Triple
T21545859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malwa region of Madhya Pradesh |
E531620
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Narsinghgarh |
—
|
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: Narsinghgarh | Statement: [Malwa region of Madhya Pradesh, hasMajorCity, Narsinghgarh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Narsinghgarh Context triple: [Malwa region of Madhya Pradesh, hasMajorCity, Narsinghgarh]
-
A.
Narsinghgarh
chosen
Narsinghgarh is a historic town in central India that once served as the administrative and cultural center of the former princely Narsinghgarh State.
-
B.
Ramgarh
Ramgarh is a town and administrative district headquarters in the Indian state of Jharkhand, known for its coal mining and industrial activities.
-
C.
Ramgarh
Ramgarh is a town in the Alwar district of Rajasthan, India, known for its historic forts, temples, and traditional Rajasthani culture.
-
D.
Jaisinghpur
Jaisinghpur is a town located in the Sultanpur district of the Indian state of Uttar Pradesh.
-
E.
Laxmangarh
Laxmangarh is a town in the Sikar district of Rajasthan, India, known for its historic fort, havelis, and traditional Rajasthani architecture.
- 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_69e0c45f17148190949c330ab9c27706 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eeb58ef2548190a81ff51baba76e48 |
completed | April 27, 2026, 1:02 a.m. |
Created at: April 16, 2026, 6:28 p.m.