Triple
T10736685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kundapura taluk |
E253212
|
entity |
| Predicate | hasLocalDialect |
P1762
|
FINISHED |
| Object | Kundagannada |
E51652
|
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: Kundagannada | Statement: [Kundapura taluk, hasLocalDialect, Kundagannada]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kundagannada Context triple: [Kundapura taluk, hasLocalDialect, Kundagannada]
-
A.
Kundagannada
chosen
Kundagannada is a regional dialect of the Kannada language spoken primarily in the coastal districts of Karnataka, India.
-
B.
Kadaru
Kadaru is a Nubian language spoken by the Kadaru people in parts of Sudan.
-
C.
Gangathura
Gangathura is the given first name of Dr. G. M. Naicker, a notable South African anti-apartheid activist and medical doctor.
-
D.
Kotagiri
Kotagiri is a hill station town in the Nilgiri district of Tamil Nadu, India, known for its cool climate, tea plantations, and scenic mountain landscapes.
-
E.
Manikonda
Manikonda is a rapidly developing residential and IT suburb in the western part of Hyderabad, known for its proximity to major technology hubs and corporate campuses.
- 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_69d6aa5e51e8819095f06881cecf152e |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d71022aee0819091a5790d3dee6777 |
completed | April 9, 2026, 2:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dff78257b88190942308719d9fa6a6 |
completed | April 15, 2026, 8:39 p.m. |
Created at: April 8, 2026, 9:14 p.m.