Triple
T4132297
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United Kingdom |
E85067
|
entity |
| Predicate | indirectImpactOn |
P54036
|
FINISHED |
| Object | China Theater logistics |
—
|
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: China Theater logistics | Statement: [United Kingdom, indirectImpactOn, China Theater logistics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: indirectImpactOn Context triple: [United Kingdom, indirectImpactOn, China Theater logistics]
-
A.
impactCategory
Indicates the type or domain of effect that one entity or action has on another, classifying the nature of its impact.
-
B.
hasUpstreamImpactOn
Indicates that one entity’s actions, changes, or outputs affect another entity that is positioned earlier in a process, flow, or dependency chain.
-
C.
examinesImpactOn
Indicates that one entity studies, evaluates, or analyzes the effects or consequences that another entity has on a specified subject or outcome.
-
D.
impactOnIndustry
Indicates the effect or influence that one entity, event, or action has on the state, performance, or development of an industry.
-
E.
majorImpact
Indicates that one entity has a significant, highly influential, or transformative effect on another entity or outcome.
- 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_69aed935ccd881909dc61f81bcdb7a78 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af03a0f3408190adba7a8513bd3d12 |
completed | March 9, 2026, 5:30 p.m. |
| PD | Predicate disambiguation | batch_69af01883b6c8190a482ead589a131a5 |
completed | March 9, 2026, 5:21 p.m. |
| PDg | Predicate description generation | batch_69af039fb19c8190b20e62a3b3ad25c1 |
completed | March 9, 2026, 5:30 p.m. |
Created at: March 9, 2026, 3:42 p.m.