Triple
T11061741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Typhoon Haiyan |
E261522
|
entity |
| Predicate | rankInPhilippinesDeadliestTyphoons |
P87240
|
FINISHED |
| Object | one of the deadliest 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 on record | Statement: [Typhoon Haiyan, rankInPhilippinesDeadliestTyphoons, one of the deadliest on record]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInPhilippinesDeadliestTyphoons Context triple: [Typhoon Haiyan, rankInPhilippinesDeadliestTyphoons, one of the deadliest on record]
-
A.
rankAmongDeadliestCyclones
chosen
Indicates how a cyclone is positioned or ordered in terms of its deadliness relative to other cyclones.
-
B.
rankByAreaInPhilippines
Indicates the relative ordering of entities based on their area size specifically within the Philippines.
-
C.
typhoonImpactYear
Indicates the year in which a typhoon had its primary or recorded impact on a given entity or location.
-
D.
rankByAtlanticHurricaneIntensity
Indicates the ordering of entities based on the strength or severity of Atlantic hurricanes associated with them.
-
E.
strongestStorm
Indicates that one storm is the most intense or powerful compared to a set of other storms.
- 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_69d6aa98650481908609c7c56bfa7902 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d798ea834c819099401e69f995c59f |
completed | April 9, 2026, 12:17 p.m. |
| PD | Predicate disambiguation | batch_69d74411d9e881908c0eeafa0f38e4b6 |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:26 p.m.