Triple
T33501985
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeff Spicoli |
E858012
|
entity |
| Predicate | schoolSubjectRelationship |
P177045
|
FINISHED |
| Object | frequently late to class |
—
|
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: frequently late to class | Statement: [Jeff Spicoli, schoolSubjectRelationship, frequently late to class]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: schoolSubjectRelationship Context triple: [Jeff Spicoli, schoolSubjectRelationship, frequently late to class]
-
A.
schoolSubjectContext
Indicates that an entity is being considered specifically in the context of a school subject or academic discipline.
-
B.
courseRelation
Indicates a relationship between courses, such as prerequisites, co-requisites, or other curricular dependencies or associations.
-
C.
examRelationship
Indicates a relationship between an exam and another entity, such as who took it, administered it, or is otherwise associated with it.
-
D.
schoolSubject
Indicates that an entity is an academic subject taught, studied, or associated with a school or educational program.
-
E.
teachingSubject
Indicates that an entity is engaged in teaching or instructing another entity in a particular subject or field of knowledge.
- 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_69f3497660508190a541826a81f7e9ab |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f85bfba48190aba95b40642a8ca7 |
completed | May 3, 2026, 7:25 a.m. |
| PD | Predicate disambiguation | batch_69f6f6619404819084662aef1238261c |
completed | May 3, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69f6f814fcf48190ae4504154d1b2c05 |
completed | May 3, 2026, 7:24 a.m. |
Created at: May 1, 2026, 1:38 a.m.