Triple
T13718936
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aubrey Drake Graham |
E328975
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Aubrey |
E328975
|
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: Aubrey | Statement: [Aubrey Drake Graham, givenName, Aubrey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aubrey Context triple: [Aubrey Drake Graham, givenName, Aubrey]
-
A.
Aubrey
Aubrey is a small suburban town in the greater Dallas–Fort Worth metropolitan area in Texas.
-
B.
Aubrey
chosen
Aubrey is the first name of Canadian rapper, singer, and actor Drake (Aubrey Drake Graham).
-
C.
Aubrey Woods
Aubrey Woods was a British actor best known for his role as Bill the Candy Man in the 1971 film "Willy Wonka & the Chocolate Factory."
-
D.
Skylar
Skylar is a compassionate and intelligent Harvard student who becomes Will Hunting’s love interest in the film "Good Will Hunting."
-
E.
Skylar
Skylar is a magical flying creature from the animated series "Elena of Avalor," serving as one of Princess Elena’s loyal and adventurous companions.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd439a121c81908cae964e7756274c |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d5a23bc8190942568658665bbb0 |
completed | May 3, 2026, 7:09 p.m. |
Created at: April 9, 2026, 9:55 p.m.