Triple
T30441761
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | アメリカ合衆国ミシガン州リボニア市 |
E774463
|
entity |
| Predicate | 経済 |
P2313
|
FINISHED |
| Object | 小売業が盛ん |
—
|
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: 小売業が盛ん | Statement: [アメリカ合衆国ミシガン州リボニア市, 経済, 小売業が盛ん]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 経済 Context triple: [アメリカ合衆国ミシガン州リボニア市, 経済, 小売業が盛ん]
-
A.
business
Indicates that an entity is engaged in commercial or professional activities, such as providing goods or services for profit.
-
B.
economicView
Indicates a relationship where one entity holds or expresses a particular economic belief, stance, or perspective regarding economic systems, policies, or issues.
-
C.
economicScope
Indicates the range or extent of economic activities, impacts, or considerations that a given entity, action, or relationship encompasses.
-
D.
economicAspect
chosen
Indicates that something is related to, characterized by, or has implications for economic factors, conditions, or outcomes.
-
E.
economicFunction
Indicates the role or purpose an entity serves within an economic system, such as how it contributes to production, distribution, or consumption of goods and services.
- 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_69f22493ef9c8190ae8c2afcb7f994c8 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6869948e481908901dbda23952cc0 |
completed | May 2, 2026, 11:19 p.m. |
| PD | Predicate disambiguation | batch_69f678d2196c8190b9d0d2fcd47cc539 |
completed | May 2, 2026, 10:21 p.m. |
Created at: April 29, 2026, 8:08 p.m.