Triple

T36815747
Position Surface form Disambiguated ID Type / Status
Subject Tage Danielsson E909729 entity
Predicate notableWork P4 FINISHED
Object Sagan om Karl-Bertil Jonssons julafton
Sagan om Karl-Bertil Jonssons julafton is a beloved Swedish animated Christmas film and satirical tale about a young boy who redistributes Christmas presents in the spirit of Robin Hood.
E2199620 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: Sagan om Karl-Bertil Jonssons julafton | Statement: [Tage Danielsson, notableWork, Sagan om Karl-Bertil Jonssons julafton]
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: Sagan om Karl-Bertil Jonssons julafton
Triple: [Tage Danielsson, notableWork, Sagan om Karl-Bertil Jonssons julafton]
Generated description
Sagan om Karl-Bertil Jonssons julafton is a beloved Swedish animated Christmas film and satirical tale about a young boy who redistributes Christmas presents in the spirit of Robin Hood.

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_69f76e7dd13c81908c60b05adb49eeb5 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f7ca93bd0481909d6eee9e950001a1 completed May 3, 2026, 10:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3d17ba3f4081909a63b91d53be2c76 completed June 25, 2026, 11:57 a.m.
NEDg Description generation batch_6a3d1e09f30481908e0b2c91a876d17e completed June 25, 2026, 12:24 p.m.
NED2 Entity disambiguation (via description) batch_6a3da6b42820819093a377a8b7d05529 completed June 25, 2026, 10:07 p.m.
Created at: May 3, 2026, 4:13 p.m.