Triple
T7029111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bagdogra Airport |
E163226
|
entity |
| Predicate | nearbyTouristDestination |
P28492
|
FINISHED |
| Object | Kalimpong |
E171432
|
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: Kalimpong | Statement: [Bagdogra Airport, nearbyTouristDestination, Kalimpong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kalimpong Context triple: [Bagdogra Airport, nearbyTouristDestination, Kalimpong]
-
A.
Kalimpong
chosen
Kalimpong is a hill station and town in the eastern Himalayas of India, known for its scenic views, Buddhist monasteries, and colonial-era architecture.
-
B.
Rairangpur
Rairangpur is a town and legislative assembly constituency in the Mayurbhanj district of Odisha, India, known as the political base of President Droupadi Murmu.
-
C.
Jalpaiguri
Jalpaiguri is a town in northeastern India known as an important administrative and commercial center near the Himalayan foothills.
-
D.
Dinapur
Dinapur is a town in the Patna district of Bihar, India, known historically as a British cantonment and now as a suburban area of Patna.
-
E.
Agargaon
Agargaon is a prominent residential and administrative neighborhood in Dhaka, Bangladesh, known for housing several government offices and institutions.
- 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_69c6885d691c81908cf7d31083113886 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6e200ecdc819098ca07473dfb272a |
completed | March 27, 2026, 8:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7943ca8548190877d2698265ce7da |
completed | March 28, 2026, 8:41 a.m. |
Created at: March 27, 2026, 2:35 p.m.