Triple
T21469270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marginal Tietê expressway |
E529680
|
entity |
| Predicate | hasAssociatedRisk |
P136373
|
FINISHED |
| Object | flooding during heavy rains |
—
|
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: flooding during heavy rains | Statement: [Marginal Tietê expressway, hasAssociatedRisk, flooding during heavy rains]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAssociatedRisk Context triple: [Marginal Tietê expressway, hasAssociatedRisk, flooding during heavy rains]
-
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.
hasRiskyStrategy
Indicates that an entity employs or is associated with a strategy characterized by a high level of risk or potential for significant loss.
-
C.
hasAssociatedDisease
Indicates that an entity is linked to, or commonly occurs with, a particular disease or medical condition.
-
D.
hasHazardRelation
Indicates a relationship where one entity poses, contributes to, or is associated with a potential hazard or risk affecting another entity.
-
E.
appliesRiskWeight
Indicates that a specified risk weighting factor is assigned to or used for a particular entity, exposure, or transaction.
- 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_69e0c459acb481909bb6ee452a0045c7 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9f58f6c8190a3d2fc8f820a9925 |
completed | April 23, 2026, 9:44 a.m. |
| PD | Predicate disambiguation | batch_69e631ec1d048190b6da97da8222e413 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:16 p.m.