Triple
T14196120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chōshū Five |
E351838
|
entity |
| Predicate | riskLevelAtTime |
P3842
|
FINISHED |
| Object | high personal risk due to travel prohibition |
—
|
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: high personal risk due to travel prohibition | Statement: [Chōshū Five, riskLevelAtTime, high personal risk due to travel prohibition]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: riskLevelAtTime Context triple: [Chōshū Five, riskLevelAtTime, high personal risk due to travel prohibition]
-
A.
riskLevel
chosen
Indicates the degree of potential harm, loss, or adverse outcome associated with a particular situation, action, or entity.
-
B.
riskType
Indicates the category or nature of risk associated with an entity, event, or relationship.
-
C.
riskProfile
Indicates the level and characteristics of potential risk associated with an entity, action, or situation.
-
D.
riskReturnProfile
Indicates how the level of risk associated with an entity or investment corresponds to its expected or historical return.
-
E.
riskElement
Indicates that one entity is a risk-related component, factor, or contributor associated with another entity within a risk context.
- 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_69d827894ac0819097803e57f3227b23 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61e1fbd48190a4864fa4443f8f29 |
completed | April 14, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69de05baed64819096590e5618a3a8ed |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 10, 2026, 1:04 a.m.