Triple

T33649894
Position Surface form Disambiguated ID Type / Status
Subject La Louvière E862064 entity
Predicate hasTransport P1298 FINISHED
Object railway station La Louvière-Sud
Railway station La Louvière-Sud is a Belgian train station serving the town of La Louvière in the province of Hainaut, providing regional rail connections within the national rail network.
E2063964 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: railway station La Louvière-Sud | Statement: [La Louvière, hasTransport, railway station La Louvière-Sud]
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: railway station La Louvière-Sud
Triple: [La Louvière, hasTransport, railway station La Louvière-Sud]
Generated description
Railway station La Louvière-Sud is a Belgian train station serving the town of La Louvière in the province of Hainaut, providing regional rail connections within the national rail network.

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_69f349840ba881908e3bfce536aeb92b completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6f9c168608190a7fd52f3fcb4b2ad completed May 3, 2026, 7:31 a.m.
NED1 Entity disambiguation (via context triple) batch_6a363c872084819092d204daaa9f928d completed June 20, 2026, 7:08 a.m.
NEDg Description generation batch_6a3645df8a90819091be19034c7f7d3f completed June 20, 2026, 7:48 a.m.
NED2 Entity disambiguation (via description) batch_6a364ef0a63c81908f41bb87ca0d1745 completed June 20, 2026, 8:27 a.m.
Created at: May 1, 2026, 1:42 a.m.