Triple
T24139918
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ralik Marshallese |
E598198
|
entity |
| Predicate | languageCodeMacro |
P154992
|
FINISHED |
| Object | mh |
—
|
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: mh | Statement: [Ralik Marshallese, languageCodeMacro, mh]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageCodeMacro Context triple: [Ralik Marshallese, languageCodeMacro, mh]
-
A.
languageOfCodes
Indicates that a particular language is used for or associated with a given set of codes.
-
B.
languageCodeISO639-1
Indicates that the subject entity is associated with the specified two-letter ISO 639-1 language code.
-
C.
languageCodeStandard
Indicates that a language code conforms to a specific standardized coding scheme (such as ISO language code standards).
-
D.
languageCodeISO639-2
Indicates that an entity is associated with a language identified by its ISO 639-2 three-letter code.
-
E.
languageCodeRelation
Indicates a relationship where one entity is associated with, identified by, or mapped to a specific language code of another entity.
- 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_69e288c92e448190ac57034fa0c863ce |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1e005f7f48190b2c538bfc79a83b2 |
completed | April 29, 2026, 10:40 a.m. |
| PD | Predicate disambiguation | batch_69f1765650fc8190a6bc1eb512b240bf |
completed | April 29, 2026, 3:09 a.m. |
| PDg | Predicate description generation | batch_69f17c28b684819084eea522126463f8 |
completed | April 29, 2026, 3:34 a.m. |
Created at: April 17, 2026, 11:28 p.m.