Triple
T10925080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warangal |
E258043
|
entity |
| Predicate | nearbyCity |
P350
|
FINISHED |
| Object |
Hanamkonda
Hanamkonda is a major urban area and historical locality in the Indian state of Telangana, forming part of the tri-city region along with Warangal and Kazipet.
|
E897715
|
NE FINISHED |
How this triple was built (4 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: Hanamkonda | Statement: [Warangal, nearbyCity, Hanamkonda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanamkonda Context triple: [Warangal, nearbyCity, Hanamkonda]
-
A.
Bhagyanagaram
Bhagyanagaram is an alternative name historically used for the Indian city of Hyderabad, particularly in Telugu contexts.
-
B.
Kondapur
Kondapur is a rapidly developing residential and commercial suburb in Hyderabad, India, known for its proximity to major IT hubs and tech parks.
-
C.
Tadipatri
Tadipatri is a town in the Anantapur district of Andhra Pradesh, India, known for its granite industries and historic temples.
-
D.
Nandyal
Nandyal is a city in the Indian state of Andhra Pradesh, known as a commercial and administrative center in the Rayalaseema region.
-
E.
Ravulapalem
Ravulapalem is a town in the Indian state of Andhra Pradesh, known for its agricultural markets and location along key transport routes in the Godavari region.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Hanamkonda Triple: [Warangal, nearbyCity, Hanamkonda]
Generated description
Hanamkonda is a major urban area and historical locality in the Indian state of Telangana, forming part of the tri-city region along with Warangal and Kazipet.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hanamkonda Target entity description: Hanamkonda is a major urban area and historical locality in the Indian state of Telangana, forming part of the tri-city region along with Warangal and Kazipet.
-
A.
Bhagyanagaram
Bhagyanagaram is an alternative name historically used for the Indian city of Hyderabad, particularly in Telugu contexts.
-
B.
Kondapur
Kondapur is a rapidly developing residential and commercial suburb in Hyderabad, India, known for its proximity to major IT hubs and tech parks.
-
C.
Tadipatri
Tadipatri is a town in the Anantapur district of Andhra Pradesh, India, known for its granite industries and historic temples.
-
D.
Nandyal
Nandyal is a city in the Indian state of Andhra Pradesh, known as a commercial and administrative center in the Rayalaseema region.
-
E.
Ravulapalem
Ravulapalem is a town in the Indian state of Andhra Pradesh, known for its agricultural markets and location along key transport routes in the Godavari region.
- F. None of above. chosen
Provenance (5 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_69d6aa864ed88190818280ab6791d065 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7708f7ab48190b60a4bb8fdb17c8e |
completed | April 9, 2026, 9:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e34455ea4c8190b6f2433f3f745b76 |
completed | April 18, 2026, 8:44 a.m. |
| NEDg | Description generation | batch_69e3556ad7ec819095b3babc67ecdfd4 |
completed | April 18, 2026, 9:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e358f860f08190bfd10519ff3806aa |
completed | April 18, 2026, 10:12 a.m. |
Created at: April 8, 2026, 9:22 p.m.