Triple

T36267888
Position Surface form Disambiguated ID Type / Status
Subject Landsmeer (village) E892280 entity
Predicate nearProtectedArea P350 FINISHED
Object Varkensland nature area
Varkensland nature area is a protected wetland and meadow landscape in the Netherlands known for its rich birdlife and traditional polder scenery.
E2181040 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: Varkensland nature area | Statement: [Landsmeer (village), nearProtectedArea, Varkensland nature 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: Varkensland nature area
Triple: [Landsmeer (village), nearProtectedArea, Varkensland nature area]
Generated description
Varkensland nature area is a protected wetland and meadow landscape in the Netherlands known for its rich birdlife and traditional polder scenery.

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_69f76e4699188190af045b11a840ce31 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b6280650819097dc045343fe553f completed May 3, 2026, 8:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39a31179cc8190b2e28183c2904ed3 completed June 22, 2026, 9:03 p.m.
NEDg Description generation batch_6a39a9542af081909f7d6e6834a575d7 completed June 22, 2026, 9:29 p.m.
NED2 Entity disambiguation (via description) batch_6a39ad2ff2c08190ad76899fe957d1ad completed June 22, 2026, 9:46 p.m.
Created at: May 3, 2026, 4:09 p.m.