Triple
T3529085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waldo County, Maine |
E74613
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
Searsmont, Maine
Searsmont, Maine is a small rural town in Waldo County known for its forests, lakes, and traditional New England character.
|
E849103
|
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: Searsmont, Maine | Statement: [Waldo County, Maine, hasTown, Searsmont, Maine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Searsmont, Maine Context triple: [Waldo County, Maine, hasTown, Searsmont, Maine]
-
A.
Shapleigh, Maine
Shapleigh, Maine is a small rural town in southwestern Maine known for its forests, lakes, and outdoor recreation.
-
B.
Rockwood, Maine
Rockwood, Maine is a small unincorporated community and popular outdoor recreation gateway on the western shore of Moosehead Lake in north-central Maine.
-
C.
Lovell, Maine
Lovell, Maine is a small rural town in Oxford County known for its scenic lakes and mountains in western Maine.
-
D.
Mount Vernon, Maine
Mount Vernon, Maine is a small rural town in central Maine known for its lakes, forests, and outdoor recreation, located within Kennebec County.
-
E.
Waterford, Maine
Waterford, Maine is a small rural town in Oxford County known for its lakes, forests, and traditional New England village character.
- 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: Searsmont, Maine Triple: [Waldo County, Maine, hasTown, Searsmont, Maine]
Generated description
Searsmont, Maine is a small rural town in Waldo County known for its forests, lakes, and traditional New England character.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Searsmont, Maine Target entity description: Searsmont, Maine is a small rural town in Waldo County known for its forests, lakes, and traditional New England character.
-
A.
Shapleigh, Maine
Shapleigh, Maine is a small rural town in southwestern Maine known for its forests, lakes, and outdoor recreation.
-
B.
Rockwood, Maine
Rockwood, Maine is a small unincorporated community and popular outdoor recreation gateway on the western shore of Moosehead Lake in north-central Maine.
-
C.
Lovell, Maine
Lovell, Maine is a small rural town in Oxford County known for its scenic lakes and mountains in western Maine.
-
D.
Mount Vernon, Maine
Mount Vernon, Maine is a small rural town in central Maine known for its lakes, forests, and outdoor recreation, located within Kennebec County.
-
E.
Waterford, Maine
Waterford, Maine is a small rural town in Oxford County known for its lakes, forests, and traditional New England village character.
- 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_69ad85d1a3948190931fd1ea1f49717b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc6e9188819093480b39f263ce75 |
completed | March 8, 2026, 6:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d5a92795cc819087578d02c2c791e4 |
completed | April 8, 2026, 1:02 a.m. |
| NEDg | Description generation | batch_69d5a9fb8150819099754db5262262fa |
completed | April 8, 2026, 1:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d5aa5356d48190a5db7d939c229cd7 |
completed | April 8, 2026, 1:07 a.m. |
Created at: March 8, 2026, 3:19 p.m.