Triple
T375058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Broad Street Station (Philadelphia) |
E8352
|
entity |
| Predicate | peakDailyTrains |
P13069
|
FINISHED |
| Object | hundreds of trains per day at peak |
—
|
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: hundreds of trains per day at peak | Statement: [Broad Street Station (Philadelphia), peakDailyTrains, hundreds of trains per day at peak]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: peakDailyTrains Context triple: [Broad Street Station (Philadelphia), peakDailyTrains, hundreds of trains per day at peak]
-
A.
dailyRidershipPeak
Indicates that the relationship specifies the highest number of riders or users recorded for a service or system within a single day.
-
B.
dailyRidership
Indicates the typical number of people who use or ride a given transportation service each day.
-
C.
trains
Indicates that one entity teaches, instructs, or coaches another entity to develop skills, knowledge, or abilities.
-
D.
railwayTimeUsage
Indicates how much time is spent using or operating a railway within a given context or period.
-
E.
isBusiestPassengerRailLineIn
Indicates that a passenger rail line is the one with the highest level of use or traffic within a specified geographic area or system.
- F. None of above. chosen
Provenance (4 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_69a2e7f2ec648190b42bc7db424f8109 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec1585648190943f1c698e9b2d81 |
completed | Feb. 28, 2026, 1:22 p.m. |
| PD | Predicate disambiguation | batch_69a2e96216048190873ae533fa5b864d |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ebcb1b2c8190a68bb3bad600c227 |
completed | Feb. 28, 2026, 1:21 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.