Triple
T6090081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Verbena, Alabama |
E135738
|
entity |
| Predicate | nearestCity |
P350
|
FINISHED |
| Object | Clanton, Alabama |
E23798
|
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: Clanton, Alabama | Statement: [Verbena, Alabama, nearestCity, Clanton, Alabama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Clanton, Alabama Context triple: [Verbena, Alabama, nearestCity, Clanton, Alabama]
-
A.
Clanton, Alabama
chosen
Clanton, Alabama is a small city in central Alabama known for its peach production and location between Birmingham and Montgomery.
-
B.
Cullman
Cullman is a small city in north-central Alabama known for its German heritage, historic downtown, and proximity to Smith Lake.
-
C.
Cleveland, Alabama
Cleveland, Alabama is a small rural town in Blount County known for its close-knit community and location in north-central Alabama.
-
D.
Tarrant, Alabama
Tarrant, Alabama is a small industrial city in Jefferson County, near Birmingham, known historically for its steel and manufacturing operations.
-
E.
Blountsville, Alabama
Blountsville, Alabama is a small historic town in northern Alabama known for its rural character and role as one of the early settlements in the region.
- 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_69c0087bcc788190b20f093d3a6c60ec |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c057a862c88190912a913973c6b6fc |
completed | March 22, 2026, 8:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11d67ab9c8190aad61d2a1be76e4e |
completed | March 23, 2026, 11 a.m. |
Created at: March 22, 2026, 4:12 p.m.