Triple

T3341346
Position Surface form Disambiguated ID Type / Status
Subject Cyberabad E70266 entity
Predicate hasArea P175 FINISHED
Object Madhapur
Madhapur is a major IT and business district in Hyderabad, India, known for its technology parks, corporate offices, and modern urban infrastructure.
 E349850 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: Madhapur | Statement: [Cyberabad, hasArea, Madhapur]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Madhapur
Context triple: [Cyberabad, hasArea, Madhapur]
  • A. Damoh
    Damoh is a city in central India that serves as the administrative headquarters of Damoh district in the state of Madhya Pradesh.
  • B. Jaipuri
    Jaipuri is a regional Indo-Aryan language variety spoken in and around Jaipur in the Indian state of Rajasthan.
  • C. Budaun
    Budaun is a historic city in the Rohilkhand region of Uttar Pradesh, India, known for its medieval heritage and cultural significance.
  • D. Warangal
    Warangal is a historic city in southern India known for its Kakatiya-era forts, temples, and monuments, and is one of the major urban centers of the state of Telangana.
  • E. Benipatti
    Benipatti is a town in the Madhubani district of the Indian state of Bihar, known for its rural setting and proximity to the region’s famed Mithila culture.
  • 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: Madhapur
Triple: [Cyberabad, hasArea, Madhapur]
Generated description
Madhapur is a major IT and business district in Hyderabad, India, known for its technology parks, corporate offices, and modern urban infrastructure.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Madhapur
Target entity description: Madhapur is a major IT and business district in Hyderabad, India, known for its technology parks, corporate offices, and modern urban infrastructure.
  • A. Damoh
    Damoh is a city in central India that serves as the administrative headquarters of Damoh district in the state of Madhya Pradesh.
  • B. Jaipuri
    Jaipuri is a regional Indo-Aryan language variety spoken in and around Jaipur in the Indian state of Rajasthan.
  • C. Budaun
    Budaun is a historic city in the Rohilkhand region of Uttar Pradesh, India, known for its medieval heritage and cultural significance.
  • D. Warangal
    Warangal is a historic city in southern India known for its Kakatiya-era forts, temples, and monuments, and is one of the major urban centers of the state of Telangana.
  • E. Benipatti
    Benipatti is a town in the Madhubani district of the Indian state of Bihar, known for its rural setting and proximity to the region’s famed Mithila culture.
  • 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_69ad85a405e48190b6e68de7cf9f319e completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb1c0ae44819091c851569eaf4565 completed March 8, 2026, 5:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69b31a92555c81909a4769b6ecea5721 completed March 12, 2026, 7:57 p.m.
NEDg Description generation batch_69b31c3aaba48190b203e344d71080f3 completed March 12, 2026, 8:04 p.m.
NED2 Entity disambiguation (via description) batch_69b31da28a04819096e7ced5f123592a completed March 12, 2026, 8:10 p.m.
Created at: March 8, 2026, 3:12 p.m.