Triple
T8698183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Quechuan |
E206453
|
entity |
| Predicate | hasNotableLanguage |
P7390
|
FINISHED |
| Object | Kichwa |
E6374
|
NE 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: Kichwa | Statement: [Quechuan, hasNotableLanguage, Kichwa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kichwa Context triple: [Quechuan, hasNotableLanguage, Kichwa]
-
A.
Kichwa
chosen
Kichwa is a Quechuan indigenous language variety widely spoken by Andean communities in Ecuador and neighboring regions.
-
B.
Ikalanga
Ikalanga is a Bantu language spoken primarily by the Kalanga people in Botswana and southwestern Zimbabwe.
-
C.
Kwanyama
Kwanyama is a major standardized dialect of the Ovambo language spoken primarily in northern Namibia and southern Angola.
-
D.
Kwéyòl
Kwéyòl is a French-based Creole language spoken primarily in the Lesser Antilles, notably in Saint Lucia and Dominica.
-
E.
Kituba
Kituba is a widely spoken Bantu-based creole language of Central Africa, serving as a major lingua franca in the Republic of the Congo and surrounding regions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca83555b6c8190abe930dd397e863b |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc58af50408190a3b81100a759795e |
completed | March 31, 2026, 11:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf6edeef308190b6acfe324552b1cf |
completed | April 3, 2026, 7:40 a.m. |
Created at: March 30, 2026, 6:34 p.m.