Triple

T32477085
Position Surface form Disambiguated ID Type / Status
Subject Noida Sector 18 E830004 entity
Predicate hasMall P16039 FINISHED
Object DLF Mall of India
DLF Mall of India is one of the largest shopping and entertainment malls in India, featuring a wide range of retail brands, dining options, and leisure attractions.
 E2008001 NE FINISHED

How this triple was built (3 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: DLF Mall of India | Statement: [Noida Sector 18, hasMall, DLF Mall of India]
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: DLF Mall of India
Triple: [Noida Sector 18, hasMall, DLF Mall of India]
Generated description
DLF Mall of India is one of the largest shopping and entertainment malls in India, featuring a wide range of retail brands, dining options, and leisure attractions.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hasMall
Context triple: [Noida Sector 18, hasMall, DLF Mall of India]
  • A. hasShoppingMall chosen
    Indicates that one entity possesses, contains, or includes a shopping mall within its area or domain.
  • B. hasShop
    Indicates that one entity owns, operates, or is associated with a shop or retail establishment.
  • C. isIndoorMall
    Indicates that a shopping mall is located indoors, typically enclosed within a single building or connected interior space.
  • D. hasShoppingMallName
    Indicates that an entity (such as a shopping mall) is associated with or identified by a specific name.
  • E. hasShoppingMallRank
    Indicates the relative position or ranking assigned to a shopping mall within a specified comparison set or evaluation.
  • F. None of above.

Provenance (6 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_69f3491ff3b48190b50a7fa00bb05b1f completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_6a0091ad8b8c8190b0f00a3358e59bc1 completed May 10, 2026, 2:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a34669dc0c88190aabe62cd4ddfc34a completed June 18, 2026, 9:43 p.m.
NEDg Description generation batch_6a34674302f081908ce094e58ee8360c completed June 18, 2026, 9:46 p.m.
NED2 Entity disambiguation (via description) batch_6a3468279dbc8190b5efcecd6f4aa23c completed June 18, 2026, 9:50 p.m.
PD Predicate disambiguation batch_6a008f2813ec81909a54c2dfa5c75dc7 completed May 10, 2026, 1:59 p.m.
Created at: May 1, 2026, 12:58 a.m.