Triple

T16386553
Position Surface form Disambiguated ID Type / Status
Subject Agra metropolitan area E397936 entity
Predicate hasPart P35 FINISHED
Object Lohamandi
Lohamandi is a locality within the city of Agra in Uttar Pradesh, India, known for its dense residential areas and bustling commercial activity.
E1209799 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: Lohamandi | Statement: [Agra metropolitan area, hasPart, Lohamandi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lohamandi
Context triple: [Agra metropolitan area, hasPart, Lohamandi]
  • A. Lambhua
    Lambhua is a town in the Sultanpur district of Uttar Pradesh, India, known as a local administrative and market center for surrounding rural areas.
  • B. Lakamadi
    Lakamadi is a regional dialect of the Moru language spoken by Moru communities in South Sudan.
  • C. Lhazar
    Lhazar is a pastoral region in southeastern Essos inhabited by peaceful shepherding people known for their devotion to the god the Great Shepherd.
  • D. Loinang
    Loinang is an alternative name for the Saluan language, an Austronesian language spoken in Central Sulawesi, Indonesia.
  • E. Lohapol
    Lohapol is one of the historic entrance gates of Mehrangarh Fort in Jodhpur, Rajasthan, known for its massive iron-studded doors and memorial handprints of royal widows.
  • 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: Lohamandi
Triple: [Agra metropolitan area, hasPart, Lohamandi]
Generated description
Lohamandi is a locality within the city of Agra in Uttar Pradesh, India, known for its dense residential areas and bustling commercial activity.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lohamandi
Target entity description: Lohamandi is a locality within the city of Agra in Uttar Pradesh, India, known for its dense residential areas and bustling commercial activity.
  • A. Lambhua
    Lambhua is a town in the Sultanpur district of Uttar Pradesh, India, known as a local administrative and market center for surrounding rural areas.
  • B. Lakamadi
    Lakamadi is a regional dialect of the Moru language spoken by Moru communities in South Sudan.
  • C. Lhazar
    Lhazar is a pastoral region in southeastern Essos inhabited by peaceful shepherding people known for their devotion to the god the Great Shepherd.
  • D. Loinang
    Loinang is an alternative name for the Saluan language, an Austronesian language spoken in Central Sulawesi, Indonesia.
  • E. Lohapol
    Lohapol is one of the historic entrance gates of Mehrangarh Fort in Jodhpur, Rajasthan, known for its massive iron-studded doors and memorial handprints of royal widows.
  • 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_69d87f2880b48190ae1a9673a3bbef80 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e3263d260081909db9ac6016d5738a completed April 18, 2026, 6:35 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00356ed47c819085aaf101459dd55c completed May 10, 2026, 7:36 a.m.
NEDg Description generation batch_6a00368287d48190b510541eb7851942 completed May 10, 2026, 7:40 a.m.
NED2 Entity disambiguation (via description) batch_6a003766d4ec8190ab98387781f85bd6 completed May 10, 2026, 7:44 a.m.
Created at: April 10, 2026, 5:08 a.m.