Triple
T11851360
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hoshangabad district |
E281914
|
entity |
| Predicate | legislativeAssemblyConstituenciesInclude |
P23217
|
FINISHED |
| Object | Sohagpur |
E478923
|
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: Sohagpur | Statement: [Hoshangabad district, legislativeAssemblyConstituenciesInclude, Sohagpur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sohagpur Context triple: [Hoshangabad district, legislativeAssemblyConstituenciesInclude, Sohagpur]
-
A.
Sohagpur
chosen
Sohagpur is a town in the Narmadapuram district of Madhya Pradesh, India, known as a local commercial center and access point to nearby forested and wildlife areas.
-
B.
Karanpur
Karanpur is a town located in the Ganganagar district of the northern Indian state of Rajasthan.
-
C.
Partapur
Partapur is a locality in Meerut district of Uttar Pradesh, India, known for its proximity to the Dr. Bhimrao Ambedkar Airstrip and its growing urban and institutional development.
-
D.
Bhopalgarh
Bhopalgarh is a town in the Indian state of Rajasthan, known for its rural setting and administrative role within the region.
-
E.
Chandanpura
Chandanpura is a locality in Chittagong, Bangladesh, known for its historic architecture and urban commercial activity.
- 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_69d6ab287ba48190a5178779fd19b9b7 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a65db52c8190a218736da17d0153 |
completed | April 10, 2026, 7:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f49cd0f8e0819081e4492e0002e48a |
completed | May 1, 2026, 12:30 p.m. |
Created at: April 8, 2026, 9:43 p.m.