Triple
T23263799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Driver (Two-Lane Blacktop) |
E582087
|
entity |
| Predicate | associatedVehicleType |
P1776
|
FINISHED |
| Object | hot rod |
—
|
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: hot rod | Statement: [The Driver (Two-Lane Blacktop), associatedVehicleType, hot rod]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedVehicleType Context triple: [The Driver (Two-Lane Blacktop), associatedVehicleType, hot rod]
-
A.
relatedVehicle
Indicates that there exists an associated or connected vehicle that has a relevant relationship to the primary entity.
-
B.
vehicleType
chosen
Indicates the specific kind or category of vehicle associated with an entity (e.g., car, bus, bicycle).
-
C.
appliedToVehicleType
Indicates that something (such as a rule, restriction, or condition) is specifically applicable to a particular type or category of vehicle.
-
D.
intendedVehicle
Indicates that one entity is the vehicle that another entity plans or is meant to use.
-
E.
associatedVehicleWeightClass
Indicates the weight classification category that is linked or assigned to a particular 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_69e246079f58819085eaa9c260906880 |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f194cb76c48190869915cd93b44fcc |
completed | April 29, 2026, 5:19 a.m. |
| PD | Predicate disambiguation | batch_69effce4d704819092826931d430e8c4 |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 4:11 p.m.