Triple
T19469927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sweet Tooth |
E487093
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Serena Frome |
—
|
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: Serena Frome | Statement: [Sweet Tooth, hasCharacter, Serena Frome]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Serena Frome Context triple: [Sweet Tooth, hasCharacter, Serena Frome]
-
A.
Serena Frome
chosen
Serena Frome is the protagonist of Ian McEwan’s novel "Sweet Tooth," a young Cambridge graduate recruited into a covert MI5 operation in 1970s Britain.
-
B.
Serena Tomalin
Serena Tomalin is a British woman best known as one of the children of acclaimed biographer and literary journalist Claire Tomalin.
-
C.
Serena Evans
Serena Evans is a British actress best known for her role in the BBC sitcom "The Thin Blue Line."
-
D.
Serena Benson
Serena Benson is the late mother of NYPD Captain Olivia Benson in the television series "Law & Order: Special Victims Unit," whose traumatic history and alcoholism deeply influenced Olivia's life.
-
E.
Serena Brown
Serena Brown is the daughter of Bob Brown.
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633e6fd988190b79be580b65746fe |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 10, 2026, 1:39 p.m.