Triple
T13367638
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abjad |
E318980
|
entity |
| Predicate | exampleScripts |
P109107
|
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: [Abjad, exampleScripts, Arabic script]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exampleScripts Context triple: [Abjad, exampleScripts, Arabic script]
-
A.
usedInScripts
chosen
Indicates that something (such as a tool, method, or resource) is employed or referenced within one or more scripts.
-
B.
codeExample
Indicates that one entity provides a snippet or sample of source code that illustrates how to use, implement, or demonstrate another entity.
-
C.
scriptCode
Indicates that an entity is associated with a particular writing system or script, identified by a standardized script code.
-
D.
scriptNameZh
Indicates the Chinese-language name or title of a script.
-
E.
script
Indicates that an entity is associated with a written text or code (such as a screenplay, program, or written instructions) that defines its content or behavior.
- 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_69d806b7bbac8190b85278c87fa7aff3 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dadcd652d48190a782fd1f57f34b6a |
completed | April 11, 2026, 11:44 p.m. |
| PD | Predicate disambiguation | batch_69d9a02c9abc8190b328e7bae747bfc5 |
completed | April 11, 2026, 1:13 a.m. |
Created at: April 9, 2026, 9:32 p.m.