Triple

T26648991
Position Surface form Disambiguated ID Type / Status
Subject North South Line E668996 entity
Predicate rollingStock P1305 FINISHED
Object Kawasaki–CRRC Qingdao Sifang C151C
The Kawasaki–CRRC Qingdao Sifang C151C is a modern electric multiple unit train used on Singapore’s MRT system, designed to increase capacity and improve reliability on the network.
 E1740259 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–CRRC Qingdao Sifang C151C | Statement: [North South Line, rollingStock, Kawasaki–CRRC Qingdao Sifang C151C]
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–CRRC Qingdao Sifang C151C
Triple: [North South Line, rollingStock, Kawasaki–CRRC Qingdao Sifang C151C]
Generated description
The Kawasaki–CRRC Qingdao Sifang C151C is a modern electric multiple unit train used on Singapore’s MRT system, designed to increase capacity and improve reliability on the 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_69ee9d00eb5481908d6c6d0ada2f0c9a completed April 26, 2026, 11:17 p.m.
NER Named-entity recognition batch_69f6167875e8819083782aec7b793143 completed May 2, 2026, 3:21 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12092f5b1881908e43cce703995dfd completed May 23, 2026, 8:08 p.m.
NEDg Description generation batch_6a120a10905c819096fa77fad68b6bb8 completed May 23, 2026, 8:12 p.m.
NED2 Entity disambiguation (via description) batch_6a120aeed5ec819097f7ac08533bcf65 completed May 23, 2026, 8:15 p.m.
Created at: April 27, 2026, 2:32 a.m.