Triple
T4769032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Atbash |
E105879
|
entity |
| Predicate | preservesLetterCount |
P58670
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Atbash, preservesLetterCount, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: preservesLetterCount Context triple: [Atbash, preservesLetterCount, true]
-
A.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
-
B.
hasApproximateNumberOfLetters
Indicates that an entity is associated with a number that roughly, but not exactly, corresponds to the count of letters it contains.
-
C.
hasStandardLetterCount
Indicates that an entity’s associated text or label contains a number of letters that matches a predefined standard or expected count.
-
D.
excludesLetter
Indicates that one entity does not contain or allow the presence of a specified letter.
-
E.
hasLetter
Indicates that one entity contains, includes, or is associated with a specific letter or character.
- F. None of above. chosen
Provenance (4 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_69bd43f226fc8190b867cc249c2a9042 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd655aceb081908100ffc0498fe183 |
completed | March 20, 2026, 3:18 p.m. |
| PD | Predicate disambiguation | batch_69bd6229d8448190a271719e5e30fd82 |
completed | March 20, 2026, 3:05 p.m. |
| PDg | Predicate description generation | batch_69bd6508e218819086a36236cfa4a249 |
completed | March 20, 2026, 3:17 p.m. |
Created at: March 20, 2026, 1:21 p.m.