Triple
T21860029
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenny Dantley |
E539734
|
entity |
| Predicate | schoolSubject |
P145958
|
FINISHED |
| Object | auto shop |
—
|
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: auto shop | Statement: [Kenny Dantley, schoolSubject, auto shop]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: schoolSubject Context triple: [Kenny Dantley, schoolSubject, auto shop]
-
A.
schoolSubjectContext
Indicates that an entity is being considered specifically in the context of a school subject or academic discipline.
-
B.
nameOfSubject
Indicates that the predicate specifies the name or label assigned to the subject entity.
-
C.
teachingSubject
Indicates that an entity is engaged in teaching or instructing another entity in a particular subject or field of knowledge.
-
D.
schoolSubjectStrength
Indicates a relationship where an entity has a particular level of ability or proficiency in a specific school subject.
-
E.
languageOfSubjects
Indicates the language used by or associated with the subjects in question.
- 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_69e0c47829648190bbe2d1d7033768ec |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0d63a22b88190b59b13e7b4788195 |
completed | April 28, 2026, 3:46 p.m. |
| PD | Predicate disambiguation | batch_69e6be9394f88190945ddd1dc004d29d |
completed | April 21, 2026, 12:02 a.m. |
| PDg | Predicate description generation | batch_69e6d054737081908aa7112975b77475 |
completed | April 21, 2026, 1:18 a.m. |
Created at: April 16, 2026, 6:56 p.m.