Triple

T23678243
Position Surface form Disambiguated ID Type / Status
Subject British Rail Class 377 E584944 entity
Predicate hasSubclass P1244 FINISHED
Object Class 377/3
Class 377/3 is a subseries of the British Rail Class 377 electric multiple units, designed for high-frequency commuter services in the Southern region of the UK rail network.
 E1606545 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 377/3 | Statement: [British Rail Class 377, hasSubclass, Class 377/3]
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 377/3
Triple: [British Rail Class 377, hasSubclass, Class 377/3]
Generated description
Class 377/3 is a subseries of the British Rail Class 377 electric multiple units, designed for high-frequency commuter services in the Southern region of the UK rail network.

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_69e24901f7c08190909fd727632e823d completed April 17, 2026, 2:51 p.m.
NER Named-entity recognition batch_69f1b4f4d4388190a0e439f4df7a0f23 completed April 29, 2026, 7:36 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f695bbe7881909aed23cac9f70e12 completed May 21, 2026, 8:21 p.m.
NEDg Description generation batch_6a0f6d3d0b548190aa6de291bffd32ce completed May 21, 2026, 8:38 p.m.
NED2 Entity disambiguation (via description) batch_6a0f6db3e3c081909f81db7080f51351 completed May 21, 2026, 8:40 p.m.
Created at: April 17, 2026, 6:51 p.m.