Triple
T7257385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kambera language |
E157754
|
entity |
| Predicate | hasSVOAlternativeOrder |
P75598
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Kambera language, hasSVOAlternativeOrder, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSVOAlternativeOrder Context triple: [Kambera language, hasSVOAlternativeOrder, yes]
-
A.
hasSVOOrder
Indicates that a language or construction follows a basic word order where the subject comes first, followed by the verb, and then the object.
-
B.
hasAlternativeNameOfOrder
Indicates that one entity is an alternative or variant name used to refer to the same order as the other entity.
-
C.
SOVOrderPossible
Indicates that a subject–object–verb (SOV) word order is grammatically possible in the language or construction being described.
-
D.
hasOrder
Indicates that one entity possesses, is associated with, or is characterized by a specific order, sequence, or arrangement relative to others.
-
E.
hasAlternativeReferent
Indicates that an entity can also be referred to or identified by an alternative name, label, or reference.
- 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_69c6882d81d4819085f7ff862951ee4f |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6eaa3d88081908f59ca5a85790290 |
completed | March 27, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69c6e7666ffc81908bf643d8257e6337 |
completed | March 27, 2026, 8:24 p.m. |
| PDg | Predicate description generation | batch_69c6e889854481908c765ce2107f2d3a |
completed | March 27, 2026, 8:28 p.m. |
Created at: March 27, 2026, 2:57 p.m.