Triple
T6386698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rolf Lohse |
E143717
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Lohse |
E143717
|
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: Lohse | Statement: [Rolf Lohse, familyName, Lohse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lohse Context triple: [Rolf Lohse, familyName, Lohse]
-
A.
Lohse
chosen
Lohse is a German surname borne by various notable individuals in fields such as science, sports, and the arts.
-
B.
Griese
Griese is a surname most prominently associated with Bob Griese, the Hall of Fame American football quarterback.
-
C.
Cloyce
Cloyce is a surname most notably associated with Sarah Cloyce, one of the women accused during the Salem witch trials in 17th-century Massachusetts.
-
D.
Taillibert
Taillibert is a French surname most notably associated with architect Roger Taillibert, known for designing major sports complexes such as Montreal's Olympic Stadium.
-
E.
Jake Hoyt
Jake Hoyt is a rookie LAPD narcotics officer whose moral integrity is tested during a tumultuous day under a corrupt veteran detective in the film "Training Day."
- 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_69c008dac1ec81909cef8157ccd69962 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c068688bfc8190a28918d58d0cfd2e |
completed | March 22, 2026, 10:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cafb3d4c8190a00e66839c3eaf01 |
completed | March 27, 2026, 6:22 p.m. |
Created at: March 22, 2026, 4:34 p.m.