Triple
T6893616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Australian Public Service |
E159111
|
entity |
| Predicate | employsOver |
P36133
|
FINISHED |
| Object | 100000 staff |
—
|
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: 100000 staff | Statement: [Australian Public Service, employsOver, 100000 staff]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employsOver Context triple: [Australian Public Service, employsOver, 100000 staff]
-
A.
employedApproximately
Indicates that one entity employs another in a manner where the number, duration, or extent of employment is approximate rather than exact.
-
B.
employedPeople
Indicates that there exists a relationship where people are currently working in jobs or positions, typically under an employer.
-
C.
employerIn
Indicates that one entity serves as the employer of another within a specified context, such as a location, organization, or time period.
-
D.
employersInclude
Indicates that a specified set or group of employers contains, as members, the employer or employers referenced by the other argument.
-
E.
hasEmployees
chosen
Indicates that one entity employs one or more other entities as its workers or staff.
- 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_69c6883568c8819081db6407e892cccc |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d931da24819096b9b205f2c0ebb0 |
completed | March 27, 2026, 7:23 p.m. |
| PD | Predicate disambiguation | batch_69c6d7b7681481909ec50509b19fcf81 |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:24 p.m.