Triple
T7673834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States–China relations |
E173810
|
entity |
| Predicate | tensionArea |
P78851
|
FINISHED |
| Object | technology export controls |
—
|
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: technology export controls | Statement: [United States–China relations, tensionArea, technology export controls]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tensionArea Context triple: [United States–China relations, tensionArea, technology export controls]
-
A.
tension
Indicates a state of strain, stress, or conflict existing between entities, often involving opposing forces, interests, or emotions.
-
B.
hasTension
Indicates the presence of strain, stress, or conflict between entities in their relationship or interaction.
-
C.
targetArea
Indicates the specific area or region that is the intended focus or destination of an action or effect.
-
D.
coreAreaOf
Indicates that one entity is the central, primary, or most important area or domain of focus for another entity.
-
E.
engagementArea
Indicates the spatial region or scope within which an entity’s actions, influence, or interactions are intended to occur.
- F. None of above. chosen
Provenance (4 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_69c6995703e0819081de77361b602e78 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7048b0b448190889bd40e0a38e51a |
completed | March 27, 2026, 10:28 p.m. |
| PD | Predicate disambiguation | batch_69c701618d3481908be84b76f36ac5a1 |
completed | March 27, 2026, 10:14 p.m. |
| PDg | Predicate description generation | batch_69c7048a01508190bc2e9ae8b863486c |
completed | March 27, 2026, 10:28 p.m. |
Created at: March 27, 2026, 4 p.m.