Triple
T35134272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richard Sapper |
E1014525
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Alessi 9090 espresso maker
The Alessi 9090 espresso maker is an iconic stainless-steel stovetop coffee maker renowned for its sleek, functional design and status as a classic of modern industrial design.
|
E2128400
|
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: Alessi 9090 espresso maker | Statement: [Richard Sapper, notableWork, Alessi 9090 espresso maker]
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: Alessi 9090 espresso maker Triple: [Richard Sapper, notableWork, Alessi 9090 espresso maker]
Generated description
The Alessi 9090 espresso maker is an iconic stainless-steel stovetop coffee maker renowned for its sleek, functional design and status as a classic of modern industrial design.
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_69f76dd9c1848190af70d4882a2c1ad7 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f78c6d6b9881909ccd12d8e2e6639e |
completed | May 3, 2026, 5:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a37d9551fc8819083bddbcbc8448fe7 |
completed | June 21, 2026, 12:30 p.m. |
| NEDg | Description generation | batch_6a37f62358048190b30eb50715d715be |
completed | June 21, 2026, 2:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a37f688edc48190830eda1cc3366d8f |
completed | June 21, 2026, 2:34 p.m. |
Created at: May 3, 2026, 4:02 p.m.