Triple
T6034978
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeff Maggioncalda |
E134394
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Coursera |
E9162
|
NE 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: Coursera | Statement: [Jeff Maggioncalda, employer, Coursera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coursera Context triple: [Jeff Maggioncalda, employer, Coursera]
-
A.
Coursera
chosen
Coursera is a major online learning platform that partners with universities and organizations worldwide to offer courses, professional certificates, and degree programs across a wide range of subjects.
-
B.
edX
edX is a leading online learning platform founded by MIT and Harvard that offers university-level courses, professional certificates, and degree programs to learners worldwide.
-
C.
Udacity
Udacity is an online learning platform specializing in technology-focused courses and career-oriented "Nanodegree" programs developed in collaboration with industry partners.
-
D.
Udemy
Udemy is a global online learning platform that hosts a vast marketplace of video-based courses across diverse subjects for learners and professionals.
-
E.
FutureLearn
FutureLearn is a digital education platform that partners with universities and institutions worldwide to deliver a wide range of online courses and learning programs.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c00875db5c819099dd5bb833ec43c2 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056b33a7c8190ad6282286199b192 |
completed | March 22, 2026, 8:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11388aec881908408d5844c96ea2d |
completed | March 23, 2026, 10:18 a.m. |
Created at: March 22, 2026, 4:08 p.m.