Triple
T22497446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ture Turesson Bielke |
E556179
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Bielke |
—
|
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: Bielke | Statement: [Ture Turesson Bielke, familyName, Bielke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bielke Context triple: [Ture Turesson Bielke, familyName, Bielke]
-
A.
Bielke
chosen
Bielke is the surname of a notable Swedish noble family historically associated with prominent political and military figures.
-
B.
Belpberg
Belpberg is a small former municipality in the canton of Bern, Switzerland, situated on a plateau above the Gürbetal valley and known for its rural, scenic landscape.
-
C.
Biegun
Biegun is a Polish surname borne by various individuals, including figures in politics, academia, and the arts.
-
D.
Beselich
Beselich is a municipality in the Limburg-Weilburg district of Hesse, Germany, known for its rural character and proximity to the Lahn River region.
-
E.
Eichelbaum
Eichelbaum is a German-language surname borne by various individuals, including figures in law, arts, and public life.
- 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_69e11e5445bc8190b6a9481926db3355 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15cb31b8081909553fa860a07e746 |
completed | April 29, 2026, 1:19 a.m. |
Created at: April 16, 2026, 8:50 p.m.