Triple
T24638101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North American commuter railroads |
E609868
|
entity |
| Predicate | typicalSchedulePattern |
P128659
|
FINISHED |
| Object | weekday peak-focused |
—
|
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: weekday peak-focused | Statement: [North American commuter railroads, typicalSchedulePattern, weekday peak-focused]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSchedulePattern Context triple: [North American commuter railroads, typicalSchedulePattern, weekday peak-focused]
-
A.
typicalSchedule
Indicates the usual or standard timing and sequence of activities or events associated with an entity.
-
B.
schedulePattern
chosen
Indicates a recurring or structured timing relationship that defines when an event or action is scheduled to occur.
-
C.
typicalMatchSchedule
Indicates the usual or standard timing and arrangement of when matches are scheduled to occur.
-
D.
workPattern
Indicates the typical schedule, structure, or arrangement according to which an entity performs its work or duties.
-
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_69e2c4d28f848190ac38c400060e943d |
completed | April 17, 2026, 11:40 p.m. |
| NER | Named-entity recognition | batch_69f2be064ff88190b5d9e5ec75a41242 |
completed | April 30, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69f2a6d0ab708190b2e3b94dd20ca76b |
completed | April 30, 2026, 12:48 a.m. |
Created at: April 18, 2026, 2:33 a.m.