Triple

T24681058
Position Surface form Disambiguated ID Type / Status
Subject NAC Breda E611129 entity
Predicate notableFormerCoach P4378 FINISHED
Object Robert Maaskant
Robert Maaskant is a Dutch football manager and former midfielder best known for coaching several Eredivisie clubs and leading Wisła Kraków to a Polish league title.
E1776082 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: Robert Maaskant | Statement: [NAC Breda, notableFormerCoach, Robert Maaskant]
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: Robert Maaskant
Triple: [NAC Breda, notableFormerCoach, Robert Maaskant]
Generated description
Robert Maaskant is a Dutch football manager and former midfielder best known for coaching several Eredivisie clubs and leading Wisła Kraków to a Polish league title.

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_69e2c4d5c2dc8190ac857dea25ec6ce9 completed April 17, 2026, 11:40 p.m.
NER Named-entity recognition batch_69f40fc0e1ec819088a7f57ee568e392 completed May 1, 2026, 2:28 a.m.
NED1 Entity disambiguation (via context triple) batch_6a12bbad52588190a372438057de5405 completed May 24, 2026, 8:49 a.m.
NEDg Description generation batch_6a12bd0d87a88190a617ee64551f7d93 completed May 24, 2026, 8:55 a.m.
NED2 Entity disambiguation (via description) batch_6a12be3a604c8190887660a427cd9f2f completed May 24, 2026, 9 a.m.
Created at: April 18, 2026, 3:10 a.m.