Triple
T10419886
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kim Lammers |
E245617
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Lammers |
E245617
|
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: Lammers | Statement: [Kim Lammers, familyName, Lammers]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lammers Context triple: [Kim Lammers, familyName, Lammers]
-
A.
Lammers
chosen
Lammers is a German surname borne by various notable individuals in fields such as politics, sports, and the arts.
-
B.
Lammer
The Lammer is a river in the Austrian state of Salzburg, known for its clear waters and popularity for kayaking and rafting before joining the Salzach River.
-
C.
Hammann
Hammann is a German-origin surname borne by various notable individuals in fields such as aviation, music, and academia.
-
D.
Klostermann
Klostermann is a German academic publishing house known for its influential works in philosophy and the humanities.
-
E.
Wesselmann
Wesselmann is a surname most notably associated with Tom Wesselmann, a prominent American Pop Art painter known for his bold, stylized depictions of the nude and everyday consumer objects.
- 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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea2aa7848190a7091ee71722fcc6 |
completed | April 7, 2026, 11:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d95e5197cc8190ad70c665ec2f8fa8 |
completed | April 10, 2026, 8:32 p.m. |
Created at: April 6, 2026, 12:11 p.m.