Triple
T15011361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NH19 |
E377843
|
entity |
| Predicate | connectsCity |
P4245
|
FINISHED |
| Object | Durgapur |
E165050
|
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: Durgapur | Statement: [NH19, connectsCity, Durgapur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Durgapur Context triple: [NH19, connectsCity, Durgapur]
-
A.
Durgapur
chosen
Durgapur is a major industrial city in eastern India known for its steel plants and planned urban infrastructure.
-
B.
Asansol
Asansol is a major industrial and coal-mining city in eastern India, known as one of the largest urban centers in the state of West Bengal.
-
C.
Kharagpur
Kharagpur is an industrial city in eastern India best known for hosting the first campus of the Indian Institute of Technology (IIT Kharagpur) and one of the country’s longest railway platforms.
-
D.
Raniganj
Raniganj is a coal-mining town in West Bengal, India, historically significant as one of the earliest centers of the Indian coal industry and now a key urban hub in the Durgapur–Asansol region.
-
E.
Chandanagar
Chandanagar is a residential and commercial suburb in the northwestern part of Hyderabad, India, known for its proximity to IT hubs and growing urban infrastructure.
- 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_69d85cd3a3c881908c71fc424d459c17 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded7613cec8190ac25e3f68c5d0edf |
completed | April 15, 2026, 12:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fea5ae816c8190a36abb46bbdaad7b |
completed | May 9, 2026, 3:10 a.m. |
Created at: April 10, 2026, 2:55 a.m.