Triple

T24187514
Position Surface form Disambiguated ID Type / Status
Subject Swansea Vale Railway E599595 entity
Predicate partOf P40 FINISHED
Object South Wales industrial railway network
The South Wales industrial railway network was an extensive system of lines serving the region’s coal mines, steelworks, and other heavy industries, crucial to the area’s economic development during the 19th and 20th centuries.
E1625542 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: South Wales industrial railway network | Statement: [Swansea Vale Railway, partOf, South Wales industrial railway network]
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: South Wales industrial railway network
Triple: [Swansea Vale Railway, partOf, South Wales industrial railway network]
Generated description
The South Wales industrial railway network was an extensive system of lines serving the region’s coal mines, steelworks, and other heavy industries, crucial to the area’s economic development during the 19th and 20th centuries.

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_69e288cdc8b88190bf2f835d3cb4ca28 completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1e24423108190a580ce7d69860a22 completed April 29, 2026, 10:49 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fbd0fb99881908f1b48772219aa1b completed May 22, 2026, 2:18 a.m.
NEDg Description generation batch_6a0fbe78822881909e04f037a60db091 completed May 22, 2026, 2:24 a.m.
NED2 Entity disambiguation (via description) batch_6a0fbf0ed7808190b64797da02f8fbac completed May 22, 2026, 2:27 a.m.
Created at: April 17, 2026, 11:35 p.m.