Triple
T7124417
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kamakhya Temple |
E166023
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Dispur |
E644413
|
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: Dispur | Statement: [Kamakhya Temple, nearCity, Dispur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dispur Context triple: [Kamakhya Temple, nearCity, Dispur]
-
A.
Dispur
chosen
Dispur is a locality in Guwahati that serves as the administrative and political center of the Indian state of Assam.
-
B.
Chandannagar
Chandannagar is a former French colonial town in West Bengal, India, known for its historic riverside architecture and cultural blend of French and Bengali influences.
-
C.
Konnagar
Konnagar is a suburban town in West Bengal, India, situated along the Hooghly River and known as part of the Kolkata metropolitan area.
-
D.
Kolkata Cantonment
Kolkata Cantonment is a major military cantonment area in Kolkata, India, housing key army installations and administrative facilities.
-
E.
Durgapur
Durgapur is a major industrial city in eastern India known for its steel plants and planned 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_69c6888350588190870cd552b427a1cd |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e64c0f688190a9b7482d86c2f033 |
completed | March 27, 2026, 8:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7eec29d8c81909d9123b48b195f98 |
completed | March 28, 2026, 3:07 p.m. |
Created at: March 27, 2026, 2:44 p.m.