Triple
T28083519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Banshiwala |
E709752
|
entity |
| Predicate | hasSourceMaterialLanguage |
P2925
|
FINISHED |
| Object | Bengali |
—
|
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 | Statement: [Banshiwala, hasSourceMaterialLanguage, Bengali]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSourceMaterialLanguage Context triple: [Banshiwala, hasSourceMaterialLanguage, Bengali]
-
A.
languageOfSources
chosen
Indicates that the specified language is the language in which the referenced sources or source materials are expressed.
-
B.
hasSourceLanguageForLoanwords
Indicates that a language serves as the original source from which loanwords are borrowed into another language.
-
C.
hasLanguageOfOrigin
Indicates that one entity has its origin or source in the language specified by another entity.
-
D.
isLanguageOf
Indicates that a particular language is used as the official or primary language associated with a given entity (such as a person, document, or region).
-
E.
indirectOriginLanguage
Indicates that something originates from a particular language, not directly but through one or more intermediate languages or sources.
- 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_69ef9b7037f0819095bb90eaccbcaf32 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f6b49436b0819094e21603054d05d4 |
completed | May 3, 2026, 2:36 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a5fd8481909433e923c5e24e55 |
completed | May 3, 2026, 2:32 a.m. |
Created at: April 27, 2026, 8:53 p.m.