Triple
T26342682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lexington, Alabama |
E662689
|
entity |
| Predicate | isInMetropolitanAreaWith |
P114292
|
FINISHED |
| Object | Florence, 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: Florence, Alabama | Statement: [Lexington, Alabama, isInMetropolitanAreaWith, Florence, Alabama]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isInMetropolitanAreaWith Context triple: [Lexington, Alabama, isInMetropolitanAreaWith, Florence, Alabama]
-
A.
isWithinMetroArea
chosen
Indicates that one location lies inside the geographic boundaries of a specified metropolitan area.
-
B.
isMetropolitanFor
Indicates that one entity serves as the primary metropolitan center or urban hub for another entity (such as a region, area, or service).
-
C.
locatedNearMetropolitanArea
Indicates that one entity is situated in close geographic proximity to a metropolitan (urban) area.
-
D.
metropolitanAreaWith
Indicates that one entity is a metropolitan area that includes, is associated with, or encompasses the other entity.
-
E.
hasMetropolitanAreaName
Indicates that an entity is associated with a metropolitan area identified by a specific name.
- F. None of above.
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_69ee81304194819092e20e0fae3aee07 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f60fa3f27c8190a6f4223beb94a82e |
completed | May 2, 2026, 2:52 p.m. |
| PD | Predicate disambiguation | batch_69f602d2ec748190ae95154f34c7878f |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 26, 2026, 10:40 p.m.