Triple
T7554249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles Geschke |
E178617
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Geschke |
E178617
|
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: Geschke | Statement: [Charles Geschke, familyName, Geschke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Geschke Context triple: [Charles Geschke, familyName, Geschke]
-
A.
Geschke
chosen
Geschke is a German-origin surname most notably associated with Charles Geschke, the co-founder of Adobe Systems.
-
B.
Kugler
Kugler is a German-language surname borne by various notable individuals across fields such as history, the arts, and public life.
-
C.
Fleischer
Fleischer is a surname of German origin borne by various notable individuals across different fields.
-
D.
Keutenberg
Keutenberg is a famously steep and decisive hill in the Dutch Limburg region, often shaping the outcome of professional cycling races.
-
E.
Gershon Kekst
Gershon Kekst was a prominent American businessman and philanthropist known for his leadership in corporate communications and his significant support of Jewish and higher education institutions.
- 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_69c69f2da22c8190a50942ac20af70e8 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f8b990148190b26a3a262cf538b3 |
completed | March 27, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c856be6e1c8190ba292d4d9cf1f37f |
completed | March 28, 2026, 10:31 p.m. |
Created at: March 27, 2026, 3:49 p.m.