Triple

T27425713
Position Surface form Disambiguated ID Type / Status
Subject Sotetsu Railway E690477 entity
Predicate rollingStockSeries P45556 FINISHED
Object Sotetsu 20000 series
The Sotetsu 20000 series is a modern Japanese electric multiple unit train operated by Sagami Railway (Sotetsu) for commuter services in the Greater Tokyo area.
E1780220 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: Sotetsu 20000 series | Statement: [Sotetsu Railway, rollingStockSeries, Sotetsu 20000 series]
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: Sotetsu 20000 series
Triple: [Sotetsu Railway, rollingStockSeries, Sotetsu 20000 series]
Generated description
The Sotetsu 20000 series is a modern Japanese electric multiple unit train operated by Sagami Railway (Sotetsu) for commuter services in the Greater Tokyo area.

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_69ef52003fb48190b0f1295246182a86 completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62d546b2881909d0acb99ce291de1 completed May 2, 2026, 4:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12d0bd144481908ca7c7aba724ad9e completed May 24, 2026, 10:19 a.m.
NEDg Description generation batch_6a12d1497cb4819085e9a1a5401d9118 completed May 24, 2026, 10:22 a.m.
NED2 Entity disambiguation (via description) batch_6a12d2747f6881909aa2a5b0c389a494 completed May 24, 2026, 10:27 a.m.
Created at: April 27, 2026, 12:40 p.m.