Triple

T25338195
Position Surface form Disambiguated ID Type / Status
Subject Tondu railway station E635333 entity
Predicate railwayRegion P15232 FINISHED
Object Valleys Lines area
The Valleys Lines area is a suburban rail network serving the former coal-mining valleys and surrounding communities of South Wales, centered on Cardiff.
 E1676009 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: Valleys Lines area | Statement: [Tondu railway station, railwayRegion, Valleys Lines area]
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: Valleys Lines area
Triple: [Tondu railway station, railwayRegion, Valleys Lines area]
Generated description
The Valleys Lines area is a suburban rail network serving the former coal-mining valleys and surrounding communities of South Wales, centered on Cardiff.

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_69e75a99bd6481909476115b35b9a8e4 completed April 21, 2026, 11:08 a.m.
NER Named-entity recognition batch_69f498b4cbb88190a8ac563ad1a73b5d completed May 1, 2026, 12:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1075f2f78c8190a53a12931482cc8a completed May 22, 2026, 3:27 p.m.
NEDg Description generation batch_6a10771a5a648190844a509e6ac507be completed May 22, 2026, 3:32 p.m.
NED2 Entity disambiguation (via description) batch_6a1077d01fa08190b5439eba879538ef completed May 22, 2026, 3:35 p.m.
Created at: April 21, 2026, 1:32 p.m.