Triple
T232171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Penn Station (New York City) |
E4431
|
entity |
| Predicate | dailyRidership |
P10158
|
FINISHED |
| Object | hundreds of thousands of passengers |
—
|
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 thousands of passengers | Statement: [Penn Station (New York City), dailyRidership, hundreds of thousands of passengers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dailyRidership Context triple: [Penn Station (New York City), dailyRidership, hundreds of thousands of passengers]
-
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.
hasLightRailSystem
Indicates that a place possesses and operates a light rail transit system.
-
C.
isDowntownEndpointOf
Indicates that a location serves as the downtown terminus or endpoint of a route, line, or path.
-
D.
terminusCity
Indicates that a transportation route or service ends or has its final stop in a particular city.
-
E.
fareSystem
Indicates a relationship where a system is used to determine, collect, or manage fares or payments for transportation or similar services.
- 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_69a257363ffc81909757bde7ab3404da |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25f14f72081908182e76300b59358 |
completed | Feb. 28, 2026, 3:20 a.m. |
| PD | Predicate disambiguation | batch_69a25b5c8c888190b5544e687736b373 |
completed | Feb. 28, 2026, 3:05 a.m. |
| PDg | Predicate description generation | batch_69a25f1450f08190872bcf58a32d506b |
completed | Feb. 28, 2026, 3:20 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.