Triple
T7029087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bagdogra Airport |
E163226
|
entity |
| Predicate | regionServed |
P82
|
FINISHED |
| Object | Sikkim |
E177807
|
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: Sikkim | Statement: [Bagdogra Airport, regionServed, Sikkim]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sikkim Context triple: [Bagdogra Airport, regionServed, Sikkim]
-
A.
Sikkim
chosen
Sikkim is a small, mountainous Indian state in the eastern Himalayas known for its dramatic landscapes, Buddhist monasteries, and proximity to Mount Kanchenjunga.
-
B.
Meghalaya
Meghalaya is a hilly state in northeastern India known for its heavy rainfall, lush forests, and diverse indigenous cultures.
-
C.
Himachal Pradesh
Himachal Pradesh is a mountainous state in northern India known for its Himalayan landscapes, hill stations, and tourism.
-
D.
Uttarakhand
Uttarakhand is a northern Indian state in the Himalayas known for its sacred rivers, pilgrimage sites, and mountainous landscapes.
-
E.
Arunachal Pradesh
Arunachal Pradesh is a northeastern Indian state known for its mountainous terrain, diverse indigenous cultures, and strategic location along the borders with China, Bhutan, and Myanmar.
- 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_69c8f2ffec108190ab60b0777d97dd89 |
completed | March 29, 2026, 9:38 a.m. |
Created at: March 27, 2026, 2:35 p.m.