Triple

T7302429
Position Surface form Disambiguated ID Type / Status
Subject Jaldapara National Park E167889 entity
Predicate nearestTown P350 FINISHED
Object Madarihat
Madarihat is a small town in West Bengal, India, known primarily as the main gateway and service hub for visitors to Jaldapara National Park.
 E654868 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: Madarihat | Statement: [Jaldapara National Park, nearestTown, Madarihat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Madarihat
Context triple: [Jaldapara National Park, nearestTown, Madarihat]
  • A. Almansa
    Almansa is a historic town in the province of Albacete, Spain, known for its imposing medieval castle and its role as the site of a major battle in the War of the Spanish Succession.
  • B. Alamata
    Alamata is a town in northern Ethiopia that serves as a local commercial and administrative center in the southern part of the Tigray Region.
  • C. Nájera
    Nájera is a historic town in northern Spain known for its medieval heritage and role as a former capital of the Kingdom of Navarre.
  • D. Zuera
    Zuera is a municipality in northeastern Spain located within the autonomous community of Aragon.
  • E. Majadahonda
    Majadahonda is a suburban municipality west of Madrid, Spain, known for its residential character, shopping centers, and sports facilities.
  • 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: Madarihat
Triple: [Jaldapara National Park, nearestTown, Madarihat]
Generated description
Madarihat is a small town in West Bengal, India, known primarily as the main gateway and service hub for visitors to Jaldapara National Park.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Madarihat
Target entity description: Madarihat is a small town in West Bengal, India, known primarily as the main gateway and service hub for visitors to Jaldapara National Park.
  • A. Almansa
    Almansa is a historic town in the province of Albacete, Spain, known for its imposing medieval castle and its role as the site of a major battle in the War of the Spanish Succession.
  • B. Alamata
    Alamata is a town in northern Ethiopia that serves as a local commercial and administrative center in the southern part of the Tigray Region.
  • C. Nájera
    Nájera is a historic town in northern Spain known for its medieval heritage and role as a former capital of the Kingdom of Navarre.
  • D. Zuera
    Zuera is a municipality in northeastern Spain located within the autonomous community of Aragon.
  • E. Majadahonda
    Majadahonda is a suburban municipality west of Madrid, Spain, known for its residential character, shopping centers, and sports facilities.
  • 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_69c6888c820881909fc68f689fe1c251 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6ebb2261c8190ae9095c8e110b528 completed March 27, 2026, 8:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7e558098c819091562566c59332e2 completed March 28, 2026, 2:27 p.m.
NEDg Description generation batch_69c7e6671e2c8190aed42aa673540efa completed March 28, 2026, 2:32 p.m.
NED2 Entity disambiguation (via description) batch_69c7e6cd820881909ef8fd3bc28d2716 completed March 28, 2026, 2:33 p.m.
Created at: March 27, 2026, 3:01 p.m.