Triple
T37418201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fresno Amtrak Station |
E929774
|
entity |
| Predicate | numberOfDailyTrains |
—
|
GENERATED |
| Object | multiple daily San Joaquins round trips |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
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.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfDailyTrains Context triple: [Fresno Amtrak Station, numberOfDailyTrains, multiple daily San Joaquins round trips]
-
A.
peakDailyTrains
Indicates the maximum number of trains operating per day on a given route, line, or segment during its busiest period.
-
B.
trainCount
chosen
Indicates the number of trains associated with a given entity, context, or time period.
-
C.
trainsOn
Indicates that one entity receives training, instruction, or practice using or based on another entity (such as a resource, dataset, tool, or subject).
-
D.
vehiclesPerTrain
Indicates the number of vehicles that are attached to or make up a single train.
-
E.
numberOfTrainsInvolved
Indicates the count of trains that are involved in a particular event, situation, or incident.
- F. None of above.
Provenance (1 batch)
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_69f76ebde49481908566cd96b37ccc84 |
completed | May 3, 2026, 3:50 p.m. |
Created at: May 3, 2026, 4:16 p.m.