Triple
T19815242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NH 65 |
E476042
|
entity |
| Predicate | passesThroughCity |
P416
|
FINISHED |
| Object | Nalgonda |
—
|
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: Nalgonda | Statement: [NH 65, passesThroughCity, Nalgonda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nalgonda Context triple: [NH 65, passesThroughCity, Nalgonda]
-
A.
Nalgonda
chosen
Nalgonda is a town and district headquarters in the Indian state of Telangana, known for its proximity to major irrigation projects and its historical significance in the region.
-
B.
Nandyal
Nandyal is a city in the Indian state of Andhra Pradesh, known as a commercial and administrative center in the Rayalaseema region.
-
C.
Jangaon
Jangaon is a town and municipal center in the Indian state of Telangana, known for its location along key road and rail routes between Hyderabad and Warangal.
-
D.
Medak
Medak is a town in the Indian state of Telangana known for its historic fort and prominent churches, serving as an important local administrative and cultural center.
-
E.
Khammam
Khammam is a city in the Indian state of Telangana known for its historical forts, temples, and proximity to the Munneru River.
- 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_69d8e51bc4208190a1c57d8c5d1b15e4 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e654f861248190a633dd8d227d9697 |
completed | April 20, 2026, 4:31 p.m. |
Created at: April 10, 2026, 1:50 p.m.