Triple
T37696281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Linn Records |
E938936
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object |
Ivor Tiefenbrun
Ivor Tiefenbrun is a Scottish audio engineer and entrepreneur best known as the founder of Linn, a high-end hi-fi company renowned for its turntables and audio equipment.
|
E2271294
|
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: Ivor Tiefenbrun | Statement: [Linn Records, foundedBy, Ivor Tiefenbrun]
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: Ivor Tiefenbrun Triple: [Linn Records, foundedBy, Ivor Tiefenbrun]
Generated description
Ivor Tiefenbrun is a Scottish audio engineer and entrepreneur best known as the founder of Linn, a high-end hi-fi company renowned for its turntables and audio equipment.
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_69f76eda6ae48190b3111071eeacc038 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fbae2169d881909a0ff9bbedfabe00 |
completed | May 6, 2026, 9:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41cc8d5cd88190af33a4e61b4d8e9f |
completed | June 29, 2026, 1:38 a.m. |
| NEDg | Description generation | batch_6a41ce0a6ae081909a96d33869cfde6b |
completed | June 29, 2026, 1:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a41ceaec9a48190bd08361fd7b3362b |
completed | June 29, 2026, 1:47 a.m. |
Created at: May 3, 2026, 4:18 p.m.