Triple
T3909108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruqʿah |
E87278
|
entity |
| Predicate | diacriticsUsage |
P2270
|
FINISHED |
| Object | often omitted in everyday writing |
—
|
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: often omitted in everyday writing | Statement: [Ruqʿah, diacriticsUsage, often omitted in everyday writing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: diacriticsUsage Context triple: [Ruqʿah, diacriticsUsage, often omitted in everyday writing]
-
A.
usesDiacritics
chosen
Indicates that the referenced text or linguistic element employs diacritical marks as part of its written form.
-
B.
diacriticType
Indicates the specific kind or category of diacritic mark associated with a character or symbol.
-
C.
usesColloquialCharacters
Indicates that an expression, name, or text is written using informal, non-standard, or colloquial characters rather than formal or standard script.
-
D.
usesPhoneticSystem
Indicates that one entity employs or is based on a particular phonetic system for representing or encoding sounds.
-
E.
hasAccent
Indicates that an entity speaks with or possesses a particular accent or distinctive pronunciation style.
- 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_69aed9424514819086e9c58adde6652d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1abe2dc81909c18aeae9b286898 |
completed | March 9, 2026, 4:13 p.m. |
| PD | Predicate disambiguation | batch_69aee75cff148190b6d5979d17fae085 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:22 p.m.