Triple
T27100037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Krasnodar trolleybus |
E686413
|
entity |
| Predicate | roadVehicleType |
P1776
|
FINISHED |
| Object | rubber-tyred electric 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: rubber-tyred electric bus | Statement: [Krasnodar trolleybus, roadVehicleType, rubber-tyred electric bus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roadVehicleType Context triple: [Krasnodar trolleybus, roadVehicleType, rubber-tyred electric bus]
-
A.
vehicleType
chosen
Indicates the specific kind or category of vehicle associated with an entity (e.g., car, bus, bicycle).
-
B.
vehicleFamily
Indicates that two vehicles belong to the same family or category based on shared design, platform, or lineage.
-
C.
poweredVehicleFamily
Indicates that one vehicle is part of a family or group of vehicles that share a common powered (motorized) design or platform.
-
D.
vehicleBase
Indicates that one entity serves as the foundational or underlying base for a vehicle-related entity or system.
-
E.
vehicleTypeFocus
Indicates that the relationship or action specifically concerns or emphasizes 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_69ef1489f8b481908e24a1985982bd26 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69f623b54dfc8190b9294ac42f940415 |
completed | May 2, 2026, 4:17 p.m. |
| PD | Predicate disambiguation | batch_69f61b40f02081909bd9c3ea73249163 |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 27, 2026, 8:47 a.m.