Triple
T12357060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bloom |
E294639
|
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: [Bloom, hasVariant, Blume]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blume Context triple: [Bloom, 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.
Blome
Blome is a German surname borne by various notable individuals across fields such as politics, science, and the arts.
-
C.
Blumenstück
Blumenstück is a lyrical piano piece in D-flat major, Op. 19, by Robert Schumann, noted for its delicate, song-like character.
-
D.
Blume in Love
Blume in Love is a 1973 romantic comedy-drama film directed by Paul Mazursky, known for its introspective look at relationships and starring George Segal as a conflicted divorce lawyer.
-
E.
Bloom
Bloom is a common English and Jewish surname borne by numerous notable figures in literature, academia, and the arts.
- 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_69d6ab6ccbec8190b09e2d357aa80064 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f8e64dc81908c2242c68cd1b86e |
completed | April 10, 2026, 6:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62ab4cdec8190849604ef2ec498ba |
completed | May 2, 2026, 4:47 p.m. |
Created at: April 8, 2026, 9:54 p.m.