Triple
T2385019
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norsk Data |
E48799
|
entity |
| Predicate | productFamily |
P11218
|
FINISHED |
| Object | NORD-10 |
E8678
|
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: NORD-10 | Statement: [Norsk Data, productFamily, NORD-10]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: NORD-10 Context triple: [Norsk Data, productFamily, NORD-10]
-
A.
NORD-500
NORD-500 was a Norwegian minicomputer system developed by Norsk Data in the late 1960s as an early member of their NORD series of computers.
-
B.
NORD series
chosen
The NORD series is a line of minicomputers developed by Norwegian company Norsk Data, notable in the 1970s and 1980s for their use in scientific, technical, and real-time computing applications.
-
C.
Nordy
Nordy is the furry, wildcat-like official mascot of the NHL’s Minnesota Wild, known for entertaining fans at games and community events.
-
D.
Nord
Nord is a department in northern France known for its industrial heritage, dense population, and proximity to Belgium.
-
E.
Nork
Nork is a suburban residential area within the borough of Reigate and Banstead in Surrey, England.
- 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_69a88aa5f63081908d07fd302029fcbd |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc7d8a918819089a210e74e13be6e |
completed | March 7, 2026, 6:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aeb3ccec008190b21c0bf84f8ecd09 |
completed | March 9, 2026, 11:49 a.m. |
Created at: March 4, 2026, 7:57 p.m.