Triple
T22569713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Geba script used as syllabary |
E558045
|
entity |
| Predicate | linguisticLevelEncoded |
P5302
|
FINISHED |
| Object | syllabic |
—
|
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: syllabic | Statement: [Geba script used as syllabary, linguisticLevelEncoded, syllabic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: linguisticLevelEncoded Context triple: [Geba script used as syllabary, linguisticLevelEncoded, syllabic]
-
A.
languageLevel
Indicates the proficiency or complexity level of a language associated with an entity.
-
B.
hasLinguisticStratum
Indicates a relationship where one element is associated with, or belongs to, a particular layer or level within a linguistic structure or system.
-
C.
linguisticType
chosen
Indicates the type or category of language or linguistic system associated with an entity (e.g., spoken, signed, written, or other linguistic modality).
-
D.
linguisticClassification
Indicates the relationship by which an entity is categorized according to its language or linguistic type.
-
E.
hasLinguisticCode
Indicates that an entity is associated with a specific linguistic identifier or code (such as a language or script code) that characterizes its linguistic properties.
- 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_69e11e5ae4ac8190b1f503457603d969 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15fad35448190b51a3dd639ca8568 |
completed | April 29, 2026, 1:32 a.m. |
| PD | Predicate disambiguation | batch_69ee626e6bb08190ada4dd8b48cc0c43 |
completed | April 26, 2026, 7:07 p.m. |
Created at: April 16, 2026, 8:52 p.m.