Triple
T37649420
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Storm Desmond |
E937130
|
entity |
| Predicate | economicDamageCountry |
P1584
|
FINISHED |
| Object | United Kingdom |
—
|
NE NERFINISHED |
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: United Kingdom | Statement: [Storm Desmond, economicDamageCountry, United Kingdom]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: economicDamageCountry Context triple: [Storm Desmond, economicDamageCountry, United Kingdom]
-
A.
economicDamageRank
Indicates the relative severity or position of an entity in terms of the economic damage it causes or experiences compared to others.
-
B.
economicDamage
chosen
Indicates that one entity causes or experiences financial loss, harm, or negative economic impact as a result of another entity or event.
-
C.
economicDamageApprox
Indicates that one entity has caused or is associated with an estimated or approximate amount of economic damage to another entity or system.
-
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.
affectedCountry
Indicates that a particular country is impacted or influenced by an event, action, or condition.
- 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_69f76ed4fe908190b8061c5c135e0971 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbc36ce1f88190a7fa1656b714e107 |
completed | May 6, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69fbbd166a488190b1bf9316b0790801 |
completed | May 6, 2026, 10:13 p.m. |
Created at: May 3, 2026, 4:18 p.m.