Triple
T19839365
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Younus |
E476683
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Younes |
—
|
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: Younes | Statement: [Younus, hasVariant, Younes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Younes Context triple: [Younus, hasVariant, Younes]
-
A.
Younes
chosen
Younes is a male given name commonly used in Arabic-speaking and Muslim-majority cultures, corresponding to the name Yunus (Jonah).
-
B.
Aymen
Aymen is a masculine given name of Arabic origin, commonly considered a variant spelling of Ayman and often associated with meanings like "blessed" or "fortunate."
-
C.
Yousef
Yousef is a masculine given name of Arabic origin, commonly used across the Middle East and Muslim-majority regions.
-
D.
Yusuf
Yusuf is a revered prophet in Islamic tradition, known for his exemplary patience, prophetic dreams, and the Qur’anic narrative of his trials and rise to power in Egypt.
-
E.
Yusuf
Yusuf is a skilled chemist and dream architect in the film "Inception," responsible for creating the powerful sedatives used in shared dreaming heists.
- 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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65804be608190b49e110c3bf381bc |
completed | April 20, 2026, 4:44 p.m. |
Created at: April 10, 2026, 1:50 p.m.