Triple
T9126785
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cyclone Nargis |
E218984
|
entity |
| Predicate | rankAmongDeadliestCyclones |
P87240
|
FINISHED |
| Object | one of the deadliest tropical cyclones on record |
—
|
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: one of the deadliest tropical cyclones on record | Statement: [Cyclone Nargis, rankAmongDeadliestCyclones, one of the deadliest tropical cyclones on record]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankAmongDeadliestCyclones Context triple: [Cyclone Nargis, rankAmongDeadliestCyclones, one of the deadliest tropical cyclones on record]
-
A.
rankByAtlanticHurricaneIntensity
Indicates the ordering of entities based on the strength or severity of Atlantic hurricanes associated with them.
-
B.
strongestStorm
Indicates that one storm is the most intense or powerful compared to a set of other storms.
-
C.
numberOfHurricanes
Indicates the total count of hurricanes associated with a specified context, such as a region, time period, or event.
-
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.
numberOfNamedStorms
Indicates the total count of distinct storms that have been formally assigned names within a specified context or period.
- 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_69ca83debfc0819095800583e97ab10f |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca8c93d3c8190b003b2b1af2003b2 |
completed | April 1, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69cc66003e3c819091e1e42c9cf7c781 |
completed | April 1, 2026, 12:25 a.m. |
| PDg | Predicate description generation | batch_69cc6a3c78388190a7436acc0e44ff55 |
completed | April 1, 2026, 12:43 a.m. |
Created at: March 30, 2026, 7:18 p.m.