Triple
T246832
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bengali |
E5055
|
entity |
| Predicate | hasAlphabeticOrder |
P1109
|
FINISHED |
| Object | Akar (অ) as first letter |
—
|
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: Akar (অ) as first letter | Statement: [Bengali, hasAlphabeticOrder, Akar (অ) as first letter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAlphabeticOrder Context triple: [Bengali, hasAlphabeticOrder, Akar (অ) as first letter]
-
A.
usesAlphabet
Indicates that one entity employs or is written using the alphabet or writing system associated with another entity.
-
B.
hasBasicWordOrder
Indicates the typical sequence in which core sentence elements (such as subject, verb, and object) are ordered in a language.
-
C.
alphabeticCode
Indicates that one entity is identified or represented by a specific alphabetic code assigned to it.
-
D.
hasOrder
chosen
Indicates that one entity possesses, is associated with, or is characterized by a specific order, sequence, or arrangement relative to others.
-
E.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
- 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_69a257c4bf688190a46ebbf411ab7473 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25d13b8088190a3f48f0388d57496 |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b63b0bc8190864d7324d339fb48 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:54 a.m.