Triple
T8010612
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Günter Grass |
E186479
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Anna Schwarz |
E186479
|
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: Anna Schwarz | Statement: [Günter Grass, spouse, Anna Schwarz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Schwarz Context triple: [Günter Grass, spouse, Anna Schwarz]
-
A.
Anna Schwarz
chosen
Anna Schwarz was the wife of Nobel Prize–winning German novelist and playwright Günter Grass.
-
B.
Anna Schloss
Anna Schloss was the wife of renowned American value investor Walter Schloss.
-
C.
Anna Wimschneider
Anna Wimschneider was a German farmer and autobiographical writer best known for her memoir "Herbstmilch," which depicts her impoverished rural upbringing and life in early 20th-century Bavaria.
-
D.
Aniela Jaffé
Aniela Jaffé was a Swiss analyst and writer best known as a close collaborator and biographer of Carl Gustav Jung, helping to record and shape his autobiographical work and ideas.
-
E.
Nena von Schlebrügge
Nena von Schlebrügge is a Swedish-born former fashion model of German and Swedish descent who worked internationally in the 1950s and 1960s and is the mother of actress Uma Thurman.
- 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_69ca82abaffc8190ab8af79cdbc31ab3 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3d70caf8819090a9f98025470c0d |
completed | March 31, 2026, 3:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc63c1f76881909ad6d7777090f2e2 |
completed | April 1, 2026, 12:16 a.m. |
Created at: March 30, 2026, 5:19 p.m.