Triple
T381057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NORD series |
E8678
|
entity |
| Predicate | producedBy |
P490
|
FINISHED |
| Object | Norsk Data AS |
E48799
|
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: Norsk Data AS | Statement: [NORD series, producedBy, Norsk Data AS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Norsk Data AS Context triple: [NORD series, producedBy, Norsk Data AS]
-
A.
Norsk Data
chosen
Norsk Data was a Norwegian computer company best known for producing the NORD series of minicomputers during the 1970s and 1980s.
-
B.
Tokyo Tsushin Kogyo
Tokyo Tsushin Kogyo was the original name of the Japanese electronics company that later became globally known as Sony.
-
C.
Norsk Data NORD-10
Norsk Data NORD-10 was a 16-bit minicomputer series from the Norwegian company Norsk Data, widely used in the 1970s and 1980s for scientific, technical, and commercial applications.
-
D.
Micros Systems
Micros Systems was a leading provider of point-of-sale and hospitality management software and hardware solutions for restaurants, hotels, and retail businesses.
-
E.
NCR Corporation
NCR Corporation is a global technology company best known for its point-of-sale systems, ATMs, and other financial and retail transaction solutions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a2e7f47dd08190a4e294ccbbe46cd4 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec2c95088190a603bb1ee076ebd6 |
completed | Feb. 28, 2026, 1:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a405f431d48190b83e2eaa2fe0e587 |
completed | March 1, 2026, 9:25 a.m. |
Created at: Feb. 28, 2026, 1:08 p.m.