Triple
T2803316
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elsa Löwenthal |
E53994
|
entity |
| Predicate | previousSpouse |
P493
|
FINISHED |
| Object | Max Löwenthal |
E61882
|
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: Max Löwenthal | Statement: [Elsa Löwenthal, previousSpouse, Max Löwenthal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Max Löwenthal Context triple: [Elsa Löwenthal, previousSpouse, Max Löwenthal]
-
A.
Max Löwenthal
chosen
Max Löwenthal was a German lawyer and civil servant best known as the first husband of Elsa Einstein, who later married Albert Einstein.
-
B.
Peter Kornbluh
Peter Kornbluh is an American historian and investigative journalist known for his work on U.S. foreign policy and declassified government documents, particularly regarding Latin America.
-
C.
Michael Rotenberg
Michael Rotenberg is a television producer and manager best known for his work on popular comedy series including It's Always Sunny in Philadelphia.
-
D.
Michael Berenbaum
Michael Berenbaum is an American film and television editor known for his work on numerous popular comedies and dramas.
-
E.
Daniel H. Weiss
Daniel H. Weiss is an American art historian and academic leader who served as president and CEO of New York’s Metropolitan Museum of Art.
- 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_69ab49dcee188190b5c6eca9ae9e3469 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde12b33481908b276760a922db9c |
completed | March 7, 2026, 8:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afe8a5c364819092b01e90ee40e155 |
completed | March 10, 2026, 9:47 a.m. |
Created at: March 6, 2026, 9:59 p.m.