Triple
T22602984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yoshinoyama area |
E574878
|
entity |
| Predicate | approximateNumberOfCherryTrees |
P25753
|
FINISHED |
| Object | thousands |
—
|
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: thousands | Statement: [Yoshinoyama area, approximateNumberOfCherryTrees, thousands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateNumberOfCherryTrees Context triple: [Yoshinoyama area, approximateNumberOfCherryTrees, thousands]
-
A.
numberOfTrees
chosen
Indicates the count or quantity of trees associated with a given entity or context.
-
B.
hasCherryTreesPlantedSince
Indicates that cherry trees have been planted on or at an entity starting from a specified point in time and continuing thereafter.
-
C.
hasTrees
Indicates that something possesses or contains one or more trees.
-
D.
isCherrySpecies
Indicates that one entity is a biological species classified as a type of cherry in relation to another entity.
-
E.
approximateNumberOfTulips
Indicates that the relationship specifies an estimated or approximate count of tulips associated with an entity.
- 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_69e245bc11308190b69d794d5d1e0bb6 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1626eb178819096866d03a78f82fc |
completed | April 29, 2026, 1:44 a.m. |
| PD | Predicate disambiguation | batch_69ee627be4248190889a88764624e174 |
completed | April 26, 2026, 7:07 p.m. |
Created at: April 17, 2026, 2:50 p.m.