Triple
T15619070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West Side Road |
E375496
|
entity |
| Predicate | recommendedVehicleType |
P27118
|
FINISHED |
| Object | high-clearance vehicle |
—
|
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: high-clearance vehicle | Statement: [West Side Road, recommendedVehicleType, high-clearance vehicle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recommendedVehicleType Context triple: [West Side Road, recommendedVehicleType, high-clearance vehicle]
-
A.
recommendedVehicle
Indicates that one entity suggests or endorses a particular vehicle as suitable or preferable for another entity or purpose.
-
B.
vehicleType
Indicates the specific kind or category of vehicle associated with an entity (e.g., car, bus, bicycle).
-
C.
vehicleTypeFocus
chosen
Indicates that the relationship or action specifically concerns or emphasizes a particular type or category of vehicle.
-
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.
vehicleEligibility
Indicates whether a given vehicle satisfies the required conditions or criteria to be considered eligible for a specified purpose or program.
- 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_69d85ccf2794819096cda4cbcb02d478 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e997ce481909b2f10d25705fbc6 |
completed | April 16, 2026, 2:51 a.m. |
| PD | Predicate disambiguation | batch_69deda844af081909e658ebc9d9b403d |
completed | April 15, 2026, 12:23 a.m. |
Created at: April 10, 2026, 4:13 a.m.