Triple
T23374385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yannick Bisson |
E593564
|
entity |
| Predicate | starredIn |
P1668
|
FINISHED |
| Object | Sue Thomas: F.B.Eye |
—
|
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: Sue Thomas: F.B.Eye | Statement: [Yannick Bisson, starredIn, Sue Thomas: F.B.Eye]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sue Thomas: F.B.Eye Context triple: [Yannick Bisson, starredIn, Sue Thomas: F.B.Eye]
-
A.
Sue Thomas: F.B.Eye
chosen
Sue Thomas: F.B.Eye is an American crime drama television series inspired by the true story of a deaf FBI surveillance expert who uses her lip-reading skills to solve cases.
-
B.
Sue
Sue is a character from the dark comedy film "Bad Santa," known as the love interest of the main antihero, Willie T. Soke.
-
C.
Sue
Sue is a town in Fukuoka Prefecture, Japan, that forms part of the greater Fukuoka–Kitakyushu metropolitan region.
-
D.
Sue
Sue is a character in the British stage play and film "Abigail's Party," known as a polite, somewhat reserved neighbor who becomes an awkward guest at a disastrous suburban drinks party.
-
E.
Sue
Sue is a common English given name, typically used as a short form of Susanna or Susan.
- 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_69e25d268a50819095f2fd479da8ef3f |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1a3b1d24881909945936cbf00876e |
completed | April 29, 2026, 6:22 a.m. |
Created at: April 17, 2026, 5:33 p.m.