Triple
T1065091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hurricane Katrina |
E22990
|
entity |
| Predicate | estimatedEconomicDamage_USD |
P4259
|
FINISHED |
| Object | over 100000000000 |
—
|
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: over 100000000000 | Statement: [Hurricane Katrina, estimatedEconomicDamage_USD, over 100000000000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: estimatedEconomicDamage_USD Context triple: [Hurricane Katrina, estimatedEconomicDamage_USD, over 100000000000]
-
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.
estimatedCost
chosen
Indicates the predicted or calculated monetary amount expected to be required for something, such as a project, item, or action.
-
C.
estimatedDevelopmentCostCurrency
Indicates the currency in which the estimated development cost is expressed.
-
D.
economicImpactRegion
Indicates the region or geographic area that experiences or is affected by a particular economic impact.
-
E.
areaDestroyed
Indicates that a specified portion or region has been damaged or ruined to the point of destruction.
- 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_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. |
Created at: March 1, 2026, 7:42 p.m.