Triple
T6352937
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Suwannee County Airport |
E142919
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object | Suwannee County |
E378864
|
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: Suwannee County | Statement: [Suwannee County Airport, operator, Suwannee County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suwannee County Context triple: [Suwannee County Airport, operator, Suwannee County]
-
A.
Suwannee County
chosen
Suwannee County is a rural county in northern Florida known for the Suwannee River, agriculture, and small-town communities.
-
B.
Lauderdale County
Lauderdale County is a county in the northwestern part of Alabama, known for its seat in Florence and location along the Tennessee River.
-
C.
Bailey County
Bailey County is a rural county in the western Texas Panhandle known for its agricultural economy and small communities.
-
D.
Levy County
Levy County is a rural county in Florida known for its Gulf Coast shoreline, small towns, and natural springs and forests.
-
E.
Sumter County
Sumter County is a rural county in western Alabama known for its agricultural landscape, small communities, and location along the Mississippi state line.
- 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_69c008d6dcbc8190aa1c2f1fd8916b42 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067dec4a88190992d57a0cc7782ad |
completed | March 22, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c640afafd48190b900fa5e0956d538 |
completed | March 27, 2026, 8:32 a.m. |
Created at: March 22, 2026, 4:31 p.m.