Triple
T3227819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Llandewey |
E67664
|
entity |
| Predicate | environmentalRisk |
P44599
|
FINISHED |
| Object | flooding |
—
|
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 | Statement: [Llandewey, environmentalRisk, flooding]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: environmentalRisk Context triple: [Llandewey, environmentalRisk, flooding]
-
A.
hasEnvironmentalRisk
Indicates that an entity poses, contributes to, or is associated with potential harm or adverse impact on the environment.
-
B.
environmentalSignificance
Indicates the importance or impact that something has on the natural environment, such as its role in conservation, degradation, or ecological balance.
-
C.
environmentalEvent
Indicates an occurrence or phenomenon related to the natural environment, such as climatic, ecological, or geophysical changes or incidents.
-
D.
environmentalIssue
Indicates that something is a problem or concern related to the natural environment, such as harm, risk, or negative impact on ecosystems or resources.
-
E.
hasNaturalHazardRisk
chosen
Indicates that an entity is exposed or subject to potential damage or impact from one or more natural hazards (e.g., earthquakes, floods, storms).
- 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_69ad858c61888190a31196310d9b30b5 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaeb5e67c819082070d108d3613ba |
completed | March 8, 2026, 5:15 p.m. |
| PD | Predicate disambiguation | batch_69ad9e0dc2248190a38c40f4e06cd41c |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:08 p.m.