Triple
T1321789
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New Guinea |
E28233
|
entity |
| Predicate | hasNotableFlora |
P22447
|
FINISHED |
| Object | tropical rainforests |
—
|
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: tropical rainforests | Statement: [New Guinea, hasNotableFlora, tropical rainforests]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableFlora Context triple: [New Guinea, hasNotableFlora, tropical rainforests]
-
A.
hasBiodiversityFeature
chosen
Indicates that an entity possesses or is associated with a specific biodiversity-related characteristic, attribute, or element.
-
B.
notableTreeSpecies
Indicates that the subject place or area is known for, or characterized by, the specified tree species.
-
C.
notableSpecies
Indicates that the subject is known for, or significantly associated with, the specified species.
-
D.
hasBotanicalResource
Indicates that an entity possesses, contains, or is associated with a plant-based resource (such as plants, plant parts, or botanical materials) used for some purpose.
-
E.
hasInvasiveSpecies
Indicates that an area, ecosystem, or habitat contains one or more species that are non-native and causing or likely to cause ecological, economic, or environmental harm.
- 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_69a498540a2481909e807a762280d3ba |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c19932888190a3d45871e84f112e |
completed | March 1, 2026, 10:45 p.m. |
| PD | Predicate disambiguation | batch_69a4beedb49c8190beb5b85cdda05013 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:55 p.m.