Triple
T15213865
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fanning Springs, Florida |
E363587
|
entity |
| Predicate | county |
P75
|
FINISHED |
| Object | Levy County |
E377614
|
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: Levy County | Statement: [Fanning Springs, Florida, county, Levy County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Levy County Context triple: [Fanning Springs, Florida, county, Levy County]
-
A.
Levy County
chosen
Levy County is a rural county in Florida known for its Gulf Coast shoreline, small towns, and natural springs and forests.
-
B.
Thomas County
Thomas County is a county in southern Georgia, United States, known for its historic city of Thomasville and its blend of agricultural and cultural heritage.
-
C.
Suwannee County
Suwannee County is a rural county in northern Florida known for the Suwannee River, agriculture, and small-town communities.
-
D.
Lee County
Lee County is a county in eastern Alabama known for being home to the city of Auburn and Auburn University.
-
E.
Lee County
Lee County is a county in northern Illinois known for its largely rural landscape, small towns, and agricultural economy.
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0076e4348819091fa91c1562e7c5c |
completed | April 15, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00baf8a2648190adf3ad3af118187f |
completed | May 10, 2026, 5:06 p.m. |
Created at: April 10, 2026, 3:11 a.m.