Triple
T21134535
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Grey |
E520780
|
entity |
| Predicate | usesAlias |
P23264
|
FINISHED |
| Object | Mr. Grey |
—
|
NE NERFINISHED |
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: Mr. Grey | Statement: [Mr. Grey, usesAlias, Mr. Grey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mr. Grey Context triple: [Mr. Grey, usesAlias, Mr. Grey]
-
A.
Mr. Grey
chosen
Mr. Grey is a mysterious, coldly efficient criminal mastermind who leads the subway hijacking in the thriller "The Taking of Pelham One Two Three."
-
B.
John Strange
John Strange is the given name of John Strange Spencer-Churchill, a British Army officer and the younger brother of Prime Minister Winston Churchill.
-
C.
Christian Grey
Christian Grey is the wealthy, enigmatic businessman and dominant love interest at the center of the erotic romance series "Fifty Shades of Grey."
-
D.
Mr. Sparks
Mr. Sparks is a friendly, mechanically skilled character in the Noddy children's stories who often helps fix things in Toyland.
-
E.
Christian Trevelyan Grey
Christian Trevelyan Grey is the wealthy, enigmatic businessman and dominant love interest in E. L. James's "Fifty Shades" series.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b50b53048190ae34e8abbe3c5ada |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e723592fd48190ba5977a1b229d51e |
completed | April 21, 2026, 7:12 a.m. |
Created at: April 16, 2026, 2:56 p.m.