Triple
T16558587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tenczynek Landscape Park |
E402275
|
entity |
| Predicate | hasFloraCharacteristic |
P68196
|
FINISHED |
| Object | mixed forests |
—
|
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: mixed forests | Statement: [Tenczynek Landscape Park, hasFloraCharacteristic, mixed forests]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFloraCharacteristic Context triple: [Tenczynek Landscape Park, hasFloraCharacteristic, mixed forests]
-
A.
hasFloraGroup
Indicates that an entity is associated with, contains, or is characterized by a particular group or category of plant life.
-
B.
hasFloralFeature
chosen
Indicates that an entity possesses a specific floral characteristic, structure, or attribute.
-
C.
hasAttractiveFoliage
Indicates that an entity possesses foliage that is visually appealing or ornamental in appearance.
-
D.
associatedFlora
Indicates a relationship where specific plants or vegetation are characteristically linked to, occur with, or are commonly found in association with a given entity or environment.
-
E.
hasFloralRegion
Indicates that one entity possesses or is associated with a specific floral region as a characteristic or component.
- 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_69d8838648088190acf97ef11fc3f61b |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3576bce0c819087ab36f7dec5c394 |
completed | April 18, 2026, 10:05 a.m. |
| PD | Predicate disambiguation | batch_69e296a47b7481909d9958158510c806 |
completed | April 17, 2026, 8:23 p.m. |
Created at: April 10, 2026, 5:15 a.m.