Triple
T16664046
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Panamint |
E404933
|
entity |
| Predicate | neighboringLanguage |
P16383
|
FINISHED |
| Object | Kawaiisu |
—
|
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: Kawaiisu | Statement: [Panamint, neighboringLanguage, Kawaiisu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kawaiisu Context triple: [Panamint, neighboringLanguage, Kawaiisu]
-
A.
Kawaiisu
chosen
Kawaiisu is a Native American people and their Uto-Aztecan language traditionally spoken in the southern Sierra Nevada and Tehachapi Mountains of California.
-
B.
Yamanakako
Yamanakako is a village in Yamanashi Prefecture, Japan, known for Lake Yamanaka, one of the Fuji Five Lakes located near Mount Fuji.
-
C.
Shizu
Shizu is the posthumous temple name by which Emperor Guangwu, the restorer and founding ruler of the Eastern Han dynasty in China, is venerated in ancestral rites.
-
D.
Yamabiko
Yamabiko is a high-speed Shinkansen train service in Japan that operates on the Tōhoku Shinkansen line, connecting Tokyo with northern regions such as Sendai.
-
E.
Shimotsuki
Shimotsuki was a Japanese destroyer of the Imperial Japanese Navy that served in World War II before being sunk in late 1944.
- 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_69d8838b5fbc81908c6575c132b82e80 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e37c9afd048190b73bbc9c423915ca |
completed | April 18, 2026, 12:44 p.m. |
Created at: April 10, 2026, 5:18 a.m.