Triple
T11121948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malegalalli Madumagalu |
E263037
|
entity |
| Predicate | setInRegion |
P285
|
FINISHED |
| Object | Malnad |
E682860
|
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: Malnad | Statement: [Malegalalli Madumagalu, setInRegion, Malnad]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malnad Context triple: [Malegalalli Madumagalu, setInRegion, Malnad]
-
A.
Malnad
chosen
Malnad is a hilly, forested region of the Western Ghats in Karnataka, India, known for its heavy rainfall, lush greenery, and rich biodiversity.
-
B.
Mundlana
Mundlana is a town located in the Sonipat district of the northern Indian state of Haryana.
-
C.
Malaun
Malaun is a historic hill town and former fortress area in Himachal Pradesh, India, known for its strategic role in early 19th-century Anglo-Gurkha conflicts.
-
D.
Madalag
Madalag is a rural municipality in the province of Aklan in the Philippines, known for its mountainous terrain and river landscapes.
-
E.
Harestua
Harestua is a village in Viken county, Norway, known for its residential community and proximity to the Harestua Solar Observatory.
- 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_69e42d8406988190837a16d7ad8048d1 |
completed | April 19, 2026, 1:19 a.m. |
Created at: April 8, 2026, 9:28 p.m.