Triple

T28372280
Position Surface form Disambiguated ID Type / Status
Subject New York City Subway rolling stock E718664 entity
Predicate includesClass P1393 FINISHED
Object R32
The R32 is a class of stainless-steel New York City Subway cars built in the 1960s that became known for their durability and long service life.
 E1815165 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: R32 | Statement: [New York City Subway rolling stock, includesClass, R32]
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: R32
Triple: [New York City Subway rolling stock, includesClass, R32]
Generated description
The R32 is a class of stainless-steel New York City Subway cars built in the 1960s that became known for their durability and long service life.

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_69eff6ee5afc8190bd7375a29f0cc6c6 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64c5b4520819082c5b119371ba557 completed May 2, 2026, 7:11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1627d7983c8190aa0aac8255cddaab completed May 26, 2026, 11:08 p.m.
NEDg Description generation batch_6a162923e55481909c09ac79ce541641 completed May 26, 2026, 11:13 p.m.
NED2 Entity disambiguation (via description) batch_6a1629baced481908056a997a85a1b84 completed May 26, 2026, 11:16 p.m.
Created at: April 28, 2026, 1 a.m.