Triple
T37418201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fresno Amtrak Station |
E929774
|
entity |
| Predicate | numberOfDailyTrains |
P23304
|
FINISHED |
| Object | multiple daily San Joaquins round trips |
—
|
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: multiple daily San Joaquins round trips | Statement: [Fresno Amtrak Station, numberOfDailyTrains, multiple daily San Joaquins round trips]
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 (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_69f76ebde49481908566cd96b37ccc84 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037c8efcd4819088c2aeead65d93df |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a13a1308190a202df66f4781855 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:16 p.m.