Triple
T19888406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dexter Public Library |
E477963
|
entity |
| Predicate | serviceArea |
P82
|
FINISHED |
| Object | Dexter, Maine region |
—
|
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 region | Statement: [Dexter Public Library, serviceArea, Dexter, Maine region]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dexter, Maine region Context triple: [Dexter Public Library, serviceArea, Dexter, Maine region]
-
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.
town of Dexter
The town of Dexter is a small community in central Maine known for its rural character, historic mill heritage, and recreational access to nearby lakes and forests.
-
C.
State of Maine (fictional setting)
The State of Maine is the fictional U.S. setting in Stephen King’s works, notably encompassing the town of Shawshank and its infamous prison.
-
D.
Castle Rock, Maine
Castle Rock, Maine is a fictional small town in Stephen King’s works, known as the setting for many of his horror and suspense stories.
-
E.
Strong, Maine
Strong, Maine is a small rural town in western Maine known historically for its lumber and toothpick manufacturing industries.
- 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_69d8e51f32b08190b3687f4f60353250 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6590c1b9c8190abbfaa04b80713b3 |
completed | April 20, 2026, 4:49 p.m. |
Created at: April 10, 2026, 1:52 p.m.