Triple
T71475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Douglas fir |
E1430
|
entity |
| Predicate | woodDensity |
P2023
|
FINISHED |
| Object | relatively high for softwood |
—
|
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: relatively high for softwood | Statement: [Douglas fir, woodDensity, relatively high for softwood]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: woodDensity Context triple: [Douglas fir, woodDensity, relatively high for softwood]
-
A.
woodProperty
Indicates that one entity specifies or characterizes a property or attribute of wood associated with another entity.
-
B.
hasMeanDensity
chosen
Indicates that one entity possesses a specified average mass per unit volume (mean density).
-
C.
maximumTrunkDiameter
Indicates the largest thickness of a trunk measured across its widest point.
-
D.
notableTreeSpecies
Indicates that the subject place or area is known for, or characterized by, the specified tree species.
-
E.
vegetationType
Indicates the specific kind or category of plant cover or flora that characterizes a given area or environment.
- 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.