Triple
T22135384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Knutsford railway station |
E547011
|
entity |
| Predicate | typicalServiceFrequencyWeekdayDaytime |
P90998
|
FINISHED |
| Object | hourly |
—
|
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: hourly | Statement: [Knutsford railway station, typicalServiceFrequencyWeekdayDaytime, hourly]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalServiceFrequencyWeekdayDaytime Context triple: [Knutsford railway station, typicalServiceFrequencyWeekdayDaytime, hourly]
-
A.
typicalFrequencyWeekdayDaytime
chosen
Indicates the usual or most common frequency with which something occurs during daytime hours on weekdays.
-
B.
offPeakServiceFrequency_minutes
Indicates the number of minutes between successive services during off-peak periods.
-
C.
serviceFrequencyType
Indicates how often a service occurs or is scheduled within a given time period.
-
D.
typicalTimes
Indicates the usual or characteristic times at which an event, activity, or condition typically occurs.
-
E.
typicalFrequencySunday
Indicates how frequently an event, action, or relationship typically occurs on Sundays.
- 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_69e11e39bf348190b541bfa16a7b71e0 |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129b9ee54819081141c4f28e1211a |
completed | April 28, 2026, 9:42 p.m. |
| PD | Predicate disambiguation | batch_69e71b384e008190b723c9a0f1089d66 |
completed | April 21, 2026, 6:37 a.m. |
Created at: April 16, 2026, 8:32 p.m.