Triple

T28623024
Position Surface form Disambiguated ID Type / Status
Subject South Extension E724437 entity
Predicate hasPart P35 FINISHED
Object South Extension Part II
South Extension Part II is a prominent upscale residential and commercial neighborhood in South Delhi, India, known for its shopping markets, eateries, and proximity to key city hubs.
 E724437 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: South Extension Part II | Statement: [South Extension, hasPart, South Extension Part II]
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: South Extension Part II
Triple: [South Extension, hasPart, South Extension Part II]
Generated description
South Extension Part II is a prominent upscale residential and commercial neighborhood in South Delhi, India, known for its shopping markets, eateries, and proximity to key city hubs.

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_69f01d822ac08190932de59ec2268ed2 completed April 28, 2026, 2:37 a.m.
NER Named-entity recognition batch_69f6526fc8a881908e77df9bc360601a completed May 2, 2026, 7:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1cc377c9d481908f9f817d00cd13b2 completed May 31, 2026, 11:25 p.m.
NEDg Description generation batch_6a1cc44ac1448190b0dc305eb5e460be completed May 31, 2026, 11:29 p.m.
NED2 Entity disambiguation (via description) batch_6a1cc571b3b481908c523e5bad5e086a completed May 31, 2026, 11:34 p.m.
Created at: April 28, 2026, 4:34 a.m.