Triple
T5714610
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1900 Galveston hurricane |
E125991
|
entity |
| Predicate | damageAdjusted |
P66042
|
FINISHED |
| Object | several billion US dollars (modern value) |
—
|
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: several billion US dollars (modern value) | Statement: [1900 Galveston hurricane, damageAdjusted, several billion US dollars (modern value)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: damageAdjusted Context triple: [1900 Galveston hurricane, damageAdjusted, several billion US dollars (modern value)]
-
A.
damageTo
Indicates a relationship where one entity causes harm, loss, or deterioration to another entity.
-
B.
damagedBy
Indicates that one entity has caused harm, impairment, or deterioration to another entity.
-
C.
affectedLevel
Indicates the degree or extent to which one entity is impacted or influenced by another entity or event.
-
D.
damagedIn
Indicates that an entity has suffered harm, impairment, or destruction as a result of a specified event, process, or condition.
-
E.
primaryDamageType
Indicates the main kind of harm or injury that an action, event, or object is responsible for causing.
- 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_69c0082e3d548190950169847b43043b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c029014588819094a2a0f6f9b66bab |
completed | March 22, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c021c47f4c81909e6849c3be3e951c |
completed | March 22, 2026, 5:07 p.m. |
| PDg | Predicate description generation | batch_69c028fec2bc819083f5dca6a8d9d435 |
completed | March 22, 2026, 5:38 p.m. |
Created at: March 22, 2026, 3:46 p.m.