Triple

T27146456
Position Surface form Disambiguated ID Type / Status
Subject Baron Aarnoud van Heemstra E681960 entity
Predicate givenName P17 FINISHED
Object Aarnoud
Aarnoud is the given name of Baron Aarnoud van Heemstra, a Dutch nobleman and politician from the early 20th century.
E1768926 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: Aarnoud | Statement: [Baron Aarnoud van Heemstra, givenName, Aarnoud]
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: Aarnoud
Triple: [Baron Aarnoud van Heemstra, givenName, Aarnoud]
Generated description
Aarnoud is the given name of Baron Aarnoud van Heemstra, a Dutch nobleman and politician from the early 20th century.

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_69eefacca3888190b67238d380e8f28b completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f624c59988819084bf9be39c3fa44b completed May 2, 2026, 4:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12a7bb41bc8190a706bdc022cf42ca completed May 24, 2026, 7:24 a.m.
NEDg Description generation batch_6a12a8b8ec608190846c55dabeec801a completed May 24, 2026, 7:28 a.m.
NED2 Entity disambiguation (via description) batch_6a12a951f04881909419d5b9d41d5c79 completed May 24, 2026, 7:31 a.m.
Created at: April 27, 2026, 9:11 a.m.