Triple
T3984998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gulfport, Mississippi |
E86848
|
entity |
| Predicate | heavilyDamagedBy |
P993
|
FINISHED |
| Object | Hurricane Katrina in 2005 |
—
|
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: Hurricane Katrina in 2005 | Statement: [Gulfport, Mississippi, heavilyDamagedBy, Hurricane Katrina in 2005]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: heavilyDamagedBy Context triple: [Gulfport, Mississippi, heavilyDamagedBy, Hurricane Katrina in 2005]
-
A.
damagedBy
chosen
Indicates that one entity has caused harm, impairment, or deterioration to another entity.
-
B.
tookHeavyDamageAt
Indicates that an entity experienced severe or substantial damage at a specific location or point in time.
-
C.
damagedIn
Indicates that an entity has suffered harm, impairment, or destruction as a result of a specified event, process, or condition.
-
D.
warDamage
Indicates damage that was caused as a direct consequence of war or armed conflict.
-
E.
sufferedDestructionIn
Indicates that an entity experienced damage, ruin, or devastation during or as part of a specified event or period.
- 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_69aed93fd9d4819085d3b2137d2346cb |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefa3ef7ac8190abe02f440ff83c43 |
completed | March 9, 2026, 4:50 p.m. |
| PD | Predicate disambiguation | batch_69aef8f492ac819089dbb9436dbcdd2b |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:33 p.m.