Triple
T10834534
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aberdeen, South Dakota |
E255717
|
entity |
| Predicate | county |
P75
|
FINISHED |
| Object |
Brown County
Brown County is a county in northeastern South Dakota that includes the city of Aberdeen as its county seat and primary population center.
|
E890915
|
NE FINISHED |
How this triple was built (4 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: Brown County | Statement: [Aberdeen, South Dakota, county, Brown County]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brown County Context triple: [Aberdeen, South Dakota, county, Brown County]
-
A.
Brown County
Brown County is a county in northeastern Wisconsin that includes the city of Green Bay and operates various public facilities and services for its residents.
-
B.
Wood County
Wood County is a county in central Wisconsin known for its mix of small cities, agricultural areas, and paper industry heritage.
-
C.
Smith County
Smith County is a county in eastern Texas that includes the city of Tyler and serves as a regional hub for healthcare, education, and commerce.
-
D.
Smith County
Smith County is a rural county in central Mississippi known for its small communities, agriculture, and pine forests.
-
E.
Medina County
Medina County is a suburban-rural county in northern Ohio known for its historic town squares, growing residential communities, and proximity to the Cleveland metropolitan area.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Brown County Triple: [Aberdeen, South Dakota, county, Brown County]
Generated description
Brown County is a county in northeastern South Dakota that includes the city of Aberdeen as its county seat and primary population center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Brown County Target entity description: Brown County is a county in northeastern South Dakota that includes the city of Aberdeen as its county seat and primary population center.
-
A.
Brown County
Brown County is a county in northeastern Wisconsin that includes the city of Green Bay and operates various public facilities and services for its residents.
-
B.
Wood County
Wood County is a county in central Wisconsin known for its mix of small cities, agricultural areas, and paper industry heritage.
-
C.
Smith County
Smith County is a county in eastern Texas that includes the city of Tyler and serves as a regional hub for healthcare, education, and commerce.
-
D.
Smith County
Smith County is a rural county in central Mississippi known for its small communities, agriculture, and pine forests.
-
E.
Medina County
Medina County is a suburban-rural county in northern Ohio known for its historic town squares, growing residential communities, and proximity to the Cleveland metropolitan area.
- F. None of above. chosen
Provenance (5 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_69d6aa81a5d08190aa86689061d1ddd2 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d74425447081908fb51c7edf54af67 |
completed | April 9, 2026, 6:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dff7c739708190b0d58fc2d6392c6c |
completed | April 15, 2026, 8:40 p.m. |
| NEDg | Description generation | batch_69e0026e7900819087327db5f625169c |
completed | April 15, 2026, 9:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e0057a7704819096becb74dc261883 |
completed | April 15, 2026, 9:39 p.m. |
Created at: April 8, 2026, 9:19 p.m.