Triple
T20808126
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Women |
E512218
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object | Miriam Aarons |
—
|
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: Miriam Aarons | Statement: [The Women, hasCharacter, Miriam Aarons]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miriam Aarons Context triple: [The Women, hasCharacter, Miriam Aarons]
-
A.
Miriam Aarons
chosen
Miriam Aarons is a sharp-tongued showgirl character in the 1939 film "The Women," known for her wit and involvement in the story’s romantic entanglements.
-
B.
Miriam David
Miriam David is a British sociologist and academic known for her work on gender, education, and social policy.
-
C.
Miriam Nelson
Miriam Nelson was an American choreographer and dancer known for her work in Hollywood films and on Broadway during the mid-20th century.
-
D.
Miriam Bryant
Miriam Bryant is a Swedish pop singer and songwriter known for her powerful vocals and emotionally charged, melodic songs.
-
E.
Miriam Matthews
Miriam Matthews was a pioneering African American librarian and historian in Los Angeles, renowned for her work preserving and documenting Black history and culture in California.
- 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_69e0b4cc69f481908e98751e697b9df4 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2d0a2a081908fb0e3d890e87aaf |
completed | April 21, 2026, 12:20 a.m. |
Created at: April 16, 2026, 12:40 p.m.