Triple
T22655564
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Susan |
E559215
|
entity |
| Predicate | hasDiminutive |
P456
|
FINISHED |
| Object | Sue |
—
|
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 | Statement: [Susan, hasDiminutive, Sue]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sue Context triple: [Susan, hasDiminutive, Sue]
-
A.
Sue
Sue is the given name of Sue Storm, the Invisible Woman and a central member of Marvel’s superhero team the Fantastic Four.
-
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 the middle name of Australian conservationist and television personality Bindi Irwin, daughter of the late Steve Irwin.
-
D.
Sue
chosen
Sue is a common English given name, typically used as a short form of Susanna or Susan.
-
E.
Sue
Sue is a town in Fukuoka Prefecture, Japan, that forms part of the greater Fukuoka–Kitakyushu metropolitan region.
- 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_69e245489dd88190b1f674acf61c8769 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1765b93dc8190a0357dd4b6ae524a |
completed | April 29, 2026, 3:09 a.m. |
Created at: April 17, 2026, 3:06 p.m.