Triple
T6524320
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monument commémoratif de guerre du Canada |
E151263
|
entity |
| Predicate | numberOfBronzeFigures |
P71406
|
FINISHED |
| Object | 22 |
—
|
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: 22 | Statement: [Monument commémoratif de guerre du Canada, numberOfBronzeFigures, 22]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfBronzeFigures Context triple: [Monument commémoratif de guerre du Canada, numberOfBronzeFigures, 22]
-
A.
numberOfFiguresDepicted
Indicates the total count of distinct figures shown within a given depiction or representation.
-
B.
goldObjectsCount
Indicates the number of objects in the relationship that are made of or classified as gold.
-
C.
numberOfSculptures
Indicates the quantity of sculptures associated with a given entity or context.
-
D.
hasAnimatedFigures
Indicates that something contains or features figures that are animated or capable of motion.
-
E.
hasApproximateNumberOfMiniatures
Indicates that an entity is associated with an estimated or non-exact count of miniatures.
- F. None of above. chosen
Provenance (4 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_69c687f522748190b3058405553cdabd |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ad9831f88190a2b64cf6bc8c9a11 |
completed | March 27, 2026, 4:17 p.m. |
| PD | Predicate disambiguation | batch_69c68abbc7148190a8270d47fe10cc31 |
completed | March 27, 2026, 1:48 p.m. |
| PDg | Predicate description generation | batch_69c69f362ee4819090e8fa48caef7d7d |
completed | March 27, 2026, 3:16 p.m. |
Created at: March 27, 2026, 1:45 p.m.