Triple
T4395248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clarington |
E99470
|
entity |
| Predicate | hasMajorCommunity |
P2321
|
FINISHED |
| Object |
Orono
Orono is a small rural village in Ontario, Canada, known for its historic downtown, agricultural surroundings, and community events.
|
E480003
|
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: Orono | Statement: [Clarington, hasMajorCommunity, Orono]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orono Context triple: [Clarington, hasMajorCommunity, Orono]
-
A.
Orono
Orono is a suburban city in Minnesota known for its affluent residential communities and scenic location along the north shore of Lake Minnetonka.
-
B.
Orono, Maine
Orono, Maine is a small town in Penobscot County best known as the home of the University of Maine’s flagship campus.
-
C.
Gardiner
Gardiner is an English surname historically associated with Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
-
D.
Gardiner, Maine
Gardiner, Maine is a small historic city in central Maine located along the Kennebec River, known for its preserved downtown and 19th-century architecture.
-
E.
Madison, Maine
Madison, Maine is a small town in central Maine known for its rural character, historic mill industry, and location along the Kennebec River.
- 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: Orono Triple: [Clarington, hasMajorCommunity, Orono]
Generated description
Orono is a small rural village in Ontario, Canada, known for its historic downtown, agricultural surroundings, and community events.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Orono Target entity description: Orono is a small rural village in Ontario, Canada, known for its historic downtown, agricultural surroundings, and community events.
-
A.
Orono
Orono is a suburban city in Minnesota known for its affluent residential communities and scenic location along the north shore of Lake Minnetonka.
-
B.
Orono, Maine
Orono, Maine is a small town in Penobscot County best known as the home of the University of Maine’s flagship campus.
-
C.
Gardiner
Gardiner is an English surname historically associated with Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
-
D.
Gardiner, Maine
Gardiner, Maine is a small historic city in central Maine located along the Kennebec River, known for its preserved downtown and 19th-century architecture.
-
E.
Madison, Maine
Madison, Maine is a small town in central Maine known for its rural character, historic mill industry, and location along the Kennebec River.
- 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_69b345506b408190b0e3dee616738a7d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352ab928c81909f4406d5df3e081b |
completed | March 12, 2026, 11:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be777b514081909832a8a520a7f7d1 |
completed | March 21, 2026, 10:48 a.m. |
| NEDg | Description generation | batch_69be77fbb3008190bf1b4066a2dbff81 |
completed | March 21, 2026, 10:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be7854677c8190aeef107e6874e4cf |
completed | March 21, 2026, 10:52 a.m. |
Created at: March 12, 2026, 11:20 p.m.