Triple
T14876062
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khairatabad |
E349869
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Ameerpet |
E353219
|
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: Ameerpet | Statement: [Khairatabad, near, Ameerpet]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ameerpet Context triple: [Khairatabad, near, Ameerpet]
-
A.
Ameerpet
chosen
Ameerpet is a major commercial and educational hub in Hyderabad, India, known for its dense concentration of training institutes, offices, and shopping centers.
-
B.
Nampally
Nampally is a central neighborhood in Hyderabad, India, known for its major railway station, commercial activity, and proximity to key administrative and cultural landmarks.
-
C.
Dilsukhnagar
Dilsukhnagar is a major commercial and residential hub in Hyderabad, India, known for its bustling markets, educational institutions, and busy transport connections.
-
D.
Hafeezpet
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.
-
E.
Nazimabad
Nazimabad is a prominent residential and commercial neighborhood in Karachi, Pakistan, known for its middle-class population and central location within the city.
- 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5e3e5d48190a132f2cf012b01e2 |
completed | April 15, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe6b52c12481908d0173a2a3ed854b |
completed | May 8, 2026, 11:01 p.m. |
Created at: April 10, 2026, 1:55 a.m.