Triple
T14372510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daniel Wellington |
E356389
|
entity |
| Predicate | logoText |
P3623
|
FINISHED |
| Object | DW |
E1095397
|
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: DW | Statement: [Daniel Wellington, logoText, DW]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DW Context triple: [Daniel Wellington, logoText, DW]
-
A.
DW
DW is the abbreviation for Deutsche Werft AG, a former German shipbuilding company based in Hamburg.
-
B.
DW
chosen
DW is the commonly used abbreviation for Daniel Wellington, a Swedish watch and accessories brand known for its minimalist, classic designs.
-
C.
WD
WD is a consumer-facing brand of Western Digital known for its hard drives, solid-state drives, and other data storage products.
-
D.
WD
WD is the National Rail station code for Woodside railway station in London, England.
-
E.
WD
WD is a UK postcode area covering parts of southwest Hertfordshire and northwest Greater London, including towns such as Watford.
- 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_69d8279163a081908aec45c0e3f1e02f |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8fb2082c8190b42cc5f2bab4f574 |
completed | April 14, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd551002948190aeb93d245e1449a7 |
completed | May 8, 2026, 3:14 a.m. |
Created at: April 10, 2026, 1:15 a.m.