Triple

T37945310
Position Surface form Disambiguated ID Type / Status
Subject Sir Frederick Burrows E946588 entity
Predicate employer P7 FINISHED
Object British railway companies
British railway companies were the privately owned firms that operated and managed the United Kingdom’s railway network, particularly during the 19th and early 20th centuries before nationalisation.
E2249718 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: British railway companies | Statement: [Sir Frederick Burrows, employer, British railway companies]
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: British railway companies
Triple: [Sir Frederick Burrows, employer, British railway companies]
Generated description
British railway companies were the privately owned firms that operated and managed the United Kingdom’s railway network, particularly during the 19th and early 20th centuries before nationalisation.

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_69f76ef531ac8190ae6d99e5786e76ec completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fbbdb5f3588190bf98b0c231d72002 completed May 6, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a4117fa088081908a187ab615978dfb completed June 28, 2026, 12:47 p.m.
NEDg Description generation batch_6a4118a395b8819080fe072ef24f41b3 completed June 28, 2026, 12:50 p.m.
NED2 Entity disambiguation (via description) batch_6a4119cd78bc8190b4f84646eea2ec12 completed June 28, 2026, 12:55 p.m.
Created at: May 3, 2026, 4:20 p.m.