Triple
T15469853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jack Griffin |
E372129
|
entity |
| Predicate | usesStudentsFor |
P118358
|
FINISHED |
| Object | personal revenge schemes |
—
|
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: personal revenge schemes | Statement: [Jack Griffin, usesStudentsFor, personal revenge schemes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesStudentsFor Context triple: [Jack Griffin, usesStudentsFor, personal revenge schemes]
-
A.
hasStudents
Indicates that an entity (such as a class, school, or teacher) is associated with one or more students.
-
B.
educationUse
Indicates the use or application of something specifically for educational purposes or in an educational context.
-
C.
studentsReceive
Indicates that one or more students are the recipients of something, such as instruction, resources, or communications, from another source.
-
D.
admitsStudentsFrom
Indicates that an educational institution accepts or enrolls students who come from a specified source, such as a school, region, or program.
-
E.
studentsAreEmployedBy
Indicates that the students have an employment relationship with, or work for, a particular employer or organization.
- 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_69d85cc8bd308190886949510b42e764 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f6b49788190b270fdfe92646842 |
completed | April 16, 2026, 1:46 a.m. |
| PD | Predicate disambiguation | batch_69ded284bd008190b31c53b4f1cebadd |
completed | April 14, 2026, 11:49 p.m. |
| PDg | Predicate description generation | batch_69ded5deee00819099fa3e43313312e1 |
completed | April 15, 2026, 12:03 a.m. |
Created at: April 10, 2026, 3:33 a.m.