Triple
T389274
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mostafa Madbouly |
E8845
|
entity |
| Predicate | academicBackground |
P778
|
FINISHED |
| Object | architecture |
—
|
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: architecture | Statement: [Mostafa Madbouly, academicBackground, architecture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: academicBackground Context triple: [Mostafa Madbouly, academicBackground, architecture]
-
A.
academicDegree
Indicates that an entity holds or has been awarded a specific academic degree.
-
B.
academicStatus
Indicates the educational or scholarly standing or level an entity holds within an academic context.
-
C.
academicFocus
chosen
Indicates the primary field of study, discipline, or subject area that an entity concentrates on academically.
-
D.
academicProfile
Indicates the relationship that captures an entity’s academic background, qualifications, and scholarly activities or achievements.
-
E.
educatedAt
Indicates that an entity received education or formal training at a specified institution or place of learning.
- 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_69a2e7f55c60819097aff65ea2ca2832 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec5bdc848190826701590070497b |
completed | Feb. 28, 2026, 1:23 p.m. |
| PD | Predicate disambiguation | batch_69a2e96960608190bdd342da9c5ddb5e |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.