Triple
T3210071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lena Gieseke |
E67256
|
entity |
| Predicate | isKnownAs |
P39
|
FINISHED |
| Object | Lena Gieseke |
E67256
|
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: Lena Gieseke | Statement: [Lena Gieseke, isKnownAs, Lena Gieseke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lena Gieseke Context triple: [Lena Gieseke, isKnownAs, Lena Gieseke]
-
A.
Lena Gieseke
chosen
Lena Gieseke is a German visual effects artist and academic known for her work in 3D animation and digital media.
-
B.
Therese Giehse
Therese Giehse was a prominent German stage and film actress, renowned for her powerful performances in works by Bertolt Brecht and for her opposition to the Nazi regime.
-
C.
Maike Kohl-Richter
Maike Kohl-Richter is a German academic and lawyer best known as the second wife and widow of former Chancellor Helmut Kohl.
-
D.
Julia Sauer
Julia Sauer was an American librarian and author best known for her atmospheric children's fantasy and historical novels, including the Newbery Honor book "Fog Magic."
-
E.
Johanna Herting
Johanna Herting was the wife of 19th-century civil engineer John A. Roebling, known for supporting him during his career designing pioneering suspension bridges such as the Brooklyn Bridge.
- 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_69ad858ac36c81909962589cd277d6e2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaab701c48190b91404ab416f7ce3 |
completed | March 8, 2026, 4:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b367e780008190b554bf1ad0c7cc59 |
completed | March 13, 2026, 1:27 a.m. |
Created at: March 8, 2026, 3:07 p.m.