired, or on leave.
-
D.
employedUnder
Indicates that one entity works as an employee under the authority, supervision, or organizational structure of another entity.
-
E.
commonEmployment
Indicates that two or more entities share the same employer or have worked for the same organization.
- 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_69ef590c3e288190ad54d2465af8ca4e |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69f67f0488bc819089fbd2d2478158d3 |
completed | May 2, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69f67e3ed894819094c067c1ef624951 |
completed | May 2, 2026, 10:44 p.m. |
Created at: April 27, 2026, 3:10 p.m.