Triple
T3333104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dane County |
E70077
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Vermont
Vermont is a small rural town located in Dane County, Wisconsin, known for its scenic landscapes and agricultural character.
|
E386728
|
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: Vermont | Statement: [Dane County, containsTown, Vermont]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vermont Context triple: [Dane County, containsTown, Vermont]
-
A.
Vermont
Vermont is a small, rural New England state in the northeastern United States, known for its Green Mountains, maple syrup production, and picturesque towns.
-
B.
New Hampshire
New Hampshire is a small New England state in the northeastern United States known for its mountainous landscapes, early presidential primary, and “Live Free or Die” motto.
-
C.
Maine
Maine is a northeastern U.S. state known for its rugged coastline, maritime history, and vast forested interior.
-
D.
Maine
Maine is a historical region in northwestern France that played a significant role in the medieval power struggles between the English and French crowns.
-
E.
Warren, Vermont
Warren, Vermont is a small New England town in the Mad River Valley known for its scenic mountain setting, outdoor recreation, and proximity to Sugarbush Resort.
- 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: Vermont Triple: [Dane County, containsTown, Vermont]
Generated description
Vermont is a small rural town located in Dane County, Wisconsin, known for its scenic landscapes and agricultural character.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vermont Target entity description: Vermont is a small rural town located in Dane County, Wisconsin, known for its scenic landscapes and agricultural character.
-
A.
Vermont
Vermont is a small, rural New England state in the northeastern United States, known for its Green Mountains, maple syrup production, and picturesque towns.
-
B.
New Hampshire
New Hampshire is a small New England state in the northeastern United States known for its mountainous landscapes, early presidential primary, and “Live Free or Die” motto.
-
C.
Maine
Maine is a northeastern U.S. state known for its rugged coastline, maritime history, and vast forested interior.
-
D.
Maine
Maine is a historical region in northwestern France that played a significant role in the medieval power struggles between the English and French crowns.
-
E.
Warren, Vermont
Warren, Vermont is a small New England town in the Mad River Valley known for its scenic mountain setting, outdoor recreation, and proximity to Sugarbush Resort.
- 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_69ad85a24f208190bcf83131bfed3521 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb194960081909333c855f06d8b03 |
completed | March 8, 2026, 5:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4e4b980888190a7df10662789f61e |
completed | March 14, 2026, 4:31 a.m. |
| NEDg | Description generation | batch_69b4e5f07c7081908e1aae715984aac4 |
completed | March 14, 2026, 4:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b4e6531b48819083c0d14c2ca4f7c1 |
completed | March 14, 2026, 4:38 a.m. |
Created at: March 8, 2026, 3:12 p.m.