Triple
T2122186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kamviri language |
E43948
|
entity |
| Predicate | alternativeName |
P39
|
FINISHED |
| Object | Kâmviri |
E228137
|
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: Kâmviri | Statement: [Kamviri language, alternativeName, Kâmviri]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kâmviri Context triple: [Kamviri language, alternativeName, Kâmviri]
-
A.
Ziza
Ziza is a lesser-known biblical figure mentioned in the Hebrew Bible as one of the descendants in the royal line of King Rehoboam of Judah.
-
B.
Senaki
Senaki is a town in western Georgia that serves as an important local administrative and transportation center in the Samegrelo region.
-
C.
Negombo
Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
-
D.
Sanglechi
chosen
Sanglechi is a lesser-known Eastern Iranian language spoken in parts of northeastern Afghanistan and adjacent regions.
-
E.
Kumba
Kumba is a renowned steel roller coaster at Busch Gardens Tampa Bay, famous for its intense inversions and smooth, high-speed layout.
- 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_69a88717cfe48190b7ecdd68c824848a |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abbb51e8088190a1aeafee4e8dff63 |
completed | March 7, 2026, 5:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae58cd22c8819096dfd06d16703bf8 |
completed | March 9, 2026, 5:21 a.m. |
Created at: March 4, 2026, 7:44 p.m.