Triple
T11380170
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gone with the Wind |
E269572
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Mammy |
E241221
|
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: Mammy | Statement: [Gone with the Wind, hasCharacter, Mammy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mammy Context triple: [Gone with the Wind, hasCharacter, Mammy]
-
A.
Nannie
Nannie is a feminine given name, often used as a diminutive or variant of names like Nancy or Anne.
-
B.
Biddy
Biddy is a kind, sensible, and unpretentious young woman in Charles Dickens's novel "Great Expectations," who serves as both a friend and moral counterpoint to the protagonist, Pip.
-
C.
Mama Reed
Mama Reed is an American blues singer best known for her close musical and personal association with influential blues musician Jimmy Reed.
-
D.
Biddy Baxter
Biddy Baxter is a British television producer best known for her long-running role as editor of the BBC children's programme Blue Peter, where she helped shape its distinctive style and legacy.
-
E.
Mammy in Gone with the Wind
chosen
Mammy in *Gone with the Wind* is the strong-willed, loyal enslaved house servant of the O’Hara family, known for her sharp tongue, moral authority, and complex, stereotype-laden portrayal in the classic 1939 film.
- 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_69d6aacca1048190b39dbbc2174616fa |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7fc331f188190a7f69f1aae53fb6b |
completed | April 9, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e556a99c8c8190b7439a548216e648 |
completed | April 19, 2026, 10:26 p.m. |
Created at: April 8, 2026, 9:34 p.m.