Triple
T5422876
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Аҧсуа бызшәа |
E121292
|
entity |
| Predicate | numberOfLettersInAlphabet |
P4429
|
FINISHED |
| Object | over 50 letters |
—
|
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: over 50 letters | Statement: [Аҧсуа бызшәа, numberOfLettersInAlphabet, over 50 letters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfLettersInAlphabet Context triple: [Аҧсуа бызшәа, numberOfLettersInAlphabet, over 50 letters]
-
A.
alphabetSizeLatin
Indicates the number of distinct letters in the Latin alphabet used in a given context or system.
-
B.
alphabet
Indicates that one entity is an alphabet or set of symbols used for representing elements (such as characters or tokens) in relation to another entity.
-
C.
hasNumberOfLetters
chosen
Indicates a relationship where an entity is associated with the count of letters it contains.
-
D.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
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_69bd463b58d88190b258261573de9e91 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd8812f84c819094d8516f69fff83d |
completed | March 20, 2026, 5:46 p.m. |
| PD | Predicate disambiguation | batch_69bd8469f5e48190bbe5c8bdfe8925ea |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:06 p.m.