Triple
T21414733
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wallyford railway station |
E528269
|
entity |
| Predicate | typicalOffPeakFrequencyToEdinburgh |
P80227
|
FINISHED |
| Object | 2 trains per hour |
—
|
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: 2 trains per hour | Statement: [Wallyford railway station, typicalOffPeakFrequencyToEdinburgh, 2 trains per hour]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalOffPeakFrequencyToEdinburgh Context triple: [Wallyford railway station, typicalOffPeakFrequencyToEdinburgh, 2 trains per hour]
-
A.
offPeakServiceFrequency_minutes
Indicates the number of minutes between successive services during off-peak periods.
-
B.
typicalOffPeakServiceTo
chosen
Indicates the usual or standard off-peak (non-peak time) service pattern that operates to a given destination.
-
C.
typicalFrequencyWeekdayDaytime
Indicates the usual or most common frequency with which something occurs during daytime hours on weekdays.
-
D.
hasOffPeakUsage
Indicates that an entity exhibits or is associated with usage occurring during designated off-peak times rather than standard peak periods.
-
E.
offPeakServicePattern
Indicates the service pattern or schedule that applies during off-peak (non-rush-hour) times.
- 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_69e0c454c248819093425d1099101c09 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e8b2032fe48190907b282e2fffa2bd |
completed | April 22, 2026, 11:33 a.m. |
| PD | Predicate disambiguation | batch_69e61633f8208190a2a849457c4e4198 |
completed | April 20, 2026, 12:04 p.m. |
Created at: April 16, 2026, 5:45 p.m.