Triple
T27496806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Strobilanthes kunthiana |
E694038
|
entity |
| Predicate | impactOnLandscape |
P107573
|
FINISHED |
| Object | turns slopes bluish-purple when in bloom |
—
|
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: turns slopes bluish-purple when in bloom | Statement: [Strobilanthes kunthiana, impactOnLandscape, turns slopes bluish-purple when in bloom]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactOnLandscape Context triple: [Strobilanthes kunthiana, impactOnLandscape, turns slopes bluish-purple when in bloom]
-
A.
landscapeImpact
chosen
Indicates how an action, project, or entity alters or affects the visual and physical character of a landscape.
-
B.
hasLandscapeInfluence
Indicates that one entity has shaped, altered, or significantly affected the characteristics, form, or development of a landscape.
-
C.
humanImpact
Indicates the effect or influence that human activities have on another entity, system, or environment.
-
D.
agriculturalImpact
Indicates the effect that an action, condition, or entity has on agricultural systems, productivity, or practices.
-
E.
都市景観への影響
Indicates the effect or impact that something has on the appearance, structure, or overall quality of the urban landscape.
- 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_69ef538370888190b1ddf53bb4831188 |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69f6978fe97081908fe568091ad9b159 |
completed | May 3, 2026, 12:32 a.m. |
| PD | Predicate disambiguation | batch_69f69661e6ec8190948251c7516a32ad |
completed | May 3, 2026, 12:27 a.m. |
Created at: April 27, 2026, 1:09 p.m.