Triple
T7635005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fuchs |
E172853
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object | Daniel Fuchs |
E370687
|
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: Daniel Fuchs | Statement: [Fuchs, hasNotableBearer, Daniel Fuchs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daniel Fuchs Context triple: [Fuchs, hasNotableBearer, Daniel Fuchs]
-
A.
Daniel Fuchs
chosen
Daniel Fuchs was an American novelist and screenwriter known for his Brooklyn-set fiction and acclaimed Hollywood screenplays, including several classic film noirs.
-
B.
Richard Hönigswald
Richard Hönigswald was a German Neo-Kantian philosopher known for his work on epistemology, logic, and the philosophy of science in the early 20th century.
-
C.
Marten Wassmann
Marten Wassmann is an architect known for his partnership role at the Dutch architecture firm Benthem Crouwel Architekten.
-
D.
Robert Ochsenfeld
Robert Ochsenfeld was a German physicist best known for co-discovering the Meissner effect, a fundamental phenomenon in superconductivity.
-
E.
Edward Anhalt
Edward Anhalt was an American screenwriter and producer best known for his Academy Award–winning work on films such as "Panic in the Streets" and "Becket."
- 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_69c69952849881908fdcea7a93bfc307 |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6faa83fcc8190a3f0bb20cbe1b2d6 |
completed | March 27, 2026, 9:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c9116b86cc8190997077243f99cc7d |
completed | March 29, 2026, 11:47 a.m. |
Created at: March 27, 2026, 3:57 p.m.