Triple
T37817356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bungi |
E942809
|
entity |
| Predicate | lexiconIncludes |
P42778
|
FINISHED |
| Object | loanwords from Cree |
—
|
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: loanwords from Cree | Statement: [Bungi, lexiconIncludes, loanwords from Cree]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lexiconIncludes Context triple: [Bungi, lexiconIncludes, loanwords from Cree]
-
A.
lexiconStatus
Indicates the current state or condition of a lexical item within a lexicon, such as whether it is active, deprecated, provisional, or otherwise classified.
-
B.
partOfLexicon
chosen
Indicates that a linguistic unit (such as a word or expression) belongs to or is included within a particular lexicon or vocabulary set.
-
C.
lexicalResources
Indicates that one entity provides or is associated with lexical materials (such as dictionaries, vocabularies, or word lists) that support or describe another entity.
-
D.
sharesLexiconWith
Indicates that two entities use or are associated with the same set of lexical items, vocabulary, or word inventory.
-
E.
spellingIncludes
Indicates that the spelling of one entity contains, as a substring or component, the spelling of another entity.
- 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_69f76ee987588190906506e759be5db3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbbae559a8819086ef839973f8d9b2 |
completed | May 6, 2026, 10:04 p.m. |
| PD | Predicate disambiguation | batch_69fbb1440fa08190abf25ba684f75b6e |
completed | May 6, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:19 p.m.