Triple
T3004981
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shina |
E81878
|
entity |
| Predicate | hasDialect |
P4251
|
FINISHED |
| Object | Tangiri Shina |
E81878
|
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: Tangiri Shina | Statement: [Shina, hasDialect, Tangiri Shina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tangiri Shina Context triple: [Shina, hasDialect, Tangiri Shina]
-
A.
Shina
chosen
Shina is an Indo-Aryan language spoken primarily in the Gilgit-Baltistan region of Pakistan and surrounding Himalayan areas.
-
B.
Shingu
Shingu is a coastal city in Japan known for its historic Kumano Hongu Taisha shrine and its role as a gateway to the sacred Kumano Kodo pilgrimage routes.
-
C.
Shinmei
Shinmei is a divine title associated with Emperor Jimmu, the legendary first emperor of Japan revered as a descendant of the sun goddess Amaterasu.
-
D.
Teisheba
Teisheba is the Urartian storm and war god, often associated with thunder, rain, and military power in the ancient Near Eastern pantheon.
-
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 (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_69ad8b1c4de88190a83b7cefaa1f2842 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a15ad9c81908255003bdb38d603 |
completed | March 8, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1dea3a31481908db6871cf6c37781 |
completed | March 11, 2026, 9:29 p.m. |
Created at: March 8, 2026, 2:59 p.m.