Triple
T2346285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret Blagge |
E45137
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Blagge |
E185792
|
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: Blagge | Statement: [Margaret Blagge, familyName, Blagge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blagge Context triple: [Margaret Blagge, familyName, Blagge]
-
A.
Blagge
chosen
Blagge is a surname that serves as an alternative spelling variant of the name Blagg.
-
B.
Braunlage
Braunlage is a German town and ski resort in the Harz Mountains, known for its winter sports, hiking opportunities, and scenic natural surroundings.
-
C.
Blomstedt
Blomstedt is a surname most prominently associated with Herbert Blomstedt, a renowned Swedish conductor known for his interpretations of the classical and romantic repertoire.
-
D.
Wolthusen
Wolthusen is a district of the seaport city of Emden in Lower Saxony, Germany, known for its residential character and proximity to the Ems estuary.
-
E.
Meesseman
Meesseman is the surname of Belgian professional basketball star Emma Meesseman, known for her success in European leagues and the WNBA.
- 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_69a88917935081909b755dbf38e81024 |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abc6c9396081908abb2b0a229bb046 |
completed | March 7, 2026, 6:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae9629f4908190ba3c51b7d12be4e4 |
completed | March 9, 2026, 9:43 a.m. |
Created at: March 4, 2026, 7:52 p.m.