Triple
T13083260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sayyid Shabab Ahl al-Jannah |
E310263
|
entity |
| Predicate | englishGloss |
P97872
|
FINISHED |
| Object | Master of the youth of Paradise |
—
|
LITERAL 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: Master of the youth of Paradise | Statement: [Sayyid Shabab Ahl al-Jannah, englishGloss, Master of the youth of Paradise]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: englishGloss Context triple: [Sayyid Shabab Ahl al-Jannah, englishGloss, Master of the youth of Paradise]
-
A.
hasEnglishGloss
chosen
Indicates that one entity serves as the English-language gloss or explanatory translation for the other entity.
-
B.
etymologyGloss
Indicates that a term’s meaning is explained by a brief gloss specifically describing its etymological origin or source.
-
C.
hasGlossonym
Indicates a relationship where an entity is associated with the specific name or term used to refer to a language (its glossonym).
-
D.
languageTerm
Indicates that one entity is a linguistic expression (word, phrase, or term) used to denote or label the other entity.
-
E.
meaningInGerman
Indicates that one entity expresses the meaning or translation of another entity in the German language.
- F. None of above.
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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d9811add9881908a92186dab5b6d48 |
completed | April 10, 2026, 11 p.m. |
| PD | Predicate disambiguation | batch_69d9803f6c508190bfadfbc2d00c2c64 |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:02 p.m.