Triple
T8212337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Members, Don’t Git Weary |
E191848
|
entity |
| Predicate | hasTrack |
P3284
|
FINISHED |
| Object | Effi |
E474491
|
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: Effi | Statement: [Members, Don’t Git Weary, hasTrack, Effi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Effi Context triple: [Members, Don’t Git Weary, hasTrack, Effi]
-
A.
Ottilie von Pogwisch
Ottilie von Pogwisch was a German noblewoman best known as the wife of August von Goethe and daughter-in-law of the writer Johann Wolfgang von Goethe.
-
B.
Ottilie Einhorn
Ottilie Einhorn is known as one of the children of American hedge fund manager and Greenlight Capital founder David Einhorn.
-
C.
Elfriede
chosen
Elfriede is a feminine given name of German origin, notably borne by Austrian Nobel Prize–winning writer Elfriede Jelinek.
-
D.
Verena
Verena is a feminine given name of Latin origin, commonly used in German-speaking and other European countries.
-
E.
Franziska
Franziska is a feminine given name of German origin, closely related to and cognate with the name Frances.
- 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_69ca82c8c054819087fedd9a5436b8a3 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb76e07a1c8190b5d1ec2ef16966ad |
completed | March 31, 2026, 7:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccedea881481909f9348778290eb63 |
completed | April 1, 2026, 10:05 a.m. |
Created at: March 30, 2026, 5:44 p.m.