Triple

T20740419
Position Surface form Disambiguated ID Type / Status
Subject Hoekse Lijn (metro line B) E510422 entity
Predicate hasStation P35 FINISHED
Object Hoek van Holland Haven
Hoek van Holland Haven is a coastal railway and metro station in the Dutch village of Hoek van Holland, serving as a key access point to the North Sea ferry terminal and nearby beaches.
E1840675 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: Hoek van Holland Haven | Statement: [Hoekse Lijn (metro line B), hasStation, Hoek van Holland Haven]
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: Hoek van Holland Haven
Triple: [Hoekse Lijn (metro line B), hasStation, Hoek van Holland Haven]
Generated description
Hoek van Holland Haven is a coastal railway and metro station in the Dutch village of Hoek van Holland, serving as a key access point to the North Sea ferry terminal and nearby beaches.

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_69e0b4c845e88190b4c5f3ae79291182 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c20e76ac8190985203b2c17aca14 completed April 21, 2026, 12:17 a.m.
NED1 Entity disambiguation (via context triple) batch_6a24d3ce324c8190878325e9da4b459b completed June 7, 2026, 2:13 a.m.
NEDg Description generation batch_6a24df49b2a481908847e23c6a8124fb completed June 7, 2026, 3:02 a.m.
NED2 Entity disambiguation (via description) batch_6a24dfb84170819092e27531cb82faaa completed June 7, 2026, 3:04 a.m.
Created at: April 16, 2026, 12:32 p.m.