Triple

T9704708
Position Surface form Disambiguated ID Type / Status
Subject Nashik district E234869 entity
Predicate containsTown P847 FINISHED
Object Malegaon
Malegaon is a major textile and powerloom town in Maharashtra, India, known for its large Muslim population and vibrant weaving industry.
E815160 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: Malegaon | Statement: [Nashik district, containsTown, Malegaon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Malegaon
Context triple: [Nashik district, containsTown, Malegaon]
  • A. Baramati
    Baramati is a town in the Pune district of Maharashtra, India, known as an agricultural and industrial hub with historical and political significance.
  • B. Latur
    Latur is a city in the Marathwada region of western India known for its agricultural economy and for being the epicenter of a devastating earthquake in 1993.
  • C. Nanded
    Nanded is a historic city in the Indian state of Maharashtra, known as an important Sikh pilgrimage center and a major urban hub in the Marathwada region.
  • D. Sangli
    Sangli is a city in the Indian state of Maharashtra known for its fertile agricultural surroundings and prominence in sugar and turmeric production.
  • E. Gondia
    Gondia is a district in the Indian state of Maharashtra, known for its rice production and proximity to forests and wildlife reserves.
  • 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: Malegaon
Triple: [Nashik district, containsTown, Malegaon]
Generated description
Malegaon is a major textile and powerloom town in Maharashtra, India, known for its large Muslim population and vibrant weaving industry.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Malegaon
Target entity description: Malegaon is a major textile and powerloom town in Maharashtra, India, known for its large Muslim population and vibrant weaving industry.
  • A. Baramati
    Baramati is a town in the Pune district of Maharashtra, India, known as an agricultural and industrial hub with historical and political significance.
  • B. Latur
    Latur is a city in the Marathwada region of western India known for its agricultural economy and for being the epicenter of a devastating earthquake in 1993.
  • C. Nanded
    Nanded is a historic city in the Indian state of Maharashtra, known as an important Sikh pilgrimage center and a major urban hub in the Marathwada region.
  • D. Sangli
    Sangli is a city in the Indian state of Maharashtra known for its fertile agricultural surroundings and prominence in sugar and turmeric production.
  • E. Gondia
    Gondia is a district in the Indian state of Maharashtra, known for its rice production and proximity to forests and wildlife reserves.
  • 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_69ca84cc78808190a56f3402b7c139a7 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9d74afb4819084174aab5bcdb6e0 completed April 1, 2026, 10:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69d19136b40c8190922052dd84d49f15 completed April 4, 2026, 10:31 p.m.
NEDg Description generation batch_69d193623fac8190a8dcdac664a977f3 completed April 4, 2026, 10:40 p.m.
NED2 Entity disambiguation (via description) batch_69d193cc61208190b98fe862b295dda3 completed April 4, 2026, 10:42 p.m.
Created at: March 30, 2026, 8:18 p.m.