Triple

T16687785
Position Surface form Disambiguated ID Type / Status
Subject Secunderabad E405509 entity
Predicate hasNeighbourhood P4813 FINISHED
Object Maredpally
Maredpally is a prominent residential and commercial neighborhood in Secunderabad, Telangana, known for its schools, eateries, and well-connected urban setting.
E1228651 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: Maredpally | Statement: [Secunderabad, hasNeighbourhood, Maredpally]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maredpally
Context triple: [Secunderabad, hasNeighbourhood, Maredpally]
  • A. Laknepalli
    Laknepalli is a village in India best known as the birthplace of former Prime Minister P. V. Narasimha Rao.
  • B. Nandipet
    Nandipet is a village located in the Nizamabad district of the Indian state of Telangana.
  • C. Nandyal
    Nandyal is a city in the Indian state of Andhra Pradesh, known as a commercial and administrative center in the Rayalaseema region.
  • D. Anakapalli
    Anakapalli is a town and municipality in the Indian state of Andhra Pradesh, known historically as a major jaggery trading center and now part of the urban area of Visakhapatnam.
  • E. Tadipatri
    Tadipatri is a town in the Anantapur district of Andhra Pradesh, India, known for its granite industries and historic temples.
  • 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: Maredpally
Triple: [Secunderabad, hasNeighbourhood, Maredpally]
Generated description
Maredpally is a prominent residential and commercial neighborhood in Secunderabad, Telangana, known for its schools, eateries, and well-connected urban setting.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Maredpally
Target entity description: Maredpally is a prominent residential and commercial neighborhood in Secunderabad, Telangana, known for its schools, eateries, and well-connected urban setting.
  • A. Laknepalli
    Laknepalli is a village in India best known as the birthplace of former Prime Minister P. V. Narasimha Rao.
  • B. Nandipet
    Nandipet is a village located in the Nizamabad district of the Indian state of Telangana.
  • C. Nandyal
    Nandyal is a city in the Indian state of Andhra Pradesh, known as a commercial and administrative center in the Rayalaseema region.
  • D. Anakapalli
    Anakapalli is a town and municipality in the Indian state of Andhra Pradesh, known historically as a major jaggery trading center and now part of the urban area of Visakhapatnam.
  • E. Tadipatri
    Tadipatri is a town in the Anantapur district of Andhra Pradesh, India, known for its granite industries and historic temples.
  • 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_69d8838c28748190b3f5967c743940ab completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e37ea75df481909a7ebb9b2a9d0afd completed April 18, 2026, 12:52 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0091966fbc81908cd1db230ddbb82b completed May 10, 2026, 2:09 p.m.
NEDg Description generation batch_6a00920dd72c8190b30d4edd779e7029 completed May 10, 2026, 2:11 p.m.
NED2 Entity disambiguation (via description) batch_6a0092e322d08190862ae42a28c9e5cf completed May 10, 2026, 2:14 p.m.
Created at: April 10, 2026, 5:19 a.m.