Triple

T36137170
Position Surface form Disambiguated ID Type / Status
Subject Goes E1045198 entity
Predicate hasLandmark P105 FINISHED
Object Klok’uus area
The Klok’uus area is a notable local landmark district within the municipality of Goes in the Netherlands, recognized for its distinct neighborhood character and urban layout.
 E2169547 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: Klok’uus area | Statement: [Goes, hasLandmark, Klok’uus 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: Klok’uus area
Triple: [Goes, hasLandmark, Klok’uus area]
Generated description
The Klok’uus area is a notable local landmark district within the municipality of Goes in the Netherlands, recognized for its distinct neighborhood character and urban layout.

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_69f76e36a4508190b5bfc8f594272a4c completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b337403481909a80e56d9f4090fb completed May 3, 2026, 8:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38de1b5c948190b2c03b9817f9b94a completed June 22, 2026, 7:02 a.m.
NEDg Description generation batch_6a38f29932788190a48a9157929af308 completed June 22, 2026, 8:30 a.m.
NED2 Entity disambiguation (via description) batch_6a38f49bd140819085e7eed992c797fd completed June 22, 2026, 8:38 a.m.
Created at: May 3, 2026, 4:08 p.m.