Triple
T8710224
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaddare script |
E206755
|
entity |
| Predicate | hasLettersFor |
P84674
|
FINISHED |
| Object | Somali long vowels |
—
|
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: Somali long vowels | Statement: [Kaddare script, hasLettersFor, Somali long vowels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLettersFor Context triple: [Kaddare script, hasLettersFor, Somali long vowels]
-
A.
hasLetter
Indicates that one entity contains, includes, or is associated with a specific letter or character.
-
B.
hasLetterBy
Indicates that an entity possesses or is associated with a letter authored or sent by another entity.
-
C.
hasNumberOfLetters
Indicates a relationship where an entity is associated with the count of letters it contains.
-
D.
hasAdditionalLetters
Indicates that one entity contains extra or more letters than another entity, beyond a specified base set or reference.
-
E.
hasLetterCount
Indicates that an entity is associated with a specific number representing how many letters it contains.
- 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_69ca835645e881908f00e3c8b51da81d |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5c3034708190b895eaf890d62198 |
completed | March 31, 2026, 11:43 p.m. |
| PD | Predicate disambiguation | batch_69cc456bda508190a9aa0fb92760739e |
completed | March 31, 2026, 10:06 p.m. |
| PDg | Predicate description generation | batch_69cc582412f48190ae819965bfb0e75d |
completed | March 31, 2026, 11:26 p.m. |
Created at: March 30, 2026, 6:35 p.m.