Triple
T19815232
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NH 65 |
E476042
|
entity |
| Predicate | passesThroughCity |
P416
|
FINISHED |
| Object | Sangareddy |
—
|
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: Sangareddy | Statement: [NH 65, passesThroughCity, Sangareddy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sangareddy Context triple: [NH 65, passesThroughCity, Sangareddy]
-
A.
Sangareddy
chosen
Sangareddy is a town and district headquarters in the Indian state of Telangana, known for its proximity to Hyderabad and its mix of administrative, commercial, and semi-urban landscapes.
-
B.
Kamareddy
Kamareddy is a town in the Indian state of Telangana known as a regional commercial and transportation hub.
-
C.
Janardhan
Janardhan is an Indian given name commonly used for males, often associated with Hindu cultural and religious traditions.
-
D.
Satyavedu
Satyavedu is a town in the Tirupati district of Andhra Pradesh, India, known for its agricultural surroundings and proximity to the Tamil Nadu border.
-
E.
Sai Madhav Burra
Sai Madhav Burra is an Indian screenwriter and dialogue writer known for his work on major Telugu films, including the epic action drama "RRR."
- 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.