Triple
T19921363
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uli Schwab |
E478802
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Uli Schwab |
—
|
NE NERFINISHED |
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: Uli Schwab | Statement: [Uli Schwab, name, Uli Schwab]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uli Schwab Context triple: [Uli Schwab, name, Uli Schwab]
-
A.
Uli Schwab
chosen
Uli Schwab is a notable individual who carries the surname Schwab, recognized for contributions significant enough to be specifically cited as a bearer of the name.
-
B.
Uli Wiesendanger
Uli Wiesendanger is an advertising executive best known as a co-founder of the global advertising agency network TBWA Worldwide.
-
C.
Uli Meyer
Uli Meyer is a German-born animator and illustrator known for his character design and animation work in film and advertising.
-
D.
Martin Breitner
Martin Breitner is the idealistic young German protagonist of the novel and film "The Mortal Storm," whose moral opposition to Nazism drives the story’s central conflict.
-
E.
Sepp Allgeier
Sepp Allgeier was a German cinematographer best known for his work on influential and controversial propaganda and documentary films in the early 20th century.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e521855c8190b41871700afc8d6a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e659c564788190a3893fc73fc4922b |
completed | April 20, 2026, 4:52 p.m. |
Created at: April 10, 2026, 1:53 p.m.