Triple
T6485449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norden |
E146496
|
entity |
| Predicate | postcodeArea |
P920
|
FINISHED |
| Object | OL |
E73664
|
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: OL | Statement: [Norden, postcodeArea, OL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OL Context triple: [Norden, postcodeArea, OL]
-
A.
OL
chosen
OL is a UK postcode area covering Oldham and surrounding parts of Greater Manchester and nearby regions in North West England.
-
B.
OL
OL is the commonly used abbreviation for Olympique Lyonnais, a major French football club best known internationally for its highly successful women's team.
-
C.
OL
OL is the vehicle registration code for the city of Oldenburg in the German state of Lower Saxony.
-
D.
OL
OL is the post-nominal abbreviation used by recipients of Papua New Guinea’s Order of Logohu, a national honor recognizing distinguished service.
-
E.
OLE
OLE (Object Linking and Embedding) is a Microsoft technology that enables embedding and linking to documents and other objects within different applications, forming a foundation for later component technologies like ActiveX.
- 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_69c0090158c08190af0df9a2348d2d52 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06a6efe1881909a044b1cdaa511af |
completed | March 22, 2026, 10:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c653b4e91c81908dfa1798a057b21a |
completed | March 27, 2026, 9:53 a.m. |
Created at: March 22, 2026, 4:52 p.m.