Triple
T25338054
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Russia and Japan |
E635328
|
entity |
| Predicate | haveAsymmetryIn |
P124554
|
FINISHED |
| Object | energy resources |
—
|
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: energy resources | Statement: [Russia and Japan, haveAsymmetryIn, energy resources]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: haveAsymmetryIn Context triple: [Russia and Japan, haveAsymmetryIn, energy resources]
-
A.
hasAsymmetry
chosen
Indicates that one entity exhibits a lack of symmetry or an uneven, non-mirrored relationship or structure relative to another entity.
-
B.
asymmetric
Indicates that the relationship between two entities never holds in both directions simultaneously, so if it holds from A to B it cannot also hold from B to A.
-
C.
hasGlobalAsymmetry
Indicates that an entity exhibits a non-uniform or directionally biased property or structure when considered at a global or overall scale.
-
D.
causeOfAsymmetry
Indicates that one entity is the source or reason for the asymmetry observed in another entity or system.
-
E.
hasObservedAsymmetry
Indicates that one entity has detected or recorded an imbalance, difference, or non-uniformity in another entity or in a relationship between entities.
- 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_69e75a99bd6481909476115b35b9a8e4 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f498b4cbb88190a8ac563ad1a73b5d |
completed | May 1, 2026, 12:12 p.m. |
| PD | Predicate disambiguation | batch_69f4683b34748190818428489a226124 |
completed | May 1, 2026, 8:45 a.m. |
Created at: April 21, 2026, 1:32 p.m.