Triple

T25142059
Position Surface form Disambiguated ID Type / Status
Subject Den Burg E629828 entity
Predicate administrativeCenterOf P383 FINISHED
Object municipality of Texel
The municipality of Texel is a Dutch local government area encompassing the island of Texel in the province of North Holland, known for its beaches, dunes, and nature reserves.
E1666863 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: municipality of Texel | Statement: [Den Burg, administrativeCenterOf, municipality of Texel]
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: municipality of Texel
Triple: [Den Burg, administrativeCenterOf, municipality of Texel]
Generated description
The municipality of Texel is a Dutch local government area encompassing the island of Texel in the province of North Holland, known for its beaches, dunes, and nature reserves.

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_69e2ff349e408190a6f4a5a66279f54d completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f46849ab4081909a2278c535b5e5bc completed May 1, 2026, 8:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a105d02862081909e976d06aa19f528 completed May 22, 2026, 1:41 p.m.
NEDg Description generation batch_6a105df5bf44819082f76c7e8c6728b2 completed May 22, 2026, 1:45 p.m.
NED2 Entity disambiguation (via description) batch_6a105f5aa33c819098ce8cc50b09ee62 completed May 22, 2026, 1:51 p.m.
Created at: April 18, 2026, 6:29 a.m.