Triple
T12997994
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sue Storm |
E322092
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Sue |
E322092
|
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: Sue | Statement: [Sue Storm, givenName, Sue]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sue Context triple: [Sue Storm, givenName, Sue]
-
A.
Sue
chosen
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 tough, resilient male protagonist of the humorous country song "A Boy Named Sue," whose life is shaped by the hardships caused by his traditionally feminine name.
-
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.
Suzie
Suzie is a brilliant, tech-savvy girl from Stranger Things who helps Dustin Henderson and his friends by providing crucial scientific and hacking assistance.
- 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_69d8076479b8819090afce3591939cdf |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97e7980288190a9fe629a8cc76a52 |
completed | April 10, 2026, 10:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6c101ea60819098e4d5fb3a9e803a |
completed | May 3, 2026, 3:29 a.m. |
Created at: April 9, 2026, 8:46 p.m.