Triple
T12030948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Owen Brewster |
E286403
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Dexter, Maine |
—
|
NE NERFINISHED |
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: Dexter, Maine | Statement: [Owen Brewster, placeOfBirth, Dexter, Maine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dexter, Maine Context triple: [Owen Brewster, placeOfBirth, Dexter, Maine]
-
A.
Dexter, Maine
chosen
Dexter, Maine is a small New England town known for its historic mill industry and lakeside setting in central Maine.
-
B.
Thorndike, Maine
Thorndike, Maine is a small rural town located in Waldo County in the central part of the state.
-
C.
Ripley, Maine
Ripley, Maine is a small rural town located in Somerset County in central Maine.
-
D.
Westbrook, Maine
Westbrook, Maine is a small city in Cumberland County, just west of Portland, known for its historic mill industry and growing residential and commercial development.
-
E.
Damariscotta, Maine
Damariscotta, Maine is a small coastal town in Lincoln County known for its historic waterfront, oyster farming, and scenic location along the Damariscotta River.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d6ab4669e48190b59246358b0383ab |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903f24490819092ec911d6ed8e24b |
completed | April 10, 2026, 2:06 p.m. |
Created at: April 8, 2026, 9:47 p.m.