Triple

T14150420
Position Surface form Disambiguated ID Type / Status
Subject Begumpet Airport E350663 entity
Predicate locatedIn P40 FINISHED
Object Begumpet
Begumpet is a prominent neighborhood in Hyderabad, India, known for its commercial centers, residential areas, and historical significance as the site of the former Begumpet Airport.
 E1082308 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: Begumpet | Statement: [Begumpet Airport, locatedIn, Begumpet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Begumpet
Context triple: [Begumpet Airport, locatedIn, Begumpet]
  • A. Bakumpai
    The Bakumpai are an indigenous Dayak ethnic group of central Kalimantan, Indonesia, traditionally living along the Barito River and known for their distinct language and river-based culture.
  • B. Bengan
    Bengan is a Swedish diminutive or nickname commonly used for the male given name Bengt.
  • C. Bompoka
    Bompoka is a small, remote island that forms part of India’s Nicobar Islands archipelago in the eastern Indian Ocean.
  • D. Ngaluma
    Ngaluma is an Aboriginal Australian people traditionally associated with the coastal and inland regions of the Pilbara in Western Australia.
  • E. Upata
    Upata is a town in southeastern Venezuela known as an agricultural and commercial center within Bolívar State.
  • 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: Begumpet
Triple: [Begumpet Airport, locatedIn, Begumpet]
Generated description
Begumpet is a prominent neighborhood in Hyderabad, India, known for its commercial centers, residential areas, and historical significance as the site of the former Begumpet Airport.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Begumpet
Target entity description: Begumpet is a prominent neighborhood in Hyderabad, India, known for its commercial centers, residential areas, and historical significance as the site of the former Begumpet Airport.
  • A. Bakumpai
    The Bakumpai are an indigenous Dayak ethnic group of central Kalimantan, Indonesia, traditionally living along the Barito River and known for their distinct language and river-based culture.
  • B. Bengan
    Bengan is a Swedish diminutive or nickname commonly used for the male given name Bengt.
  • C. Bompoka
    Bompoka is a small, remote island that forms part of India’s Nicobar Islands archipelago in the eastern Indian Ocean.
  • D. Ngaluma
    Ngaluma is an Aboriginal Australian people traditionally associated with the coastal and inland regions of the Pilbara in Western Australia.
  • E. Upata
    Upata is a town in southeastern Venezuela known as an agricultural and commercial center within Bolívar State.
  • 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_69d8278775fc8190b0802d22ca2f495d completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de6124e23481909e5132a40a1d8624 completed April 14, 2026, 3:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcdf21f8c8819097ff26e6bb345b52 completed May 7, 2026, 6:51 p.m.
NEDg Description generation batch_69fce0cc78e881909090ac42a97ebb12 completed May 7, 2026, 6:58 p.m.
NED2 Entity disambiguation (via description) batch_69fce1f53a8881909fd1258729a9879d completed May 7, 2026, 7:03 p.m.
Created at: April 10, 2026, 12:56 a.m.