Triple
T19243681
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | School Bus Replacement and Clean Transportation grants |
E481192
|
entity |
| Predicate | targetsVehicleType |
P23423
|
FINISHED |
| Object | school bus |
—
|
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: school bus | Statement: [School Bus Replacement and Clean Transportation grants, targetsVehicleType, school bus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetsVehicleType Context triple: [School Bus Replacement and Clean Transportation grants, targetsVehicleType, school bus]
-
A.
vehicleType
Indicates the specific kind or category of vehicle associated with an entity (e.g., car, bus, bicycle).
-
B.
vehicleTypeFocus
Indicates that the relationship or action specifically concerns or emphasizes a particular type or category of vehicle.
-
C.
intendedVehicle
Indicates that one entity is the vehicle that another entity plans or is meant to use.
-
D.
intendedVehicleClass
Indicates that one entity is designed or specified to be used with, or is appropriate for, a particular class or category of vehicle.
-
E.
appliedToVehicleType
chosen
Indicates that something (such as a rule, restriction, or condition) is specifically applicable to a particular type or category of vehicle.
- 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_69d8e8cd9d1081908a181d02b88b59b8 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5faf3018881909ef866616c45ff2e |
completed | April 20, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e4dd002d00819088b625056edfb74e |
completed | April 19, 2026, 1:47 p.m. |
Created at: April 10, 2026, 1:27 p.m.