Triple
T23570504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SECAmb |
E580091
|
entity |
| Predicate | usesStaffType |
P127311
|
FINISHED |
| Object | paramedics |
—
|
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: paramedics | Statement: [SECAmb, usesStaffType, paramedics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesStaffType Context triple: [SECAmb, usesStaffType, paramedics]
-
A.
usesStaffCategory
chosen
Indicates that an entity employs or applies a particular category or classification of staff in its operations or context.
-
B.
usesStaffCode
Indicates that one entity performs an action or operates by referencing or applying a specific staff code associated with another entity.
-
C.
hadStaffRole
Indicates that an entity served in a specific staff role or position for another entity during some period.
-
D.
hasWorkforceType
Indicates the type or category of workforce associated with an entity (such as permanent, temporary, contract, or part-time).
-
E.
hasStaffTypeInStory
Indicates that a story involves or is associated with a particular type or category of 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_69e24601a9108190bc31e83833c980e4 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1afd259e4819094b09f3ed76664ea |
completed | April 29, 2026, 7:14 a.m. |
| PD | Predicate disambiguation | batch_69f118bcc0b08190b25a8dddfd461a0e |
completed | April 28, 2026, 8:29 p.m. |
Created at: April 17, 2026, 6:36 p.m.