Triple

T8171308
Position Surface form Disambiguated ID Type / Status
Subject Russell Square E190825 entity
Predicate adjacentTo P224 FINISHED
Object Russell Square Street
Russell Square Street is a road in the Bloomsbury district of central London, closely associated with the historic Russell Square and its surrounding academic and cultural institutions.
 E2295420 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 Street | Statement: [Russell Square, adjacentTo, Russell Square Street]
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 Street
Triple: [Russell Square, adjacentTo, Russell Square Street]
Generated description
Russell Square Street is a road in the Bloomsbury district of central London, closely associated with the historic Russell Square and its surrounding academic 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_69ca82c1c0a08190bf8692b4d91a03ca completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb48056d0c819094575090a41e0083 completed March 31, 2026, 4:05 a.m.
NED1 Entity disambiguation (via context triple) batch_6a7d507132808190bf1ded74ab30647a completed Aug. 13, 2026, 5:04 a.m.
NEDg Description generation batch_6a7d52ea63108190a84e591be85451fd completed Aug. 13, 2026, 5:15 a.m.
NED2 Entity disambiguation (via description) batch_6a7d533929e08190a2e3553574425d03 completed Aug. 13, 2026, 5:16 a.m.
Created at: March 30, 2026, 5:39 p.m.