Triple

T26175141
Position Surface form Disambiguated ID Type / Status
Subject Madonna in a Fur Coat E654517 entity
Predicate mainCharacter P1183 FINISHED
Object Maria Puder
Maria Puder is a complex, independent, and melancholic young artist who serves as the enigmatic female protagonist in Sabahattin Ali’s novel "Madonna in a Fur Coat."
E1711451 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: Maria Puder | Statement: [Madonna in a Fur Coat, mainCharacter, Maria Puder]
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: Maria Puder
Triple: [Madonna in a Fur Coat, mainCharacter, Maria Puder]
Generated description
Maria Puder is a complex, independent, and melancholic young artist who serves as the enigmatic female protagonist in Sabahattin Ali’s novel "Madonna in a Fur Coat."

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_69ee5b45873c81909499203612d05d07 completed April 26, 2026, 6:36 p.m.
NER Named-entity recognition batch_69f60c6bc408819098c4288f77d75e38 completed May 2, 2026, 2:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a11277d70688190af8859def1a55084 completed May 23, 2026, 4:05 a.m.
NEDg Description generation batch_6a112d8ab4a481908ccfe11f16d1b4e5 completed May 23, 2026, 4:31 a.m.
NED2 Entity disambiguation (via description) batch_6a1132067ae881909318388b7671cfe6 completed May 23, 2026, 4:50 a.m.
Created at: April 26, 2026, 8:37 p.m.