Triple
T3942391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amtrak Wolverine service |
E92063
|
entity |
| Predicate | numberOfDailyRoundTrips |
P52142
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Amtrak Wolverine service, numberOfDailyRoundTrips, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfDailyRoundTrips Context triple: [Amtrak Wolverine service, numberOfDailyRoundTrips, 3]
-
A.
passesUsedForTransportation
Indicates that the passes are utilized as a means or instrument for transporting people or goods.
-
B.
distanceTraveled
Indicates the total length of the path an entity has moved over a period of time or between two points.
-
C.
hasDailyPassengerTraffic
Indicates the number of passengers that regularly use or pass through something (such as a station or route) each day.
-
D.
numberOfFlights
Indicates the total count of flights associated with a given entity or within a specified context.
-
E.
numberOfRoutes
Indicates the total count of distinct routes or paths associated with a given entity or between specified entities.
- F. None of above. chosen
Provenance (4 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_69aed965502c8190904ebad1203a4ae8 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1515c688190a38332aedeed8a76 |
completed | March 9, 2026, 4:12 p.m. |
| PD | Predicate disambiguation | batch_69aee764235081909309b3c982f322a9 |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aef14f9bb4819098e64b527b546d74 |
completed | March 9, 2026, 4:11 p.m. |
Created at: March 9, 2026, 3:24 p.m.