Triple
T13346663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sherman, Ohio |
E317968
|
entity |
| Predicate | county |
P75
|
FINISHED |
| Object | Huron 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: Huron County | Statement: [Sherman, Ohio, county, Huron County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Huron County Context triple: [Sherman, Ohio, county, Huron County]
-
A.
Huron County
Huron County is a predominantly rural county in southwestern Ontario, Canada, known for its agriculture, small towns, and Lake Huron shoreline.
-
B.
Huron County
chosen
Huron County is a county in northern Ohio known for its mix of small cities, rural communities, and agricultural land.
-
C.
Chippewa County
Chippewa County is a county-level local government jurisdiction in the U.S. state of Michigan that administers regional services and infrastructure for its residents.
-
D.
Missaukee County
Missaukee County is a rural county in northern Michigan known for its forests, lakes, and agricultural landscape.
-
E.
Ottawa County
Ottawa County is a county in western Michigan known for its Lake Michigan shoreline, agricultural communities, and cities such as Holland and Grand Haven.
- 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99e89c65c819093f3bea11d6073c5 |
completed | April 11, 2026, 1:06 a.m. |
Created at: April 9, 2026, 9:31 p.m.