Triple
T15282383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norristown Branch |
E365300
|
entity |
| Predicate | railUsage |
P33182
|
FINISHED |
| Object | daily commuter traffic |
—
|
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: daily commuter traffic | Statement: [Norristown Branch, railUsage, daily commuter traffic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: railUsage Context triple: [Norristown Branch, railUsage, daily commuter traffic]
-
A.
railwayLineUsage
chosen
Indicates how a railway line is used, such as the type or purpose of traffic or operations it supports.
-
B.
railwayStationUsage
Indicates how frequently or extensively a railway station is used, such as by measuring passenger numbers, train traffic, or overall activity levels.
-
C.
railwayUse
Indicates that something is used as, or functions in the capacity of, a railway or rail-based transportation facility.
-
D.
railwayTimeUsage
Indicates how much time is spent using or operating a railway within a given context or period.
-
E.
railAccessModel
Indicates the type or pattern of how rail infrastructure or services are accessed or connected between locations or entities.
- 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_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00e51f82081909f63d14b589d5587 |
completed | April 15, 2026, 10:16 p.m. |
| PD | Predicate disambiguation | batch_69deca90739081909bd1b797cdb8af2b |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:15 a.m.