Triple
T5625947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Puluwatese language |
E147717
|
entity |
| Predicate | hasDomainVocabulary |
P17147
|
FINISHED |
| Object | navigation |
—
|
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: navigation | Statement: [Puluwatese language, hasDomainVocabulary, navigation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDomainVocabulary Context triple: [Puluwatese language, hasDomainVocabulary, navigation]
-
A.
hasVocabularyFrom
Indicates that one entity’s vocabulary, terminology, or set of terms is derived from, based on, or taken from another entity.
-
B.
hasLinguisticDomain
chosen
Indicates that something (such as a term, expression, or resource) is associated with or applies within a particular linguistic domain or language context.
-
C.
hasDistinctVocabulary
Indicates that one entity’s vocabulary is different or distinguishable from that of another entity.
-
D.
hasKnownVocabulary
Indicates that an entity possesses a defined, identifiable set of terms or words that it can recognize or use.
-
E.
containsRegionalVocabulary
Indicates that the subject includes vocabulary items that are specific to a particular geographic region or dialect.
- 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_69c00906f2a88190a992c66b13d606d4 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c02235b4e48190a529f70605bf47ca |
completed | March 22, 2026, 5:09 p.m. |
| PD | Predicate disambiguation | batch_69c01b1d4b108190846ce586dc783acf |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:40 p.m.