Triple
T16188642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kemantney |
E392874
|
entity |
| Predicate | hasWordOrderVariation |
P108180
|
FINISHED |
| Object | VSO and SOV patterns reported |
—
|
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: VSO and SOV patterns reported | Statement: [Kemantney, hasWordOrderVariation, VSO and SOV patterns reported]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWordOrderVariation Context triple: [Kemantney, hasWordOrderVariation, VSO and SOV patterns reported]
-
A.
hasBasicWordOrder
Indicates the typical sequence in which core sentence elements (such as subject, verb, and object) are ordered in a language.
-
B.
hasV2WordOrder
Indicates that a clause or language follows verb-second (V2) word order, where the finite verb consistently appears in the second position of the sentence.
-
C.
alsoExhibitsWordOrder
chosen
Indicates that one linguistic element displays the same or an additional word order pattern as another element or construction.
-
D.
hasVariantSpelling
Indicates that one term is an alternative spelling form of another term.
-
E.
hasVerseOrder
Indicates that one verse is ordered or sequenced in relation to another verse within a structured text.
- 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_69d87f1e49ac8190a311b54d32990576 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e222d3a8e48190bdf29a633f4b0490 |
completed | April 17, 2026, 12:08 p.m. |
| PD | Predicate disambiguation | batch_69e219e11f6081909106b1240a17fd37 |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:02 a.m.