Triple
T10018717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black Bears |
E199560
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Orono, Maine |
E65873
|
NE FINISHED |
How this triple was built (2 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, Maine | Statement: [Black Bears, city, Orono, Maine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Orono, Maine Context triple: [Black Bears, city, Orono, Maine]
-
A.
Orono, Maine
chosen
Orono, Maine is a small town in Penobscot County best known as the home of the University of Maine’s flagship campus.
-
B.
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.
-
C.
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.
-
D.
Monroe, Maine
Monroe, Maine is a small rural town in Waldo County known for its quiet countryside and close-knit community in central coastal Maine.
-
E.
Orono
Orono is a small rural village in Ontario, Canada, known for its historic downtown, agricultural surroundings, and community events.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca8315a1a08190ab310f25620f362b |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd4f3a988190892cc698109be8b8 |
completed | April 2, 2026, 1:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64b78e7ec819093e5e631197ed295 |
completed | May 2, 2026, 7:07 p.m. |
Created at: March 30, 2026, 8:53 p.m.