Triple
T8824954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ware railway station |
E209990
|
entity |
| Predicate | serviceFrequencyOffPeak |
P55256
|
FINISHED |
| Object | typically 2 trains per hour to London Liverpool Street |
—
|
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: typically 2 trains per hour to London Liverpool Street | Statement: [Ware railway station, serviceFrequencyOffPeak, typically 2 trains per hour to London Liverpool Street]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: serviceFrequencyOffPeak Context triple: [Ware railway station, serviceFrequencyOffPeak, typically 2 trains per hour to London Liverpool Street]
-
A.
typicalOffPeakServiceTo
Indicates the usual or standard off-peak (non-peak time) service pattern that operates to a given destination.
-
B.
offPeakServicePattern
Indicates the service pattern or schedule that applies during off-peak (non-rush-hour) times.
-
C.
serviceFrequencyType
chosen
Indicates how often a service occurs or is scheduled within a given time period.
-
D.
serviceFrequencyContext
Indicates the contextual conditions or circumstances under which a service’s frequency is defined, applied, or interpreted.
-
E.
peakHours
Indicates that an action, event, or condition occurs during the busiest or most heavily trafficked time period.
- 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_69ca8365b28081909e48e45e95dfc405 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc603220508190b64e22dec3ee5ceb |
completed | April 1, 2026, midnight |
| PD | Predicate disambiguation | batch_69cc5c21e64c81908490e3b0875dc0d6 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:46 p.m.