Triple
T19192700
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mohammed Amer |
E469882
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Mohammed Amer |
—
|
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: Mohammed Amer | Statement: [Mohammed Amer, name, Mohammed Amer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mohammed Amer Context triple: [Mohammed Amer, name, Mohammed Amer]
-
A.
Mohammed Amer
chosen
Mohammed Amer is a Palestinian-American stand-up comedian and actor best known for his role on the TV series "Ramy" and his Netflix comedy specials.
-
B.
Mohamed Amer
Mohamed Amer is a common Arabic personal name shared by several notable individuals, including figures in politics, sports, and the arts.
-
C.
Abdelrahman Mohamed
Abdelrahman Mohamed is a researcher in machine learning and speech recognition known for his contributions to deep learning models, including work related to the BART architecture.
-
D.
Youssef Amer
Youssef Amer is a notable individual recognized for bearing the surname Amer.
-
E.
Mohamed Maait
Mohamed Maait is an Egyptian economist and politician who serves as Egypt’s Minister of Finance, overseeing the country’s fiscal and economic reform policies.
- 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_69d8dd0ad9088190a173b32657ae2e7a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f8a3434881908acc4063a9ee9386 |
completed | April 20, 2026, 9:57 a.m. |
Created at: April 10, 2026, 12:07 p.m.