Triple
T6295970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bidayuh people |
E141131
|
entity |
| Predicate | languageSituation |
P16608
|
FINISHED |
| Object | highly diverse dialects |
—
|
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: highly diverse dialects | Statement: [Bidayuh people, languageSituation, highly diverse dialects]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageSituation Context triple: [Bidayuh people, languageSituation, highly diverse dialects]
-
A.
ethnicLanguageStatus
Indicates the status or role of a language in relation to a particular ethnic group (e.g., primary, secondary, heritage, or minority language).
-
B.
languageUse
Indicates the language or languages an entity uses for communication, expression, or interaction.
-
C.
languageDiversity
chosen
Indicates the degree to which multiple distinct languages are present and used within a given context or population.
-
D.
isWidelySpokenIn
Indicates that a language is spoken by a large portion of the population across many regions or communities within a specified area.
-
E.
sociolinguisticSituation
Indicates the social and cultural context in which language is used, including factors like participants, setting, norms, and power relations that shape linguistic behavior.
- 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_69c008cdf2ac8190bb640c94478fb4ed |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0643ac2b48190b2db036ce709e7ea |
completed | March 22, 2026, 9:50 p.m. |
| PD | Predicate disambiguation | batch_69c060df0d8881908215575862ef6831 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:27 p.m.