Triple
T21323567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arundel, Maine |
E525686
|
entity |
| Predicate | proximityToResortCommunity |
P4647
|
FINISHED |
| Object | Kennebunkport, 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: Kennebunkport, Maine | Statement: [Arundel, Maine, proximityToResortCommunity, Kennebunkport, Maine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: proximityToResortCommunity Context triple: [Arundel, Maine, proximityToResortCommunity, Kennebunkport, Maine]
-
A.
hasResortCommunity
Indicates that one entity includes, contains, or is associated with a resort-style residential or vacation community.
-
B.
hasNearbyCommunity
chosen
Indicates that one entity has another community located close to it in geographic or spatial terms.
-
C.
nearbyResortArea
Indicates that a resort area is located close to or within a short distance of a specified place or entity.
-
D.
nearbyResortSection
Indicates that one resort section is located close to another resort section in physical proximity.
-
E.
locatedInResortTown
Indicates that something is situated within the boundaries of a town that is primarily known as a resort or vacation destination.
- F. None of above.
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_69e0b51ad810819098c12392c8e55f6c |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e77ed572548190bd71ef690fc7befe |
completed | April 21, 2026, 1:42 p.m. |
| PD | Predicate disambiguation | batch_69e6161feea4819091d13bb003363279 |
completed | April 20, 2026, 12:03 p.m. |
Created at: April 16, 2026, 4:40 p.m.