Triple
T11472121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bergstrom |
E271933
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Bergström |
E679637
|
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: Bergström | Statement: [Bergstrom, hasVariant, Bergström]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bergström Context triple: [Bergstrom, hasVariant, Bergström]
-
A.
Bergström
chosen
Bergström is a common Swedish surname borne by numerous notable figures in fields such as science, sports, and the arts.
-
B.
Bäckström
Bäckström is a Swedish surname most prominently associated with NHL ice hockey star Nicklas Bäckström.
-
C.
Sundström
Sundström is a Swedish surname borne by various notable individuals, including actress Rebecca Ferguson.
-
D.
Wikström
Wikström is the Swedish family name of Maud Adams, the actress best known for her roles in James Bond films.
-
E.
Enström
Enström is a Swedish surname most notably associated with professional ice hockey player Tobias Enström.
- 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_69d6aae0c8d881908a5a360c0be3242e |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8294b3f388190a587c358313f7260 |
completed | April 9, 2026, 10:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5e95a479c81909a330c9721bd3902 |
completed | April 20, 2026, 8:52 a.m. |
Created at: April 8, 2026, 9:35 p.m.