Triple
T7857753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weiner |
E182419
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Weinerová |
E182419
|
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: Weinerová | Statement: [Weiner, hasVariant, Weinerová]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Weinerová Context triple: [Weiner, hasVariant, Weinerová]
-
A.
Weiner
chosen
Weiner is a surname of Germanic origin borne by various notable individuals across fields such as business, politics, and entertainment.
-
B.
Ninove
Ninove is a city in the Belgian province of East Flanders, known for its historical center and past role in major cycling events.
-
C.
Neubauer
Neubauer is a German surname borne by various notable individuals, including activists, politicians, and academics.
-
D.
Rubin
Rubin is a surname most famously associated with American astronomer Vera Rubin, whose work on galaxy rotation curves provided key evidence for the existence of dark matter.
-
E.
Olitski
Olitski is the surname of Jules Olitski, a prominent Russian-American abstract painter associated with Color Field painting and the Washington Color School.
- 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_69ca82887fd48190975896bf38c4596b |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb1a76f8648190976b488d0d8658ef |
completed | March 31, 2026, 12:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5b32eaf88190aae55aaeb963c50b |
completed | March 31, 2026, 5:27 a.m. |
Created at: March 30, 2026, 4:52 p.m.