Triple
T14249461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miyapur |
E353218
|
entity |
| Predicate | nearbyLocality |
P4647
|
FINISHED |
| Object | Hafeezpet |
E1082302
|
NE FINISHED |
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: Hafeezpet | Statement: [Miyapur, nearbyLocality, Hafeezpet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hafeezpet Context triple: [Miyapur, nearbyLocality, Hafeezpet]
-
A.
Hafeezpet
chosen
Hafeezpet is a residential and commercial suburb in the western part of Hyderabad, Telangana, known for its proximity to major IT hubs and growing urban infrastructure.
-
B.
Nazimabad
Nazimabad is a prominent residential and commercial neighborhood in Karachi, Pakistan, known for its middle-class population and central location within the city.
-
C.
Kukatpally
Kukatpally is a major residential and commercial suburb in Hyderabad, India, known for its dense population, shopping areas, and proximity to the IT corridor.
-
D.
Khairatabad
Khairatabad is a prominent commercial and administrative locality in central Hyderabad, India, known for its major government offices, busy junction, and proximity to key city landmarks.
-
E.
Ameerpet
Ameerpet is a major commercial and educational hub in Hyderabad, India, known for its dense concentration of training institutes, offices, and shopping centers.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d8278c43e08190824146f4632b89a5 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6295ef9081909cfb0c1283bca21a |
completed | April 14, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3d1081148190b8830615a34711c0 |
completed | May 8, 2026, 1:32 a.m. |
Created at: April 10, 2026, 1:08 a.m.