Triple
T71488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Douglas fir |
E1430
|
entity |
| Predicate | shadeTolerance |
P1346
|
FINISHED |
| Object | moderate shade tolerance |
—
|
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: moderate shade tolerance | Statement: [Douglas fir, shadeTolerance, moderate shade tolerance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: shadeTolerance Context triple: [Douglas fir, shadeTolerance, moderate shade tolerance]
-
A.
sunRequirement
chosen
Indicates the amount or type of sunlight an entity (such as a plant or object) needs or is designed to receive.
-
B.
tolerates
Indicates that one entity endures, accepts, or allows the presence, behavior, or condition of another entity without intervening to stop or change it.
-
C.
hasAlbedo
Indicates that an entity possesses a specific reflectivity or albedo value, describing how much incoming light it reflects.
-
D.
hasLighting
Indicates that one entity is equipped with, contains, or is characterized by a particular type or configuration of lighting.
-
E.
camouflageEffectiveness
Indicates how well one entity’s appearance or behavior conceals it from detection by another entity or sensing system.
- 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_69a24c06b3bc8190aa4ac89026115efc |
completed | Feb. 28, 2026, 1:59 a.m. |
| NER | Named-entity recognition | batch_69a24f6997c081908b202f937eb2b14f |
completed | Feb. 28, 2026, 2:14 a.m. |
| PD | Predicate disambiguation | batch_69a24eab7f408190a8275cb82474f575 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.