Triple
T31964112
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Counties of Tennessee |
E816126
|
entity |
| Predicate | hasSmallestCountyByArea |
P14659
|
FINISHED |
| Object | Trousdale County, Tennessee |
—
|
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: Trousdale County, Tennessee | Statement: [Counties of Tennessee, hasSmallestCountyByArea, Trousdale County, Tennessee]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSmallestCountyByArea Context triple: [Counties of Tennessee, hasSmallestCountyByArea, Trousdale County, Tennessee]
-
A.
isSmallestByAreaIn
chosen
Indicates that an entity has the smallest area among all comparable entities within a specified set, group, or context.
-
B.
smallestStateAdministered
Indicates that the subject is the smallest administrative unit or territory governed or managed by the object.
-
C.
largestCountyByArea
Indicates that one county has the greatest land area compared to all other counties within a specified region or set.
-
D.
isSmallCountyByArea
Indicates that a county is classified as small based on the size of its geographic area.
-
E.
isLeastPopulousStateOf
Indicates that a state has the smallest population among all states within a specified country or region.
- 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_69f348f5ae5481909da0247869f51955 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6b49436b0819094e21603054d05d4 |
completed | May 3, 2026, 2:36 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a7bdb481908d16a32f49e38c2c |
completed | May 3, 2026, 2:32 a.m. |
Created at: May 1, 2026, 12:09 a.m.