Triple
T6565205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rumi script |
E153885
|
entity |
| Predicate | usesAdditionalLetter |
P10533
|
FINISHED |
| Object | letter É in some Malay orthographies |
—
|
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: letter É in some Malay orthographies | Statement: [Rumi script, usesAdditionalLetter, letter É in some Malay orthographies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesAdditionalLetter Context triple: [Rumi script, usesAdditionalLetter, letter É in some Malay orthographies]
-
A.
usesAdditionalLettersFrom
chosen
Indicates that one entity forms or derives its representation by incorporating extra letters taken from another entity beyond those originally present.
-
B.
hasAdditionalLetters
Indicates that one entity contains extra or more letters than another entity, beyond a specified base set or reference.
-
C.
usesAlphabet
Indicates that one entity employs or is written using the alphabet or writing system associated with another entity.
-
D.
hasLetter
Indicates that one entity contains, includes, or is associated with a specific letter or character.
-
E.
hasLetterBy
Indicates that an entity possesses or is associated with a letter authored or sent by another 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_69c6880cb35881909b763eb0125236b9 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6cc9c6cb0819084fec8e0beb430de |
completed | March 27, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69c6acf93cb48190b54f5dd6febd34dc |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:52 p.m.