Triple
T15111066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Windham Region |
E360911
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Searsburg, Vermont |
—
|
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: Searsburg, Vermont | Statement: [Windham Region, contains, Searsburg, Vermont]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Searsburg, Vermont Context triple: [Windham Region, contains, Searsburg, Vermont]
-
A.
Searsburg, Vermont
chosen
Searsburg, Vermont is a small rural town in southern Vermont known for its forested landscape and wind energy installations.
-
B.
Sutton, Vermont
Sutton, Vermont is a small rural town in northeastern Vermont known for its scenic landscapes and quiet, close-knit community.
-
C.
Sudbury, Vermont
Sudbury, Vermont is a small rural town in Rutland County known for its scenic landscapes and quiet, agricultural character.
-
D.
Ferrisburgh, Vermont
Ferrisburgh, Vermont is a rural town in northwestern Vermont known for its Lake Champlain shoreline, historic sites, and agricultural landscape.
-
E.
Shelburne, Vermont
Shelburne, Vermont is a suburban town near Burlington known for its scenic Lake Champlain shoreline, Shelburne Museum, and Shelburne Farms.
- 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_69d85a0491ec8190830960be8fafb994 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0058c04f481909deeac0271d961b6 |
completed | April 15, 2026, 9:39 p.m. |
Created at: April 10, 2026, 3:05 a.m.