Triple
T4368663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chautauqua County, New York |
E98839
|
entity |
| Predicate | adjacentToCountry |
P25929
|
FINISHED |
| Object | Canada (across Lake Erie) |
—
|
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: Canada (across Lake Erie) | Statement: [Chautauqua County, New York, adjacentToCountry, Canada (across Lake Erie)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: adjacentToCountry Context triple: [Chautauqua County, New York, adjacentToCountry, Canada (across Lake Erie)]
-
A.
borderingCountryNearby
chosen
Indicates that one country is geographically close to, but does not necessarily share a direct land border with, another country.
-
B.
neighboringCountryBySea
Indicates that one country is adjacent to another with their territories touching via a shared sea boundary rather than solely by land.
-
C.
countryBorderProximity
Indicates that one country is geographically close to or directly bordering another country.
-
D.
countryBordering
Indicates that one country shares a land or maritime boundary directly with another country.
-
E.
provinceBordering
Indicates that two provinces share a common boundary or border with each other.
- 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_69b3454db3708190aeafd814413c4c3d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b352034d3881909ed4b2f9eef5e823 |
completed | March 12, 2026, 11:53 p.m. |
| PD | Predicate disambiguation | batch_69b34f53e3cc8190bf5d4dbe2413bf65 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:17 p.m.