Triple
T24101084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FDNY Engine Company 159 |
E597073
|
entity |
| Predicate | hasStaffingType |
P11922
|
FINISHED |
| Object | full-time firefighters |
—
|
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 firefighters | Statement: [FDNY Engine Company 159, hasStaffingType, full-time firefighters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStaffingType Context triple: [FDNY Engine Company 159, hasStaffingType, full-time firefighters]
-
A.
hasStaffingModel
Indicates that an entity is associated with or operates under a particular staffing model or staffing approach.
-
B.
hasWorkforceType
chosen
Indicates the type or category of workforce associated with an entity (such as permanent, temporary, contract, or part-time).
-
C.
usesStaffCategory
Indicates that an entity employs or applies a particular category or classification of staff in its operations or context.
-
D.
hasStaffTypeInStory
Indicates that a story involves or is associated with a particular type or category of staff.
-
E.
staffingLevel
Indicates the degree or adequacy of personnel assigned to perform a particular function, task, or operation.
- 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_69e288c548048190a5c1018da1166a21 |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1dd29fe708190a94195ea607bd69e |
completed | April 29, 2026, 10:27 a.m. |
| PD | Predicate disambiguation | batch_69f17651458c8190bbfd301883e46085 |
completed | April 29, 2026, 3:09 a.m. |
Created at: April 17, 2026, 11 p.m.