Triple

T25115715
Position Surface form Disambiguated ID Type / Status
Subject Kingsway Camp E629122 entity
Predicate near P350 FINISHED
Object Model Town, Delhi
Model Town, Delhi is a planned residential neighborhood in North Delhi known for its wide roads, parks, and proximity to major educational institutions and commercial areas.
 E1660830 NE FINISHED

How this triple was built (2 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: Model Town, Delhi | Statement: [Kingsway Camp, near, Model Town, Delhi]
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: Model Town, Delhi
Triple: [Kingsway Camp, near, Model Town, Delhi]
Generated description
Model Town, Delhi is a planned residential neighborhood in North Delhi known for its wide roads, parks, and proximity to major educational institutions and commercial areas.

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_69e2ff3169d08190973b6061d5009abd completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f465c66cc48190b66b14e4b9036432 completed May 1, 2026, 8:35 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1048f93d548190b6dba081fbf8a913 completed May 22, 2026, 12:15 p.m.
NEDg Description generation batch_6a1049d7d4bc819081cf52476b0c0a1d completed May 22, 2026, 12:19 p.m.
NED2 Entity disambiguation (via description) batch_6a104a50e59c81908e576aeb2cebc1c5 completed May 22, 2026, 12:21 p.m.
Created at: April 18, 2026, 6:27 a.m.