Triple
T38612306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Provincial Municipality of La Convención |
E934512
|
entity |
| Predicate | mayIncludeLanguagesUsed |
P2177
|
FINISHED |
| Object | 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: Quechua | Statement: [Provincial Municipality of La Convención, mayIncludeLanguagesUsed, Quechua]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mayIncludeLanguagesUsed Context triple: [Provincial Municipality of La Convención, mayIncludeLanguagesUsed, Quechua]
-
A.
mayUseLanguagesOf
Indicates that an entity is permitted to use the languages associated with another entity.
-
B.
includesLanguage
chosen
Indicates that one entity contains, supports, or makes use of a specified language as part of its content, functionality, or representation.
-
C.
usesWorkingLanguagesOf
Indicates that one entity employs or operates using the working languages associated with another entity.
-
D.
usesLanguageFor
Indicates that an entity employs a particular language as a tool or medium to perform some activity, function, or purpose.
-
E.
hasLanguages
Indicates that an entity is associated with one or more languages it uses, supports, or is expressed in.
- 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_69f76eccd6d081909ccce171011739a1 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcdb0de8c08190928cd1323f80ab5c |
completed | May 7, 2026, 6:33 p.m. |
| PD | Predicate disambiguation | batch_69fcd9017dd88190b32a73fe78909740 |
completed | May 7, 2026, 6:25 p.m. |
Created at: May 3, 2026, 4:32 p.m.