Triple
T320715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Korean won |
E6407
|
entity |
| Predicate | isAffectedBy |
P9
|
FINISHED |
| Object | South Korea’s trade balance |
—
|
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: South Korea’s trade balance | Statement: [South Korean won, isAffectedBy, South Korea’s trade balance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isAffectedBy Context triple: [South Korean won, isAffectedBy, South Korea’s trade balance]
-
A.
isSubjectTo
Indicates that one entity is governed, affected, or constrained by the authority, rules, conditions, or influence of another entity.
-
B.
appliedBecauseOf
Indicates that one entity is applied, used, or enacted as a consequence or result of another specified cause or condition.
-
C.
hasLexicalInfluenceOn
Indicates that one linguistic element (such as a word, phrase, or lexicon) has affected or shaped the form, usage, or meaning of another linguistic element.
-
D.
influenced
chosen
Indicates that one entity has affected, shaped, or altered another entity’s state, behavior, or characteristics.
-
E.
affectedArea
Indicates the specific region or extent over which an event, condition, or influence has an impact.
- 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_69a2e7933d6c8190bb2592ad13286ef2 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea8047c08190872c875e00f6e7dd |
completed | Feb. 28, 2026, 1:15 p.m. |
| PD | Predicate disambiguation | batch_69a2e946607081909c8b97473aaf8d1b |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.