Triple
T14017408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japan–China relations |
E337237
|
entity |
| Predicate | tensionCharacteristic |
P11899
|
FINISHED |
| Object | periodic diplomatic crises |
—
|
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: periodic diplomatic crises | Statement: [Japan–China relations, tensionCharacteristic, periodic diplomatic crises]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tensionCharacteristic Context triple: [Japan–China relations, tensionCharacteristic, periodic diplomatic crises]
-
A.
hasTension
Indicates the presence of strain, stress, or conflict between entities in their relationship or interaction.
-
B.
tension
chosen
Indicates a state of strain, stress, or conflict existing between entities, often involving opposing forces, interests, or emotions.
-
C.
tensionArea
Indicates the region or extent over which mechanical or emotional tension is distributed or experienced.
-
D.
tightens
Indicates that one entity makes another entity more secure, compact, or taut by applying constricting force or reducing looseness.
-
E.
strainType
Indicates the specific variety or subtype classification within a broader category of strains (e.g., biological, chemical, or product strains).
- 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_69d81c6543a48190bd5ba93d7419e797 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2f3b5b088190a58715779d2c46a6 |
completed | April 14, 2026, 12:12 p.m. |
| PD | Predicate disambiguation | batch_69de05a802ac819090604025aae6a4d5 |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:19 p.m.