Triple
T2151131
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Winston (cigarette) |
E47181
|
entity |
| Predicate | hasAddictionPotential |
P36259
|
FINISHED |
| Object | high |
—
|
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 | Statement: [Winston (cigarette), hasAddictionPotential, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAddictionPotential Context triple: [Winston (cigarette), hasAddictionPotential, high]
-
A.
addiction
Indicates a compulsive dependence of one entity on a substance, activity, or behavior, typically despite negative consequences and difficulty stopping.
-
B.
hasNotableDrug
Indicates that an entity is associated with a drug that is considered notable or significant in some recognized context.
-
C.
drugPolicy
Indicates the rules, regulations, or guidelines governing the use, control, or management of drugs within a given context.
-
D.
protectedDrugClassesInclude
Indicates that the specified set of protected drug classes includes the referenced drug class or classes.
-
E.
hasCommonAdverseEffect
Indicates that two or more entities share at least one adverse effect that occurs in response to them.
- 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_69a88a1933e0819094f18426ed74180f |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbe4747a0819080e2234f3ea8995f |
completed | March 7, 2026, 5:57 a.m. |
| PD | Predicate disambiguation | batch_69abbd9a60648190b20b116be5c7ad98 |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abbe4252688190944491a450383450 |
completed | March 7, 2026, 5:57 a.m. |
Created at: March 4, 2026, 7:44 p.m.