Triple
T26572187
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shelton v. Tucker |
E666854
|
entity |
| Predicate | teacherStatusOfPetitioner |
P40561
|
FINISHED |
| Object | public school teacher |
—
|
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: public school teacher | Statement: [Shelton v. Tucker, teacherStatusOfPetitioner, public school teacher]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teacherStatusOfPetitioner Context triple: [Shelton v. Tucker, teacherStatusOfPetitioner, public school teacher]
-
A.
studentOrTeacherOf
Indicates that one entity is either a student of, or a teacher of, another entity within some educational or instructional context.
-
B.
hasTeachingStatus
chosen
Indicates that an entity holds a particular teaching-related role, capacity, or status in relation to another entity or context.
-
C.
hasTeacher
Indicates that one entity serves as an instructor or educator for another entity.
-
D.
hasTeacherType
Indicates that an entity is associated with a teacher characterized by a specific type or category (e.g., role, specialization, or employment status).
-
E.
hasEducationalRole
Indicates that an entity holds a specific function, position, or responsibility within an educational context or setting.
- 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_69ee9cfa21c081909e4e36e087debfc6 |
completed | April 26, 2026, 11:17 p.m. |
| NER | Named-entity recognition | batch_69f6562fd3488190be1acd8c526a28d2 |
completed | May 2, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69f651a731508190bb0c8c2462eba224 |
completed | May 2, 2026, 7:33 p.m. |
Created at: April 27, 2026, 1:58 a.m.