Triple
T33379400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William S. Gray |
E854727
|
entity |
| Predicate | hasNotableStudentMaterial |
P10464
|
FINISHED |
| Object | elementary school reading textbooks |
—
|
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: elementary school reading textbooks | Statement: [William S. Gray, hasNotableStudentMaterial, elementary school reading textbooks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableStudentMaterial Context triple: [William S. Gray, hasNotableStudentMaterial, elementary school reading textbooks]
-
A.
hasEducationalMaterial
chosen
Indicates that an entity provides, contains, or is associated with educational content or learning resources for another entity.
-
B.
notableStudentWork
Indicates that the subject has a student whose work is particularly notable or significant in relation to the subject.
-
C.
hasStudentWorkComponent
Indicates that something includes or is associated with a component consisting of work produced by students.
-
D.
hasStudents
Indicates that an entity (such as a class, school, or teacher) is associated with one or more students.
-
E.
notableStudent
Indicates that a person is a distinguished or particularly significant student of another individual or institution.
- 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_69f3496ca10c8190908640d18fa00832 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a01b50490e481908eb1d675561fdde2 |
completed | May 11, 2026, 10:52 a.m. |
| PD | Predicate disambiguation | batch_6a01b44b106481908aa98a3f1eb2c119 |
completed | May 11, 2026, 10:49 a.m. |
Created at: May 1, 2026, 1:35 a.m.