Triple
T21054036
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tana River County |
E518662
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Lamu 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: Lamu County | Statement: [Tana River County, borders, Lamu County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lamu County Context triple: [Tana River County, borders, Lamu County]
-
A.
Lamu County
chosen
Lamu County is an administrative county in Kenya’s coastal region, known for encompassing the historic Lamu Archipelago and its UNESCO-listed Swahili Old Town.
-
B.
Bailey County
Bailey County is a rural county in the western Texas Panhandle known for its agricultural economy and small communities.
-
C.
Suwannee County
Suwannee County is a rural county in northern Florida known for the Suwannee River, agriculture, and small-town communities.
-
D.
Wilcox County
Wilcox County is a rural county in south-central Alabama known for its rich Civil Rights history and location along the Alabama River in the state's Black Belt region.
-
E.
Colbert County
Colbert County is a county in northwestern Alabama known for its location along the Tennessee River and its role in the Muscle Shoals metropolitan area.
- 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_69e0b5053ac48190921529544959e906 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fd7e087c81908712ddc63e8b1e6c |
completed | April 21, 2026, 4:30 a.m. |
Created at: April 16, 2026, 2:36 p.m.