Triple

T28249825
Position Surface form Disambiguated ID Type / Status
Subject R142 subway car class E712281 entity
Predicate designedToReplace P101 FINISHED
Object R26 subway cars
The R26 subway cars were a mid-20th-century fleet of New York City Subway rolling stock that served primarily on the IRT lines before being retired and replaced by newer models.
 E1808703 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: R26 subway cars | Statement: [R142 subway car class, designedToReplace, R26 subway cars]
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: R26 subway cars
Triple: [R142 subway car class, designedToReplace, R26 subway cars]
Generated description
The R26 subway cars were a mid-20th-century fleet of New York City Subway rolling stock that served primarily on the IRT lines before being retired and replaced by newer models.

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_69efb51fb98881909692421959ec0170 completed April 27, 2026, 7:12 p.m.
NER Named-entity recognition batch_69f643eecbe481908f4c9be0fa878f36 completed May 2, 2026, 6:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15e6d7e6b88190b525d8f9dba3944c completed May 26, 2026, 6:30 p.m.
NEDg Description generation batch_6a15ee5360c08190b2d41f5094658ccb completed May 26, 2026, 7:02 p.m.
NED2 Entity disambiguation (via description) batch_6a15f3567468819084dba00f9ed8ff68 completed May 26, 2026, 7:24 p.m.
Created at: April 27, 2026, 11:04 p.m.