Triple
T19250208
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warangal district |
E481367
|
entity |
| Predicate | contains |
P35
|
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: [Warangal district, contains, Kazipet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kazipet Context triple: [Warangal district, contains, Kazipet]
-
A.
Kazipet
chosen
Kazipet is a major railway and educational hub in the Hanamkonda/Warangal urban area of Telangana, India.
-
B.
Yaseenabad
Yaseenabad is a residential neighborhood located within the Federal B Area of Karachi, Pakistan.
-
C.
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.
-
D.
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.
-
E.
Kammala
Kammala was a historical figure known primarily as one of the children of Zhenjin, the Crown Prince of the Yuan dynasty and son of Kublai Khan.
- 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_69d8e8cd9d1081908a181d02b88b59b8 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fb3001308190913e24343769be8d |
completed | April 20, 2026, 10:08 a.m. |
Created at: April 10, 2026, 1:27 p.m.