Triple
T11984795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike Teavee |
E285249
|
entity |
| Predicate | lessonSymbolized |
P13197
|
FINISHED |
| Object | dangers of too much television |
—
|
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: dangers of too much television | Statement: [Mike Teavee, lessonSymbolized, dangers of too much television]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lessonSymbolized Context triple: [Mike Teavee, lessonSymbolized, dangers of too much television]
-
A.
lesson
Indicates that one entity provides or conducts an instructional session or teaching activity for another entity.
-
B.
lessonsLearned
Indicates that certain insights, knowledge, or understanding have been gained from a prior experience, event, or process.
-
C.
structureLearning
Indicates a process in which an agent infers or constructs the underlying structure or dependency relationships within a set of variables, data, or a model.
-
D.
lectureSeries
Indicates a relationship where a set of lectures is organized and presented as a coherent, thematically linked series.
-
E.
isTaughtAs
chosen
Indicates that something is presented or delivered as instructional content, typically within an educational or training context.
- 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_69d6ab44a77c8190a652f4b27164e4ef |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d903acbb9081908fe7f8360057785c |
completed | April 10, 2026, 2:05 p.m. |
| PD | Predicate disambiguation | batch_69d902abca70819098291aa51b593708 |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:46 p.m.