Triple
T2147777
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Union Station (Baltimore) |
E47107
|
entity |
| Predicate | railwayStationUsage |
P36616
|
FINISHED |
| Object | passenger rail |
—
|
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: passenger rail | Statement: [Union Station (Baltimore), railwayStationUsage, passenger rail]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: railwayStationUsage Context triple: [Union Station (Baltimore), railwayStationUsage, passenger rail]
-
A.
railwayLineUsage
Indicates how a railway line is used, such as the type or purpose of traffic or operations it supports.
-
B.
railwayTimeUsage
Indicates how much time is spent using or operating a railway within a given context or period.
-
C.
railwayStationOnLine
Indicates that a particular railway station is located on and served by a specified railway line.
-
D.
railwayTraffic
Indicates the presence, flow, or management of train movements along railway lines between locations.
-
E.
railwayStationFor
Indicates a relationship where a railway station serves, is designated for, or primarily associated with a particular place, line, or service.
- 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_69a88a1933e0819094f18426ed74180f |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbeaa14bc81908486683decd7ae42 |
completed | March 7, 2026, 5:59 a.m. |
| PD | Predicate disambiguation | batch_69abbd9846e88190b6c2941dd9ce7749 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abbea8bd4881908f72019a5acf6174 |
completed | March 7, 2026, 5:59 a.m. |
Created at: March 4, 2026, 7:44 p.m.