Triple
T2616637
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brown County, South Dakota |
E58901
|
entity |
| Predicate | hasCountyNumberInSouthDakota |
P42228
|
FINISHED |
| Object | 13 |
—
|
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: 13 | Statement: [Brown County, South Dakota, hasCountyNumberInSouthDakota, 13]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCountyNumberInSouthDakota Context triple: [Brown County, South Dakota, hasCountyNumberInSouthDakota, 13]
-
A.
hasCountyCode
Indicates that an entity is associated with a specific county identified by a standardized county code.
-
B.
inCounty
Indicates that one entity is geographically or administratively located within the boundaries of a specified county.
-
C.
hasNumberOfCounties
Indicates the relationship that specifies how many counties are associated with or contained within a given entity.
-
D.
startCounty
Indicates the county in which something (such as an event, route, or process) begins or originates.
-
E.
hasCountyNumberPlateCode
Indicates that an entity (such as a vehicle or registration) bears a number plate code that corresponds to a specific county.
- 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_69ab4ac444dc819099614e534dd6021f |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abdaca581881908fe8d3d820f839b7 |
completed | March 7, 2026, 7:59 a.m. |
| PD | Predicate disambiguation | batch_69abd80f48888190afdf7e3e042157d0 |
completed | March 7, 2026, 7:47 a.m. |
| PDg | Predicate description generation | batch_69abdac82b688190886a6ec2d6e2abc7 |
completed | March 7, 2026, 7:59 a.m. |
Created at: March 6, 2026, 9:50 p.m.