Triple
T4663804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Waitress |
E102795
|
entity |
| Predicate | hasAddictionIssues |
P16449
|
FINISHED |
| Object | alcohol |
—
|
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: alcohol | Statement: [The Waitress, hasAddictionIssues, alcohol]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAddictionIssues Context triple: [The Waitress, hasAddictionIssues, alcohol]
-
A.
hasAddictiveSubstance
Indicates that an entity contains or involves a substance capable of causing addiction in those who use or consume it.
-
B.
addiction
chosen
Indicates a compulsive dependence of one entity on a substance, activity, or behavior, typically despite negative consequences and difficulty stopping.
-
C.
hasAddictionPotential
Indicates that one entity (typically a substance or activity) has the capacity to cause another entity (typically a person) to develop dependence or addictive behavior toward it.
-
D.
associatedWithSubstance
Indicates that one entity has a relevant connection or involvement with a particular substance, such as use, presence, exposure, or composition.
-
E.
hasDrugAddictedProtagonist
Indicates that the work’s main character is portrayed as being addicted to drugs.
- 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_69bd43d9cba4819086c1ab1c2d9d2133 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd633aeba88190a8329ed022d685b6 |
completed | March 20, 2026, 3:09 p.m. |
| PD | Predicate disambiguation | batch_69bd62126b0c81909ba3f21b21e30d54 |
completed | March 20, 2026, 3:04 p.m. |
Created at: March 20, 2026, 1:15 p.m.