Triple
T9491
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | School of Engineering and Applied Science |
E191
|
entity |
| Predicate | hasDiscipline |
P531
|
FINISHED |
| Object | engineering |
—
|
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: engineering | Statement: [School of Engineering and Applied Science, hasDiscipline, engineering]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDiscipline Context triple: [School of Engineering and Applied Science, hasDiscipline, engineering]
-
A.
supportsDiscipline
Indicates that one entity provides assistance, resources, or endorsement that helps sustain or advance a particular discipline.
-
B.
hasNotableSubject
Indicates that an entity is associated with a subject that is particularly significant, prominent, or noteworthy in relation to it.
-
C.
hasSchool
Indicates that an entity possesses, is associated with, or is served by a particular school.
-
D.
hasConcept
chosen
Indicates that an entity includes, embodies, or is associated with a particular concept.
-
E.
hasStudents
Indicates that an entity (such as a class, school, or teacher) is associated with one or more students.
- 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_69a23bb612708190b09f25385e4b63d1 |
completed | Feb. 28, 2026, 12:49 a.m. |
| NER | Named-entity recognition | batch_69a240b249788190af8dbf7e80e9c91b |
completed | Feb. 28, 2026, 1:11 a.m. |
| PD | Predicate disambiguation | batch_69a23fe52ec48190a4d24101c91434ed |
completed | Feb. 28, 2026, 1:07 a.m. |
Created at: Feb. 28, 2026, 12:54 a.m.