Triple
T5858421
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tom Nissalke |
E130212
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Tom Nissalke |
E130212
|
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: Tom Nissalke | Statement: [Tom Nissalke, name, Tom Nissalke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Nissalke Context triple: [Tom Nissalke, name, Tom Nissalke]
-
A.
Tom Nissalke
chosen
Tom Nissalke was an American professional basketball coach best known for his work in the ABA and NBA during the 1970s and 1980s.
-
B.
Dieter Löhr
Dieter Löhr is a person notable enough to be recognized as a namesake of the surname Löhr, though specific widely known biographical details about him are not clearly established.
-
C.
Jürgen Ovens
Jürgen Ovens was a 17th-century German-Danish Baroque painter known for his portraits and history paintings, active primarily in Schleswig and the Netherlands.
-
D.
Uli Meyer
Uli Meyer is a German-born animator and illustrator known for his character design and animation work in film and advertising.
-
E.
Johann Löhr
Johann Löhr is a person notable enough to be recognized as a bearer of the surname Löhr, though specific widely known biographical details about him are not well documented.
- 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_69c0084f3bb08190a7720f55f7aa4252 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0358654e48190908e7390a0164726 |
completed | March 22, 2026, 6:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a1c3a69881908ffeee1ddbeb8618 |
completed | March 23, 2026, 2:13 a.m. |
Created at: March 22, 2026, 3:56 p.m.