Triple

T21668131
Position Surface form Disambiguated ID Type / Status
Subject Old Dhaka E534773 entity
Predicate contains P35 FINISHED
Object Nawabpur
Nawabpur is a historic commercial neighborhood in Old Dhaka, Bangladesh, known especially for its bustling hardware and electronics markets.
 E1494989 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: Nawabpur | Statement: [Old Dhaka, contains, Nawabpur]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nawabpur
Context triple: [Old Dhaka, contains, Nawabpur]
  • A. Sikandarpur
    Sikandarpur is a metro station in the Delhi Metro network that serves the Gurugram area and provides an interchange with the Rapid Metro system.
  • B. Muradnagar
    Muradnagar is a town and municipal area in Uttar Pradesh, India, known for its proximity to Ghaziabad and its role as a local commercial and residential hub.
  • C. Mauzamabad
    Mauzamabad is a village-level settlement located within the Jaipur district of the Indian state of Rajasthan.
  • D. Pakpattan
    Pakpattan is a historic town in Pakistan renowned as a major Sufi center, especially associated with the shrine of the revered saint Baba Farid of the Chishti Order.
  • E. Sheikhupura
    Sheikhupura is a major city in Pakistan’s Punjab province known for its historical sites, including the Hiran Minar complex, and its role as an industrial and agricultural center.
  • 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: Nawabpur
Triple: [Old Dhaka, contains, Nawabpur]
Generated description
Nawabpur is a historic commercial neighborhood in Old Dhaka, Bangladesh, known especially for its bustling hardware and electronics markets.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nawabpur
Target entity description: Nawabpur is a historic commercial neighborhood in Old Dhaka, Bangladesh, known especially for its bustling hardware and electronics markets.
  • A. Sikandarpur
    Sikandarpur is a metro station in the Delhi Metro network that serves the Gurugram area and provides an interchange with the Rapid Metro system.
  • B. Muradnagar
    Muradnagar is a town and municipal area in Uttar Pradesh, India, known for its proximity to Ghaziabad and its role as a local commercial and residential hub.
  • C. Mauzamabad
    Mauzamabad is a village-level settlement located within the Jaipur district of the Indian state of Rajasthan.
  • D. Pakpattan
    Pakpattan is a historic town in Pakistan renowned as a major Sufi center, especially associated with the shrine of the revered saint Baba Farid of the Chishti Order.
  • E. Sheikhupura
    Sheikhupura is a major city in Pakistan’s Punjab province known for its historical sites, including the Hiran Minar complex, and its role as an industrial and agricultural center.
  • 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_69e0c46898008190aa618a4af55bd1ee completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69ef6c0cf6208190a8bd9fa423c65a40 completed April 27, 2026, 2 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a15a32c1c8190a9ae4efb9d09e1dc completed May 17, 2026, 7:23 p.m.
NEDg Description generation batch_6a0a1635915081908c46794cdbaa4cde completed May 17, 2026, 7:25 p.m.
NED2 Entity disambiguation (via description) batch_6a0a16c70cfc81908125358316e2a89a completed May 17, 2026, 7:28 p.m.
Created at: April 16, 2026, 6:37 p.m.