Triple

T37520349
Position Surface form Disambiguated ID Type / Status
Subject Jewellery Line E932752 entity
Predicate rollingStock P1305 FINISHED
Object Class 172 diesel multiple unit
The Class 172 diesel multiple unit is a British commuter train used on regional and suburban services, known for its modern design and improved performance over earlier DMU classes.
 E2230171 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 172 diesel multiple unit | Statement: [Jewellery Line, rollingStock, Class 172 diesel 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 172 diesel multiple unit
Triple: [Jewellery Line, rollingStock, Class 172 diesel multiple unit]
Generated description
The Class 172 diesel multiple unit is a British commuter train used on regional and suburban services, known for its modern design and improved performance over earlier DMU classes.

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_69f76ec730988190b5aa4f9cb9afd518 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fba3cfc04c8190b9547d11232eecd1 completed May 6, 2026, 8:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40953f4c2481909c758cc384516100 completed June 28, 2026, 3:30 a.m.
NEDg Description generation batch_6a40964c81d0819084e6e2f36cedab7a completed June 28, 2026, 3:34 a.m.
NED2 Entity disambiguation (via description) batch_6a4096a8a74881908e231a1e18b16449 completed June 28, 2026, 3:36 a.m.
Created at: May 3, 2026, 4:17 p.m.