Triple
T395827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malayalam |
E8980
|
entity |
| Predicate | spokenIn |
P2266
|
FINISHED |
| Object | Kerala |
E34853
|
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: Kerala | Statement: [Malayalam, spokenIn, Kerala]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kerala Context triple: [Malayalam, spokenIn, Kerala]
-
A.
Kerala
chosen
Kerala is a coastal state in southwestern India known for its backwaters, high literacy rate, distinctive Malayalam culture, and strong traditions in art, Ayurveda, and religious diversity.
-
B.
Tamil Nadu
Tamil Nadu is a state in southern India known for its rich Dravidian cultural heritage, classical arts, and major urban centers like Chennai.
-
C.
Karnataka
Karnataka is a state in southwestern India known for its diverse languages and cultures, major tech hub Bengaluru, and rich historical and architectural heritage.
-
D.
Gujarat
Gujarat is a western coastal state of India known for its significant role in trade and industry, rich cultural heritage, and historic cities such as Ahmedabad.
-
E.
Goa
Goa is a coastal state on India’s western shore known for its beaches, distinctive blend of Indian and Portuguese heritage, and vibrant tourism industry.
- 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_69a2e7f55c60819097aff65ea2ca2832 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec792dd081909b6b18a854139f8c |
completed | Feb. 28, 2026, 1:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5554286788190b7ae0c81d2e352e8 |
completed | March 2, 2026, 9:15 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.