Triple
T24932943
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moonee Ponds railway station |
E623236
|
entity |
| Predicate | hasStaffingStatus |
P160827
|
FINISHED |
| Object | part-time staffed |
—
|
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: part-time staffed | Statement: [Moonee Ponds railway station, hasStaffingStatus, part-time staffed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStaffingStatus Context triple: [Moonee Ponds railway station, hasStaffingStatus, part-time staffed]
-
A.
hasStaffingStatus
chosen
Indicates the current staffing condition or level associated with an entity, such as whether it is adequately, under-, or over-staffed.
-
B.
hasStaffingModel
Indicates that an entity is associated with or operates under a particular staffing model or staffing approach.
-
C.
hasStaffedHours
Indicates that specific hours or time periods are assigned during which staff are present and available.
-
D.
staffingLevel
Indicates the degree or adequacy of personnel assigned to perform a particular function, task, or operation.
-
E.
hasWorkStatus
Indicates the current employment or occupational state associated with an entity, such as whether it is active, inactive, or in a specific work condition.
- 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_69e2fac6b5a48190a1c38857f00915a9 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f60c3b09488190ade1b69ff7f0df0e |
completed | May 2, 2026, 2:37 p.m. |
| PD | Predicate disambiguation | batch_69f60b8461ac81908c5bd3d73eed59f4 |
completed | May 2, 2026, 2:34 p.m. |
Created at: April 18, 2026, 5:30 a.m.