Triple
T23743738
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kedayan Malay |
E586755
|
entity |
| Predicate | sharesMorphosyntaxWith |
P10003
|
FINISHED |
| Object | Malayic languages |
—
|
NE NERFINISHED |
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: Malayic languages | Statement: [Kedayan Malay, sharesMorphosyntaxWith, Malayic languages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sharesMorphosyntaxWith Context triple: [Kedayan Malay, sharesMorphosyntaxWith, Malayic languages]
-
A.
sharesLinguisticFamilyWith
Indicates that two languages belong to the same linguistic family or branch within a language family.
-
B.
hasCommonLoanwordsFrom
Indicates that two languages share loanwords that originate from the same source language.
-
C.
linguisticallyRelatedTo
chosen
Indicates that two entities are connected through a linguistic relationship, such as sharing a common language, origin, structure, or other language-based association.
-
D.
hasDialectalFeaturesSharedWith
Indicates that two language varieties share specific dialectal features or characteristics in common.
-
E.
hasLanguageSimilarTo
Indicates that one entity uses or is associated with a language that is similar or closely related to the language used or associated with another entity.
- 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_69e24908efb08190bf755c3a9b91f222 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1bcbbcf988190b56d74af4b126bd8 |
completed | April 29, 2026, 8:09 a.m. |
| PD | Predicate disambiguation | batch_69f155f012808190a4b1cbc155558ade |
completed | April 29, 2026, 12:50 a.m. |
Created at: April 17, 2026, 7:12 p.m.