Triple
T6265031
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hurricane Maria |
E140391
|
entity |
| Predicate | economicDamageRank |
P69050
|
FINISHED |
| Object | one of the costliest hurricanes on record |
—
|
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: one of the costliest hurricanes on record | Statement: [Hurricane Maria, economicDamageRank, one of the costliest hurricanes on record]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: economicDamageRank Context triple: [Hurricane Maria, economicDamageRank, one of the costliest hurricanes on record]
-
A.
economicDamage
Indicates that one entity causes or experiences financial loss, harm, or negative economic impact as a result of another entity or event.
-
B.
economicDamageApprox
Indicates that one entity has caused or is associated with an estimated or approximate amount of economic damage to another entity or system.
-
C.
infrastructureDamage
Indicates damage or destruction affecting physical infrastructure such as buildings, roads, utilities, or other constructed facilities.
-
D.
countryRankByDamage
Indicates the relative position of a country in an ordered list based on the amount of damage it has caused or received.
-
E.
currencyOfDamageCost
Indicates the monetary currency in which a specified damage cost amount is expressed.
- 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_69c008cabc4081909723e2547c9d6cc0 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0639e64588190875e4e1e772fb336 |
completed | March 22, 2026, 9:48 p.m. |
| PD | Predicate disambiguation | batch_69c05606fb50819082d1a5a91e5030b6 |
completed | March 22, 2026, 8:50 p.m. |
| PDg | Predicate description generation | batch_69c056c965ac8190b938502fa8c74e1b |
completed | March 22, 2026, 8:53 p.m. |
Created at: March 22, 2026, 4:25 p.m.