Triple
T12598270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alabama Black Belt |
E300789
|
entity |
| Predicate | includesCity |
P3207
|
FINISHED |
| Object | Marion, Alabama |
E351670
|
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: Marion, Alabama | Statement: [Alabama Black Belt, includesCity, Marion, Alabama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marion, Alabama Context triple: [Alabama Black Belt, includesCity, Marion, Alabama]
-
A.
Marion, Alabama
chosen
Marion, Alabama is a small historic city in Perry County known for its significant role in the civil rights movement and as the home of several notable educational institutions.
-
B.
Morris, Alabama
Morris, Alabama is a small town in central Alabama that forms part of the Birmingham metropolitan area.
-
C.
Sylvania, Alabama
Sylvania, Alabama is a small rural town in northeastern Alabama known for its close-knit community and location atop Sand Mountain.
-
D.
Roanoke, Alabama
Roanoke, Alabama is a small city in eastern Alabama known for its rural character and role as a local commercial and community hub.
-
E.
Courtland, Alabama
Courtland, Alabama is a small historic town in northern Alabama known for its 19th-century architecture and role in the region’s early transportation and cotton 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_69d7bdea2ca881908f379526c13b1145 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d954d096d08190afa1f685bad68d35 |
completed | April 10, 2026, 7:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716b1c7c081909154a59516b59d63 |
completed | May 3, 2026, 9:34 a.m. |
Created at: April 9, 2026, 5:09 p.m.