Triple
T23076783
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kwanzan cherry |
E575352
|
entity |
| Predicate | flowerClusterType |
P150856
|
FINISHED |
| Object | large clusters |
—
|
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: large clusters | Statement: [Kwanzan cherry, flowerClusterType, large clusters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: flowerClusterType Context triple: [Kwanzan cherry, flowerClusterType, large clusters]
-
A.
flowerType
Indicates the specific kind or category of flower associated with an entity.
-
B.
flowerPosition
Indicates the spatial location or arrangement of a flower relative to a reference object or coordinate system.
-
C.
floweringPattern
Indicates how and when a plant produces flowers, such as the timing, frequency, and arrangement of its blooming.
-
D.
floweringPlantGroup
Indicates that the subject belongs to, or is classified within, a particular group or category of flowering plants.
-
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. 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_69e245be28d48190ad1348d5a73db37d |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18c63870c81909a08a3b410c0d417 |
completed | April 29, 2026, 4:43 a.m. |
| PD | Predicate disambiguation | batch_69ef89e5ce748190b2c3ac3843484127 |
completed | April 27, 2026, 4:08 p.m. |
| PDg | Predicate description generation | batch_69ef9b7494f4819088ae59ea3d0ae8ab |
completed | April 27, 2026, 5:23 p.m. |
Created at: April 17, 2026, 3:56 p.m.