Triple
T22527433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amina Rizk |
E556943
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Amina Rizk |
—
|
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: Amina Rizk | Statement: [Amina Rizk, name, Amina Rizk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amina Rizk Context triple: [Amina Rizk, name, Amina Rizk]
-
A.
Amina Rizk
chosen
Amina Rizk was a renowned Egyptian film and stage actress celebrated for her powerful dramatic roles in classic Arabic cinema.
-
B.
Fathia Rizk
Fathia Rizk, later known as Fathia Nkrumah, was an Egyptian-born First Lady of Ghana and the wife of the country’s first president, Kwame Nkrumah.
-
C.
Yasmine El Rashidi
Yasmine El Rashidi is an Egyptian writer and journalist known for her essays and reportage on contemporary Egyptian politics and society.
-
D.
Aiman Ezzat
Aiman Ezzat is a French-Moroccan business executive best known as the Chief Executive Officer of the global consulting and technology services company Capgemini.
-
E.
Layla El-Faouly
Layla El-Faouly is a fictional character in the Marvel Cinematic Universe series "Moon Knight," known as an adventurous archaeologist who becomes the heroic Scarlet Scarab.
- 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_69e11e57483c8190b0887c4f8ff26446 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15ed411488190a51320930b9805c2 |
completed | April 29, 2026, 1:28 a.m. |
Created at: April 16, 2026, 8:51 p.m.