Triple
T21556847
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mascarene forests |
E531913
|
entity |
| Predicate | originalVegetationType |
P953
|
FINISHED |
| Object | evergreen broadleaf forest |
—
|
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: evergreen broadleaf forest | Statement: [Mascarene forests, originalVegetationType, evergreen broadleaf forest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalVegetationType Context triple: [Mascarene forests, originalVegetationType, evergreen broadleaf forest]
-
A.
vegetationType
chosen
Indicates the specific kind or category of plant cover or flora that characterizes a given area or environment.
-
B.
vegetationCoverage
Indicates the extent or proportion of an area that is covered by vegetation.
-
C.
typicalVegetationAdaptation
Indicates the characteristic ways in which vegetation is adapted to the prevailing environmental conditions of a given area.
-
D.
forestCoverCharacteristic
Indicates a relationship where a forested area possesses a specific attribute or quality related to its tree or vegetation cover.
-
E.
vegetation
Indicates that an area or object is covered with, contains, or is characterized by plant life.
- 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_69e0c460232c81908de2c3819d17c00e |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eed2e04b048190ac3a9913094b4625 |
completed | April 27, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69e6320766308190ba5dca2f7c826aa4 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:29 p.m.