Triple
T22197906
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turkic scripts |
E548593
|
entity |
| Predicate | usedForLanguage |
P907
|
FINISHED |
| Object | Yakut |
—
|
NE NERFINISHED |
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: Yakut | Statement: [Turkic scripts, usedForLanguage, Yakut]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yakut Context triple: [Turkic scripts, usedForLanguage, Yakut]
-
A.
Yakut
chosen
Yakut is a Turkic language spoken primarily in the Sakha Republic (Yakutia) in northeastern Siberia, Russia.
-
B.
Yura
Yura is a common Slavic diminutive form of the male given name Yuri (or Yuriy), often used as a familiar or affectionate nickname.
-
C.
Daur
The Daur are a Mongolic ethnic group of northeastern China, traditionally living along the Heilongjiang (Amur) River and known for their distinct language, culture, and history as borderland farmers and hunters.
-
D.
Daur
Daur is a town in Pakistan’s Sindh province, known as a local commercial and agricultural center within the Nawabshah (Shaheed Benazirabad) District.
-
E.
Sibirskaya
Sibirskaya is a station on the Novosibirsk Metro system in Novosibirsk, Russia.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e3ecc7c8190b5f94cd8f42e9d37 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12ae8c74c819080c7f9e8383ecaa7 |
completed | April 28, 2026, 9:47 p.m. |
Created at: April 16, 2026, 8:36 p.m.