Triple
T19839391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Younus |
E476683
|
entity |
| Predicate | transliterationOf |
P5923
|
FINISHED |
| Object | يُونُس |
E110373
|
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: يُونُس | Statement: [Younus, transliterationOf, يُونُس]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: يُونُس Context triple: [Younus, transliterationOf, يُونُس]
-
A.
Giona
Giona is an Italian given name, equivalent to Jonah, with biblical origins and shared etymological roots with the name Yonas.
-
B.
Nerio
Nerio is an Italian masculine given name of ancient origin, historically borne by several notable figures including medieval nobles.
-
C.
Ulis
Ulis is a surname most notably associated with American basketball player and coach Tyler Ulis.
-
D.
Nassim
Nassim is the first name of Nassim Nicholas Taleb, a Lebanese-American scholar, statistician, and former trader known for his work on risk, probability, and uncertainty.
-
E.
Yonas
chosen
Yonas is a given name, often used as a variant of Jonas in various cultures.
- 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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65804be608190b49e110c3bf381bc |
completed | April 20, 2026, 4:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07d436990481908bb488d3935fa773 |
completed | May 16, 2026, 2:19 a.m. |
Created at: April 10, 2026, 1:50 p.m.