Triple
T6646215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Max Warburg |
E150707
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Warburg |
E152756
|
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: Warburg | Statement: [Max Warburg, familyName, Warburg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Warburg Context triple: [Max Warburg, familyName, Warburg]
-
A.
Warburg
Warburg is a historic small city in the German state of Hesse, known for its well-preserved medieval old town and hilltop castle.
-
B.
Warburg
chosen
Warburg is a prominent German-Jewish banking and philanthropic family historically influential in international finance and economic policy.
-
C.
Landsberg
Landsberg is a town in the Saalekreis district of the German state of Saxony-Anhalt.
-
D.
Löwenberg
Löwenberg is a town in Germany known for its cultural and municipal partnership as a twin town of Weilburg.
-
E.
Winsum
Winsum is a historic village and former municipality in the Dutch province of Groningen, known for its old churches, windmills, and picturesque canals.
- 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_69c687f1a3048190828b7342f7125d5c |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b01cecc48190a6d2c26d8d5ab80c |
completed | March 27, 2026, 4:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6eef48b488190ac08dc37d4a5d514 |
completed | March 27, 2026, 8:56 p.m. |
Created at: March 27, 2026, 2 p.m.