Triple
T15737259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Central Wisconsin |
E381504
|
entity |
| Predicate | containsRiver |
P165
|
FINISHED |
| Object | Sugar River |
—
|
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: Sugar River | Statement: [South Central Wisconsin, containsRiver, Sugar River]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sugar River Context triple: [South Central Wisconsin, containsRiver, Sugar River]
-
A.
Sugar River
Sugar River is a New Hampshire river that drains Lake Sunapee and flows westward to join the Connecticut River.
-
B.
Sugar River
chosen
Sugar River is a tributary waterway in the Midwestern United States that flows through southern Wisconsin and northern Illinois before joining the Rock River.
-
C.
Mad River
Mad River is a tributary stream in New Hampshire that feeds into the Pemigewasset River within the Merrimack River watershed.
-
D.
Mad River
Mad River is a waterway in northwestern California that flows through the ancestral lands of the Wiyot people before emptying into the Pacific Ocean.
-
E.
Mad River
Mad River is a tributary of the Great Miami River in western Ohio known for its clear, cold waters and popularity for fishing and paddling.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04fd6eb888190b7a9b07b76e62c0d |
completed | April 16, 2026, 2:56 a.m. |
Created at: April 10, 2026, 4:46 a.m.