Triple
T19209205
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aymaran languages |
E480312
|
entity |
| Predicate | contactPhenomena |
P134983
|
FINISHED |
| Object | lexical borrowing with Quechua |
—
|
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: lexical borrowing with Quechua | Statement: [Aymaran languages, contactPhenomena, lexical borrowing with Quechua]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: contactPhenomena Context triple: [Aymaran languages, contactPhenomena, lexical borrowing with Quechua]
-
A.
contactWith
Indicates that two entities are in direct or indirect physical or communicative interaction or touch with each other.
-
B.
notableExplorerContact
Indicates that an entity has had a significant interaction or encounter with a well-known explorer.
-
C.
earlyContact
Indicates that one entity initiates contact with another earlier than a typical, expected, or reference time.
-
D.
contactLanguageWith
Indicates that two entities communicate with each other using a particular language as the medium of contact.
-
E.
examplePhenomenon
Indicates a representative or illustrative occurrence used to demonstrate or clarify a broader phenomenon or pattern.
- F. None of above. chosen
Provenance (4 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_69d8e8cb8c348190b52075823911c869 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5f9a000188190afb762ea24bc3deb |
completed | April 20, 2026, 10:02 a.m. |
| PD | Predicate disambiguation | batch_69e4dcf22b3c8190bee02e3af946e114 |
completed | April 19, 2026, 1:47 p.m. |
| PDg | Predicate description generation | batch_69e4ddcf50108190a09d0f1291c17374 |
completed | April 19, 2026, 1:51 p.m. |
Created at: April 10, 2026, 1:20 p.m.