Triple

T24320692
Position Surface form Disambiguated ID Type / Status
Subject Liverpool–Southport line E612951 entity
Predicate usesRollingStock P5426 FINISHED
Object Class 777
Class 777 is a modern electric multiple unit train built for Merseyrail’s network around Liverpool, designed to provide faster, more reliable, and accessible commuter services.
 E1629894 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: Class 777 | Statement: [Liverpool–Southport line, usesRollingStock, Class 777]
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: Class 777
Triple: [Liverpool–Southport line, usesRollingStock, Class 777]
Generated description
Class 777 is a modern electric multiple unit train built for Merseyrail’s network around Liverpool, designed to provide faster, more reliable, and accessible commuter services.

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_69e2d7da491c8190b6e6218af50923db completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f292ab2fa08190bc19c3edc0a1a9b2 completed April 29, 2026, 11:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fc9e213dc8190ad9a56715b74bc35 completed May 22, 2026, 3:13 a.m.
NEDg Description generation batch_6a0fcecc34808190b1b853c9c471a382 completed May 22, 2026, 3:34 a.m.
NED2 Entity disambiguation (via description) batch_6a0fcf5008e08190b0744a9be634cedb completed May 22, 2026, 3:36 a.m.
Created at: April 18, 2026, 1:49 a.m.