Triple
T26776632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Department of Finance and Business Economics |
E670134
|
entity |
| Predicate | offersCourseTopic |
P42231
|
FINISHED |
| Object | investment 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: investment analysis | Statement: [Department of Finance and Business Economics, offersCourseTopic, investment analysis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: offersCourseTopic Context triple: [Department of Finance and Business Economics, offersCourseTopic, investment analysis]
-
A.
offersCourseType
Indicates that an entity provides or makes available a course of a specified type.
-
B.
offersCurriculum
Indicates that one entity provides or makes available a specific curriculum to another entity.
-
C.
offersEducationIn
chosen
Indicates that an entity provides or delivers educational programs, courses, or instruction in a specified field, subject, or area.
-
D.
offersEducationTo
Indicates that one entity provides educational services, instruction, or learning opportunities to another entity.
-
E.
offersDiscipline
Indicates that one entity provides or makes available a particular field of study, training, or area of specialization to another entity.
- 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_69eeb31c925881909b597f6e40056d28 |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f71422adac8190a5ceb32dcf820833 |
completed | May 3, 2026, 9:23 a.m. |
| PD | Predicate disambiguation | batch_69f712764d2c819081b64b27e5de4a13 |
completed | May 3, 2026, 9:16 a.m. |
Created at: April 27, 2026, 4:05 a.m.