Triple
T7029086
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bagdogra Airport |
E163226
|
entity |
| Predicate | regionServed |
P82
|
FINISHED |
| Object | Darjeeling |
E162673
|
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: Darjeeling | Statement: [Bagdogra Airport, regionServed, Darjeeling]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Darjeeling Context triple: [Bagdogra Airport, regionServed, Darjeeling]
-
A.
Darjeeling
chosen
Darjeeling is a famous hill station in the Indian Himalayas renowned for its tea plantations, scenic mountain views, and colonial-era charm.
-
B.
Ranikhet
Ranikhet is a hill station and cantonment town in the Kumaon region of Uttarakhand, India, known for its scenic Himalayan views and pleasant climate.
-
C.
Jalpaiguri
Jalpaiguri is a town in northeastern India known as an important administrative and commercial center near the Himalayan foothills.
-
D.
Nainital
Nainital is a popular hill station and lake town in northern India, known for its scenic beauty and colonial-era charm.
-
E.
Mussoorie
Mussoorie is a popular hill station in the Indian state of Uttarakhand, known for its scenic Himalayan views, colonial-era architecture, and role as a major educational and administrative training hub.
- 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.