Triple

T30859006
Position Surface form Disambiguated ID Type / Status
Subject In the Name of the King: A Dungeon Siege Tale E786007 entity
Predicate musicBy P1952 FINISHED
Object Jessica de Rooij
Jessica de Rooij is a German film composer known for scoring numerous feature films, often collaborating with director Uwe Boll on action and fantasy projects.
E1971032 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: Jessica de Rooij | Statement: [In the Name of the King: A Dungeon Siege Tale, musicBy, Jessica de Rooij]
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: Jessica de Rooij
Triple: [In the Name of the King: A Dungeon Siege Tale, musicBy, Jessica de Rooij]
Generated description
Jessica de Rooij is a German film composer known for scoring numerous feature films, often collaborating with director Uwe Boll on action and fantasy projects.

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_69f224b91c14819084e764832fe67a57 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f691a7353c8190bf1cf3ead5cc6421 completed May 3, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2b79ad4f248190844af3d093c3623e completed June 12, 2026, 3:14 a.m.
NEDg Description generation batch_6a2b7ae829ac8190816c4c7ffdfc3a1c completed June 12, 2026, 3:20 a.m.
NED2 Entity disambiguation (via description) batch_6a2b7bdca59c81909eeabf535985fd97 completed June 12, 2026, 3:24 a.m.
Created at: April 29, 2026, 8:46 p.m.