Triple
T2673058
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fanny Koch |
E56392
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Koch |
E52535
|
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: Koch | Statement: [Fanny Koch, familyName, Koch]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Koch Context triple: [Fanny Koch, familyName, Koch]
-
A.
Koch
chosen
Koch is a German surname most famously associated with Robert Koch, the pioneering microbiologist who discovered the causative agents of tuberculosis and cholera.
-
B.
Kocher
Kocher is a Swiss surname most notably associated with Nobel Prize–winning surgeon Emil Theodor Kocher.
-
C.
Krieblowitz
Krieblowitz was a village in Silesia (now Krobielowice, Poland) historically notable as the estate and place of death of Prussian field marshal Gebhard Leberecht von Blücher.
-
D.
Kretschmann
Kretschmann is a German surname most prominently associated with actor Thomas Kretschmann, known for his roles in international film and television.
-
E.
Ehrlich
Ehrlich is a German-origin surname borne by numerous notable individuals across fields such as science, medicine, and the arts.
- 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_69ab4a4b13fc81909dfdb3f23da46832 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd9b08b1c8190824342fc63e555d2 |
completed | March 7, 2026, 7:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afa06170108190a6f4be82fa4ecd2a |
completed | March 10, 2026, 4:38 a.m. |
Created at: March 6, 2026, 9:54 p.m.