Triple
T927670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dallas Chaparrals |
E20019
|
entity |
| Predicate | notableCoach |
P550
|
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: [Dallas Chaparrals, notableCoach, Tom Nissalke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Nissalke Context triple: [Dallas Chaparrals, notableCoach, 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.
Detlef Schrempf
Detlef Schrempf is a German former professional basketball player best known in the NBA for his versatile forward play, multiple All-Star selections, and winning the Sixth Man of the Year Award.
-
C.
Thomas Borsch
Thomas Borsch is a German botanist and academic known for his leadership of the Berlin Botanical Garden and his research on plant systematics and biodiversity.
-
D.
Peter Vermes
Peter Vermes is an American soccer coach and former defender best known for his long tenure leading Sporting Kansas City in Major League Soccer.
-
E.
Martin Benrath
Martin Benrath was a German actor known for his extensive work in film, television, and theater from the mid-20th century onward.
- 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_69a493af3dc48190adb7263e6e445ea1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b32de3cc81908a0ef885795677ff |
completed | March 1, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac7631d5d081909c1229d1b792ddb6 |
completed | March 7, 2026, 7:02 p.m. |
Created at: March 1, 2026, 7:40 p.m.