Triple
T34888537
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Montreal–Quebec City |
E1006216
|
entity |
| Predicate | hasCommonTravelTimeByTrainHours |
P181490
|
FINISHED |
| Object | about 3 to 3.5 |
—
|
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: about 3 to 3.5 | Statement: [Montreal–Quebec City, hasCommonTravelTimeByTrainHours, about 3 to 3.5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommonTravelTimeByTrainHours Context triple: [Montreal–Quebec City, hasCommonTravelTimeByTrainHours, about 3 to 3.5]
-
A.
railTravelTimeRangeHours
chosen
Indicates the range of time, in hours, that a journey by rail between the related entities is expected to take.
-
B.
railwayTimeUsage
Indicates how much time is spent using or operating a railway within a given context or period.
-
C.
hasApproximateFastRailJourneyTimeHours
Indicates that there is an estimated duration, measured in hours, for a fast rail journey between the related entities.
-
D.
hasNightTrainService
Indicates that a transportation route or station is served by trains that operate during nighttime hours.
-
E.
hasRegionalTrainService
Indicates that a location or route is served by regional train services connecting it to nearby areas.
- 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_69f76dbedb288190afe5780710847410 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a037c8c34f88190ace26f555827f23e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379ff1ba081908eda86acefcf69fb |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4 p.m.