Triple
T6145461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bernie |
E137063
|
entity |
| Predicate | hasSpellingVariant |
P457
|
FINISHED |
| Object | Berny |
E133458
|
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: Berny | Statement: [Bernie, hasSpellingVariant, Berny]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Berny Context triple: [Bernie, hasSpellingVariant, Berny]
-
A.
Berny
chosen
Berny is a given name or nickname, typically used as a familiar or informal variant of the name Bernard.
-
B.
Bordon
Bordon is a town in East Hampshire, England, historically known for its large army camp and military training facilities.
-
C.
Berri
Berri is a regional town in South Australia's Riverland area, known for its citrus and grape production along the Murray River.
-
D.
Nantz
Nantz is the surname of Jim Nantz, a prominent American sportscaster best known for his long-running work with CBS Sports covering events like the NFL, NCAA basketball, and The Masters.
-
E.
Beyton
Beyton is a small rural village and civil parish in the English county of Suffolk, known for its traditional village green and historic buildings.
- 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_69c008a2c6308190a56519b22d55d083 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05cdd2dc0819080a4adf0cead3603 |
completed | March 22, 2026, 9:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c135fe8ae48190bfb20c335c7d32be |
completed | March 23, 2026, 12:45 p.m. |
Created at: March 22, 2026, 4:16 p.m.