Triple

T33626726
Position Surface form Disambiguated ID Type / Status
Subject Vernon Place E861423 entity
Predicate hasAdjacentArea P17964 FINISHED
Object Russell Square area
The Russell Square area is a central London neighborhood in Bloomsbury known for its large garden square, Georgian architecture, and proximity to major universities and cultural institutions.
 E2059561 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: Russell Square area | Statement: [Vernon Place, hasAdjacentArea, Russell Square area]
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: Russell Square area
Triple: [Vernon Place, hasAdjacentArea, Russell Square area]
Generated description
The Russell Square area is a central London neighborhood in Bloomsbury known for its large garden square, Georgian architecture, and proximity to major universities and cultural institutions.

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_69f34981c54c81909b33c3fa2208a52d completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6f85724048190be13f0503898a67e completed May 3, 2026, 7:25 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3611ac06fc8190a26f974ce6d9dcb6 completed June 20, 2026, 4:06 a.m.
NEDg Description generation batch_6a3612ddd714819084e5c57e306bb3cd completed June 20, 2026, 4:11 a.m.
NED2 Entity disambiguation (via description) batch_6a361366b0d48190be19bf37db10848b completed June 20, 2026, 4:13 a.m.
Created at: May 1, 2026, 1:41 a.m.