Triple

T36506031
Position Surface form Disambiguated ID Type / Status
Subject South Clerk Street E899462 entity
Predicate hasNearbyArea P4647 FINISHED
Object Newington Road corridor
The Newington Road corridor is a busy urban thoroughfare and commercial strip in Edinburgh’s Newington district, lined with shops, services, and residential buildings.
E2187564 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: Newington Road corridor | Statement: [South Clerk Street, hasNearbyArea, Newington Road corridor]
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: Newington Road corridor
Triple: [South Clerk Street, hasNearbyArea, Newington Road corridor]
Generated description
The Newington Road corridor is a busy urban thoroughfare and commercial strip in Edinburgh’s Newington district, lined with shops, services, and residential buildings.

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_69f76e5b92088190933afda3f7531dd4 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7c1c731508190b45000bf01654e6e completed May 3, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39dbd567448190b9e3e3aa8ec774c5 completed June 23, 2026, 1:05 a.m.
NEDg Description generation batch_6a39dd5a787c819089c278f86de8078c completed June 23, 2026, 1:11 a.m.
NED2 Entity disambiguation (via description) batch_6a39e17ec0f08190acfb9ec12f86249d completed June 23, 2026, 1:29 a.m.
Created at: May 3, 2026, 4:10 p.m.