Triple
T7164771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hengduan Mountains |
E167037
|
entity |
| Predicate | floraRichIn |
P953
|
FINISHED |
| Object | rhododendrons |
—
|
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: rhododendrons | Statement: [Hengduan Mountains, floraRichIn, rhododendrons]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: floraRichIn Context triple: [Hengduan Mountains, floraRichIn, rhododendrons]
-
A.
vegetation
Indicates that an area or object is covered with, contains, or is characterized by plant life.
-
B.
famousPlants
Indicates that the plants in question are widely known or celebrated, typically for their distinctive characteristics, history, or cultural significance.
-
C.
vegetationType
chosen
Indicates the specific kind or category of plant cover or flora that characterizes a given area or environment.
-
D.
hasAttractiveFoliage
Indicates that an entity possesses foliage that is visually appealing or ornamental in appearance.
-
E.
hasBiodiversityFeature
Indicates that an entity possesses or is associated with a specific biodiversity-related characteristic, attribute, or element.
- 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_69c68888c10c819095e0383020225758 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e83168a08190937ff46797d94f3e |
completed | March 27, 2026, 8:27 p.m. |
| PD | Predicate disambiguation | batch_69c6e1cd5c948190a9113b23f7308c21 |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:47 p.m.