Triple
T31509009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Danu Self-Administered Zone |
E803892
|
entity |
| Predicate | hasLegalLanguage |
P102087
|
FINISHED |
| Object | Burmese |
—
|
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: Burmese | Statement: [Danu Self-Administered Zone, hasLegalLanguage, Burmese]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLegalLanguage Context triple: [Danu Self-Administered Zone, hasLegalLanguage, Burmese]
-
A.
hasLanguageLegislation
chosen
Indicates that there exists formal legislation or legal provisions governing the use, status, or regulation of language in relation to the subject entity.
-
B.
hasLanguageInCountry
Indicates that a particular language is used or recognized within a specified country.
-
C.
eligibleLanguage
Indicates that a particular language satisfies the required conditions to be considered valid or allowed in a given context.
-
D.
hasLanguageType
Indicates that an entity is associated with a particular type or category of language (e.g., spoken, written, programming, sign).
-
E.
hasLanguageStatus
Indicates that an entity has a particular status or condition regarding its language use, recognition, or classification.
- 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_69f348ceb0a48190ae7feca263b6296c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69fd37b695c88190855801626f91c4cd |
completed | May 8, 2026, 1:09 a.m. |
| PD | Predicate disambiguation | batch_69fd374cccf08190a230e87164af5938 |
completed | May 8, 2026, 1:07 a.m. |
Created at: April 30, 2026, 9:49 p.m.