Triple
T27361343
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kent Menthol |
E685832
|
entity |
| Predicate | associatedHealthRisk |
P149293
|
FINISHED |
| Object | lung cancer risk |
—
|
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: lung cancer risk | Statement: [Kent Menthol, associatedHealthRisk, lung cancer risk]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedHealthRisk Context triple: [Kent Menthol, associatedHealthRisk, lung cancer risk]
-
A.
hasRiskFrom
Indicates that one entity is exposed to or may suffer potential harm, loss, or adverse effects as a result of another entity.
-
B.
hasRiskFactorFor
chosen
Indicates that one entity contributes to or increases the likelihood of another entity experiencing a particular risk or adverse outcome.
-
C.
associatedHealthClassification
Indicates a relationship where one entity is linked to a specific health-related category, status, or classification.
-
D.
hasRiskFor
Indicates that one entity is susceptible or exposed to the possibility of experiencing a harmful event, condition, or outcome associated with another entity.
-
E.
hasRiskStatus
Indicates the level or category of risk currently associated with an entity.
- 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_69ef14887c288190931b8431fdbf53c4 |
completed | April 27, 2026, 7:47 a.m. |
| NER | Named-entity recognition | batch_69ffe23081408190a121d901dbce1403 |
completed | May 10, 2026, 1:41 a.m. |
| PD | Predicate disambiguation | batch_69ffe18aed348190912a5996b2da728b |
completed | May 10, 2026, 1:38 a.m. |
Created at: April 27, 2026, 11:53 a.m.