Triple
T473623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosa Luxemburg |
E9012
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Rosa |
E14716
|
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: Rosa | Statement: [Rosa Luxemburg, givenName, Rosa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rosa Context triple: [Rosa Luxemburg, givenName, Rosa]
-
A.
Rosa
chosen
Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
-
B.
Irises
Irises is a famous 1889 oil painting by Vincent van Gogh depicting a vibrant cluster of blooming irises, celebrated for its expressive color and dynamic composition.
-
C.
Mariposa
Mariposa is a small historic town in central California known for its Gold Rush heritage and proximity to Yosemite National Park.
-
D.
Roberta
Roberta is a feminine given name commonly used in various languages, derived from the masculine name Robert.
-
E.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
- 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_69a2e7ff81708190b0507a24a997232c |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f0208c788190a96cdabcf593fda7 |
completed | Feb. 28, 2026, 1:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4711cd9ac8190bc95a6560950525b |
completed | March 1, 2026, 5:02 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.