Triple
T5965269
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Opelika |
E132736
|
entity |
| Predicate | county |
P75
|
FINISHED |
| Object | Lee County |
E163376
|
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: Lee County | Statement: [Opelika, county, Lee County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lee County Context triple: [Opelika, county, Lee County]
-
A.
Lee County
Lee County is a coastal county on Florida’s Gulf Coast known for its beaches, barrier islands, and the city of Fort Myers.
-
B.
Lee County
chosen
Lee County is a county in eastern Alabama known for being home to the city of Auburn and Auburn University.
-
C.
Lee County
Lee County is a county in northern Illinois known for its largely rural landscape, small towns, and agricultural economy.
-
D.
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.
-
E.
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.
- 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_69c0086c2364819091e9fe2f58fa2517 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c03a3ca1dc819098cde8ae5ec1d845 |
completed | March 22, 2026, 6:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c20d2e42c88190927bba51caec186f |
completed | March 24, 2026, 4:03 a.m. |
Created at: March 22, 2026, 4:03 p.m.