Triple
T20213446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Howrah–Sahibganj loop |
E493550
|
entity |
| Predicate | connectsCity |
P4245
|
FINISHED |
| Object | Sahibganj |
—
|
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: Sahibganj | Statement: [Howrah–Sahibganj loop, connectsCity, Sahibganj]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sahibganj Context triple: [Howrah–Sahibganj loop, connectsCity, Sahibganj]
-
A.
Sahibganj
chosen
Sahibganj is a town and district headquarters in Jharkhand, India, situated along the Ganges River and known as a regional rail and river transport hub.
-
B.
Maharajganj
Maharajganj is a city in the Purvanchal region of Uttar Pradesh, India, serving as an administrative and commercial center near the Indo-Nepal border.
-
C.
Sitarganj
Sitarganj is a town in the Udham Singh Nagar district of Uttarakhand, India, known for its agricultural surroundings and growing industrial development.
-
D.
Saharsa
Saharsa is a city in the northeastern Indian state of Bihar, known as a major agricultural and commercial center in the Kosi river region.
-
E.
Santalpur
Santalpur is a small town in the Patan district of Gujarat, India, known primarily as a local administrative and trading center for surrounding rural areas.
- 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.