Triple
T21872202
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Badger |
E540033
|
entity |
| Predicate | hasFormerMember |
P1168
|
FINISHED |
| Object | Roy Dyke |
—
|
NE NERFINISHED |
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: Roy Dyke | Statement: [Badger, hasFormerMember, Roy Dyke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roy Dyke Context triple: [Badger, hasFormerMember, Roy Dyke]
-
A.
Roy Dyke
chosen
Roy Dyke is a musician best known as the drummer for the British rock band Badger.
-
B.
Robert Dyke
Robert Dyke is a film director best known for helming the 1989 science fiction horror movie "Moontrap."
-
C.
John Dury
John Dury was a 17th-century Scottish Calvinist minister and ecumenical reformer known for his efforts to promote Protestant unity across Europe.
-
D.
Roy Foltrigg
Roy Foltrigg is a hard-driving, politically ambitious U.S. Attorney who serves as the chief legal antagonist in the legal thriller film "The Client."
-
E.
Giles Brindley
Giles Brindley is a British physiologist and neuroscientist known for pioneering work in visual neuroscience and for early research on treatments for erectile dysfunction.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c478f59081909d54302b57fc1ce3 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f0f3368d488190a37224b587858ab0 |
completed | April 28, 2026, 5:49 p.m. |
Created at: April 16, 2026, 6:59 p.m.