Triple
T14908852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mediterranean forests, woodlands and scrub |
E371206
|
entity |
| Predicate | majorVegetationType |
P953
|
FINISHED |
| Object | shrubland |
—
|
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: shrubland | Statement: [Mediterranean forests, woodlands and scrub, majorVegetationType, shrubland]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: majorVegetationType Context triple: [Mediterranean forests, woodlands and scrub, majorVegetationType, shrubland]
-
A.
vegetationType
chosen
Indicates the specific kind or category of plant cover or flora that characterizes a given area or environment.
-
B.
vegetation
Indicates that an area or object is covered with, contains, or is characterized by plant life.
-
C.
landscapeType
Indicates the kind or category of natural terrain or scenery that characterizes a place or area.
-
D.
treeHabit
Indicates the growth form or structural habit characteristic of a tree, such as its typical shape, branching pattern, or overall stature.
-
E.
vegetationCoverage
Indicates the extent or proportion of an area that is covered by vegetation.
- 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_69d85cc7ea3481908228b5acb7d06f12 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded61b3c808190b4f6df4e5cb401ad |
completed | April 15, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69de9a4a14a88190951bb8f4c60bd37b |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:25 a.m.