Triple
T13036120
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 42 (school) |
E326563
|
entity |
| Predicate | mainFieldOfStudy |
P2582
|
FINISHED |
| Object | computer programming |
—
|
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: computer programming | Statement: [42 (school), mainFieldOfStudy, computer programming]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainFieldOfStudy Context triple: [42 (school), mainFieldOfStudy, computer programming]
-
A.
hasSubjectOfStudy
Indicates that an entity (such as a person or organization) focuses on, researches, or specializes in a particular field or topic of study.
-
B.
isPartOfStudyField
Indicates that one subject, topic, or subfield belongs to, is included within, or is a component of a broader academic or research field.
-
C.
dimensionOfStudy
Indicates the specific field, aspect, or perspective that characterizes or structures a particular study or research activity.
-
D.
offersFieldOfStudy
chosen
Indicates that an institution or program provides a particular field of study as an available area of academic focus.
-
E.
regionOfStudy
Indicates the academic or research area that is the focus of someone’s study or investigation.
- 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_69d8076cc45c81908123123f43e69266 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97f2a71a0819098bb6cf8a4b2208a |
completed | April 10, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69d97dc39a0881908119c62e31bf6182 |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 8:55 p.m.