Triple
T500522
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fredericksburg Line |
E10388
|
entity |
| Predicate | commuterRailFor |
P3916
|
FINISHED |
| Object | I-95 corridor in Northern Virginia |
—
|
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: I-95 corridor in Northern Virginia | Statement: [Fredericksburg Line, commuterRailFor, I-95 corridor in Northern Virginia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commuterRailFor Context triple: [Fredericksburg Line, commuterRailFor, I-95 corridor in Northern Virginia]
-
A.
subwayServices
Indicates that one entity provides or operates subway transportation services for another entity or area.
-
B.
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.
-
C.
hasLightRailSystem
Indicates that a place possesses and operates a light rail transit system.
-
D.
hasCommuterOrientation
chosen
Indicates that an entity is designed or intended primarily for use by commuters, emphasizing suitability for regular travel between home and work or study.
-
E.
hasRailStation
Indicates that one entity possesses, contains, or is served by a rail station.
- 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_69a2e847df8481909239ec08ccf1e376 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f13096248190a622a58dcf540b00 |
completed | Feb. 28, 2026, 1:44 p.m. |
| PD | Predicate disambiguation | batch_69a2edfbb7e0819092cf29c2c68fe8fb |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.