Triple
T17903153
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jason Stackhouse |
E447632
|
entity |
| Predicate | hasAddictionTo |
P110100
|
FINISHED |
| Object | V (vampire blood) |
—
|
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: V (vampire blood) | Statement: [Jason Stackhouse, hasAddictionTo, V (vampire blood)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAddictionTo Context triple: [Jason Stackhouse, hasAddictionTo, V (vampire blood)]
-
A.
hasAddictionOrIssue
chosen
Indicates that an entity experiences a dependency, compulsion, or problematic issue related to a substance, behavior, or condition.
-
B.
hasAddictiveSubstance
Indicates that an entity contains or involves a substance capable of causing addiction in those who use or consume it.
-
C.
addiction
Indicates a compulsive dependence of one entity on a substance, activity, or behavior, typically despite negative consequences and difficulty stopping.
-
D.
hasDrugAddictedProtagonist
Indicates that the work’s main character is portrayed as being addicted to drugs.
-
E.
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.
- 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_69d8b9f59bd48190a6fc925a855b8bac |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49e99c3188190aead24cd3d48c7a7 |
completed | April 19, 2026, 9:21 a.m. |
| PD | Predicate disambiguation | batch_69e3d8ec2f6881909d7f54b878cbed37 |
completed | April 18, 2026, 7:18 p.m. |
Created at: April 10, 2026, 10:19 a.m.