Triple
T882068
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oman |
E19046
|
entity |
| Predicate | usesScriptForArabic |
P7160
|
FINISHED |
| Object | Arabic script |
—
|
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: Arabic script | Statement: [Oman, usesScriptForArabic, Arabic script]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesScriptForArabic Context triple: [Oman, usesScriptForArabic, Arabic script]
-
A.
associatedLanguageScript
Indicates that there is a relationship between a language and the script or writing system used to represent it.
-
B.
hasNameInArabic
Indicates that an entity is associated with a specific name expressed in the Arabic language.
-
C.
dominantStyleForArabicTexts
Indicates the prevailing or primary stylistic form used when presenting or formatting Arabic texts.
-
D.
usesAlphabet
chosen
Indicates that one entity employs or is written using the alphabet or writing system associated with another entity.
-
E.
hasUnicodeScript
Indicates that a character or text element belongs to a specific Unicode script category (such as Latin, Cyrillic, or Han).
- 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_69a4939c32488190a7ccd41cf0abb22b |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4accc863c8190be9e5350732c30b1 |
completed | March 1, 2026, 9:17 p.m. |
| PD | Predicate disambiguation | batch_69a4aa8eca748190b58e0f08b30fba43 |
completed | March 1, 2026, 9:07 p.m. |
Created at: March 1, 2026, 7:39 p.m.