Triple
T25422268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maliku Dhivehi |
E637014
|
entity |
| Predicate | hasGrammaticalSimilarityWith |
P8039
|
FINISHED |
| Object | Maldivian Dhivehi |
—
|
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: Maldivian Dhivehi | Statement: [Maliku Dhivehi, hasGrammaticalSimilarityWith, Maldivian Dhivehi]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGrammaticalSimilarityWith Context triple: [Maliku Dhivehi, hasGrammaticalSimilarityWith, Maldivian Dhivehi]
-
A.
hasGrammaticalSimilarityTo
chosen
Indicates that two linguistic elements share similar grammatical structure, form, or function.
-
B.
hasLexicalSimilarityWith
Indicates that two linguistic items share a significant degree of similarity in form, structure, or wording.
-
C.
hasPhonologicalSimilarityTo
Indicates that two linguistic elements share similar sound patterns or phonological features.
-
D.
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.
-
E.
hasSimilarityTo
Indicates that one entity shares common characteristics, features, or qualities with another entity to a notable degree.
- 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_69e75db4135881909acc287ebcb7a505 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f606c79ad081908369605f72e65ca6 |
completed | May 2, 2026, 2:14 p.m. |
| PD | Predicate disambiguation | batch_69f602ce79ec8190b8336c2b9de18ac7 |
completed | May 2, 2026, 1:57 p.m. |
Created at: April 21, 2026, 1:56 p.m.