Triple
T19966717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nagpur–Secunderabad line |
E479953
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Kazipet |
—
|
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: Kazipet | Statement: [Nagpur–Secunderabad line, passesThrough, Kazipet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kazipet Context triple: [Nagpur–Secunderabad line, passesThrough, Kazipet]
-
A.
Kazipet
chosen
Kazipet is a major railway and educational hub in the Hanamkonda/Warangal urban area of Telangana, India.
-
B.
Kodad
Kodad is a town in the Suryapet district of Telangana, India, known as a local commercial and transport hub in the region.
-
C.
Yaseenabad
Yaseenabad is a residential neighborhood located within the Federal B Area of Karachi, Pakistan.
-
D.
Shamirpet
Shamirpet is a suburban area and emerging residential and educational hub on the outskirts of Hyderabad, known for its lake, deer park, and proximity to major city infrastructure.
-
E.
Laksar
Laksar is a town in the Haridwar district of Uttarakhand, India, known primarily as a significant railway junction connecting various parts of northern India.
- 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_69d8e523c19881909f9197037200dde6 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65bc5e41881908c1e8867820f1c0c |
completed | April 20, 2026, 5 p.m. |
Created at: April 10, 2026, 1:54 p.m.