Triple

T4533311
Position Surface form Disambiguated ID Type / Status
Subject New Delhi Metro station E106347 entity
Predicate servesArea P82 FINISHED
Object Paharganj
Paharganj is a densely populated, budget-friendly neighborhood in central Delhi known for its bustling markets, cheap hotels, and popularity among backpackers.
 E450845 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: Paharganj | Statement: [New Delhi Metro station, servesArea, Paharganj]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Paharganj
Context triple: [New Delhi Metro station, servesArea, Paharganj]
  • A. Chenab Nagar
    Chenab Nagar is a town in Pakistan’s Punjab province known as the headquarters of the Ahmadiyya Muslim Community and formerly called Rabwah.
  • B. Barwala
    Barwala is a prominent town in the Hisar district of Haryana, India, known as a local commercial and administrative center for surrounding rural areas.
  • C. Nagaur
    Nagaur is a historic city in Rajasthan, India, known for its medieval fort, cultural heritage, and role as an important center in the Marwar region.
  • D. Nilokheri
    Nilokheri is a town in the Karnal district of Haryana, India, known for its agricultural surroundings and local educational institutions.
  • E. Randhawa
    Randhawa is an Indian-origin Punjabi surname notably borne by American politician Nikki Haley.
  • 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: Paharganj
Triple: [New Delhi Metro station, servesArea, Paharganj]
Generated description
Paharganj is a densely populated, budget-friendly neighborhood in central Delhi known for its bustling markets, cheap hotels, and popularity among backpackers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Paharganj
Target entity description: Paharganj is a densely populated, budget-friendly neighborhood in central Delhi known for its bustling markets, cheap hotels, and popularity among backpackers.
  • A. Chenab Nagar
    Chenab Nagar is a town in Pakistan’s Punjab province known as the headquarters of the Ahmadiyya Muslim Community and formerly called Rabwah.
  • B. Barwala
    Barwala is a prominent town in the Hisar district of Haryana, India, known as a local commercial and administrative center for surrounding rural areas.
  • C. Nagaur
    Nagaur is a historic city in Rajasthan, India, known for its medieval fort, cultural heritage, and role as an important center in the Marwar region.
  • D. Nilokheri
    Nilokheri is a town in the Karnal district of Haryana, India, known for its agricultural surroundings and local educational institutions.
  • E. Randhawa
    Randhawa is an Indian-origin Punjabi surname notably borne by American politician Nikki Haley.
  • 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_69bd43f3d6e08190a91824f833d51bbe completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd57a08ec4819091b84d53d7b564a7 completed March 20, 2026, 2:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdacea41bc8190b49c9d1a31d7930f completed March 20, 2026, 8:24 p.m.
NEDg Description generation batch_69bdb220bc6481908955c5c953bbfb97 completed March 20, 2026, 8:46 p.m.
NED2 Entity disambiguation (via description) batch_69bdb2871e3481908d143c52d9e7141f completed March 20, 2026, 8:48 p.m.
Created at: March 20, 2026, 1:04 p.m.