Triple
T1792076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Washington State Ferries |
E39517
|
entity |
| Predicate | annualVehicleTraffic |
P12939
|
FINISHED |
| Object | millions of vehicles |
—
|
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: millions of vehicles | Statement: [Washington State Ferries, annualVehicleTraffic, millions of vehicles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: annualVehicleTraffic Context triple: [Washington State Ferries, annualVehicleTraffic, millions of vehicles]
-
A.
annualTraffic
chosen
Indicates the typical amount or volume of traffic associated with something over the course of a year.
-
B.
trafficDirection
Indicates the direction in which traffic is intended or allowed to move relative to a given reference point or segment.
-
C.
roadTraffic
Indicates the presence, flow, or conditions of vehicles and movement along roads or streets.
-
D.
passengerTraffic
Indicates the flow or volume of passengers moving through or using a particular transport service, route, or facility.
-
E.
majorTrafficType
Indicates the primary kind of traffic or flow that predominantly characterizes a given route, segment, or transportation context.
- 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_69a88631854081909723959921e45c2b |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69ab61b6ea188190aab9fb839bf1e367 |
completed | March 6, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69aa61d2f7a8819090301f92d3e358c7 |
completed | March 6, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:32 p.m.