Triple
T35005911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | پاک سرزمین شاد باد |
E1009807
|
entity |
| Predicate | fullLiteralGloss |
P97872
|
FINISHED |
| Object | May the pure land be happy |
—
|
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: May the pure land be happy | Statement: [پاک سرزمین شاد باد, fullLiteralGloss, May the pure land be happy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fullLiteralGloss Context triple: [پاک سرزمین شاد باد, fullLiteralGloss, May the pure land be happy]
-
A.
meaningGloss
Indicates that the predicate provides a brief explanatory phrase or paraphrase capturing the meaning or sense of another expression or item.
-
B.
hasEnglishGloss
chosen
Indicates that one entity serves as the English-language gloss or explanatory translation for the other entity.
-
C.
hasMultilingualGlosses
Indicates that an entity is associated with glosses or explanatory labels available in multiple languages.
-
D.
hasGlossonym
Indicates a relationship where an entity is associated with the specific name or term used to refer to a language (its glossonym).
-
E.
hasLiteralMeaning
Indicates that one entity expresses the direct, explicit meaning or sense of another entity (such as a word, phrase, or symbol).
- 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_69f76dcb716881909f75e4fd60ab2284 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f7858aa5508190a07dde993b3356fc |
completed | May 3, 2026, 5:27 p.m. |
| PD | Predicate disambiguation | batch_69f7841812f081909d878955d114088e |
completed | May 3, 2026, 5:21 p.m. |
Created at: May 3, 2026, 4:01 p.m.