Triple
T20638037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ujjain district |
E507137
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Nagda |
—
|
NE NERFINISHED |
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: Nagda | Statement: [Ujjain district, containsTown, Nagda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nagda Context triple: [Ujjain district, containsTown, Nagda]
-
A.
Nagda
chosen
Nagda is an industrial town in the Indian state of Madhya Pradesh, known especially for its large textile and chemical manufacturing units.
-
B.
Nawalgarh
Nawalgarh is a historic town in Rajasthan, India, renowned for its richly painted havelis and cultural heritage within the Shekhawati region.
-
C.
Agarpara
Agarpara is a suburban locality in the northern part of Kolkata, West Bengal, known primarily as a residential and industrial area within the Kolkata metropolitan region.
-
D.
Panagarh
Panagarh is a town in West Bengal, India, known as a key railway and military hub in the Bardhaman district.
-
E.
Jwalapur
Jwalapur is a prominent suburban town and commercial hub near Haridwar in the Indian state of Uttarakhand.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4be702c8190a3d2410a881d310a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6ad1163008190aa9df36750a952d2 |
completed | April 20, 2026, 10:47 p.m. |
Created at: April 16, 2026, 11:42 a.m.