Triple
T36870195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | schools of government |
E911203
|
entity |
| Predicate | typicalCurriculumComponents |
P62594
|
FINISHED |
| Object | core courses in policy analysis |
—
|
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: core courses in policy analysis | Statement: [schools of government, typicalCurriculumComponents, core courses in policy analysis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCurriculumComponents Context triple: [schools of government, typicalCurriculumComponents, core courses in policy analysis]
-
A.
curriculumIncluded
chosen
Indicates that a particular subject, topic, or component is part of a defined curriculum or course of study.
-
B.
curriculumType
Indicates the classification or category of curriculum associated with an educational program or course.
-
C.
typicalCourse
Indicates that one entity is a standard or commonly taken course associated with another entity, such as a program, curriculum, or field of study.
-
D.
offersCurriculum
Indicates that one entity provides or makes available a specific curriculum to another entity.
-
E.
typicalCourseTopic
Indicates that a given topic is commonly or characteristically covered as part of a particular course.
- 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_69f76e80f6f0819091cba8e19b269615 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0e039481908a4a2666f76c5363 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:13 p.m.