Triple

T37607707
Position Surface form Disambiguated ID Type / Status
Subject Birmingham Cross-City Line E935699 entity
Predicate usesRollingStock P5426 FINISHED
Object Class 730 electric multiple unit
The Class 730 electric multiple unit is a modern British commuter train designed for high-capacity, high-frequency suburban and regional services.
 E2236702 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 730 electric multiple unit | Statement: [Birmingham Cross-City Line, usesRollingStock, Class 730 electric multiple unit]
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 730 electric multiple unit
Triple: [Birmingham Cross-City Line, usesRollingStock, Class 730 electric multiple unit]
Generated description
The Class 730 electric multiple unit is a modern British commuter train designed for high-capacity, high-frequency suburban and regional 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_69f76ed0a85481909254a8a89090c826 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fba9026df4819093c25a9373f32691 completed May 6, 2026, 8:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40afdf21b08190931b6be0f6c105d9 completed June 28, 2026, 5:23 a.m.
NEDg Description generation batch_6a40b4601c5c81909f7d083d89b30257 completed June 28, 2026, 5:42 a.m.
NED2 Entity disambiguation (via description) batch_6a40b4b4ff608190aec2eda0ac8afe21 completed June 28, 2026, 5:44 a.m.
Created at: May 3, 2026, 4:18 p.m.