Triple
T23089324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | M-40 |
E575699
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Van Buren County |
—
|
NE NERFINISHED |
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: Van Buren County | Statement: [M-40, passesThrough, Van Buren County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Van Buren County Context triple: [M-40, passesThrough, Van Buren County]
-
A.
Van Buren County
chosen
Van Buren County is a county in southwestern Michigan known for its Lake Michigan shoreline, agricultural areas, and small towns.
-
B.
Taney County
Taney County is a county in southwestern Missouri known for encompassing the popular tourist destination city of Branson.
-
C.
Yates County
Yates County is a rural county in New York State’s Finger Lakes region, known for its vineyards, agriculture, and lakeside communities.
-
D.
Stephenson County
Stephenson County is a county in northern Illinois known for its agricultural landscape, small towns, and historic connection to figures like Jane Addams, who was born in Cedarville.
-
E.
Upton County
Upton County is a sparsely populated, oil-producing county in western Texas known for its role in the Permian Basin energy region.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245bf3e3c819086d3448720efc01b |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18da8818481908d768a0462f3f837 |
completed | April 29, 2026, 4:48 a.m. |
Created at: April 17, 2026, 3:57 p.m.