Triple
T8694830
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Earth Simulator |
E206380
|
entity |
| Predicate | fieldOfApplication |
P3
|
FINISHED |
| Object | climate science |
—
|
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: climate science | Statement: [Earth Simulator, fieldOfApplication, climate science]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fieldOfApplication Context triple: [Earth Simulator, fieldOfApplication, climate science]
-
A.
appliedPrimarilyTo
Indicates that something is used mainly or chiefly in relation to a particular target, context, or purpose, rather than being used broadly or equally elsewhere.
-
B.
fieldOfWork
chosen
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
C.
appliesPrimarilyTo
Indicates that a property, rule, or characteristic is mainly relevant or intended for a particular entity or group, more than for others.
-
D.
developedInField
Indicates that an entity (such as a method, theory, or technology) was created, formulated, or advanced within a particular academic or professional field.
-
E.
typeOfApplication
Indicates the specific category or kind of application involved in the relationship or action.
- 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_69ca83555b6c8190abe930dd397e863b |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc58284c58819091beb05a7d6b3a1b |
completed | March 31, 2026, 11:26 p.m. |
| PD | Predicate disambiguation | batch_69cc4569f9048190b9c86b4c81103d35 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:33 p.m.