Triple
T19794142
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jabalpur–Nainpur–Gondia route |
E475494
|
entity |
| Predicate | hasJunction |
P1018
|
FINISHED |
| Object | Nainpur |
—
|
NE NERFINISHED |
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: Nainpur | Statement: [Jabalpur–Nainpur–Gondia route, hasJunction, Nainpur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nainpur Context triple: [Jabalpur–Nainpur–Gondia route, hasJunction, Nainpur]
-
A.
Nainpur
chosen
Nainpur is a town in Madhya Pradesh, India, historically known as an important railway junction in the region.
-
B.
Siddharthnagar
Siddharthnagar is a town and district headquarters in the Purvanchal region of eastern Uttar Pradesh, India, known for its proximity to important Buddhist heritage sites.
-
C.
Jaunpur
Jaunpur is a historic city in the Indian state of Uttar Pradesh, known for its medieval architecture and cultural heritage.
-
D.
Tekanpur
Tekanpur is a town in Madhya Pradesh, India, best known for hosting the Border Security Force’s main training academy.
-
E.
Jalaun
Jalaun is a town in the Indian state of Uttar Pradesh known for its administrative role within the surrounding Jalaun district.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51b014081908b263e167370529a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e653c5a7d48190b2a384f768d13750 |
completed | April 20, 2026, 4:26 p.m. |
Created at: April 10, 2026, 1:49 p.m.