Triple
T7222973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. Route 27 in Georgia |
E150305
|
entity |
| Predicate | runsThroughPartOfState |
P54144
|
FINISHED |
| Object | western Georgia |
—
|
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: western Georgia | Statement: [U.S. Route 27 in Georgia, runsThroughPartOfState, western Georgia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runsThroughPartOfState Context triple: [U.S. Route 27 in Georgia, runsThroughPartOfState, western Georgia]
-
A.
passesThroughState
Indicates that something (such as a route, path, or process) traverses or goes through a particular state or region during its course.
-
B.
passesThroughCounty
Indicates that a route, path, or boundary traverses or goes through a specified county.
-
C.
runsAcross
Indicates that one entity moves quickly on foot from one side of another entity, area, or boundary to the opposite side, traversing it in a roughly straight path.
-
D.
travelsThrough
chosen
Indicates that something moves along, passes across, or is routed via a particular path, medium, or location.
-
E.
passesThroughDistrict
Indicates that something (such as a route, boundary, or path) traverses or goes through a particular district.
- 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_69c687effb44819092b95d07d0368c9f |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6e9b54a5c8190a4a289f32853a8fe |
completed | March 27, 2026, 8:33 p.m. |
| PD | Predicate disambiguation | batch_69c6e761b7fc8190857794d78af1b468 |
completed | March 27, 2026, 8:24 p.m. |
Created at: March 27, 2026, 2:54 p.m.