Triple
T13211839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Online MBA |
E314510
|
entity |
| Predicate | similarCredentialAs |
P108524
|
FINISHED |
| Object | traditional MBA degree |
—
|
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: traditional MBA degree | Statement: [Online MBA, similarCredentialAs, traditional MBA degree]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: similarCredentialAs Context triple: [Online MBA, similarCredentialAs, traditional MBA degree]
-
A.
relatedIdentity
Indicates that two identities are connected or associated with each other in some meaningful way, such as being alternate, linked, or otherwise contextually related.
-
B.
lessSimilarTo
Indicates that one entity is considered to share fewer similarities or a weaker resemblance with another entity compared to some reference or alternative.
-
C.
credentialType
Indicates the specific kind or category of credential associated with an entity or relationship.
-
D.
securityModelSimilarTo
Indicates that one entity’s security model is similar or comparable to that of another entity.
-
E.
namedForSimilarityTo
Indicates that one entity is given its name because of a perceived resemblance or likeness to another entity.
- F. None of above. chosen
Provenance (4 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_69d806aee7308190b70a237ba2a6e3e1 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c9f0f148190a0698ef27573c885 |
completed | April 10, 2026, 11:49 p.m. |
| PD | Predicate disambiguation | batch_69d98bc938f081909f123bdf1263ff7f |
completed | April 10, 2026, 11:46 p.m. |
| PDg | Predicate description generation | batch_69d98c959ba08190adf29dc0c4e1fca6 |
completed | April 10, 2026, 11:49 p.m. |
Created at: April 9, 2026, 9:17 p.m.