Triple
T17895070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moscow–Saint Petersburg railway corridor |
E447411
|
entity |
| Predicate | travelTimeFastestServiceHours |
P122349
|
FINISHED |
| Object | about 4 |
—
|
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 4 | Statement: [Moscow–Saint Petersburg railway corridor, travelTimeFastestServiceHours, about 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelTimeFastestServiceHours Context triple: [Moscow–Saint Petersburg railway corridor, travelTimeFastestServiceHours, about 4]
-
A.
bestTravelTime
chosen
Indicates the most optimal duration or period required to travel between specified locations under given conditions.
-
B.
travelTimeTypical
Indicates the usual or expected amount of time it takes to travel between two locations under normal conditions.
-
C.
EVAtime
Indicates a temporal relationship specifying when an electric vehicle (EV)–related event, action, or state occurs or is valid.
-
D.
travelTimeCategory
Indicates the qualitative classification of how long a given travel or trip duration is (e.g., short, medium, long).
-
E.
nearbyTransit
Indicates that one location has public transportation options situated within a short distance or easy access from it.
- 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_69d8b9f59bd48190a6fc925a855b8bac |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49d7eb4f48190951e26975b57873b |
completed | April 19, 2026, 9:16 a.m. |
| PD | Predicate disambiguation | batch_69e3d8e9b77c8190bbfb508f28dfacfa |
completed | April 18, 2026, 7:18 p.m. |
Created at: April 10, 2026, 10:19 a.m.