Triple
T35794188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tsutsuji Matsuri |
E1034780
|
entity |
| Predicate | typicalFlowers |
P579
|
FINISHED |
| Object | azalea bushes |
—
|
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: azalea bushes | Statement: [Tsutsuji Matsuri, typicalFlowers, azalea bushes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalFlowers Context triple: [Tsutsuji Matsuri, typicalFlowers, azalea bushes]
-
A.
flowerType
chosen
Indicates the specific kind or category of flower associated with an entity.
-
B.
nationalFlower
Indicates that a particular flower is officially designated as the national flower of a country or region.
-
C.
hasRegionalFlower
Indicates that a region is associated with a specific flower designated as its symbolic or representative flower.
-
D.
flowerSpecies
Indicates that one entity is a flower and the other specifies its biological species classification.
-
E.
flowerUse
Indicates how a flower is used or purposed in a particular context (e.g., decorative, medicinal, culinary, or symbolic 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_69f76e1575908190aaa306d843b41c14 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037ce70f54819082946dad8d380825 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a069e6c8190857b611fffb7b867 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:06 p.m.