Triple
T3733975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Executive MBA |
E79133
|
entity |
| Predicate | hasTypicalClassProfile |
P41733
|
FINISHED |
| Object | students with 8–15 years of work experience |
—
|
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: students with 8–15 years of work experience | Statement: [Executive MBA, hasTypicalClassProfile, students with 8–15 years of work experience]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalClassProfile Context triple: [Executive MBA, hasTypicalClassProfile, students with 8–15 years of work experience]
-
A.
hasTypicalCharacterType
Indicates that an entity is commonly associated with or exemplified by a particular type of character or persona.
-
B.
hasProfile
Indicates that an entity is associated with or possesses a specific profile representation or account.
-
C.
typicalProfile
chosen
Indicates that an entity represents the standard or most representative profile or pattern for another entity.
-
D.
hasTypicalSubject
Indicates that something is commonly or characteristically used as the subject (agent or topic) of a given relation or action.
-
E.
areClassifiedBy
Indicates that entities are assigned to one or more categories, types, or classes according to a specified classification scheme.
- 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_69ad8b0e4650819090ad7cef094285e8 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcb2457f08190a6b94e9895fced2c |
completed | March 8, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_69adc04746588190b0dc535638f23546 |
completed | March 8, 2026, 6:30 p.m. |
Created at: March 8, 2026, 3:34 p.m.