Triple
T978207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Great Hanshin earthquake |
E21104
|
entity |
| Predicate | countryRankByDamage |
P22117
|
FINISHED |
| Object | one of the costliest earthquakes in Japanese history |
—
|
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 costliest earthquakes in Japanese history | Statement: [Great Hanshin earthquake, countryRankByDamage, one of the costliest earthquakes in Japanese history]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryRankByDamage Context triple: [Great Hanshin earthquake, countryRankByDamage, one of the costliest earthquakes in Japanese history]
-
A.
countryRankContext
Indicates the relative position or ranking of a country within a specified contextual framework (such as economic, political, or performance-based criteria).
-
B.
rankInWorldByArea
Indicates the position of an entity in a global ordering based on its total area size.
-
C.
affectedCountry
Indicates that a particular country is impacted or influenced by an event, action, or condition.
-
D.
populationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
E.
economicDamage
Indicates that one entity causes or experiences financial loss, harm, or negative economic impact as a result of another entity or event.
- 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_69a493c2b62c8190b616351789ec47f8 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b47861808190be56a7bbd926e658 |
completed | March 1, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a8a3b08190b4538e119b13f7f5 |
completed | March 1, 2026, 9:42 p.m. |
| PDg | Predicate description generation | batch_69a4b344f6f48190ba03ce593c94176b |
completed | March 1, 2026, 9:44 p.m. |
Created at: March 1, 2026, 7:40 p.m.