Triple
T25573072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hackbridge railway station |
E641027
|
entity |
| Predicate | typicalOffPeakTrainsPerHour |
P20359
|
FINISHED |
| Object | 4 towards central London |
—
|
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: 4 towards central London | Statement: [Hackbridge railway station, typicalOffPeakTrainsPerHour, 4 towards central London]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalOffPeakTrainsPerHour Context triple: [Hackbridge railway station, typicalOffPeakTrainsPerHour, 4 towards central London]
-
A.
peakDailyTrains
Indicates the maximum number of trains operating per day on a given route, line, or segment during its busiest period.
-
B.
offPeakServiceFrequency_minutes
Indicates the number of minutes between successive services during off-peak periods.
-
C.
peakHours
Indicates that an action, event, or condition occurs during the busiest or most heavily trafficked time period.
-
D.
transitFrequencyApprox
chosen
Indicates an approximate rate or regularity with which a transit event or service occurs between entities.
-
E.
typicalFrequencyWeekdayDaytime
Indicates the usual or most common frequency with which something occurs during daytime hours on weekdays.
- 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_69e75dc281bc819095ec04dc0c3a94d0 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69fd592e48cc81909d754cc6c4bd99ae |
completed | May 8, 2026, 3:31 a.m. |
| PD | Predicate disambiguation | batch_69fd58b7f9b881909dc099b28d567784 |
completed | May 8, 2026, 3:30 a.m. |
Created at: April 21, 2026, 3:59 p.m.