Triple
T5213867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blum |
E117700
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Blume |
E72917
|
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: Blume | Statement: [Blum, hasVariant, Blume]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blume Context triple: [Blum, hasVariant, Blume]
-
A.
Blume
chosen
Blume is the family name of acclaimed English actress Claire Bloom, known for her work in film, television, and theatre.
-
B.
Bloom
Bloom is a common English and Jewish surname borne by numerous notable figures in literature, academia, and the arts.
-
C.
Bloom
Bloom is a large open-access multilingual language model developed by the BigScience research workshop for text generation and understanding tasks.
-
D.
Violeta
Violeta is a novel by Chilean author Isabel Allende that follows the tumultuous, century-long life of a woman born during the 1918 Spanish flu pandemic.
-
E.
Rosa
Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
- 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_69bd4464ba3c8190bc16b2ebbe42ddb0 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7a911d40819086621537274dc0f0 |
completed | March 20, 2026, 4:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beefe325988190b35e3502f147c9c2 |
completed | March 21, 2026, 7:22 p.m. |
Created at: March 20, 2026, 1:47 p.m.