Triple
T12734503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allen Weisselberg |
E304327
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Weisselberg |
E304327
|
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: Weisselberg | Statement: [Allen Weisselberg, familyName, Weisselberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Weisselberg Context triple: [Allen Weisselberg, familyName, Weisselberg]
-
A.
Weisselberg
chosen
Weisselberg is a surname most prominently associated with Allen Weisselberg, the longtime chief financial officer of the Trump Organization.
-
B.
Wirsberg
Wirsberg is a small market town in the Upper Franconia region of Bavaria, Germany, known for its scenic location in the Franconian Forest and its historic architecture.
-
C.
Wielenbach
Wielenbach is a small municipality in the Upper Bavarian region of Germany, situated within the district of Weilheim-Schongau.
-
D.
Voitsberg
Voitsberg is a small town in southeastern Austria known for its industrial heritage and location within the federal state of Styria.
-
E.
Irschenberg
Irschenberg is a Bavarian municipality in southern Germany known for its scenic location in the Alpine foothills and its prominent motorway ascent on the A8 between Munich and Salzburg.
- 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_69d7bdf1426c8190a4402e1c4cdec33a |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9646902408190b29268d864833b80 |
completed | April 10, 2026, 8:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6a53e9fa08190805d55e9fcf7e79f |
completed | May 3, 2026, 1:30 a.m. |
Created at: April 9, 2026, 5:26 p.m.