Triple
T6361002
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Berwick railway station |
E143106
|
entity |
| Predicate | timetableFrequency |
P55256
|
FINISHED |
| Object | regular services throughout the day |
—
|
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: regular services throughout the day | Statement: [North Berwick railway station, timetableFrequency, regular services throughout the day]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: timetableFrequency Context triple: [North Berwick railway station, timetableFrequency, regular services throughout the day]
-
A.
serviceFrequencyType
chosen
Indicates how often a service occurs or is scheduled within a given time period.
-
B.
typicalFrequencyUnit
Indicates the unit of measurement typically used to express the frequency of an event, action, or occurrence.
-
C.
timeUnitOfFrequency
Indicates the unit of time (e.g., day, week, month) in which a given frequency is measured or expressed.
-
D.
timeCharacteristic
Indicates a relationship where one entity specifies a temporal property, feature, or constraint that characterizes another entity or event.
-
E.
rotationPeriod_hours
Indicates the length of time, measured in hours, that an object takes to complete one full rotation on its axis.
- 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_69c008d7a9c4819098d647ec47776917 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067fa0d0c819098d01545849142fc |
completed | March 22, 2026, 10:06 p.m. |
| PD | Predicate disambiguation | batch_69c060ec091c8190912aac44e1b8b1c9 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:32 p.m.