Triple
T14028109
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jalaun district |
E337514
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Jalaun |
E1096225
|
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: Jalaun | Statement: [Jalaun district, hasSettlement, Jalaun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jalaun Context triple: [Jalaun district, hasSettlement, Jalaun]
-
A.
Jalaun
chosen
Jalaun is a town in the Indian state of Uttar Pradesh known for its administrative role within the surrounding Jalaun district.
-
B.
Jaunpur
Jaunpur is a historic city in the Indian state of Uttar Pradesh, known for its medieval architecture and cultural heritage.
-
C.
Chandauli
Chandauli is a town and administrative district headquarters in the eastern Indian state of Uttar Pradesh, known for its agricultural economy and proximity to Varanasi.
-
D.
Amroha
Amroha is a town and municipal board in Uttar Pradesh, India, known for its historical significance and cultural heritage.
-
E.
Azamgarh
Azamgarh is a city in the Purvanchal region of eastern Uttar Pradesh, India, known as an important cultural and educational center.
- 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_69d81c6543a48190bd5ba93d7419e797 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2fa830ac81908cb7df7c9e81e42a |
completed | April 14, 2026, 12:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd54fa19c081908e6467ee7b79f02a |
completed | May 8, 2026, 3:14 a.m. |
Created at: April 9, 2026, 10:20 p.m.