Triple
T15284844
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hurricane Camille |
E365366
|
entity |
| Predicate | damage_USD_2010_adjusted_approx |
P25888
|
FINISHED |
| Object | 9000000000 |
—
|
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: 9000000000 | Statement: [Hurricane Camille, damage_USD_2010_adjusted_approx, 9000000000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: damage_USD_2010_adjusted_approx Context triple: [Hurricane Camille, damage_USD_2010_adjusted_approx, 9000000000]
-
A.
economicDamageApprox
chosen
Indicates that one entity has caused or is associated with an estimated or approximate amount of economic damage to another entity or system.
-
B.
economicDamageRank
Indicates the relative severity or position of an entity in terms of the economic damage it causes or experiences compared to others.
-
C.
economicDamage
Indicates that one entity causes or experiences financial loss, harm, or negative economic impact as a result of another entity or event.
-
D.
currencyOfDamageCost
Indicates the monetary currency in which a specified damage cost amount is expressed.
-
E.
damageYear
Indicates the year in which the damage to an entity occurred or was recorded.
- 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_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00e53c9588190a6cb61ac8805c706 |
completed | April 15, 2026, 10:16 p.m. |
| PD | Predicate disambiguation | batch_69deca90739081909bd1b797cdb8af2b |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:15 a.m.