Triple
T2045414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris, Maine |
E45438
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object |
Greenwood, Maine
Greenwood, Maine is a small rural town in Oxford County known for its scenic lakes, forests, and outdoor recreation in western Maine.
|
E529215
|
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: Greenwood, Maine | Statement: [Paris, Maine, adjacentTo, Greenwood, Maine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Greenwood, Maine Context triple: [Paris, Maine, adjacentTo, Greenwood, Maine]
-
A.
Greenfield, Maine
Greenfield, Maine is a small rural town located in Penobscot County in the central part of the state.
-
B.
Greene, Maine
Greene, Maine is a small rural town in Androscoggin County known for its quiet residential character and proximity to the Lewiston–Auburn area.
-
C.
Garland, Maine
Garland, Maine is a small rural town located in Penobscot County in central Maine, known for its quiet countryside and close-knit community.
-
D.
Woodville, Maine
Woodville, Maine is a small rural town located in Penobscot County in the central part of the state.
-
E.
Howland, Maine
Howland, Maine is a small rural town in Penobscot County known for its location along the Penobscot and Piscataquis Rivers and its outdoor recreation opportunities.
- 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: Greenwood, Maine Triple: [Paris, Maine, adjacentTo, Greenwood, Maine]
Generated description
Greenwood, Maine is a small rural town in Oxford County known for its scenic lakes, forests, and outdoor recreation in western Maine.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Greenwood, Maine Target entity description: Greenwood, Maine is a small rural town in Oxford County known for its scenic lakes, forests, and outdoor recreation in western Maine.
-
A.
Greenfield, Maine
Greenfield, Maine is a small rural town located in Penobscot County in the central part of the state.
-
B.
Greene, Maine
Greene, Maine is a small rural town in Androscoggin County known for its quiet residential character and proximity to the Lewiston–Auburn area.
-
C.
Garland, Maine
Garland, Maine is a small rural town located in Penobscot County in central Maine, known for its quiet countryside and close-knit community.
-
D.
Woodville, Maine
Woodville, Maine is a small rural town located in Penobscot County in the central part of the state.
-
E.
Howland, Maine
Howland, Maine is a small rural town in Penobscot County known for its location along the Penobscot and Piscataquis Rivers and its outdoor recreation opportunities.
- 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_69a8891948208190ab7898da21824c77 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9728f688190939d7c4df524f9b4 |
completed | March 7, 2026, 5:36 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0276db4e8819090ece339aba3a13b |
completed | March 22, 2026, 5:31 p.m. |
| NEDg | Description generation | batch_69c037fca93881908d4d7403bfb1f866 |
completed | March 22, 2026, 6:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c03898327c8190bd3b889bd7663003 |
completed | March 22, 2026, 6:44 p.m. |
Created at: March 4, 2026, 7:39 p.m.