Triple
T2288306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maltese |
E51443
|
entity |
| Predicate | hasLoanwordStratum |
P11431
|
FINISHED |
| Object | Romance vocabulary for culture and administration |
—
|
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: Romance vocabulary for culture and administration | Statement: [Maltese, hasLoanwordStratum, Romance vocabulary for culture and administration]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLoanwordStratum Context triple: [Maltese, hasLoanwordStratum, Romance vocabulary for culture and administration]
-
A.
hasCommonLoanwordsFrom
Indicates that two languages share loanwords that originate from the same source language.
-
B.
hasLinguisticHeritage
Indicates that one entity possesses or is associated with the linguistic background, tradition, or ancestry of another entity.
-
C.
loanwordsFrom
chosen
Indicates that one language has borrowed words from another language.
-
D.
areAgglutinativeLanguages
Indicates that the related languages primarily form words by stringing together distinct morphemes, each carrying a specific grammatical meaning, in a clear and segmentable way.
-
E.
hasGlottologName
Indicates that an entity is associated with a specific name as recorded in the Glottolog linguistic database.
- 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_69a88b09c644819090b503456d96bf70 |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abc2497ce881909b05eb9cec67d9e7 |
completed | March 7, 2026, 6:14 a.m. |
| PD | Predicate disambiguation | batch_69abbdbb9e4c819085fc588626ec7c09 |
completed | March 7, 2026, 5:55 a.m. |
Created at: March 4, 2026, 7:48 p.m.