Triple
T15365637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Two Harbors |
E367405
|
entity |
| Predicate | county |
P75
|
FINISHED |
| Object | Lake County |
E436179
|
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: Lake County | Statement: [Two Harbors, county, Lake County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lake County Context triple: [Two Harbors, county, Lake County]
-
A.
Lake County
Lake County is a county in northwestern Indiana known for its industrial cities, including Gary, and its location along the southern shore of Lake Michigan.
-
B.
Lake County
Lake County is a rural county in western Montana known for encompassing much of Flathead Lake and parts of the Flathead Indian Reservation.
-
C.
Lake County
Lake County is a county in northeastern Illinois, north of Chicago, known for its suburban communities, forest preserves, and location along Lake Michigan.
-
D.
Lake County
Lake County is a rural county in Northern California known for Clear Lake, extensive vineyards and wineries, and its mountainous, volcanic landscape.
-
E.
Lake County
chosen
Lake County is a county in northeastern Minnesota known for its North Shore scenery along Lake Superior and extensive forests and lakes.
- 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_69d85a1483788190ad93c2748e8af34b |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e497de48190be249b110999ec5c |
completed | April 16, 2026, 1:41 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff13457418819088232270b092c969 |
completed | May 9, 2026, 10:58 a.m. |
Created at: April 10, 2026, 3:18 a.m.