Triple
T2451267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lemon Breeland |
E53708
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Bluebell, Alabama |
E352473
|
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: Bluebell, Alabama | Statement: [Lemon Breeland, residence, Bluebell, Alabama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bluebell, Alabama Context triple: [Lemon Breeland, residence, Bluebell, Alabama]
-
A.
Bluebell, Alabama
chosen
Bluebell, Alabama is a fictional small Southern town featured as the primary setting in the television series "Hart of Dixie."
-
B.
Brent, Alabama
Brent, Alabama is a small city in central Alabama known for its rural character and location within Bibb County.
-
C.
Billingsley, Alabama
Billingsley, Alabama is a small rural town in central Alabama known for its close-knit community and agricultural surroundings.
-
D.
Townley, Alabama
Townley, Alabama is a small unincorporated community located in Walker County in the north-central part of the state.
-
E.
Steele, Alabama
Steele, Alabama is a small town in northeastern Alabama known for its rural character and location within St. Clair County.
- 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_69ab495d227c8190b26ae6548eeb1019 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd0f402b48190b871b2475983af7e |
completed | March 7, 2026, 7:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b37e3d80d081908bb563323e250978 |
completed | March 13, 2026, 3:02 a.m. |
Created at: March 6, 2026, 9:43 p.m.