Triple
T7589985
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kovai |
E179708
|
entity |
| Predicate | hasNearbyHillStation |
P19493
|
FINISHED |
| Object | Coonoor |
E183720
|
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: Coonoor | Statement: [Kovai, hasNearbyHillStation, Coonoor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Coonoor Context triple: [Kovai, hasNearbyHillStation, Coonoor]
-
A.
Coonoor
chosen
Coonoor is a scenic hill station in the Nilgiri Hills of Tamil Nadu, India, known for its tea plantations, cool climate, and colonial-era charm.
-
B.
Yercaud
Yercaud is a scenic hill station in Tamil Nadu’s Eastern Ghats, known for its cool climate, coffee plantations, and views over the surrounding plains.
-
C.
Ooty
Ooty is a popular hill station in the Nilgiri Hills of southern India, known for its cool climate, tea plantations, and scenic mountain landscapes.
-
D.
Munnar
Munnar is a popular hill station in the Western Ghats of southern India, renowned for its sprawling tea plantations, cool climate, and scenic mountain landscapes.
-
E.
Coorg
Coorg, also known as Kodagu, is a scenic hill district in Karnataka, India, famed for its coffee plantations, lush forests, and mist-covered landscapes.
- 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_69c69f335248819093c1006f30513708 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9b615f481908b2fe7e8aaed81bc |
completed | March 27, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b4ed90808190852ca09c06a3dc3d |
completed | March 29, 2026, 5:13 a.m. |
Created at: March 27, 2026, 3:53 p.m.