Triple

T27109458
Position Surface form Disambiguated ID Type / Status
Subject The Virgin Spring E686669 entity
Predicate editedBy P1954 FINISHED
Object Oscar Rosander
Oscar Rosander was a prominent Swedish film editor known for his extensive work in classic Scandinavian cinema, including collaborations with renowned directors like Ingmar Bergman.
E1787853 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: Oscar Rosander | Statement: [The Virgin Spring, editedBy, Oscar Rosander]
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: Oscar Rosander
Triple: [The Virgin Spring, editedBy, Oscar Rosander]
Generated description
Oscar Rosander was a prominent Swedish film editor known for his extensive work in classic Scandinavian cinema, including collaborations with renowned directors like Ingmar Bergman.

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_69ef148accd48190b6ed6e13a15f2a4f completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f6240111c08190a863d9786d36af1d completed May 2, 2026, 4:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12ec8c01048190b3058418338a28c1 completed May 24, 2026, 12:18 p.m.
NEDg Description generation batch_6a12ed2afa9481909cc0ca56270ba2a0 completed May 24, 2026, 12:20 p.m.
NED2 Entity disambiguation (via description) batch_6a12edccfe54819094da363072bdf7a6 completed May 24, 2026, 12:23 p.m.
Created at: April 27, 2026, 8:53 a.m.