Triple
T14751532
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarah Black |
E346618
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object | Rachel Black |
E400445
|
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: Rachel Black | Statement: [Sarah Black, relative, Rachel Black]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rachel Black Context triple: [Sarah Black, relative, Rachel Black]
-
A.
Rachel Black
chosen
Rachel Black is a minor character in Stephenie Meyer’s Twilight series, known as one of Jacob Black’s older twin sisters from the Quileute reservation.
-
B.
Aleisha Allen
Aleisha Allen is an American actress best known for her childhood roles in films such as "Are We There Yet?" and "School of Rock."
-
C.
Talisha Searcy
Talisha Searcy is an American local politician who serves as the mayor of Takoma Park, Maryland.
-
D.
Kaitlyn Black
Kaitlyn Black is an American actress best known for her role as Annabeth Nass on the television series "Hart of Dixie."
-
E.
Lila Garrett
Lila Garrett was an American television producer, screenwriter, and political activist known for her work on socially conscious TV projects and her outspoken progressive views.
- 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_69d822e6f1c88190bc494d491a907114 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7d40efc8190bb1be34c19a2b57c |
completed | April 14, 2026, 11:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9dba87c481908084c3cba5df3fcd |
completed | May 9, 2026, 2:36 a.m. |
Created at: April 10, 2026, 1:30 a.m.