Triple
T21590379
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donald Ross |
E532761
|
entity |
| Predicate | designedApproximatelyNumberOfCourses |
P49837
|
FINISHED |
| Object | over 400 |
—
|
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: over 400 | Statement: [Donald Ross, designedApproximatelyNumberOfCourses, over 400]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: designedApproximatelyNumberOfCourses Context triple: [Donald Ross, designedApproximatelyNumberOfCourses, over 400]
-
A.
numberOfCourses
chosen
Indicates the quantity of courses associated with a given entity.
-
B.
approximateCourseLength
Indicates an estimated or rough value for the length or duration of a course rather than an exact measurement.
-
C.
hasMultipleCourses
Indicates that an entity is associated with more than one course within the given context.
-
D.
hasNumberOfLessons
Indicates the specific count of lessons associated with an entity.
-
E.
hasApproximateStudents
Indicates that an entity is associated with an estimated or approximate number of students, rather than an exact count.
- 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_69e0c46251648190876f0427cf2d321b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eefadd0ec88190929c76137bd1603e |
completed | April 27, 2026, 5:57 a.m. |
| PD | Predicate disambiguation | batch_69e632109d048190b4ac3f14fe48d1a0 |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:32 p.m.