Triple
T3326387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aubrey Beardsley |
E69925
|
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 Beardsley, givenName, Aubrey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aubrey Context triple: [Aubrey Beardsley, givenName, Aubrey]
-
A.
Aubrey
chosen
Aubrey is the first name of Canadian rapper, singer, and actor Drake (Aubrey Drake Graham).
-
B.
Skylar
Skylar is a compassionate and intelligent Harvard student who becomes Will Hunting’s love interest in the film "Good Will Hunting."
-
C.
Ainsley
Ainsley is a secondary character in Margaret Atwood's novel "The Edible Woman," known for her conventional femininity and contrasting attitudes toward gender roles compared to the protagonist.
-
D.
Brenna
Brenna is a Norwegian surname most notably borne by Tonje Brenna, a contemporary Norwegian politician.
-
E.
Ashley
Ashley is a character featured in the animated children’s series "¡Dos!"
- 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_69ad85a1829881908942c14075644d0d |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb16dc170819086a63e033e17d8b3 |
completed | March 8, 2026, 5:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b31a7ce34c81908df0c30a41fd925c |
completed | March 12, 2026, 7:56 p.m. |
Created at: March 8, 2026, 3:12 p.m.