Triple
T32402124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Banalata Sen (English translation) |
E827979
|
entity |
| Predicate | sourceWorkLanguageCommunity |
P85137
|
FINISHED |
| Object | Bengali literature |
—
|
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: Bengali literature | Statement: [Banalata Sen (English translation), sourceWorkLanguageCommunity, Bengali literature]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sourceWorkLanguageCommunity Context triple: [Banalata Sen (English translation), sourceWorkLanguageCommunity, Bengali literature]
-
A.
hasLanguageCommunity
Indicates that an entity is associated with or serves a particular language community.
-
B.
lenguaDeTrabajo
Indicates that something functions as a working language used for communication in a specific context or setting.
-
C.
usesWorkingLanguagesOf
Indicates that one entity employs or operates using the working languages associated with another entity.
-
D.
languageOfUnderlyingWork
chosen
Indicates the language in which the original or underlying work (from which a derived or related work stems) is expressed.
-
E.
primaryLanguageInWork
Indicates that a specified language is the main or predominant language used within a particular work (such as a book, film, or document).
- 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_69f34919342c8190a4c3bf35a90d4e58 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f739a638748190808e7a2930dce16e |
completed | May 3, 2026, 12:03 p.m. |
| PD | Predicate disambiguation | batch_69f732f2dc6c8190a4e86da98cc5eb05 |
completed | May 3, 2026, 11:35 a.m. |
Created at: May 1, 2026, 12:52 a.m.