Triple
T20140313
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Virginia |
E491145
|
entity |
| Predicate | title |
P38
|
FINISHED |
| Object | Miss Virginia |
—
|
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: Miss Virginia | Statement: [Miss Virginia, title, Miss Virginia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miss Virginia Context triple: [Miss Virginia, title, Miss Virginia]
-
A.
Miss Virginia
chosen
Miss Virginia is a 2019 drama film starring Uzo Aduba as a determined mother who fights for better educational opportunities for her son and underserved children in Washington, D.C.
-
B.
Sweet Virginia
Sweet Virginia is a 2017 neo-noir thriller film about a former rodeo star whose quiet small-town life is upended by a series of violent events.
-
C.
Sweet Virginia
"Sweet Virginia" is a country-influenced rock song by The Rolling Stones, featured on their 1972 album Exile on Main St.
-
D.
Miss America
Miss America is a World War II–era Marvel Comics superheroine, often associated with the Invaders and known for her enhanced strength, durability, and patriotic theme.
-
E.
Miss America
Miss America is a 1995 autobiographical book by radio personality Howard Stern, known for its provocative humor and candid personal revelations.
- 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_69da6265f8f0819080b29c752a574088 |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e66798d59c81908ebcd6644b1b3744 |
completed | April 20, 2026, 5:51 p.m. |
Created at: April 11, 2026, 11:32 p.m.