Triple

T27406461
Position Surface form Disambiguated ID Type / Status
Subject Louis Marcoussis E692013 entity
Predicate birthName P65 FINISHED
Object Ludwik Kazimierz Markus
Ludwik Kazimierz Markus, better known as Louis Marcoussis, was a Polish-French painter and engraver associated with the Cubist movement in early 20th-century Paris.
 E1770693 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: Ludwik Kazimierz Markus | Statement: [Louis Marcoussis, birthName, Ludwik Kazimierz Markus]
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: Ludwik Kazimierz Markus
Triple: [Louis Marcoussis, birthName, Ludwik Kazimierz Markus]
Generated description
Ludwik Kazimierz Markus, better known as Louis Marcoussis, was a Polish-French painter and engraver associated with the Cubist movement in early 20th-century Paris.

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_69ef5205fc808190ad3efc5525b8e6d6 completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62cd639388190bc2e0daf2aa164e3 completed May 2, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12a7fc9a3c8190a5fad04ea0fb87d3 completed May 24, 2026, 7:25 a.m.
NEDg Description generation batch_6a12a8d49ae88190a9fba80993659445 completed May 24, 2026, 7:29 a.m.
NED2 Entity disambiguation (via description) batch_6a12aa0a89b88190ad4e1c5b0e26205b completed May 24, 2026, 7:34 a.m.
Created at: April 27, 2026, 12:30 p.m.