Triple
T5819632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samsung Techwin |
E129074
|
entity |
| Predicate | hasFormerName |
P65
|
FINISHED |
| Object | Samsung Precision |
E548168
|
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: Samsung Precision | Statement: [Samsung Techwin, hasFormerName, Samsung Precision]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samsung Precision Context triple: [Samsung Techwin, hasFormerName, Samsung Precision]
-
A.
Samsung Precision
chosen
Samsung Precision was a South Korean company specializing in precision machinery and optics that later evolved into Samsung Techwin.
-
B.
OptiPlex
OptiPlex is Dell’s line of business-oriented desktop computers designed for reliability, manageability, and long-term corporate use.
-
C.
Surface Laptop
Surface Laptop is a premium line of thin, lightweight Windows ultrabooks designed and produced by Microsoft under its Surface brand.
-
D.
Surface Laptop Studio
Surface Laptop Studio is a high-end Microsoft 2-in-1 laptop featuring a unique pull-forward hinged display designed for both traditional notebook use and creative, pen-enabled workflows.
-
E.
ThinkStation
ThinkStation is Lenovo’s line of high-performance workstation computers designed for demanding professional applications such as engineering, 3D modeling, and content creation.
- 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_69c0084869e881908d7859492183ca7b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c033e477c08190a8bd37c879e6b6b8 |
completed | March 22, 2026, 6:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a185cc78819098c0f04e7ebfb3c4 |
completed | March 23, 2026, 2:12 a.m. |
Created at: March 22, 2026, 3:53 p.m.