Triple
T16911383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KCKCC |
E410201
|
entity |
| Predicate | county |
P75
|
FINISHED |
| Object | Wyandotte County |
E98574
|
NE 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: Wyandotte County | Statement: [KCKCC, county, Wyandotte County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wyandotte County Context triple: [KCKCC, county, Wyandotte County]
-
A.
Wyandotte County
chosen
Wyandotte County is an urban county in northeastern Kansas that includes and is largely defined by the city of Kansas City, Kansas.
-
B.
Isabella County
Isabella County is a county in the U.S. state of Michigan, known for being home to the city of Mount Pleasant and Central Michigan University.
-
C.
Dewey County
Dewey County is a rural county in northwestern Oklahoma known for its agricultural economy and small-town communities.
-
D.
Wayne County
Wayne County is a county in western New York State, situated along the southern shore of Lake Ontario and known for its agricultural production and small rural communities.
-
E.
Wayne County
Wayne County is a county-level jurisdiction in the U.S. state of North Carolina, known for its mix of rural communities, small towns, and agricultural activity.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d886c7b1e481908c3766dfa8c13458 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3ca3d6be88190bca5e1fdf727141b |
completed | April 18, 2026, 6:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00c7bb4ac481909318d3d61a2d10e1 |
completed | May 10, 2026, 6 p.m. |
Created at: April 10, 2026, 5:30 a.m.