Triple
T22150581
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abzhua area |
E547400
|
entity |
| Predicate | hasDialectBasis |
P40502
|
FINISHED |
| Object | Abzhywa dialect of Abkhaz language |
—
|
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: Abzhywa dialect of Abkhaz language | Statement: [Abzhua area, hasDialectBasis, Abzhywa dialect of Abkhaz language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDialectBasis Context triple: [Abzhua area, hasDialectBasis, Abzhywa dialect of Abkhaz language]
-
A.
isBasedOnDialect
chosen
Indicates that something (such as a language variety, system, or representation) is derived from, structured around, or primarily influenced by a particular dialect.
-
B.
hasDialectCounterpart
Indicates that one linguistic form or expression has a corresponding equivalent in another dialect.
-
C.
hasDialects
Indicates that an entity (typically a language) possesses one or more distinct dialectal varieties.
-
D.
hasDialectsIn
Indicates that a language or linguistic variety possesses distinct dialects that are used or found within a specified region or context.
-
E.
haveDialect
Indicates that an entity uses, speaks, or is associated with a particular dialect or regional linguistic variety.
- 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_69e11e3b52088190ad5df386d01eb2fb |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129f37dac8190a7cecb12f4271515 |
completed | April 28, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69e71b384e008190b723c9a0f1089d66 |
completed | April 21, 2026, 6:37 a.m. |
Created at: April 16, 2026, 8:33 p.m.