Triple
T3584492
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hans |
E75878
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Hansi |
E75878
|
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: Hansi | Statement: [Hans, hasVariant, Hansi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hansi Context triple: [Hans, hasVariant, Hansi]
-
A.
Hans
chosen
Hans is a masculine given name of Germanic origin commonly used in Germanic and Scandinavian countries.
-
B.
Günther
Günther is a German masculine given name traditionally associated with figures of Germanic origin and culture.
-
C.
Hermann
Hermann Minkowski was a German mathematician best known for developing the geometric formulation of special relativity using four-dimensional spacetime.
-
D.
Hermann
Hermann is a German surname borne by various notable individuals across fields such as philosophy, science, and the arts.
-
E.
Helmut
Helmut is a masculine given name of German origin, historically common in German-speaking countries.
- 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_69ad85d6dc3c8190b491b79b83e25461 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc10f9b508190bde4a4e4711dd452 |
completed | March 8, 2026, 6:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4882803e481908bc716c8beda3c73 |
completed | March 13, 2026, 9:56 p.m. |
Created at: March 8, 2026, 3:21 p.m.