Triple
T26333606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zilog Z8000 |
E662458
|
entity |
| Predicate | usedIn |
P98
|
FINISHED |
| Object |
Olivetti M20 workstation
The Olivetti M20 workstation is an early 1980s Italian personal computer notable for its advanced 16-bit architecture and distinctive design, aimed at professional and business users.
|
E1717290
|
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: Olivetti M20 workstation | Statement: [Zilog Z8000, usedIn, Olivetti M20 workstation]
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: Olivetti M20 workstation Triple: [Zilog Z8000, usedIn, Olivetti M20 workstation]
Generated description
The Olivetti M20 workstation is an early 1980s Italian personal computer notable for its advanced 16-bit architecture and distinctive design, aimed at professional and business users.
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_69ee812f32748190871d970c4e2a8ddf |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f60f6b1cac8190acae634753161983 |
completed | May 2, 2026, 2:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a118fe2353c819094917ee14c8468b3 |
completed | May 23, 2026, 11:30 a.m. |
| NEDg | Description generation | batch_6a119071f348819093c113dab0fcea45 |
completed | May 23, 2026, 11:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a1190efe1a8819097407e675292a7e4 |
completed | May 23, 2026, 11:35 a.m. |
Created at: April 26, 2026, 10:35 p.m.