Triple
T30774388
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Catharines–Niagara metropolitan area |
E783617
|
entity |
| Predicate | isBinationallyOriented |
P144875
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [St. Catharines–Niagara metropolitan area, isBinationallyOriented, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isBinationallyOriented Context triple: [St. Catharines–Niagara metropolitan area, isBinationallyOriented, true]
-
A.
isBinational
Indicates that an entity is associated with or recognized by two distinct nations, such as holding dual nationality or operating under the authority of two countries.
-
B.
isBinationalComponentOf
Indicates that an entity is a component or part of a larger structure, project, or system that is jointly established, managed, or recognized by two nations.
-
C.
isBilingual
Indicates that an entity is able to communicate fluently in two distinct languages.
-
D.
isBinationalUrbanArea
chosen
Indicates that an urban area spans across and functionally integrates cities or communities in two different nations.
-
E.
isBilingualRegion
Indicates that a region officially uses two languages or has two predominant languages in regular use.
- 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_69f224b1519081908b9db003fd2073e0 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f68fe016688190b2fe1f6931ee1e48 |
completed | May 2, 2026, 11:59 p.m. |
| PD | Predicate disambiguation | batch_69f686140aa08190a35f62572b2db9b6 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 29, 2026, 8:40 p.m.