Triple
T1919052
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Old Nubian script |
E40082
|
entity |
| Predicate | hasDistinctLettersFor |
P33153
|
FINISHED |
| Object | Old Nubian phonemes not in Greek |
—
|
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: Old Nubian phonemes not in Greek | Statement: [Old Nubian script, hasDistinctLettersFor, Old Nubian phonemes not in Greek]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDistinctLettersFor Context triple: [Old Nubian script, hasDistinctLettersFor, Old Nubian phonemes not in Greek]
-
A.
hasDistinctLetters
Indicates that all letters in the given string or word are unique, with no character repeated.
-
B.
hasDistinctVowelLetters
Indicates that the subject contains vowel letters that are all different from one another, with no vowel repeated.
-
C.
hasDistinctCharacterSet
Indicates that two compared items use different sets of characters, with no character set being a subset or duplicate of the other.
-
D.
hasNumberOfLetters
Indicates a relationship where an entity is associated with the count of letters it contains.
-
E.
hasAdditionalLetters
Indicates that one entity contains extra or more letters than another entity, beyond a specified base set or reference.
- 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_69a8864298748190a2f2fd34f7ef8d77 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb211eda88190865de7a0522a453d |
completed | March 7, 2026, 5:05 a.m. |
| PD | Predicate disambiguation | batch_69abafed2ab481908920334e77b1021b |
completed | March 7, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69abb1b180e481908bbe893d6ba6208b |
completed | March 7, 2026, 5:03 a.m. |
Created at: March 4, 2026, 7:35 p.m.