Triple
T21453601
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AL 25 |
E529279
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Moody, Alabama |
—
|
NE NERFINISHED |
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: Moody, Alabama | Statement: [AL 25, passesThrough, Moody, Alabama]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moody, Alabama Context triple: [AL 25, passesThrough, Moody, Alabama]
-
A.
Moody, Alabama
chosen
Moody, Alabama is a small suburban city in central Alabama that forms part of the Birmingham metropolitan area.
-
B.
Elmore, Alabama
Elmore, Alabama is a small town in central Alabama that forms part of the Montgomery metropolitan area.
-
C.
Gadsden, Alabama
Gadsden, Alabama is a small industrial city in northeastern Alabama known historically for its manufacturing plants and labor history.
-
D.
Jackson, Alabama
Jackson, Alabama is a small city in Clarke County known historically as a regional center for timber and paper industries in southwestern Alabama.
-
E.
Tyler, Alabama
Tyler, Alabama is a small unincorporated rural community located in Dallas County in the central part of the state.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c457579481909db68053ed99750c |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9d50b88819081a771596d0a2b2b |
completed | April 23, 2026, 9:43 a.m. |
Created at: April 16, 2026, 6:07 p.m.