Triple
T4682649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flag of Canada |
E103840
|
entity |
| Predicate | widthToLengthRatio |
P1991
|
FINISHED |
| Object | 1:2 |
—
|
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: 1:2 | Statement: [Flag of Canada, widthToLengthRatio, 1:2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: widthToLengthRatio Context triple: [Flag of Canada, widthToLengthRatio, 1:2]
-
A.
aspectRatio
chosen
Indicates the proportional relationship between an entity’s width and its height.
-
B.
width
Indicates the measurement of how wide an entity is, typically the extent of its horizontal dimension from side to side.
-
C.
isProportionalTo
Indicates that one quantity varies in constant ratio to another, so when one changes, the other changes by a fixed multiplicative factor.
-
D.
proportionalTo
Indicates that one quantity varies in constant ratio to another, so changes in one are directly reflected by proportional changes in the other.
-
E.
relativeLength
Indicates a comparative relationship between entities based on how long they are relative to one another.
- 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_69bd43debbf08190b4bc372e286ec234 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd67c9c3c08190a6c4944cdd1362a8 |
completed | March 20, 2026, 3:29 p.m. |
| PD | Predicate disambiguation | batch_69bd6217e0088190836570522e324dc6 |
completed | March 20, 2026, 3:04 p.m. |
Created at: March 20, 2026, 1:16 p.m.