Triple
T11122103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kannada literature |
E263040
|
entity |
| Predicate | hasNotableAuthor |
P4244
|
FINISHED |
| Object | Ranna |
E53438
|
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: Ranna | Statement: [Kannada literature, hasNotableAuthor, Ranna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ranna Context triple: [Kannada literature, hasNotableAuthor, Ranna]
-
A.
Ranna
chosen
Ranna was a prominent 10th-century Kannada poet, celebrated as one of the “three gems” of early Kannada literature for his influential epic and courtly works.
-
B.
Maritta
Maritta is a feminine given name, typically considered a variant of names like Marita or Maria used in various European cultures.
-
C.
Noor
Noor is the American-born widow of King Hussein who served as Queen consort of Jordan and became known for her humanitarian and peace-building work.
-
D.
Noor
Noor is a science fiction novel by Nnedi Okorafor that blends Africanfuturism with themes of identity, technology, and survival in a near-future Nigeria.
-
E.
Suawa
Suawa is an Austronesian language spoken by the Suwawa people of North Sulawesi, Indonesia.
- 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_69d6aa9b46cc8190b19f9f0cc45bf322 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79afa0ab88190ab6d61df8cf485ec |
completed | April 9, 2026, 12:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e441d376f8819080effd5bf29c6bc1 |
completed | April 19, 2026, 2:45 a.m. |
Created at: April 8, 2026, 9:28 p.m.