Triple
T20205973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samira Wiley |
E493351
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Samira |
—
|
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: Samira | Statement: [Samira Wiley, givenName, Samira]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samira Context triple: [Samira Wiley, givenName, Samira]
-
A.
Samira
chosen
Samira is a feminine given name of Arabic origin commonly used across the Middle East, North Africa, and South Asia.
-
B.
Nasim
Nasim is an Iranian-American actress and comedian best known for her work on "Saturday Night Live" and the sitcom "New Girl."
-
C.
Salma
Salma is a feminine given name of Arabic origin, commonly used in various cultures around the world.
-
D.
सुनैना
सुनैना एक स्त्रीलिंग भारतीय नाम है जो मुख्यतः हिंदी भाषी समुदायों में प्रचलित है।
-
E.
Shireen
Shireen is a feminine given name of Persian origin, commonly used in various cultures across the Middle East and South Asia.
- 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_69da6269614c8190bb40475d9d477358 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66d922ebc8190ae012da8ceba74dd |
completed | April 20, 2026, 6:16 p.m. |
Created at: April 11, 2026, 11:38 p.m.