Triple
T37501870
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canada–United States border crossings |
E931983
|
entity |
| Predicate | connectRegion |
P24718
|
FINISHED |
| Object | Canadian provinces |
—
|
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: Canadian provinces | Statement: [Canada–United States border crossings, connectRegion, Canadian provinces]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: connectRegion Context triple: [Canada–United States border crossings, connectRegion, Canadian provinces]
-
A.
connectsProvinceOrRegion
Indicates that one entity serves to link or provide a connection between a specific province or region and another entity.
-
B.
connectsRegions
chosen
Indicates a relationship where one entity links or joins two or more distinct regions, enabling passage, interaction, or continuity between them.
-
C.
connectsPortRegion
Indicates that one entity serves as a linkage or interface between a specific port and a defined region.
-
D.
connectsCountryOrRegion
Indicates that one entity establishes a connection, link, or association to a specific country or region.
-
E.
connectsRegionalCity
Indicates a relationship where one entity serves as a link or transport route between a regional city and another location.
- 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_69f76ec5268481909ea01c73aeeefd42 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69ffe8ebe9fc8190b1934a3c4074370c |
completed | May 10, 2026, 2:09 a.m. |
| PD | Predicate disambiguation | batch_69ffe83a5de88190bed3e8bac86e8760 |
completed | May 10, 2026, 2:06 a.m. |
Created at: May 3, 2026, 4:17 p.m.