Triple
T35509483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Machigenga |
E1026244
|
entity |
| Predicate | hasBilingualEducation |
P71934
|
FINISHED |
| Object | Spanish–Machiguenga programs in some communities |
—
|
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: Spanish–Machiguenga programs in some communities | Statement: [Machigenga, hasBilingualEducation, Spanish–Machiguenga programs in some communities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBilingualEducation Context triple: [Machigenga, hasBilingualEducation, Spanish–Machiguenga programs in some communities]
-
A.
isBilingual
Indicates that an entity is able to communicate fluently in two distinct languages.
-
B.
usesBilingualInstruction
chosen
Indicates that an entity employs two languages as the medium of instruction within an educational or communicative context.
-
C.
isBilingualRegion
Indicates that a region officially uses two languages or has two predominant languages in regular use.
-
D.
bilingualismWith
Indicates a relationship where an entity possesses or is associated with the ability to use two specified languages.
-
E.
hasSecondaryLanguage
Indicates that an entity possesses or uses a secondary language in addition to its primary language.
- 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_69f76dfd61208190b93ec6dc439cab41 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7c777e924819081a6634f549fe552 |
completed | May 3, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69f7c475c58c8190a883554231e88c88 |
completed | May 3, 2026, 9:56 p.m. |
Created at: May 3, 2026, 4:04 p.m.