Triple
T31830663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Delmar, Maryland |
E812522
|
entity |
| Predicate | borderCommunityType |
P55550
|
FINISHED |
| Object | bi-state community |
—
|
LITERAL 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: bi-state community | Statement: [Delmar, Maryland, borderCommunityType, bi-state community]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: borderCommunityType Context triple: [Delmar, Maryland, borderCommunityType, bi-state community]
-
A.
crossBorderCommunity
chosen
Indicates a relationship where a community spans or connects populations across one or more political or geographic borders.
-
B.
borderCultureWith
Indicates that two regions or entities share a common boundary across which cultural traits, practices, or influences are actively exchanged or intertwined.
-
C.
borderStreet
Indicates that a street forms or lies along the boundary between two geographic areas or properties.
-
D.
borderRegime
Indicates the type, rules, or control system governing how movement or interaction is managed across a border between entities.
-
E.
borderTownAcrossBorder
Indicates that a town lies on one side of a border directly opposite or adjacent to a town on the other side of that border.
- 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_69f348ea7ffc8190a2ab43d80277cf59 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fed6da0390819096b88ef4714b144e |
completed | May 9, 2026, 6:40 a.m. |
| PD | Predicate disambiguation | batch_69fed53517d081909966f31707625f1a |
completed | May 9, 2026, 6:33 a.m. |
Created at: April 30, 2026, 11:47 p.m.