Triple

T26792019
Position Surface form Disambiguated ID Type / Status
Subject East West MRT line E670541 entity
Predicate hasRollingStock P1305 FINISHED
Object Kawasaki–CSR Qingdao Sifang C151A trains
The Kawasaki–CSR Qingdao Sifang C151A trains are a fleet of modern electric multiple units operating on Singapore’s Mass Rapid Transit system.
 E1746122 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: Kawasaki–CSR Qingdao Sifang C151A trains | Statement: [East West MRT line, hasRollingStock, Kawasaki–CSR Qingdao Sifang C151A trains]
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: Kawasaki–CSR Qingdao Sifang C151A trains
Triple: [East West MRT line, hasRollingStock, Kawasaki–CSR Qingdao Sifang C151A trains]
Generated description
The Kawasaki–CSR Qingdao Sifang C151A trains are a fleet of modern electric multiple units operating on Singapore’s Mass Rapid Transit system.

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_69eeb31d45f8819089f52ebdbc556218 completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f619bd27908190ac9152693dd37658 completed May 2, 2026, 3:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a121e8b36d0819085b386ee1212abdf completed May 23, 2026, 9:39 p.m.
NEDg Description generation batch_6a121f0ade0481909f63ec824a028120 completed May 23, 2026, 9:41 p.m.
NED2 Entity disambiguation (via description) batch_6a121f80ab148190a642473aff894d9c completed May 23, 2026, 9:43 p.m.
Created at: April 27, 2026, 4:17 a.m.