Triple

T30145563
Position Surface form Disambiguated ID Type / Status
Subject MAN Lion’s City solo E766243 entity
Predicate hasVariant P455 FINISHED
Object MAN Lion’s City articulated
The MAN Lion’s City articulated is a low-floor, high-capacity articulated city bus designed by MAN for urban public transport routes with heavy passenger demand.
 E212635 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: MAN Lion’s City articulated | Statement: [MAN Lion’s City solo, hasVariant, MAN Lion’s City articulated]
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: MAN Lion’s City articulated
Triple: [MAN Lion’s City solo, hasVariant, MAN Lion’s City articulated]
Generated description
The MAN Lion’s City articulated is a low-floor, high-capacity articulated city bus designed by MAN for urban public transport routes with heavy passenger demand.

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_69f2247909048190ae86c2160cf8b566 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67e8b21048190b0fbccb10b2d3523 completed May 2, 2026, 10:45 p.m.
NED1 Entity disambiguation (via context triple) batch_6a274cbc5e488190be62c5cbb3e1614a completed June 8, 2026, 11:14 p.m.
NEDg Description generation batch_6a2751008c0c8190823c91096f60a00f completed June 8, 2026, 11:32 p.m.
NED2 Entity disambiguation (via description) batch_6a27515ddfd88190be08dfcf4659a411 completed June 8, 2026, 11:33 p.m.
Created at: April 29, 2026, 7:18 p.m.