Triple
T24396022
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Acle railway station |
E615028
|
entity |
| Predicate | serviceFrequencyTypical |
P55256
|
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: [Acle railway station, serviceFrequencyTypical, hourly]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: serviceFrequencyTypical Context triple: [Acle railway station, serviceFrequencyTypical, hourly]
-
A.
serviceFrequencyType
chosen
Indicates how often a service occurs or is scheduled within a given time period.
-
B.
serviceFrequencyContext
Indicates the contextual conditions or circumstances under which a service’s frequency is defined, applied, or interpreted.
-
C.
typicalUpdateFrequency
Indicates how often an entity is usually updated or refreshed over time.
-
D.
recommendedUsageFrequency
Indicates how often something is advised or prescribed to be used within a given time period.
-
E.
typicalPeriod
Indicates the usual or characteristic time interval or duration associated with an event, process, or state.
- 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_69e2d7e509b88190a53155d4f3de45ce |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f294d69b5c81908cf6143374934f5c |
completed | April 29, 2026, 11:31 p.m. |
| PD | Predicate disambiguation | batch_69f287c4a2b48190b80fb7a3c0e9b018 |
completed | April 29, 2026, 10:35 p.m. |
Created at: April 18, 2026, 2:04 a.m.