Triple
T1507483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sara |
E33933
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Sára |
E33933
|
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: Sára | Statement: [Sara, hasVariant, Sára]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sára Context triple: [Sara, hasVariant, Sára]
-
A.
Sara
Sara is a language spoken in parts of Central Africa, particularly in Chad.
-
B.
Sara
chosen
Sara is a feminine given name of Hebrew origin meaning "princess," historically borne by notable figures including Sara Ann Delano Roosevelt, the mother of U.S. President Franklin D. Roosevelt.
-
C.
Sandra
Sandra is the given name of Sandra Day O’Connor, the first woman to serve as a Justice on the United States Supreme Court.
-
D.
Sabina
Sabina is a historical region of central Italy, traditionally inhabited by the Sabines and known for its rugged landscape and proximity to ancient Rome.
-
E.
Sonia
Sonia is a central female character in the romantic comedy film "Think Like a Man," whose relationships and personal growth intersect with the movie’s ensemble cast and themes about modern dating.
- 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_69a885f352a4819099b24ff15489dede |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a8891c80a88190a813aab099abe2b0 |
completed | March 4, 2026, 7:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adeabea0d88190b0bd83aece8c7b55 |
completed | March 8, 2026, 9:31 p.m. |
Created at: March 4, 2026, 7:24 p.m.