Triple
T212318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Perso-Arabic script |
E4744
|
entity |
| Predicate | hasContextualLetterForms |
P9189
|
FINISHED |
| Object | initial |
—
|
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: initial | Statement: [Perso-Arabic script, hasContextualLetterForms, initial]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasContextualLetterForms Context triple: [Perso-Arabic script, hasContextualLetterForms, initial]
-
A.
usesDiacritics
Indicates that the referenced text or linguistic element employs diacritical marks as part of its written form.
-
B.
alternativeTransliteration
Indicates that one written form represents an alternative way of transliterating the same original text or name into another script or orthography.
-
C.
hasLetterBy
Indicates that an entity possesses or is associated with a letter authored or sent by another entity.
-
D.
hasStandardOrthographySince
Indicates that a language or writing system has used a particular standardized orthography starting from a specified point in time.
-
E.
usesAlphabet
Indicates that one entity employs or is written using the alphabet or writing system associated with another entity.
- 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_69a2575cb1dc8190a01ad332426dc339 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25d35aa288190966b6e15af1525cb |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b4f71b88190866c8262922ae204 |
completed | Feb. 28, 2026, 3:04 a.m. |
| PDg | Predicate description generation | batch_69a25d3463648190ac716d7475378536 |
completed | Feb. 28, 2026, 3:12 a.m. |
Created at: Feb. 28, 2026, 2:52 a.m.