Triple
T11861512
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clean, Old-Fashioned Hate |
E282168
|
entity |
| Predicate | featuresStudentPranks |
P66477
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Clean, Old-Fashioned Hate, featuresStudentPranks, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresStudentPranks Context triple: [Clean, Old-Fashioned Hate, featuresStudentPranks, yes]
-
A.
Prank Encounters
Indicates a relationship where one party orchestrates a deceptive or surprising prank scenario that another party unexpectedly experiences or becomes the target of.
-
B.
notablePrankTarget
chosen
Indicates that the subject is a well-known or frequent target of pranks carried out by the object.
-
C.
humorSource
Indicates that one entity is the origin or cause of humor experienced in relation to another entity.
-
D.
featuresStudentSections
Indicates that something includes or provides specific sections or groupings designated for students.
-
E.
gimmick
Indicates that an entity uses or features a novel, attention-grabbing trick or device primarily intended to attract interest rather than provide substantive value.
- 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_69d6ab2945d081908a5851c916cbcfb5 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a69b16bc8190999a0c1240f9ce6a |
completed | April 10, 2026, 7:28 a.m. |
| PD | Predicate disambiguation | batch_69d8a2573dbc8190ab432e8e28fde6cc |
completed | April 10, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:43 p.m.