Triple
T1065086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hurricane Katrina |
E22990
|
entity |
| Predicate | percentageOfNewOrleansFlooded |
P23038
|
FINISHED |
| Object | approximately 80% |
—
|
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: approximately 80% | Statement: [Hurricane Katrina, percentageOfNewOrleansFlooded, approximately 80%]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: percentageOfNewOrleansFlooded Context triple: [Hurricane Katrina, percentageOfNewOrleansFlooded, approximately 80%]
-
A.
hasFloodRisk
Indicates that an entity is exposed to a potential or expected risk of flooding under certain conditions.
-
B.
hasSeasonalFlooding
Indicates that an area regularly experiences flooding during specific, recurring times of the year.
-
C.
hasFloodplain
Indicates that an area or location lies within the floodplain associated with a particular water body or flooding source.
-
D.
hurricaneKatrinaLandfallYear
Indicates the calendar year in which Hurricane Katrina made landfall.
-
E.
areaWaterPercentage
Indicates the proportion of an entity’s total area that is covered by water, typically expressed as a percentage.
- 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_69a493dada0481909c43649f9843ea91 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b90f91248190ace1534a51b82bdd |
completed | March 1, 2026, 10:09 p.m. |
| PD | Predicate disambiguation | batch_69a4b7359eb881909c868a558861cc18 |
completed | March 1, 2026, 10:01 p.m. |
| PDg | Predicate description generation | batch_69a4b7da38888190a118ef20ce4ae9aa |
completed | March 1, 2026, 10:04 p.m. |
Created at: March 1, 2026, 7:42 p.m.