Triple
T31095723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black pearl farms |
E792520
|
entity |
| Predicate | facesRiskFrom |
P136373
|
FINISHED |
| Object | cyclones |
—
|
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: cyclones | Statement: [Black pearl farms, facesRiskFrom, cyclones]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: facesRiskFrom Context triple: [Black pearl farms, facesRiskFrom, cyclones]
-
A.
hasRiskFrom
chosen
Indicates that one entity is exposed to or may suffer potential harm, loss, or adverse effects as a result of another entity.
-
B.
riskToUser
Indicates that something poses a potential danger, harm, or adverse impact to the user.
-
C.
riskElement
Indicates that one entity is a risk-related component, factor, or contributor associated with another entity within a risk context.
-
D.
socialRisk
Indicates the degree to which an action, relationship, or situation exposes someone to potential negative social consequences, such as loss of status, reputation, or acceptance.
-
E.
riskFeature
Indicates that one entity possesses or exhibits a characteristic, condition, or attribute that increases the likelihood or severity of a negative outcome for another entity or situation.
- 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_69f224cf157c81909e2d2bd88c9282c3 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f69c234d648190a243fb2b107136a9 |
completed | May 3, 2026, 12:51 a.m. |
| PD | Predicate disambiguation | batch_69f69665cd9c819088c388fc82fec42e |
completed | May 3, 2026, 12:27 a.m. |
Created at: April 29, 2026, 9:03 p.m.