Triple
T32281443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ned Faraday |
E824701
|
entity |
| Predicate | illnessAsPlotDevice |
P173920
|
FINISHED |
| Object | drives much of the drama |
—
|
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: drives much of the drama | Statement: [Ned Faraday, illnessAsPlotDevice, drives much of the drama]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: illnessAsPlotDevice Context triple: [Ned Faraday, illnessAsPlotDevice, drives much of the drama]
-
A.
fictionalDisease
Indicates that an entity is associated with, afflicted by, or otherwise characterized by a disease that is imaginary or does not exist in reality.
-
B.
illness
Indicates that an entity is affected by, or suffering from, a particular disease or medical condition.
-
C.
settingOfIllness
Indicates the context, environment, or circumstances in which an illness occurs or manifests.
-
D.
causeOfIllness
Indicates that one entity is the reason or source responsible for another entity’s illness or disease.
-
E.
interpretsDiseaseAs
Indicates that one entity understands, explains, or conceptualizes a disease in terms of another entity (such as a model, category, cause, or interpretation).
- 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_69f3490f404081908450db66884f4334 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6bccb5ea8819081b497d5667fa19a |
completed | May 3, 2026, 3:11 a.m. |
| PD | Predicate disambiguation | batch_69f6b632cf788190a3d0c08cd026b84b |
completed | May 3, 2026, 2:42 a.m. |
| PDg | Predicate description generation | batch_69f6b960ca4081909a77690c2b122f5e |
completed | May 3, 2026, 2:56 a.m. |
Created at: May 1, 2026, 12:43 a.m.