Triple
T4510548
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cedar River |
E102041
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Waterloo, Iowa |
E59849
|
NE 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: Waterloo, Iowa | Statement: [Cedar River, flowsThrough, Waterloo, Iowa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Waterloo, Iowa Context triple: [Cedar River, flowsThrough, Waterloo, Iowa]
-
A.
Waterloo, Iowa
chosen
Waterloo, Iowa is a mid-sized city in northeastern Iowa known as an industrial and commercial hub of the Cedar Valley region along the Cedar River.
-
B.
Wheatland, Iowa
Wheatland, Iowa is a small rural city in eastern Iowa known for its agricultural community and location within Clinton County.
-
C.
Waverly, Iowa
Waverly, Iowa is a small city in northeastern Iowa known as the county seat of Bremer County and home to Wartburg College.
-
D.
Washington, Iowa
Washington, Iowa is a small city in southeastern Iowa known for its historic downtown, agricultural surroundings, and role as a local commercial and cultural hub.
-
E.
Williamsburg, Iowa
Williamsburg, Iowa is a small city in eastern Iowa known for its rural community character and proximity to the Tanger Outlet Center shopping area.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69bd43d6251c81909deecce3e6e9d69c |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5712b9308190876c117b50d12635 |
completed | March 20, 2026, 2:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd7f814dd081908b07ce0c2abee3c1 |
completed | March 20, 2026, 5:10 p.m. |
Created at: March 20, 2026, 1:01 p.m.