Triple
T3844047
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Union County, Tennessee |
E93523
|
entity |
| Predicate | countyNumberInStateFormation |
P52352
|
FINISHED |
| Object | 94th county of Tennessee |
—
|
LITERAL 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: 94th county of Tennessee | Statement: [Union County, Tennessee, countyNumberInStateFormation, 94th county of Tennessee]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countyNumberInStateFormation Context triple: [Union County, Tennessee, countyNumberInStateFormation, 94th county of Tennessee]
-
A.
hasNumberOfCounties
Indicates the relationship that specifies how many counties are associated with or contained within a given entity.
-
B.
numberOfStates
Indicates the total count of distinct states or conditions associated with an entity or system.
-
C.
numberPerCounty
Indicates the quantity or count of something associated with each individual county.
-
D.
foundingMemberState
Indicates that a state is one of the original founding members of an organization, union, or similar entity.
-
E.
numberOfStatesRepresented
Indicates how many distinct states are represented or covered in a given context or entity.
- F. None of above. chosen
Provenance (4 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_69aed96ce578819084ab16e3439976c9 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeebb4fd308190a636ba9dbbe57ed6 |
completed | March 9, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69aee74dcecc819098285483ec721b40 |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aeeb828fb08190901d51edbe8bd304 |
completed | March 9, 2026, 3:47 p.m. |
Created at: March 9, 2026, 3:18 p.m.