Triple
T22087563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Emma Blair Scribner |
E545821
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Emma |
—
|
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: Emma | Statement: [Emma Blair Scribner, givenName, Emma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Emma Context triple: [Emma Blair Scribner, givenName, Emma]
-
A.
Emma
chosen
Emma is a common feminine given name of Germanic origin, widely used in English-speaking and many other countries.
-
B.
Emma
"Emma" is a 2009 British television miniseries adaptation of Jane Austen's novel, starring Romola Garai in the title role.
-
C.
Emma
Emma is a fictional character portrayed by American actress Ashley Scott, known for her roles in film and television.
-
D.
Emma
Emma is a central character in the sitcom "The Great Indoors," portrayed as a savvy, socially conscious millennial who works on the magazine’s digital team.
-
E.
Emma
Emma is the protagonist of Taylor Jenkins Reid’s novel "One True Loves," a woman forced to reevaluate love and loyalty when her presumed-dead husband returns just as she is about to remarry.
- 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_69e11e36d03c8190a83a1ba802b7231b |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f128e3a98481908a7b3dc3f2a90276 |
completed | April 28, 2026, 9:38 p.m. |
Created at: April 16, 2026, 8:29 p.m.