Triple
T24974554
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | white pine weevil |
E624981
|
entity |
| Predicate | effectOnTrees |
P41548
|
FINISHED |
| Object | causes crooked and forked stems |
—
|
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: causes crooked and forked stems | Statement: [white pine weevil, effectOnTrees, causes crooked and forked stems]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnTrees Context triple: [white pine weevil, effectOnTrees, causes crooked and forked stems]
-
A.
hasTrees
Indicates that something possesses or contains one or more trees.
-
B.
affectsPlantPart
chosen
Indicates that one entity produces an influence, change, or impact on a specific part of a plant.
-
C.
numberOfTrees
Indicates the count or quantity of trees associated with a given entity or context.
-
D.
treeUse
Indicates the way in which a tree is utilized or purposed within a given context.
-
E.
hasEnvironmentalEffect
Indicates that one entity causes, contributes to, or is associated with an impact on the environment of another entity or context.
- 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_69e2ff24512481908e9a72315b8d0354 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f5ffc74fa481909b4fe24a9337f9eb |
completed | May 2, 2026, 1:44 p.m. |
| PD | Predicate disambiguation | batch_69f5f7f99dc08190afcfb3bc4dfbec1d |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 18, 2026, 6:01 a.m.