Triple
T3927207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heterodera glycines |
E93303
|
entity |
| Predicate | impactCategory |
P51728
|
FINISHED |
| Object | major agricultural pest |
—
|
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: major agricultural pest | Statement: [Heterodera glycines, impactCategory, major agricultural pest]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactCategory Context triple: [Heterodera glycines, impactCategory, major agricultural pest]
-
A.
impactOnIndustry
Indicates the effect or influence that one entity, event, or action has on the state, performance, or development of an industry.
-
B.
impactOnBusiness
Indicates the effect or influence that one factor, event, or action has on a business’s performance, operations, or outcomes.
-
C.
impactEvent
Indicates that one entity physically strikes or collides with another, producing a resulting effect or change.
-
D.
impactBuilding
Indicates that one entity physically collides with or strikes a building, causing an impact event.
-
E.
impactOnMarket
Indicates the effect or influence that one factor, event, or action has on market conditions, behavior, or outcomes.
- F. None of above. chosen
Provenance (4 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_69aed96bfa1081908f7b30f2c647dee6 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeed80a1e48190aa39748b9db42701 |
completed | March 9, 2026, 3:55 p.m. |
| PD | Predicate disambiguation | batch_69aee7609c4081908000ce12ae827c3f |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aeeb5f859c819095b61f5ba8eace4f |
completed | March 9, 2026, 3:46 p.m. |
Created at: March 9, 2026, 3:23 p.m.