Triple
T20213404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Howrah–New Jalpaiguri line |
E493549
|
entity |
| Predicate | majorJunctionOnRoute |
P64350
|
FINISHED |
| Object | Kishanganj |
—
|
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: Kishanganj | Statement: [Howrah–New Jalpaiguri line, majorJunctionOnRoute, Kishanganj]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kishanganj Context triple: [Howrah–New Jalpaiguri line, majorJunctionOnRoute, Kishanganj]
-
A.
Kishanganj
chosen
Kishanganj is a town and district headquarters in the northeastern part of the Indian state of Bihar, known for its significant Muslim population and proximity to the borders of West Bengal and Nepal.
-
B.
Dhanpur
Dhanpur is a town located in the Dahod district of the Indian state of Gujarat.
-
C.
Muzaffarpur
Muzaffarpur is a major city in northern India known for its litchi production and role as an important commercial and educational center in the region.
-
D.
Gorakhpur
Gorakhpur is a prominent city in northern India known as a regional commercial, transportation, and cultural hub near the border with Nepal.
-
E.
Kashipur
Kashipur is a town in the Udham Singh Nagar district of Uttarakhand, India, known as an important industrial and commercial center in the region.
- 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_69da6269614c8190bb40475d9d477358 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66ed6fe888190b553ba6879cb2d8d |
completed | April 20, 2026, 6:22 p.m. |
Created at: April 11, 2026, 11:38 p.m.