Triple
T22849402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roman script |
E566314
|
entity |
| Predicate | hasBasicLetterCount |
P73319
|
FINISHED |
| Object | 23 letters in Classical Latin |
—
|
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: 23 letters in Classical Latin | Statement: [Roman script, hasBasicLetterCount, 23 letters in Classical Latin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBasicLetterCount Context triple: [Roman script, hasBasicLetterCount, 23 letters in Classical Latin]
-
A.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
-
B.
hasBasicLetters
Indicates that an entity contains or is composed of fundamental alphabetic characters, without additional symbols or diacritics.
-
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.
hasNumberOfLetters
Indicates a relationship where an entity is associated with the count of letters it contains.
-
E.
hasNumberOfBasicCharacters
chosen
Indicates the quantity of basic (non-accented or fundamental) characters associated with an entity.
- 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_69e2458750b481908a8e4cf4609cc6cf |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17eb74700819090d191b3a7a17034 |
completed | April 29, 2026, 3:44 a.m. |
| PD | Predicate disambiguation | batch_69eed2d507c08190895ed971af0fc755 |
completed | April 27, 2026, 3:07 a.m. |
Created at: April 17, 2026, 3:36 p.m.