Triple
T2178342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moose Pond |
E48583
|
entity |
| Predicate | locatedNearTown |
P3883
|
FINISHED |
| Object |
Denmark, Maine
Denmark, Maine is a small rural town in Oxford County known for its lakeside setting in western Maine’s Lakes Region.
|
E241984
|
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: Denmark, Maine | Statement: [Moose Pond, locatedNearTown, Denmark, Maine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Denmark, Maine Context triple: [Moose Pond, locatedNearTown, Denmark, Maine]
-
A.
Norway, Maine
Norway, Maine is a small New England town known for its historic downtown, lakes and outdoor recreation, located in western Maine.
-
B.
Caribou, Maine
Caribou, Maine is a small city in northern Maine known for its agricultural economy, especially potato farming, and its proximity to outdoor recreation in Aroostook County.
-
C.
Strong, Maine
Strong, Maine is a small rural town in western Maine known historically for its lumber and toothpick manufacturing industries.
-
D.
Peru, Maine
Peru, Maine is a small rural town in Oxford County known for its scenic western Maine landscape and close-knit community.
-
E.
Greenland, New Hampshire
Greenland, New Hampshire is a small New England town in Rockingham County known for its historic character and proximity to the Seacoast region.
- 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: Denmark, Maine Triple: [Moose Pond, locatedNearTown, Denmark, Maine]
Generated description
Denmark, Maine is a small rural town in Oxford County known for its lakeside setting in western Maine’s Lakes Region.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Denmark, Maine Target entity description: Denmark, Maine is a small rural town in Oxford County known for its lakeside setting in western Maine’s Lakes Region.
-
A.
Norway, Maine
Norway, Maine is a small New England town known for its historic downtown, lakes and outdoor recreation, located in western Maine.
-
B.
Caribou, Maine
Caribou, Maine is a small city in northern Maine known for its agricultural economy, especially potato farming, and its proximity to outdoor recreation in Aroostook County.
-
C.
Strong, Maine
Strong, Maine is a small rural town in western Maine known historically for its lumber and toothpick manufacturing industries.
-
D.
Peru, Maine
Peru, Maine is a small rural town in Oxford County known for its scenic western Maine landscape and close-knit community.
-
E.
Greenland, New Hampshire
Greenland, New Hampshire is a small New England town in Rockingham County known for its historic character and proximity to the Seacoast region.
- 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_69a88aa3faa48190995b233af6525815 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc5af20808190902031d8c0bba376 |
completed | March 7, 2026, 6:29 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae5da36db0819093f832d44d228bba |
completed | March 9, 2026, 5:41 a.m. |
| NEDg | Description generation | batch_69ae5e48eefc8190b459278860bb3217 |
completed | March 9, 2026, 5:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae5ec233788190a52dc1a135f7dfb9 |
completed | March 9, 2026, 5:46 a.m. |
Created at: March 4, 2026, 7:45 p.m.