Triple
T13712407
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Babatpur |
E328805
|
entity |
| Predicate | roadConnection |
P385
|
FINISHED |
| Object | Jaunpur |
E352178
|
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: Jaunpur | Statement: [Babatpur, roadConnection, Jaunpur]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jaunpur Context triple: [Babatpur, roadConnection, Jaunpur]
-
A.
Jaunpur
chosen
Jaunpur is a historic city in the Indian state of Uttar Pradesh, known for its medieval architecture and cultural heritage.
-
B.
Shahjahanpur
Shahjahanpur is a prominent city in the Rohilkhand region of Uttar Pradesh, India, known for its historical significance and regional commercial importance.
-
C.
Amroha
Amroha is a town and municipal board in Uttar Pradesh, India, known for its historical significance and cultural heritage.
-
D.
Unnao
Unnao is a city in northern India known for its historical significance and its location between the major urban centers of Lucknow and Kanpur in Uttar Pradesh.
-
E.
Ghazipur
Ghazipur is a city in the Indian state of Uttar Pradesh, known for its historical significance and as a regional hub in eastern Uttar Pradesh.
- 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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd4395e8c0819098719c8cd344aa33 |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c6fcddf08190bee3dce22be39a34 |
completed | May 3, 2026, 10:06 p.m. |
Created at: April 9, 2026, 9:54 p.m.